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Electronic Theses, Treatises and Dissertations The Graduate School
2007 Mutual and Exchange Traded Funds: Market Effects and the Impact of Investor Sentiment Vaneesha Boney
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THE FLORIDA STATE UNIVERSITY
COLLEGE OF BUSINESS
MUTUAL AND EXCHANGE TRADED FUNDS: MARKET EFFECTS AND THE IMPACT OF INVESTOR SENTIMENT
By
VANEESHA BONEY
A Dissertation submitted to the Department of Finance in partial fulfillment of the requirements for the degree of Doctor of Philosophy
Degree Awarded: Summer Semester, 2007 The members of the Committee approve the Dissertation of Vaneesha Boney defended on May 25, 2007.
David Peterson Professor Directing Dissertation
Thomas Zuelhke Outside Committee Member
Yingmei Cheng Committee Member
James Doran Committee Member
Stacy Sirmans Committee Member
Approved:
Caryn Beck-Dudley, Dean, College of Business
The Office of Graduate Studies has verified and approved the above named committee members.
ii This dissertation is dedicated to my late maternal Grandmother, Mary Jenice Boney, who passed away Friday, January 19, 2007. I love and miss you Grandma.
iii ACKNOWLEDGEMENTS
I would like to thank my committee for all of their support and guidance. Specifically, I would like to acknowledge the leadership, counsel and encouragement of my Chair, David Peterson, for guiding me through the dissertation process, and enlightening this young and eager mind throughout my entire tenure at Florida State University. I also acknowledge my wonderful family and close friends. Your loving support helped make this process a little easier to manage. I am very grateful for the academic and financial support of my McKnight and KPMG PhD Project families. You all have been instrumental in this process and I thank you.
iv TABLE OF CONTENTS
List of Tables ...... Page vi Abstract ...... Page viii
1. Essay Introductions...... Page 1
2. Effect of the SPIDER Exchange Traded Fund on the Cash Flow of Funds of S&P 500 Index Mutual Funds...... Page 7
Background ETF Information...... Page 9 Data ...... Page 14 Methodology...... Page 14 Empirical Results...... Page 20 Conclusions ...... Page 23
3. Investor Sentiment: Its’ Effect on ETF Pricing and Creations And Deletions ...... Page 31
ETF Literature...... Page 37 The Formal Hypothesis...... Page 39 Data ...... Page 40 Methodology...... Page 42 Results ...... Page 51 Conclusions ...... Page 55
4. REIT CEF Returns: The Impact of Irrational Investor Sentiment And Changes in the Federal Funds Interest Rate...... Page 68
The Literature...... Page 71 Data ...... Page 75 Methodology...... Page 76 Results ...... Page 82 Conclusions ...... Page 85
5. Dissertation Summary...... Page 92
v
REFERENCES ...... Page 93
BIOGRAPHICAL SKETCH ...... Page 98
vi LIST OF TABLES
Table 1: Descriptive Statistics ...... Page 25
Table 2: Flow of Funds Regression Estimates...... Page 26
Table 3: Differences in Traditional Explanatory Variables Predicting Fund Flows Page 27
Table 4: Flow of Funds Regression Controlling for Distinct Traders ...... Page 28
Table 5: Flow of Funds Regression Controlling for Investor Type...... Page 29
Table 6: Descriptive Statistics ...... Page 56
Table 7: ARMA Results...... Page 57
Table 8: ETFP Results By Ticker ...... Page 58
Table 9: ETFP Aggregate Results...... Page 63
Table 10: ETFP By Size ...... Page 64
Table 11: Creation and Deletion Aggregate Results...... Page 65
Table 12: Descriptive Statistics ...... Page 87
Table 13: Impact of Sentiment on REIT CEF Returns...... Page 89
Table 14: Impact of Sentiment on REIT CEF Returns Given Relative Return to the NAREIT all equity REIT Index and Interest Rate Environment...... Page 90
Table 15: Chow Test Results ...... Page 91
vii ABSTRACT
I have composed a three essay dissertation. I examine mutual and/or exchange traded funds in an attempt to give a deeper understanding of the interplay between our financial markets and fund investors, in order to: 1) determine the effects new products have on the market of existing and similar products and 2) determine whether investor perceptions and potential biases (as proxied by measures of investor sentiment) affect fundamental aspects of exchange traded funds (ETFs) and closed-end funds (CEFs). Specifically, this dissertation seeks to examine the impacts of the introduction of the ETF and determine whether there are significant effects on the creation and deletion activity common to ETFs and the market price deviations from net asset value per share (NAV) of REIT CEFs given various real estate market environments.
viii CHAPTER 1: ESSAY INTRODUCTIONS
1.1 Exchange Traded Funds Exchange traded funds (ETFs) are an emerging class of low cost index securities that are often compared to mutual funds. An ETF is a security that mirrors an existing index by holding the same component stocks and matching the weighting scheme of a specific index. Some of the more popular ETFs include Standard and Poor’s Depository Receipts, also known as Spiders (SPDRs), which track the holdings and returns of the S&P 500, DIAMONDS (DIA), which track the Dow Jones Industrial Average (DJIA), and Qubes (QQQQ), which track the NASDAQ 100.1 The first ETF traded in the US was the SPDR, which was introduced in January, 1993, while DIAMONDS and Qubes began trading in 1998 and 1999, respectively. ETF products can be found that track a wide variety of indexes; this not only includes the broad stock and bond markets, but industry sectors and international stock indexes. The objective of an ETF is to match, not exceed, the returns of the underlying index; however, ETFs offer services and investment flexibility that indexed mutual funds generally do not. As a result, ETFs have received a great deal of attention in the academic and practitioner literature. The literature on ETFs is either descriptive in nature or provides tests of market effects given their introduction. Fuhr (2001) offers a general introduction to the overall characteristics of ETFs while Elton, Gruber, Comer and Li (2002) detail ETF characteristics, and examine the performance of the SPIDER relative to its benchmark and low cost index funds. Hedge and McDermott (2004) examine changes in the liquidity of the underlying stocks of the Dow Jones Industrial Average and the NASDAQ after the introduction of DIAMONDS and Qubes2 while Ackert and Tian (2000) examine pricing errors of the SPIDER relative to its underlying index, the S&P 500. Poterba and Shoven (2002) look at the tax benefits ETFs offer investors and point out that ETFs take advantage of a specialized tax treatment referred to as redemption in kind, which allows the trustee of the ETF to distribute shares of the underlying stock, in place of cash, when units of the trust are deleted, thus avoiding potential capital gains.
1 SPY, DIA, and QQQQ are the tickers for the SPDRs, Diamonds, and Qubes, respectively. ETF operating expenses for SPY and DIA currently are not allowed to exceed 18 basis points vs. the 0.5% to 2% charged by many mutual funds. 2 DIAMONDS are units of the exchange traded fund that mimics the Dow Jones Industrial Average as it holds the same 30 component stocks of the index. Qubes are units of the exchange traded fund that mimics the NASDAQ 100 as it holds the same component stocks of this index.
1 Park and Switzer (1995), Switzer, Varson and Zghidi (2000), and Chu and Hsieh (2002) examine the effects ETFs have on the index futures market. Park and Switzer look at the effects TIPs (Toronto 35 Index participation Units3) have on the Canadian index futures market and find that TIPs increase the overall trading volume in index futures and significantly improve the overall pricing efficiency within that market. Switzer, Varson, and Zghidi (2000) and Chu and Hsieh (2002) examine how the introduction of the SPDR affected the pricing efficiency of the S&P futures market. Switzer et al conclude that the introduction of the SPDR mitigates the positive pricing errors in S&P 500 Index futures contracts. Chu and Hsieh conclude the SPDR improves pricing efficiency through the simplification of the short arbitrage process. Although the above is not exhaustive, the literature on ETFs is relatively new and generally centers around getting a better understanding of how this financial innovation affects our markets and market participants. Accordingly, my dissertation adds to the literature on ETFs and closed end funds (CEFs) by furthering our understanding of the mechanics of the ETF and the interplay between the market and various market participants actively trading in ETFs and CEFs.
1.2 Investor Sentiment ETFs have become an increasingly important and popular security in the index mutual fund product category. ETFs have experienced tremendous growth in both total assets as well as the number of funds offered since their 1993 introduction in the US market with the SPIDER. This growth and popularity has prompted many to question what drives investors to invest in these products and the role ETFs play in our financial markets. Of interest is whether ETF pricing is affected by investor sentiment. Investor sentiment is defined as an aggregate measure of investors’ attitude toward market conditions and is generally categorized as bullish, bearish or neutral. The literature on investor sentiment generally examines investors’ perceptions and effect on the market independent of risk considerations. Often nested within literature discussing the effects of “noise traders” or irrational investors, investor sentiment studies generally either support of refute whether non risk related measures have the ability to change some fundamental aspect of a security such as price.
3 In March of 1990 the Toronto Stock Exchange developed TIPs, which represent an interest in a trust that holds baskets of the stocks that make up the Toronto 35 Index. TIPs are akin to ETFs in the US market.
2 Numerous papers examine the effect measures of investor sentiment have on financial assets; however, this research is generally confined to closed-end mutual funds and equities and returns. However, prior studies do not examine how and whether investor sentiment affects the pricing and creation and deletion activity of ETFs.
2.0 The Essays I am composing a three essay dissertation. All three essays attempt to give a deeper understanding of the interplay between our financial markets and fund investors, in order to: 1) determine the effects new products have on the market of existing and similar products and 2) determine whether investor perceptions and potential biases (as proxied by measures of investor sentiment) affect fundamental aspects of ETFs and CEFs. Specifically, this dissertation seeks to determine whether there are significant effects on the creation and deletion activity common to ETFs and the market price deviations from net asset value per share (NAV) of REIT CEFs given various real estate market environments.
2.1 Chapter 2, Essay 1: In essay 1 I examine the growth of ETFs in order to determine whether their growth may be at the expense of traditional index mutual funds. Prior studies by Park and Switzer (1995), Switzer Varson and Zghidi (2000), Chu and Hsieh (2002), and Berkman, Brailsford and Frino (2005) find that ETFs affect futures markets. Gruber (1996), Zheng (1999), Sirri and Tufano (1998), Jain and Wu (2000) and O’Neal (2004) all find important factors affecting the flow of funds into mutual funds. However, the effect of the introduction of ETFs on mutual fund cash flows is unexplored. I explore whether ETFs and traditional index mutual funds are competing for investor cash flows by testing the null hypothesis that these two products are redundant. To do this I first examine whether there are significant differences in returns, fund flows, and total net assets between the SPDR and index mutual funds tracking the S&P 500. Next I examine the flow of funds into and out of the SPDR and these index mutual funds. By tracking the flow of funds of index mutual funds and the SPDR, I seek to determine whether there is a significant difference in the flow of funds of indexed mutual funds and ETFs, whether the flow of funds into ETFs is at
3 the expense of indexed mutual fund flows, and if there are any specific characteristics of these funds that significantly affect the level of fund flows. This study should be interesting to both individual investors holding money in index products, and those mutual fund companies with indexed products in their family of funds. If I find that the growth in ETF fund flows is partially at the expense of index mutual funds and that the market sees these two products as redundant, then I might expect to see reductions in traditional index mutual fund fees or an eventual pricing out of index products. Both could have large profit implications for fund families with a significant proportion of their managed funds in these products.
2.2 Chapter 3 Essay 2: In essay 2 I seek to determine whether the market price deviations from NAV and subsequent creation and deletion activity that is common to ETFs are affected by investor biases, or factors unrelated to risk, as proxied by investor sentiment. To do this I examine whether factors, such as fund size affect the pricing and creations and deletions of a sample of broad based ETFs. This chapter seeks to increase understanding of ETFs by analyzing the effects of institutional and individual investor sentiment, not related to risk, in determining: 1) whether investor sentiment orthogonal to risk plays a role in the pricing deviations from the NAV of ETFs, 2) whether there is a distinction between measures of institutional versus individual investor sentiment with respect to these pricing deviations, and 3) whether the significance of sentiment varies according to ETF market capitalization. The effect of investor sentiment on security prices has received extensive attention in recent literature. Lee, Shleifer, and Thaler (1991), Neal and Wheatley (1998), Kaniel, Saar and Titman (2004), Brown and Cliff (2004, 2005), and Lemmon and Portniagina (2006) all find that various measures of investor sentiment are related to prices of various investment securities. Despite this extensive analysis, the impact of sentiment on ETFs is unexplored. My analysis is important as it attempts to shed light on the factors that drive ETF investors and, to my knowledge, it is the first study that helps increase our understanding of the motivations behind pricing deviations and the creation and deletion mechanism common to ETFs; specifically, I explore whether institutional or individual investors likely drive these changes.
4 Poterba and Shoven (2002) show that individual investors may prefer ETFs over similar investment options given their preferential tax treatment. Hughen (2001) shows that ETFs may be attractive to many short term traders given their high turnover rates and the ability investors have to short shares and trade them on margin. Moreover, institutional investors should be attracted to ETFs given the growth in index investing in the US (see Bhattacharya and Galpin (2005)) and the ability to arbitrage by creating and deleting shares in response to pricing deviations from NAV. Accordingly, it is of interest to disentangle the motives of these respective traders to determine whether they are strictly driven by knowledge about changes in fundamental risk as the efficient market framework would suggest, or whether they are motivated by sentiment or factors that are considered unrelated to risk.
2.3 Chapter 4 Essay 3: Over the past five to seven years real estate has received a great deal of attention in the academic, practitioner and popular press. In accordance, real estate investment has become increasingly popular amongst both retail and institutional investors. This has been the case with real property investment as well as real estate securities. In particular, there has been a significant growth in real estate exchange traded security investment. Not only were real estate exchange traded funds (ETFs) introduced in 2001, but there has been significant growth in exchange traded real estate CEFs. CEFs differentiate themselves from ETFs given they trade at market prices that are generally not at the NAV, while the creation and deletion mechanism common to ETFs generally ensures that market price is close to the NAV. As a result, the examination of REIT CEFs allows an opportunity to examine investor behavior and what impact they have on market price, given various real estate market cycles. The literature on REITs is quite extensive and has examined issues including the effect management has on REIT performance (see Kallberg, Liu and Trzcinka (2000) and Sirmans, Friday and Price (2006)), financial distress costs and the effects of leverage on owner-managed properties held by REITs (see Brown 2000), REIT IPO pricing (see Chen and Lu (2006)), REITs and capital structure (see Howe and Shilling (1988) and Jaffe (1991)), the effect of interest rates on REITs (see Liang and Webb (1995) and Swanson, Theis and Casey (2002)), and the impact of investor sentiment on REITs (see Barkham and Ward (1999). However, the
5 literature has yet to explore how investor irrational bias, as gauged by investor sentiment orthogonalized to risk, and changes in the federal fund interest rate affect REIT CEF pricing. This paper examines how measures of investor sentiment, or those factors that impact investors perceptions that are unrelated to risk, affect REIT CEF pricing deviations of the NAV from market price. Moreover, this analysis examines whether these effects differ according to various stages in the real estate market cycle as gauged by changes in the federal funds rate.
3.0 Remainder of Dissertation Chapter 2 presents essay 1. Essay 2 is provided in Chapter 3. Essay 3 is presented in Chapter 4. The results are summarized in Chapter 5.
6 CHAPTER 2: THE EFFECT OF THE SPIDER EXCHANGE TRADED FUND ON THE CASH FLOW OF FUNDS OF S&P INDEX MUTUAL FUNDS
1. Introduction Exchange traded funds (ETFs) are an emerging class of low cost index securities that are often compared to mutual funds. Some of the more popular ETFs include Standard and Poor’s Depository Receipts, also known as Spiders (SPDRs), which track the holdings and returns of the S&P 500, DIAMONDS (DIA), which track the Dow Jones Industrial Average (DJIA), and Qubes (QQQQ), which track the NASDAQ 100.4 The first ETF traded in the US was the SPDR, which was introduced in January, 1993. DIAMONDS were second and began trading in January, 1998. Both trade on the New York and American Stock Exchanges and represent ownership in a Trust Series 1 (Trust), which is a unit investment trust established to hold a portfolio of the equity securities that are comprised of the firms in the underlying index. State Street Bank and Trust Company serves as the trustee for these ETFs. In general, these funds are priced to trade at a designed level of its underlying index. For instance, DIAMONDS trade around 1/100 the level of the DJIA and SPDRs trade about 1/10 the level of the S&P 500. ETFs mirror an existing index by holding the same component stocks and matching the weighting scheme of a specific index. Although the objective of the ETF is to match, not exceed, the returns of the underlying index, ETFs are said to offer services and investment flexibility that indexed mutual funds generally do not. As a result, indexed mutual funds and ETFs are often said to compete for investor funds. However, the extent of this potential competition is not clear. ETFs are an increasingly popular form of investment. This is substantiated by financial news commentaries such as “As if a growling bear market isn’t enough, mutual funds have another beast breathing down their necks — exchange-traded funds,” or “…investors are drawn to ETFs because they can get broad market exposure at lower cost than they do with conventional mutual funds run by stock pickers who might not even beat their benchmarks,5” or
4 SPY, DIA, and QQQQ are the tickers for the SPDR, Diamond, and Qube, respectively. ETF operating expenses for SPY and DIA currently are not allowed to exceed 18 basis points vs. the 0.5% to 2% charged by many mutual funds. 5Comments are from the Quarterly Mutual Fund Review of the Wall Street Journal; January 4, 2007.
7 even still “At the end of January (2000), the combined assets of the nation’s ETFs totaled $72.1 billion, a 10% increase from the level a month earlier. That’s on top of last year’s doubling of ETF assets. Assets in the nation’s 4,415 stock mutual funds, meanwhile, dropped 2% in 2000 and climbed 3.3% in January.”6 While the later quote is slightly dated it remains relevant. In aggregate these quotes point to the fact that there is significant demand and growth in the ETF market. Moreover, as of December 2006, ETFs have over $422 billion in assets in approximately 357 funds. This represents a staggering 450% growth in total assets under management since 2000 and an annual total asset growth of over 40% since December of 2005. Moreover, the combined annual asset growth in mutual funds traded in the US was less than 17% between December of 2005 and December of 2006, up from 3.2% between December of 2004 and 20057. SPDRs, in particular, have experienced extraordinary growth. Elton, Gruber, Comer, and Li (2002) note that by the end of 1999 there were 19.8 billion dollars invested in SPDRs and no other stock had more daily dollar trading volume. Figure 1 shows the growth in total net assets of the SPDR ETF from 1997 through 2004. For comparison, the growth in total net assets for the largest S&P 500 fund, the Vanguard 500 Index Fund (VFINX), and the average of 33 index mutual funds that also track the S&P 500 are plotted. The rapid growth of the SPDR is clearly seen. The growth in demand for ETFs may be at the expense of traditional index mutual funds. We explore whether these investment vehicles are competing for investor cash flows by testing the null hypothesis that these two products are redundant. To do this we first examine whether there are significant differences in returns, demand, and total net assets between the SPDR and index mutual funds tracking the S&P 500. Next we examine the creation and deletion activity of the SPDR and the flow of funds into and out of the sample of index mutual funds. By doing so we seek to determine whether there is a significant and inverse relationship in the demand for the SPDR and the sample of traditional funds and whether a casual relationship exists. A confirmative finding would indicate that the growth of the ETF is at the expense of indexed mutual fund demand, thus flows, after controlling for the unique characteristics of the ETF that may generate demand not common to the traditional index mutual fund.
6 Comments are from the Investment Company Institute, a mutual fund trade group in Washington, DC (www.ici.org). 7 Data from the Investment Company Institute website http://www.ici.org/stats/etf/etfs_12_06.html#TopOfPage.
8 This study should be interesting to both investors holding money in index products, and practitioners, the mutual fund companies and managers, with indexed products in their family of funds. If we find that the growth in ETF demand significantly and adversely impacts the flows in and out of index mutual funds then investors might expect to see further reductions in index mutual fund fees. Moreover, some mutual fund companies, like Vanguard and Rydex have opted to include ETFs in their family of offered products. Whether a move to hedge the risk of a shift in investor demand or a means to attract additional investment dollars, it is evident that an understanding of the demand in the market for index securities could have significant profit implications for fund families with a significant proportion of their managed funds in these products.
2. Background ETF Information 2.1 ETF characteristics An ETF’s objective is not to beat the index, but to seek returns that, before expenses, correspond to the underlying index. An investor purchasing shares/units of the ETF receives monthly or quarterly cash dividends8 and has the ability to trade those shares at any time during the day, at prices that fluctuate on a continuous basis like a share of common stock. This is in contrast to an investor who buys a share of an open-end mutual fund, since all redemptions and subscriptions of shares are valued at a net asset value per share (NAV) that is calculated once a day. There is no assurance that the performance of the ETF will exactly correspond to the underlying index due to reasons such as incorrect security weighting and failing to carry the exact composition of the index. However, ETF investors share advantages over investors in many actively managed mutual funds due to the absence of sales loads and sponsor fees such as 12b-1 fees.9 ETFs also have lower management fees, there is the ability to sell them short, you can trade options on them, there is increased tax-efficiency, and there is the ability to create and delete units of the Trust. Creations and deletions, depending on the ETF, are done for a fee of $1000-4000 plus any accumulated dividends for the month of the ETF. Any investor holding all of the individual stocks that comprise the underlying index for a particular ETF may turn in those
8 The frequency of dividend payments varies among ETFs. Dividend payments for the SPDR are quarterly. 9 Brokerage fees/commissions will apply, however.
9 shares and create more outstanding units of the ETF in aggregate units of 50,00010. Deletions are done in a similar fashion, as an investor can turn in units of the ETF and receive the underlying stocks. The creation and deletion mechanism is a rather significant feature of the ETF. It can be used by Authorized Participants, or those institutional investors that are pre approved to implement such trades in order to meet the changing demand in the market for the ETF or its underlying securities. Moreover, a function of the creation and deletion process is to maintain pricing efficiency in the market for ETFs. Specifically, similar to closed-end funds, the market price for a share/unit of an ETF has the ability to deviate from its net asset value. Given that a share/unit can trade at a premium or discount, creations and deletions can be implemented by these approved participants to arbitrage pricing discrepancies. For instance, if an ETF is selling at a discount, then the true value of the underlying assets held in the Trust is not realized. Therefore, someone holding the actual underlying assets would have an opportunity to make a profit by deleting shares of the ETF and, in exchange, receive shares of the underlying securities which could, in turn, be sold in the open market. The effectiveness of this mechanism is supported by the findings of Ackert and Tian (2000) who find that the SPDR tends to be priced correctly on a day to day basis relative to its underlying index. Gruber (1996) examines why investors choose to hold actively managed mutual funds over index funds given that he finds that the actively managed funds under perform their appropriate benchmark index by 65 basis points per year. Upon their introduction, Gruber shows that there was a reluctance to invest funds in indexed mutual funds given that the average expense of these funds was 1.24 percent per year, or almost twice the underperformance of actively managed funds. Although actively managed funds were not performing better than the passive index funds, the high fees associated with these index funds overcame the benefit of the higher return offered by index funds. This is interesting because Gruber finds that investors look at more than raw returns and care about the fees associated with an investment opportunity. As a result, if we find a shift in demand from index funds into ETFs, a significant portion of this shift may be attributable to the lower expenses associated with ETFs.
10 Aggregate units can vary by ETF. Information on aggregate creation and deletion units can be found in the ETF prospectus.
10 Alternatively, it is possible that the increase in demand and total assets of ETFs may not be associated with index mutual fund demand. It may be the result of money from new investment. These investment dollars could represent demand from investors that are attracted the ETF that would not otherwise be interested in traditional index mutual funds. A potential supporting argument is offered by Berkman, Brailsford, and Frino (2005), who look at whether trading in stock index futures is driven by information or liquidity needs. This is a relevant study because ETFs, unlike regular index mutual funds, allow hedging positions similar to index futures. Berkman, Brailsford, and Frino find that trading in stock index futures is primarily driven by liquidity needs. Therefore, it is possible that the growing demand for the ETF could be from an investor set orthogonal to that of the traditional index mutual fund. It may be the result of demand from index futures traders who find the ETF a more efficient means of participating in the market. Park and Switzer (1995), Switzer, Varson and Zghidi (2000), and Chu and Hsieh (2002) provide further potential evidence for this alternative point of view. They examine the effects ETFs have on the index futures market. Park and Switzer look at the effects TIPs (Toronto 35 Index participation Units11) have on the Canadian index futures market. They find that TIPs increase the overall trading volume in index futures and significantly improve the overall pricing efficiency within that market. Switzer, Varson, and Zghidi (2000) and Chu and Hsieh (2002) examine how the introduction of the SPDR affected the pricing efficiency of the S&P futures market. They conclude that the introduction of the SPDR mitigates the positive pricing errors in S&P 500 Index futures contracts. Chu and Hsieh conclude the SPDR improves pricing efficiency since it simplifies the process of short arbitrage. These studies provide additional evidence that the introduction of the SPDR tends to have significant relevance to the market for and the behavior of market participants in the index futures market.
2.2 ETF tax benefits ETFs are promoted as offering greater tax efficiency than traditional mutual funds. Poterba and Shoven (2002) look at the benefits ETFs offer investors who take into consideration tax effects of their investment opportunity set. They point out that mutual funds operate under
11 In March of 1990 the Toronto Stock Exchange developed TIPs, which represent an interest in a trust that holds baskets of the stocks that make up the Toronto 35 Index. TIPs are akin to ETFs in the US market.
11 specialized tax treatments called “pass throughs” where realized capital gains must be passed on to the shareholders. This pass through effect can raise the tax liability of mutual fund investors. Although ETFs are technically classified as mutual funds, a fundamental aspect of their structural form allows them to avoid a substantial amount of these passed through capital gains. ETFs take advantage of a specialized tax treatment referred to as redemption in kind, which is a strategy any investment company can adopt operating under the Investment Act of 1940, and allows the trustee of the ETF to distribute shares of the underlying stock, in place of cash, when units of the trust are deleted. Although redemption in kind is also available to traditional mutual funds, Poterba and Shoven (2002) point out that it is very rarely used by them and is primarily used when very large trades, generally by arbitrageurs, are frequent to the fund. This makes redemption in kind a particularly attractive strategy for ETF investors given that arbitrageurs can create and delete units of the ETF and frequently do so to take advantage of (adjust for) deviations in an ETF’s NAV from its actual trading price. Additionally, unlike traditional mutual funds, ETFs trade like common stock so that investors who want to liquidate their shares do so in the secondary market, which means the Trust does not have to sell shares of the underlying assets to meet redemption needs. As a result, no capital gains are created for the owner of the ETF and there is a lessened tax burden.
2.3 Flow of funds and demand Our focus is the demand and subsequent flow of assets between exchange traded and indexed mutual funds. Specifically, we isolate the demand for the SPDR and a sample of index mutual funds that also track the S&P 500. Numerous studies have used mutual fund flows to gauge investor demand and to examine relevant issues in the mutual fund area. Some of these studies have focused on the effects of variables such as advertising expenditures, trading volume, manager risk taking, past performance, and investors’ reaction to funds perceived to be past winners. Gruber (1996) finds that new aggregate cash flows into funds outperform former cash flows into the same fund because investors have the ability to select superior fund managers, but Zheng (1999) refutes this smart money finding. Chevalier and Ellison (1997) find that mutual funds alter their levels of risk over various time periods in order to increase the flow of funds into the fund.
12 Jain and Wu (2000) examine if the level of mutual fund advertising sends a signal to the market about a fund manager’s skill and if these advertising dollars affect future performance and the subsequent demand for the fund. As found in Sirri and Tufano (1998), Jain and Wu examine three fund-specific measures that are the most critical for explaining flow of funds. These measures include prior period performance, prior period fund flow, and the size of the fund. Their results show that although advertised funds have greater demand, represented by larger fund inflows over a control sample, they do not have superior post-advertising performance. Sirri and Tufano find that mutual fund advertising reduces investors’ search costs because it aids in determining which mutual funds will come under consideration as investors make investment decisions. O’Neal (2004) examines the purchase and redemption patterns of equity mutual funds with the primary objective of uncovering the determinants of mutual fund inflows and outflows. Of particular interest is his finding that cash flow into mutual funds continues to be positive in recent years; however, redemption rates in mutual funds are also increasing. He suggests this means that the holding period of mutual fund investors in decreasing. Bhattacharya and Galpin (2005) also find that stock picking, as a whole, is declining. In the US stock picking has declined from a high of 60% in the 1960s to 24% in the 2000s. Findings by O’Neal and by Bhattacharya and Galpin point to reasons why investors may prefer ETFs over traditional mutual funds since ETFs represent a passive strategy and are said to offer greater liquidity and exhibit characteristics that may be attractive to shorter-term investors. We examine the change in total net assets (TNA) of traditional index mutual funds and the ETF. The change in TNA is a key component in this analysis of the demand relationship between these two security types. Accordingly, it is important to note the difference in how TNAs change for index mutual funds and ETFs and how we interpret these changes in our analysis. After controlling for index level changes, the TNAs for a traditional index mutual fund change directly when investors choose to buy or sell shares of the open-end fund in accordance with their demand. This is not the case with ETFs. Although ETFs are similar to closed-end funds where fixed amounts of shares are initially offered in the market, changes in demand for the ETF may indirectly be implied through its creation and deletion activity as authorized participants respond to general investor demand. This should be the case once one controls for the most reasonable alternatives an authorized participant may choose to create and delete
13 outstanding shares of the ETF. Specifically, we control for the short minded ETF investors attracted to the ETF for activities such as index hedging or arbitrage who undoubtedly contribute to the fluctuating demand, thus creation and deletion activity of the SPDR, but would not otherwise be attracted to the traditional index mutual fund. This is the fund flow relationship we attempt to capture in this analysis. Moreover, while it is true that we do not observe all trade activity in the market, a significant and adverse finding should prove to be a conservative estimate of the impact of the relationship.
3. Data The sample consists of monthly data from January 1997 through December 2004. ETF and index mutual fund data on fund TNA, NAV, 12b-1 fees, expenses, and returns are gathered from Bloomberg and the Center for Research in Security Prices (CRSP). Samples are collected for mutual funds that have the stated objective of matching the holdings and weighting scheme of the S&P 500, and the SPDR ETF. Using Morningstar Principia from 1997-2004, we identify 33 index mutual funds with the stated objective of tracking the S&P 500 and with median total net assets over the sample period in excess of $100 million. We impose the minimum total net asset requirement because some of the smaller funds have highly volatile fund flows from month to month. For a mutual fund to be included it needs to have a minimum of 48 consecutive monthly observations. We separately examine the largest Vanguard index fund, the Vanguard 500 (VFINX). Funds in the Vanguard family are known to be larger in size and assess lower fees than the average index mutual fund. Thus, we compare findings for our general sample of indexed mutual funds to the findings of VFINX alone to infer whether these factors (size, fees) affect the results.
4. Methodology 4.1 Initial Framework Our first step is to calculate the demand for each of the monthly observations for each fund in the sample. The calculation is:
⎛ TNA − TNA ⎞ FF = ⎜ ,it − ,1 it − RET ⎟TNA (1) ,it ⎜ TNA ,it ⎟ − ,1 it ⎝ − ,1 it ⎠
14 where FFt,i represents the dollar flow of funds in month t for fund i, RETt,i is the return in month t for fund i, and TNA is the total net assets at the end of month t for fund i. This calculation for the ETF observation indirectly captures demand by calculating the flow of funds as a result of creation and deletion activity for the SPDR. We calculate the percent flow of funds over month t for each fund i by dividing the flow of funds over month t, as calculated above, by the total net assets at t-1, or:
FF ,it PFF = (2) ,it TNA − ,1 it
We employ the percent flow of funds, PFFt,i, as used by Gruber (1996) and Sirri and Tufano (1998), to test whether there is a significant difference in the general demand, represented through flow of funds, in the various partitioned samples of indexed mutual funds and the SPDR ETF. Next, we test for mean differences in return, total net assets, and percent flow of funds for the SPDR versus and 1) our entire mutual fund sample, 2) all funds except VFINX and 3) VFINX, the low cost provider, alone. This will tell if the funds in our sample, having the stated objective of matching the returns of the S&P 500, have returns equivalent to the SPDR. If they do, then we may examine differences in the flow of funds in and out of these investment products knowing that they offer equivalent returns. Given the longitudinal nature of the data, we then estimate a fixed-effects regression to test the significance of variables most likely to influence the percent of fund flows in and out of our sample funds. Specifically, we test the hypothesis that the introduction of the SPDR has had a significant effect on the demand for index mutual funds. The fixed-effects regression is used to capture fund specific factors that are time invariant. The regression equation is: 2 2 1 PFF ,it = αi + ∑ β j PFF − ,ijt + ∑ β j+3 SPYPFF − jt + β 6 EXPENSE ,it + ∑ β j+7 RET − ,ijt j=1 j=0 j=0 (3) 1 1 + β 9 LNTNA ,it + ∑ β j+10 RRET − jt + ∑ β j+12CRET − jt + e ,it j=0 j=0 where SPYPFFt is the percent flow of funds for the SPDR in month t, EXPENSEt,i is the monthly expense for month t for fund i, RETt,i is the return for month t for fund i, LNTNAt,i is the natural log of total net assets at the beginning of month t for fund i, RRETt is the return on the NAREIT
15 all equity real estate index for month t, and CRETt is the return for month t on a commodity index12. Two lagged values of percent fund flows are included as independent variables to determine whether previous fund flows are related to current fund flows, and we expect their coefficients to be positive. The current percent fund flow of the ETF SPDR, as well as its lagged flows (from the previous two months), is included to test our main hypothesis that the fund flows of the SPDR have an effect on the fund flows of indexed mutual funds. We expect to find negative coefficients on the SPDR fund flow variables, especially for the contemporaneous variable, as that indicates a simultaneous relation with the dependent variable. We examine expenses to determine whether they have a significant effect on our dependent variable fund flow.13 We examine the current returns of the index mutual funds as well as a monthly lag to determine whether investors react to return levels, and we expect to find positive coefficients on these variables. We examine total net assets to determine whether there are any size effects and expect to find negative coefficients on them given that the flow of funds of smaller index funds may be adversely affected to a greater extent than that of larger index funds. To test whether the demand and growth in assets for the SPDR comes at the expense of the sample of index mutual fund flows we control for the fact that index mutual fund flows could be flowing into alternative investment opportunities instead of the SPDR. Investors may substitute one investment type for another that might offer, say, a higher ex-ante risk-adjusted return or, alternatively, into an investment that may offer a safe haven. We test the null hypothesis that alternative investments do not affect the percent fund flow between index mutual funds and the SPDR. The return on a real estate and commodity index is chosen because they may be considered popular alternative investment opportunities to the S&P 500 during the time The real estate index is included given the widely publicized high returns to this sector. Investors may also be attracted to the returns offered by commodities given the increase in commodity prices such as that for oil over the sample period. Given that Zheng (1999) finds that investor flows have a tendency to naively chase returns, it is feasible that the returns to REITs and commodities could be a significant cause of any significant outflows from traditional index
12 Monthly data on the NAREIT all equity real estate index is from the National Association of Real Estate Investment Trusts (NAREIT) website. Monthly data on the Commodity Index includes both fuel and non-fuel price indices and are collected from the World Bank website. 13 12b-1 fees were initially included, but had very little effect, and were subsequently dropped from the specification.
16 mutual funds. We include current and one month lagged return variables for real estate and commodities and we expect negative signs on all of their coefficients.
4.2 Distinct Traders We distinguish between different types of investors because the variability of the fund flows between indexed mutual funds and the SPDR ETF are vastly different. Long-term investors may prefer ETFs due to lower management fees, while active investors may prefer mutual funds due to no commission costs.14 However, if the investor is a day trader, the ETF would be preferred because of intra-day pricing. In addition, since options trade on ETFs, hedgers and speculators may be more active in ETFs to minimize exposure or maximize profits through leverage. To test for the distinction in traders, we first examine if the SPDR percent flow of funds is affected by the same explanatory variables as the index mutual funds percent flow of funds. This exploratory investigation uses the explanatory variables in equation (3) to estimate:
3 1 1 (4a) SPYPFFt = α + ∑ β j SPYPFF − jt + β 4 SPYRETt + β 5 LNTNAt + ∑ β j+6 RRET − jt + ∑ β j+8CRET − jt + et j=1 j=0 j=0 3 1 1 (4b) PFF ,it = α + ∑ β j PFF − ,ijt + β 4 RET ,it + β 5 LNTNA ,it + ∑ β j+6 RRET − jt + ∑ β j+8CRET − jt + e ,it j=1 j=0 j=0 Equation (4a) is for the SPDR and hence the variables have no i subscripts. Equation (4b) is the same as equation (4a) but uses index mutual funds percent flow of funds as the dependent variable, and is estimated as a fixed-effects regression. If investors in these two products are distinct, then the independent variables used to explain the flow of funds into index mutual funds should have little to no predictive power in explaining the flows into the SPDR. Next, to further explain the demand and creation/deletion activity for the SPDR ETF, we include alternative independent variables to draw the distinction between investors. Traders that are more inclined to invest in ETFs may be more sensitive to volatility, take greater option positions, or trade more frequently on an intra-day basis. To proxy for these traits, we construct three variables to control for volume and use two market indicators for volatility. For volatility,
14 As of December 2005, the listed management fee for the SPDR was 0.11% of total net assets while for the Vanguard 500 Index it was 0.19%.
17 the VIX index (VIX) is included as a proxy for short-term sensitivity to volatility, while the term premium (TERM), the difference between rates for the 10-year Treasury note and 1-year Treasury bill, is a macroeconomic variable that may predict long-term volatility. It is generally accepted that an inverse yield curve is related to higher levels of market volatility so a narrower spread should insinuate higher market volatility. The data for VIX is from the CBOE. The data for the 10-year Treasury note and 1-year Treasury bill are from the Federal Reserve. Investors that prefer ETFs, such as day traders, should prefer higher volatility since it will result in greater price fluctuations. Greater volatility is expressed in terms of higher levels of VIX and potential inverse yield curves. We construct three volume proxy variables to control for trading frequency and option effects. The first is the ratio of the average daily volume traded on the SPDR, SPY_VOLUME, over the past 22 trading days divided by the average daily volume on the SPDR over the past 252 trading days. Thus, the relative volume in month t, VOLUMEt, is given as:
1 22 SPY _VOLUME 22 ∑ −nj VOLUME = n=1 (5) t 1 252 SPY _VOLUME ∑ −nj 252 n=1 where j is the first trading day in month t. Higher volume may be the result of two competing effects. The first is that higher volume today may be due to more active day traders, and thus more flow of funds into or out of the ETF. However, since it is a monthly variable, it may also signal more frequent traders of the index mutual fund because of the higher commissions trading ETFs. The second and third variables are constructed similar to equation (5), but use option volume, OPT_VOL, and open interest, OPT_OI, respectively. Data is acquired from Goldman Sachs and includes all options on the OEX and SPX.15 On any given day, all maturities, strike prices, and option types are used to construct the variables. Higher option volume or open interest may imply greater trading in the ETF associated with either hedging or speculative purchases. All three variables are used to address the presence of distinct traders. To test for
15 Options on the OEX are used since they are most active prior to 2000 and SPX is used given the associated high activity post 2000.
18 these effects, SPDR and index mutual fund percent flow of funds, respectively, are dependent variables in:
(6a) SPYPFFt = α + β1SPYRETt + β 2 BULLt + β 3VOLUMEt + β 4VIX t + β 5TERM t
+ β 6OPTt + et (6b)
PFF ,it = α + β1RET ,it + β 2 BULLt + β 3VOLUMEt + β 4VIX t + β 5TERM t + β OPT + e 6 t ,it where equation (6b) is a fixed-effects regression estimated on the full sample of index mutual funds. The variable OPT represents either OPT_VOL or OPT_OI. Both are not included simultaneously since the two variables are highly correlated. The regressions include returns in the current month and a binary variable, BULL, that equals one for the years 1997-2000 and equals zero otherwise. We expect the coefficients on BULL to be positive since in periods of generally rising prices all equity products should have an increase in flow of funds. We expect the coefficients on RET and VOLUME to be positive in both equations. We expect the coefficients on VIX and OPT to be positive in equation (6a). We have no expectations for the signs on VIX and OPT in equation (6b). We expect the coefficients on TERM to be negative in both equations since wider spreads may represent a ‘flight to quality’ and a movement from all equity products to safer government securities. Finally, after controlling for the variables in equation (6a) that represent distinct traders, the residuals from the estimation of equation (6a) are used as a proxy for the SPDR flow of funds associated with investors that take money from index mutual funds and re-invest into ETFs. The regression of index fund percent flow of funds in equation (3) is then re-estimated, but current and lagged values of the residual, RESID, replace current and lagged values of SPYPFF as independent variables, and distinct trader variables that are significant from the estimation of equation (6b) are also included. Our expectation is that by controlling for different types of investors, the effect of the residual should have a stronger negative effect than SPYPFF on the percent fund flows into index mutual funds. By controlling for traders specific to the SPDR we are reducing the variability in fund flows attributable to day traders and option traders, and in the residual we are capturing a more precise measure of fund flows moving from indexed mutual funds to the SPDR ETF.
19
5. Empirical Results 5.1 Initial Results Table 1 offers descriptive statistics for the sample of indexed mutual funds and the SPDR ETF. Specifically, Table 1 shows the mean, standard deviation, and minimum and maximum values for percent fund flow, total net assets, and returns. Data are presented separately for the full sample of 33 index mutual funds, all funds excluding VFINX, Vanguard’s VFINX, and the SPDR (SPY). Table 1 shows a large variation in total net assets across funds, with the VFINX net assets especially large. The SPDR has a large difference in minimum and maximum total net assets, reflecting its rapid growth over the period. Also presented in Table 1 are t-values comparing the three variables between the three index mutual fund categories and the SPDR. For flow of funds and total net assets, all three index mutual fund groupings are significantly different than the SPDR. However, there is no significant difference in any of the returns.16 Figure 2 plots the monthly percent change in flow of funds for the SPDR (SPY), the Vanguard 500 index fund, and an average of all 33 index funds, for the period 1997-2004. The plots demonstrate that the SPDR has both greater average inflows over the period and more volatile inflows through time. Given that we find significant differences in the flow of funds of these products, our next step is to analyze the direction of these fund flows and attempt to explain what factors are significant in explaining these differences. We examine the direction of the fund flows, as indicated by the sign on the regression coefficient of the SPDR, in order to determine whether the significantly smaller flow of funds in the sample of index mutual funds is inversely related to the flow of funds into the SPDR. In other words, we want to examine our main hypothesis that the flow of funds of index mutual funds are decreasing as a result of the introduction of the SPDR. To analyze these questions we estimate fixed-effects regression (3). In all regressions the coefficient standard errors are adjusted for clustering and adjusted for potential autocorrelation and heteroskedasticity following procedures in Newey and West (1987). Table 2 offers the results from this regression. We estimate three different versions of equation (3) with the following dependent variables: fund flows of all funds in the sample, fund
16 In Table 1 there is a slight, but noticeable difference in mean returns between the all funds sample and the SPDR ETF. This is because we have more index mutual funds (more observations) in more recent years, when the S&P 500 performed relatively worse.
20 flows to all funds in the sample excluding VFINX, and VFINX. For VFINX none of the coefficients on the explanatory variables are significant at the 5% level; this large fund seems unaffected by any of hypothesized factors and, in particular, is unaffected by the SPDR ETF.17,18 For the other two fund samples the results are virtually identical to each other. The coefficients on the two lagged index fund flow measures are positive, as expected, and the coefficients on the two-month lag are significant at the 1% level. Thus, past fund flows are clearly related to current fund flows. Of great importance is the role of the current SPDR fund flows. As predicted, the signs on the coefficients are negative and they are significant at the 5% level. Thus, there is evidence that index mutual funds are adversely impacted by the SPDR. The coefficients on the two lagged SPDR fund flows are insignificant. As expected, the coefficients on expenses are significantly negative at the 1% level. The coefficients on current and past index mutual fund returns are significantly positive at the 1% level, suggesting, as we expected, that investors are influenced by recent performance. The coefficients on the size of the fund are significantly negative at the 1% level. All coefficients on real estate returns are significantly negative at the 1% level and all the coefficients on commodity returns are negative, but none are significant. Thus, real estate seems to be a significant alternative to index mutual funds for fund flows while commodities do not seem to be as important.
5.2 Results for Distinct Traders The previous results show that the introduction of the SPDR has a negative effect on the flow of funds into index mutual funds. However, this evidence may not reveal the whole story. As shown in Table 1, the flow of funds into the SPDR is approximately five times as variable as the index mutual funds. This may be due to additional features that are offered by ETFs, such as trading options on ETFs and intra-day trading, thus attracting different types of investors. Using equations (4a) and (4b), we first demonstrate that the flow of funds into the SPDR is not explained by the same set of variables as used for index mutual funds. The results of the regressions are shown in Table 3. What is most revealing is that the flow of funds for the prior two months has no significant relation with current flow of funds for the SPDR. This is in direct
17 Fund expenses are excluded from the regression for VFINX because of their very low intertemporal variability. 18 This analysis was also estimated to include both Vanguard and Fidelity’s S&P 500 index fund flows (the largest and lowest cost providers of an S&P 500 index fund) to examine whether results are Vanguard specific. All results for the Vanguard (VFINX) only regressions are the same when Fidelity flows are included.
21 contrast to the index funds where the coefficient for the two-month lag is significantly positive at the 1% level. Only the current commodity return is significantly related to the flow into the SPDR, while many variables have coefficients with statistical significance for the index mutual funds. This difference must be a direct result of the extra features associated with the ETF, as there is no significant difference in the returns of the two products. This suggests there is a distinct difference in the investing behavior of those that invest in the SPDR versus those that invest in index mutual funds. Using the proxies for differences in traders, we estimate equations (6a) and (6b), and the results are reported in Table 4. There are two regressions on both SPDR flows and index mutual fund flow of funds, using either option volume or option open interest as proxies for option trader behavior. The results highlight the differences between these two products. The percent flow of funds into the SPDR is positively affected by the VIX index, consistent with our expectation, and suggests that the higher the market volatility the greater the flow into the SPDR. This is consistent with day trading the ETF, which is not possible with index mutual funds. The coefficients on relative volume for SPDR fund flows are insignificant while they are positively significant at the 5% level or better for index fund flows. This difference is indicative of traders using index funds for more frequent trading, as long as it is not on a daily basis. The BULL coefficients are positive and significant for the index funds, but insignificant for the SPDR. This is not surprising given the patterns in Figure 1 showing a steady increase in SPDR investment over the entire period as compared to VFINX, which has stronger inflows from 1997-2000. The option volume coefficient is positively significant for the SPDR flows and is negative and significant for index mutual fund flows. The coefficients on option open interest are not significant but have signs in a similar direction to the option volume coefficients. Thus, higher option trading results in more money flowing out of index mutual funds while more money flows into the SPDR. This evidence is consistent with expectations and supports the notion that option traders are more inclined to either hedge or speculate through ETFs. Given there appears to be distinct differences in the types of traders using index mutual funds and the SPDR ETF, we now control for the variables explaining these differences. In addition, the residual, RESID, from the SPDR percent flow of funds regression in equation (6a) is included as a replacement for SPYPFF in the index mutual fund percent flow of funds regression equation (3). Lagged values of RESID also replace lagged values of SPYPFF. Using
22 RESID instead of SPYPFF helps to control for the effect of day traders and option traders on the flow of funds into the SPDR, since these traders have minimal impact on the flows into index mutual funds. The residual from the regression should contain information on all other traders not specific to the SPDR. Equation (3) is modified to include the RESID variables along with VOLUME, BULL, and OPT_VOL. The modified regression equation (3) is estimated on the three samples and the results are reported in Table 5. By controlling for the different types of traders, the effect of the SPDR on the cash flow of index mutual funds is increased. For all three samples the coefficient on the current RESID is more negative than the coefficient on the current SPYPFF in Table 2, and for the all funds sample and the all funds but VFINX sample the coefficient is significant at the 1% level instead of at the 5% level. Further, the coefficient on RESID lagged two months is significantly negative, at the 1 % level, for all three samples, whereas coefficients on lagged SPYPFF variables were insignificant in Table 2. The coefficient of -.038 on the current RESID suggests that index mutual funds are losing 3.8% per month in fund flows to the SPDR ETF. Other coefficients reported both in Tables 2 and 5 are generally similar between the two tables. The coefficient on the bull market binary is positive and significant at the 1% level in all estimations. Relative trading volume and option volume are related to fund flows for VFINX. Overall, these findings enhance the prior sections results. Especially important is the stronger impact of the SPDR flow of funds on index mutual fund flow of funds. Based on results reported in Tables 1 and 5, index mutual funds seem to be losing substantial dollars to the SPDR. From Table 1, the average total net assets for the SPDR are $23.912 billion and the mean monthly flow of funds is .0333 (3.33% per month). Focusing on the results for all 33 funds, the coefficient on current SPDR fund flows (RESID), reported in Table 5, is -.038. There are 96 months in our sample. Thus, an approximate total amount of dollars lost by index funds to the SPDR over the period 1997-2004 is: $23.912 x .0333 x .038 x 96 = $2.905 billion.
6. Conclusion Our objective is to discover whether investors find value in the additional features of ETFs. We address this question by examining the flow of funds in and out of a sample of index mutual funds that track the S&P 500 and the SPDR ETF. We find that our sample of 33 index
23 mutual funds lose about 3.8% per month in fund flows after controlling for alternative investments and differences among traders. This is an interesting finding given that indexing is becoming an increasing popular investment strategy and given the extraordinary growth in the SPDR ETF. While we do not account for every possible economic factor, our results indicate that a feasible explanation for this flow loss is a result of the introduction of the SPDR. Interestingly, the introduction of SPDRs has had little to no impact on the largest S&P 500 index mutual fund, Vanguard’s VFINX. Overall, we are able to reject the null hypothesis that the fund flows of our sample of indexed mutual funds are not impacted by the introduction of the SPDR and we show that over the period 1997-2004, the 33 index mutual funds that track the S&P 500 have lost more than $2.9 billion to the SPDR.
24
Table 1 Descriptive Statistics
This table offers descriptive statistics for the sample of mutual funds used in the analysis from January 1997 through December 2004. The values are calculated by taking the average across all funds in a given month, and then taking the average through time for a total of 96 monthly observations. The full sample (all funds) includes 33 index mutual funds with median total net assets over the period over $100 million. All funds without VFINX are the full sample excluding Vanguard’s VFINX. VFINX and SPDR (SPY) statistics are listed separately. Total net assets (TNA) are stated in millions and fund flow represents the percent flow in or out of the fund on a monthly basis. The return is calculated as the monthly change in NAV. The t-values represent a difference in means test between the sample group of variables (Flow of Funds, TNA, and Return) and the corresponding SPDR variable, assuming unequal variances.
Standard Mean Minimum Maximum T-Value All Funds Deviation Flow of Funds 1.03% 1.75% -3.68% 7.08% 2.52* Total Net Assets (TNA) 5288.62 963.47 2920.1 7996.5 12.51** Return 0.53% 4.82% -14.44% 9.73% 0.44
All Funds without VFINX Flow of Funds 1.05% 1.79% -3.80% 7.28% 2.50* Total Net Assets (TNA) 2820.7 509.6 940.9 4534.2 14.19** Return 0.53% 4.81% -14.44% 9.73% 0.44
VFINX Flow of Funds 0.43% 1.40% -6.46% 5.29% 3.18** Total Net Assets (TNA) 72,597.2 18,385.4 33,738.0 110,525.9 33.26** Return 0.70% 4.86% -14.47% 9.76% 0.09
SPY Flow of Funds 3.33% 8.88% -18.14% 47.33% Total Net Assets (TNA) 23,912.1 14,308.8 2,265.3 55,943.7 Return 0.69% 4.85% -14.43% 9.75%
** Indicates statistical significance at the 1% level. * Indicates statistical significance at the 5% level.
25
Table 2 Flow of Funds Regression Estimates
This table reports the results of the fixed-effects regressions, controlling for fund, specified in equation (3) with the dependent variable the percent flow of funds (PFFt). The regressions are estimated on three samples over monthly observations from January 1997 through December 2004: all funds indexed to the S&P 500 with total net assets greater than $100 million, all funds with total net assets greater than $100 million excluding VFINX, and Vanguard’s VFINX. The regression with VFINX is time-series. The independent variables are two months of lagged flow of funds (PFFt-1-PFFt-2), the exchange traded SPDR flow (SPYPFFt), and two months of lagged SPDR flow of funds (SPYPFFt-1- SPYPFFt-2). Additional controls include the expense of the fund (EXPENSEt), current monthly fund returns (RETt), one-month lagged fund returns (RETt-1), and the log of total net assets (LNTNAt). To control for alternative investments, the return on the NAREIT all equity real estate index (RRETt), its one month lag (RRETt-1), a commodities index (CRETt), and its one-month lag (CRETt-1) are included. Standard errors are adjusted for clustering and adjusted for autocorrelation and heteroskedasticity following procedures in Newey and West (1987). Absolute values of t-statistics are in parentheses below the coefficients.
All Funds without All Funds VFINX VFINX PFFt-1 0.079 0.078 0.289 (1.19) (1.18) (0.75) PFFt-2 0.170 0.170 -0.052 (5.16)** (5.16)** (0.19) SPYPFFt -0.028 -0.029 0.002 (2.40)* (2.42)* (0.11) SPYPFFt-1 0.016 0.017 -0.016 (1.64) (1.71) (1.00) SPYPFFt-2 -0.017 -0.016 -0.015 (1.92) (1.84) (1.72) EXPENSEt -5.185 -5.271 (7.78)** (7.83)** RETt 0.076 0.076 0.058 (4.10)** (3.99)** (1.72) RETt-1 0.069 0.068 0.098 (3.84)** (3.66)** (1.96) LNTNAt -0.015 -0.015 -0.011 (3.36)** (3.32)** (1.20) RRETt -0.083 -0.086 -0.006 (3.49)** (3.46)** (0.24) RRETt-1 -0.110 -0.113 -0.055 (2.62)** (2.61)** (1.45) CRETt -0.019 -0.021 0.034 (1.10) (1.20) (1.06) CRETt-1 -0.016 -0.016 -0.006 (0.76) (0.75) (0.26) Intercept 0.134 0.133 0.125 (4.20)** (4.19)** (1.24) Observations 2622 2529 93 2 R 0.15 0.15 0.36
** Indicates statistical significance at the 1% level. * Indicates statistical significance at the 5% level.
26 Table 3 Differences in Traditional Explanatory Variables Predicting Fund Flows
This table reports the results of the time-series regression of SPDR flow of funds and the fixed-effects regression, controlling for fund, specified in equations (4a) and (4b), respectively, with the dependent variable the percent flow of funds for SPDR (SPYPFFt) and index funds (PFFt), respectively. The independent variables include two months of lagged flow of funds (PFFt-1-PFFt-2), with the SPDR regression using lagged SPDR flows (SPYPFFt-1-SPYPFFt-2). Additional controls include the expense of the fund (EXPENSEt), current monthly fund returns (RETt) for the fixed-effects regression and SPDR returns (SPYRETt) for the SPDR regression, one-month lagged returns (SPYRETt-1, RETt-1), and the log of total net assets (LNTNAt). To control for alternative investments, the return on the NAREIT all equity real estate index (RRETt), its one month lag (RRETt-1), a commodities index (CRETt), and its one-month lag (CRETt-1) are included. Standard errors are adjusted for clustering and adjusted for autocorrelation and heteroskedasticity following procedures in Newey and West (1987). Absolute values of t-statistics are in parentheses below the coefficients.
SPYPFFt PFFt SPYPFFt-1(PFFt-1) 0.030 0.076 (0.17) (1.15) SPYPFFt-2(PFFt-2) -0.128 0.171 (1.37) (5.20)**
EXPENSEt -6.026 (8.63)**
SPYRETt (RETt) 0.428 0.066 (1.60) (3.76)**
SPYRETt-1 (RETt-1) -0.145 0.079 (0.73) (4.41)**
LNTNAt -0.026 -0.015 (0.68) (3.32)** RRETt -0.105 -0.079 (0.43) (3.37)** RRETt-1 -0.075 -0.109 (0.34) (2.60)** CRETt -0.289 -0.015 (2.07)* (0.83) CRETt-1 0.295 -0.024 (1.33) (1.09) Constant 0.328 0.133 (0.76) (4.20)** Observations 93 2622 R2 0.13 0.15
27 Table 4 Flow of Funds Regressions Controlling for Distinct Traders
This table reports the results of the time-series regression of SPDR flow of funds and the fixed-effects regression, controlling for fund, specified in equations (6a) and (6b), respectively, with the dependent variable the percent flow of funds for the SPDR (SPYPFF) and index funds (PFF), respectively. The first two regressions are estimated for the SPDR, the second two are for all index mutual funds in the sample. The independent variables are current monthly fund returns (RET) for the fund regressions and SPDR returns (SPYRET) for the SPDR regression, a binary variable with the value one for the bull market in years 1997-2000 (BULL), a measure of current volume relative to the past year volume (VOLUME), and the VIX index (VIX). Additional variables include the difference between interest rates for the 10-year Treasury note and the 1-year Treasury bill (TERM), a measure of option volume (OPT_VOL), and option open interest (OPT_OI). Standard errors are adjusted for clustering and adjusted for autocorrelation and heteroskedasticity following procedures in Newey and West (1987). Absolute values of t-statistics are in parentheses below the coefficients.
SPYPFF SPYPFF PFF PFF SPYRET(RET) 0.306 0.302 0.044 0.042 (1.65) (1.62) (2.12)* (2.06)* BULL -0.018 -0.014 0.024 0.026 (0.70) (0.53) (6.13)** (6.59)** VOLUME 0.036 0.044 0.008 0.011 (1.08) (1.27) (2.32)* (2.99)** VIX 0.585 0.554 -0.025 -0.031 (2.07)* (1.96)* (1.11) (1.41) TERM -2.024 -1.721 0.031 0.167 (1.81) (1.50) (0.17) (0.93) OPT_VOL 0.006 -0.005 (2.30)* (2.01)* OPT_OI 0.022 0.004 (1.02) (0.72) Constant -0.059 -0.078 -0.009 -0.005 (0.91) (1.19) (1.30) (0.88) Observations 93 93 2686 2686 R2 0.21 0.20 0.11 0.11
** Indicates statistical significance at the 1% level. * Indicates statistical significance at the 5% level.
28 Table 5 Flow of Funds Regressions Controlling for Investor Type
This table reports the results of the fixed-effects regressions, controlling for fund, specified in equation (3) with the dependent variable the percent flow of funds (PFFt). The regressions are estimated on three samples over monthly observations from January 1997 through December 2004: all funds indexed to the S&P 500 with total net assets greater than $100 million, all funds with total net assets greater than $100 million excluding VFINX, and Vanguard’s VFINX. The regression with VFINX is time-series. The independent variables are two months of lagged flow of funds (PFFt-1-PFFt-2) and current monthly fund returns (RETt). Controls include the log of total net assets (LNTNAt), the return on a real estate index (RRETt), its one-month lag (RRETt-1), a commodities index (CRETt), and its one-month lag (CRETt-1). Additional controls include the expense of the fund (EXPENSEt), a binary variable with the value one for the bull market in years 1997-2000 (BULLt), a measure of current volume relative to the past year volume (VOLUMEt), and a measure of option volume (OPT_VOLt). To control for flows into the SPDR, the residual of equation (6a) is included (RESID) along with its two lags (RESIDt-1-RESIDt-2). Standard errors are adjusted for clustering and adjusted for autocorrelation and heteroskedasticity following procedures in Newey and West (1987). Absolute values of t-statistics are in parentheses below the coefficients.
All Funds All Funds without VFINX VFINX PFFt-1 0.062 0.061 0.224 (0.97) (0.96) (0.58) PFFt-2 0.148 0.149 -0.190 (4.66)** (4.66)** (0.69) EXPENSEt -4.376 -4.475 (4.83)** (4.84)** RETt 0.050 0.049 0.048 (2.61)** (2.51)* (1.80) RETt-1 0.048 0.047 0.073 (2.52)* (2.41)* (1.70) LNTNAt -0.016 -0.016 -0.018 (3.56)** (3.52)** (2.23)* RRETt -0.051 -0.053 0.015 (2.29)* (2.30)* (0.54) RRETt-1 -0.083 -0.086 -0.04 (2.03)* (2.02)* (1.17) CRETt -0.015 -0.017 0.043 (0.83) (0.92) (1.25) CRETt-1 -0.026 -0.026 -0.005 (1.30) (1.28) (0.23) BULLt 0.011 0.011 0.009 (4.24)** (4.17)** (3.78)** VOLUMEt -0.003 -0.003 -0.007 (0.91) (0.83) (2.03)* OPT_VOLt -0.002 -0.002 0.008 (0.51) (0.54) (2.16)* RESIDt -0.038 -0.039 -0.006 (2.91)** (2.92)** (0.37) RESIDt-1 0.011 0.013 -0.021 (1.20) (1.28) (1.32) RESIDt-2 -0.033 -0.033 -0.032 (2.77)** (2.67)** (3.10)** Constant 0.138 0.136 0.202 (4.25)** (4.25)** (2.25)* Observations 2622 2529 93 R2 0.16 0.16 0.44 ** Indicates significance at the 1% level. *Indicates significance at the 5% level.
29 The figure plots the monthly total net assets (TNA) between the SPDR exchange traded fund (SPDR), Vanguard 500 index fund (VFINX), and the average of 33 index mutual funds for the period of January 1997 to December 2004. The values are measured for each year in December.
120,000 SPY TNA VFINX TNA Average TNA
100,000
80,000
60,000
40,000
20,000
‐ 1997 1998 1999 2000 2001 2002 2003 2004
Figure 1 Total Net Assets
30
CHAPTER 3: INVESTOR SENTIMENT: ITS’ EFFECT ON ETF PRICING AND CREATIONS AND DELETIONS
1.1 Introduction Exchange traded funds (ETFs) have become an increasingly important and popular security in the index mutual fund product category. ETFs have experienced tremendous growth in both total assets as well as the number of funds offered since their 1993 introduction in the US market with Standard & Poor's Depositary Receipts (SPDRs). This growth and popularity has prompted many to question what drives investors to invest in these products and the role ETFs play in our financial markets. Recent literature has examined numerous issues related to the ETFs’ impact on our financial markets. This includes, but is not limited to, the examination of the impact ETFs have on the index futures market as well as changes in the market liquidity and pricing errors of the underlying stocks these ETFs hold (see Switzer, Varson and Zghidi (2000), Hedge and McDermott (2004) and Ackert and Tian (2000), respectively.) Alternatively, less has been done to further our understanding of what actually drives investors to invest in these products and what factors are significant in this decision. The question of what motivates ETF investors is of interest given that these investors often pay market prices that deviate from the fundamental net asset value per share (NAV). Panel A of Table 1 offers descriptive statistics for the sample while Panel B of Table 1 shows that on average, market prices deviate from the NAV over 96% of the time for the ETFs in the sample. Moreover, these deviations may be economically significant given that unlike closed end funds whose prices also deviate from the NAV, ETFs, through a mechanism known as redemption in-kind, allow institutional investors, also called Authorized Participants19, to potentially earn a profit by arbitraging away these pricing deviations by creating and deleting outstanding shares of the ETF. Given that it is possible to earn a profit as a result of a pricing imbalance, it is of interest to identify factors that may be relevant in determining these premiums and discounts to the NAV. Specifically, I seek to determine whether these pricing deviations and subsequent creations and deletions are affected by investor biases, or factors
19 Authorized Participants are those institutional investors authorized to initiate creations and deletions.
31 unrelated to risk as proxied by investor sentiment surveys20. I also examine whether market capitalization affects the pricing and creation and deletion activity of the sample of ETFs used in this study. This chapter seeks to increase our understanding of ETFs by analyzing the effects of institutional and individual investor sentiment not related to risk, in determining 1) whether investor sentiment orthogonal to common risk factors, plays a role in the pricing deviations from the NAV of ETFs, 2) whether there is a distinction between the impact of institutional versus individual investor sentiment with respect to these pricing deviations, 3) whether the significance of sentiment varies across various ETF market capitalizations, and 4) whether creations and deletions are related to individual and/or institutional investor sentiment. This analysis is important as it attempts to shed light on whether factors unrelated to risk drive ETF investors, subsequently affecting price, and is to my knowledge the first study that helps increase our understanding of the motivations behind pricing deviations and the creation and deletion mechanism of ETFs; specifically, I explore whether institutional or individual investors significantly affect these changes. Poterba and Shoven (2002) show that individual investors may prefer ETFs over similar investment options given their preferential tax treatment. Hughen (2001) and essay 1 of my dissertation show that ETFs may be attractive to many short term traders given their high turnover rates and the ability to short ETF shares and trade them on margin. Moreover, institutional investors should be attracted to ETFs given the growth in index investing in the US (see Bhattacharya and Galpin (2005)) and the ability to arbitrage by creating and deleting shares in response to pricing deviations from NAV. Institutional investors may also be attracted to ETFs since the creation and deletion mechanism may offer them an efficient means of transacting in small and medium capitalized securities. Accordingly, it is of interest to disentangle the motives of these respective traders to determine whether they are strictly driven by knowledge about changes in fundamental risk as the efficient market framework would suggest, or whether they may be motivated by factors that are considered unrelated to risk.
20 The investor sentiment surveys used in this study are by American Association of Individual Investors (RETAIL) and Investor Intelligence. Both surveys are conducted on a weekly basis and “measures the percentage of individual investors who are bullish, bearish, and neutral on the stock market short term” and compiles attitudes toward market conditions. These surveys represent individual and institutional investor sentiment, respectively.
32 Lee, Shleifer and Thaler (1991) find that small investor sentiment has the ability to affect the pricing of smaller capitalized stocks. Moreover, it has been shown that institutional money managers have a tendency to trade for liquidity needs. In this regard, my analysis looks to see if these findings hold for ETFs. I seek to determine whose bias most affects ETFs by examining direct measures of both individual and institutional investor sentiment21. Of interest is whether short term investor biases significantly drive the trading and, thus, the price deviations of ETFs, in order to infer whether the trades of institutional investors are primarily driven by the desire to profit from arbitrage through creations and deletions, or whether other factors such as liquidity are a motivator22. Alternatively, given that institutional investors can play such a large role in the ETF market through the creation and deletion mechanism, it is of interest to examine the relationship between individual and institutional investor sentiment. Due to intraday trading the price an investor pays for an ETF on the open market may be driven by non-risk related measures, more so than for open-end index funds, as investors’ beliefs and perceptions of the market may affect demand and, thus, market prices. Measures of investor sentiment, which serve as a proxy for how investors form their beliefs about the state of the market, i.e. how bullish or bearish they are, offer a means of testing the impact of these non-risk related factors. Moreover, the data sets used in this study allow me to analyze individual and institutional investor sentiment separately. This is critical to this study given that I want to disentangle the effects these two investor sets have on ETFs. Accordingly, further examination of the ETF through investor sentiment measures gives an opportunity to analyze differences between individual and institutional behavior, provide incite on the role of this product in our securities market, and could have implications for the traditional risk-based asset pricing model literature. Specifically, after accounting for the portion of sentiment not attributable to risk23, I seek to determine whether sentiment plays a significant role in driving market price and creation and
21 This analysis uses institutional and individual investor sentiment measures from Investor Intelligence and American Association of Individual Investors, respectively. These measures are independent of each other and represent direct survey data. (see Brown and Cliff (2004, 2005)). 22 See Chordia, Roll and Subrahmanyam (2001) and Datar (2001) for a discussion on the role of liquidity in trading activity and closed end funds, respectively. 23 It is of interest to determine whether there is a significant difference in the level of institutional versus individual investor sentiment not attributable to risk.
33 deletion activity of the ETF and further whether creation/deletion activity may be driven by liquidity needs. I also examine whether any such relationship differs across ETF products based on size. A significant relationship would add to the literature on ETFs by highlighting the interplay between exchange traded products and investor behavior in the market. Moreover, this study contributes by adding to the literature of whether investor sentiment affects security characteristics such as pricing.
2.1. Investor Sentiment and Noise Trader Literature Indexing is increasingly becoming the preferred method of investment. Bhattacharya and Galpin (2005) show that active management and stock picking has steadily declined in the US from a high of nearly 60% in the 1960s to a low of 24% in the 2000s. These findings are likely the product of results that show that active managers fail to consistently beat the market’s return, net of expenses Gruber (1996). Moreover, of interest is the examination of financial innovation in the index market via ETFs and whether, given its unique trading mechanism, non risk related perceptions play a part in driving demand and, thus, prices for ETFs. The literature on investor sentiment generally examines investors’ perceptions of the market independent of fundamentals. Often nested within literature discussing the effects of “noise traders” or those irrational investors that base their trading decisions on factors other than fundamentals, investor sentiment studies either support of refute whether non risk related measures have the ability to change some fundamental aspect of a security such as price. Black’s (1986) theory of “noise traders” holds that these investors trade on signals unrelated to fundamentals, yet adds that noise is necessary for the liquidity of our markets. Numerous papers examine the effect measures of investor sentiment have on financial assets; however, this research is generally confined to closed-end mutual funds and stock and returns. Using a large sample of closed-end funds from the U.K., Gemmill and Thomas (2002) find that the discounts on closed-end funds result from the dynamic relationship between noise trades and the rational arbitrageurs to these funds. Specifically, they find that closed-end fund discounts are a function of noise trader demand. Shleifer and Summers (1990), DeLong, Shleifer, Summers and Waldman (1990), and Barber, Odean and Zhu (2005) all show that noise traders play a significant
34 role in our financial markets by showing that these less than fully rational traders have the ability to affect prices, thus presenting a challenge to the efficient market framework. Lee, Shleifer and Thaler (1991) examine closed-end funds and whether changes in individual investor sentiment drive fluctuations in the discounts of these funds. They primarily attempt to shed light on the “closed-end fund puzzle” which describes the life cycle of a closed-end fund. Lee et al, conclude that not only does individual investor sentiment serve as a proxy for discount levels, but changes in investor sentiment make funds riskier than the underlying assets they hold. Another interesting finding is that investor sentiment affects smaller stocks to a larger degree, thus making them more risky. This is interesting because I will test the effects of investor sentiment across varying sizes of ETFs and, if their size conjecture is true, the price deviations of ETFs tracking benchmarks holding smaller stocks should be greater than the deviations for ETFs that track indexes holding large-capitalization stocks24. By contrast, Chen, Kan and Miller (1993) and Elton, Gruber and Busse (1998) offer results that do not support the findings of Lee et al. Elton et al test whether individual investor sentiment, as proxied by changes in closed-end fund discounts, is a significant factor in determining the return generating process for shares of common stock. They test whether closed-end funds, which are found by Lee et al to be significantly related to the closed-end fund discount sentiment measure, offer higher expected returns. Elton et al find that closed-end fund discounts can be explained by factors that are not related to sentiment. Moreover, Chen et al conclude that the Lee et al findings are not supported by the data and are highly sensitive to the time period tested. Using a sample of US stock funds, Neal and Wheatley (1998) test the ability of three indirect measures of investor sentiment to predict returns. They conclude that closed-end fund discounts and mutual fund net redemptions have the ability to predict the premium in returns between small and large firms. They fail to find evidence that the third indirect measure, odd-lot ratios, have return predictive power. Bodurtha, Kim and Lee (1995) examine how measures of US investor sentiment affect the premiums and discounts of closed-end country funds. Country funds are
24 Delong, Shleifer, Summer and Waldmann (1990) and Lee, Shleifer and Thaler (1991) note that if noise traders, as proxied by investor sentiment, affect the mutual fund as well as the assets likely held by the fund, then there should be changes in NAV as well as the price of the underlying stock. Given that they move in the same direction there should not be significant changes in the discounts of closed-end funds in this situation.
35 investment vehicles that invest solely in a specific foreign market25, say Singapore or Chile, and because they are closed-end they sell at premiums or discounts to their NAV. Bodurtha, Kim and Lee attempt to provide evidence either against or for the investor sentiment hypothesis, which states that premiums and discounts of closed-end funds reflect levels of investor optimism or pessimism about a security. Accordingly, as investors become more optimistic or pessimistic about a security, prices move away from fundamentals and subsequently mean-revert. Consistent with the findings of DeLong, Shleifer, Summer and Waldman (1990), Bodurtha et al find higher (lower) country fund premiums associated with lower (higher) future returns, which provides additional support for the investor sentiment hypothesis. Kaniel, Saar and Titman (2004) examine the interplay between sentiment and individual investor buy and sell patterns. Among other things, Kaniel et al find that individuals exhibit contrarian behavior and that these risk-averse investors offer the liquidity needed to meet the demand of institutional investors. Using the University of Michigan Index of Consumer Sentiment (ICS) as a proxy, Lemmon and Portniagina (2006) find that this index of sentiment has the ability to forecast the returns of smaller capitalized stocks. Brown and Cliff (2004, 2005) investigate investor sentiment measures and the effect these measures have on stock market returns in the near-term and the valuation of assets, respectively. These two studies provide the focal point for this study given their unique data set, which includes direct measures of investor sentiment for both individual and institutional investors. Brown and Cliff (2004) test the ability of various sentiment measures to predict short-run market returns. They find that market returns cause changes to future investor sentiment, but that the inverse does not hold. They also find that changes in sentiment measures are highly correlated with contemporaneous returns. Finally, and perhaps more significant to this study, is the finding that institutional sentiment is strongly related to the contemporaneous returns of large stocks. Brown and Cliff (2004) note that this is a key finding given that prior literature on noise trading generally concludes that sentiment represents the individual side of investing and only small stocks tend to be affected. This result is important given that I test the effects of
25 A foreign market is labeled as such if the shares in the country fund are foreign to the exchange it sells on.
36 sentiment on ETFs with holdings of varying market capitalizations, which might allow me to either refute or add support to the Brown and Cliff (2004) findings with respect to institutional investor sentiment and its possible effects across size categories. Brown and Cliff (2005), similar to Brown and Cliff (2004), use direct measures of investor sentiment to test whether sentiment serves as a proxy for over-optimism in the market. Subsequently they test whether price run-ups from over-optimism mean reverts. Brown and Cliffs’ (2005) study differs from others such as Lee, Shleifer and Thaler (1990) and Neal and Wheatley (1998) in that it also examines the long horizon effects of investor sentiment on the return generating process. Finally, Brown (1999) finds that investor sentiment is significantly related to the price volatility of closed-end funds. By examining ETFs I provide evidence on whether and how individual and institutional investors behave in the market, as well as how non risk related measures, as measured by direct measures of investor sentiment orthogonalized to common risk factors, affect the pricing and levels of creations and deletions of ETFs.
2.2 ETF Literature The literature on ETFs is either descriptive in nature or provides tests of market effects given their introduction. Fuhr (2001) offers a general introduction to the overall characteristics of ETFs. Elton, Gruber, Comer and Li (2002) also detail ETF characteristics, and examine the performance of the SPIDER relative to its benchmark and low cost index funds. Boehmer and Boehmer (2003) examine the entry of ETFs to the NYSE in 2001 to analyze the effect the entry of a new product, the ETF, has on the market liquidity. They find that liquidity for securities held by the ETF significantly improves and is likely the result of the reduction of the role of market makers. Hedge and McDermott (2004) examine changes in the liquidity of the underlying stocks of the Dow Jones Industrial Average and the NASDAQ after the introduction of DIAMONDS and Qubes,26 while Ackert and Tian (2000) examine pricing errors of the SPIDER relative to its underlying index, the S&P 500. Hedge and McDermott find that DIAMONDS and Qubes, or alternatively “basket trading,” is associated with significantly lower liquidity costs than a portfolio holding the same component stock.
26 DIAMONDS are units of the exchange traded fund that mimics the Dow Jones Industrial Average as it holds the same 30 component stocks of the index. Qubes are units of the exchange traded fund that mimics the NASDAQ 100 as it holds the same component stocks of this index.
37 Additionally, they find that basket trading not only improves the liquidity of the underlying stocks held by the ETF but also generally improves the liquidity of all assets through increased volume in the index futures market for the underlying index. By examining the SPDR, Ackert and Tian conclude that basket trading improves the pricing efficiency in the options market. Poterba and Shoven (2002) look at the tax benefits ETFs offer investors. They point out that mutual funds operate under specialized tax treatments called “pass throughs” where realized capital gains must be passed on to the shareholders. This pass through effect can raise the tax liability of mutual fund investors. However, ETFs take advantage of a specialized tax treatment referred to as redemption in kind, which allows the trustee of the ETF to distribute shares of the underlying stock, in place of cash, when units of the trust are deleted, thus avoiding potential capital gains. Park and Switzer (1995), Switzer, Varson and Zghidi (2000), and Chu and Hsieh (2002) examine the effects ETFs have on the index futures market. Park and Switzer look at the effects TIPs (Toronto 35 Index participation Units27) have on the Canadian index futures market. They find that TIPs increase the overall trading volume in index futures and significantly improve the overall pricing efficiency within that market. Switzer, Varson, and Zghidi (2000) and Chu and Hsieh (2002) examine how the introduction of the SPDR affected the pricing efficiency of the S&P futures market. They conclude that the introduction of the SPDR mitigates the positive pricing errors in S&P 500 Index futures contracts. Chu and Hsieh conclude the SPDR improves pricing efficiency since it simplifies the process of short arbitrage. Although the above is not exhaustive, the literature on ETFs is relatively new and generally centers around getting a better understanding of what Fuhr (2001) comments “may well be considered the leading financial innovation of the last decade.” Accordingly, this study adds to the literature by furthering our understanding of the mechanics of the ETF and its interplay between the market and various market participants. This study helps shed light on the role investor sentiment plays in the role of determining the level of creations and deletions in ETFs. Specifically, I provide evidence
27 In March of 1990 the Toronto Stock Exchange developed TIPs, which represent an interest in a trust that holds baskets of the stocks that make up the Toronto 35 Index. TIPs are akin to ETFs in the US market.
38 of whether institutional and/or individual investor sentiment orthogonal to risk significantly effects pricing deviations and the net change in creation and deletion activity of ETFs. Further, given the market capitalizations findings of Lee, Shleifer and Thaler (1991) and Brown and Cliff (2004), I test whether the significance of the sentiment measures differs across ETFs holding stocks with varying market capitalization. Specifically, I test whether institutional investor sentiment has a greater impact on ETFs containing large capitalization stocks and whether ETFs with smaller capitalization stocks are impacted to a greater extent by individual investor sentiment measures.
3.1. The Formal Hypotheses Brown (1999) notes “ it would be interesting to replicate our cross-sectional tests with US data and thereby verify, in a different environment, that it is the interplay of noise, arbitrage and expenses which cause [closed end] funds to trade at market prices that [deviate from] fundamental values.” Accordingly, this paper tests the following null hypotheses: 1a) H0: Measures of investor sentiment do not have a significant effect on the pricing of exchange traded funds. 1b) H0: Measures of institutional and individual investor sentiment have an equally significant impact on the pricing of exchange traded funds in the sample. 2) H0: Investor sentiment measures have an equal impact on the pricing of exchange traded funds holding stocks of varying market capitalizations. 3) H0: The change in the level of daily creation and deletion activity is not significantly impacted by investor sentiment.
Alternatively, my expectation is to reject the null of no significance. When comparing measures of individual investor sentiment against institutional investor sentiment my expectation is that these measures will not have an equal impact on the pricing deviations of the ETFs in the sample. In aggregate, I expect individual investor sentiment to have the stronger effect on the sample given my belief that it is the irrationally biased perceptions of these investors, often considered less sophisticated, that move prices away from fundamental value.
39 I also expect the impact of these respective measures on pricing deviations to vary across the sample grouped by market capitalizations. My expectation here is that smaller capitalized ETFs should be affected to a greater extent by individual investor sentiment, while ETFs characterized as holding large capitalized stocks should be impacted to a greater extent by institutional investor sentiment, since institutional investors likely overwhelmingly represent the greatest level of trading volume for larger capitalized securities. Medium capitalized ETFs are also examined and it is not clear which measure will have a significant effect on these funds. I expect that investor sentiment, both individual and institutional, will have a significant and positive relationship with the change in creation and deletion activity. Namely, as sentiment is high, in other words more bullish, I expect Authorized Participants to initiate creations and deletions. In other words, if sentiment affects pricing deviations it is of interest to see whether these pricing deviations significantly drive creation/deletion activity as opposed to, say, a liquidity driven reason. It is not clear whether individual or institutional investor sentiment will have a greater impact on this relationship. Given that institutions are the primary investors engaging in creation and deletion activity it is possible that institutional investor sentiment would be the primary or sole significant measure. Alternatively, it is possible that institutional investors are merely passive participants with respect to creations and deletions and as individual investor sentiment moves prices, institutional investors reactively engage in creation and deletions to take advantage of arbitrage opportunities.
4.0 Data 4.1 ETF Sample Data Daily and/or weekly data on all ETF returns, volume, net asset value per share, net creation and deletion activity and market price is from the Center for Research in Security Prices (CRSP) mutual fund database and Bloomberg. The sample includes 28 ETFs that track a variety of broad based indexes28 including the S&P 500, NASDAQ 100 and Dow Jones Industrial Average, to name a few. The sample also identifies ETFs according to their market capitalizations of the stocks they hold. Data is collected from
28 As determined by trading volume.
40 the date of inception for each ETF included in the sample through December 30, 2005. Moreover, data for the SPDR starts in January of 1995 given the availability of data.
4.2 Sentiment Proxies I examine the impact of two sentiment proxies. Gemmill and Thomas (2002), Shleifer and Summers (1990), DeLong, Shleifer, Summers and Waldman (1990) and Lee, Shleifer and Thaler (1991) all examine investor sentiment using what are considered indirect measures of investor sentiment. These measures include closed-end fund discounts, odd-lot sales and mutual fund redemptions. However, Brown and Cliff (2004, 2005), and Han (2006) are amongst the first to use investor survey data which can be classified as direct measures of investor sentiment. Moreover, Lemmon and Portniagina (2006) also use a monthly direct measure of investor sentiment through the University of Michigan index of consumer sentiment poll. This measure is not used in this study given the daily and weekly frequency of my data. Moreover, my study focuses on direct measures of investor sentiment over indirect measures given that direct measures may offer the most efficient means of testing the feelings and expectations of investors. In addition, the literature continues to argue the validity of many of the indirect measures often used to proxy for investor sentiment; therefore, I rely on direct measures. Similar to that used in Brown and Cliff (2004, 2005), and Han (2006), data for the direct measures of individual and institutional investor sentiment is from The American Association of Individual Investors and Investor Intelligence, respectively. The American Association of Individual Investors (RETAIL) conducts a random sentiment survey of its members in order to “measure the percentage of individual investors who are bullish, bearish, and neutral on the stock market short term.” Individuals are polled on a weekly basis. The direct measure of institutional investor sentiment is from Investor Intelligence (INST). The INST survey analyzes more than 120 independent financial advisory newsletters and classifies their attitudes toward market conditions. Specifically, they compile the number of newsletters that are bullish, bearish and neutral to market conditions on a weekly basis. Both retail and institutional surveys are released on Friday.
41 5.1 Methodology The focus of this examination is to test whether investor sentiment, as a measure of non-risk related bias, affects the pricing and subsequent creation and deletion activity of ETFs. This question is tested using direct measures of individual and institutional investor sentiment on the sample of ETFs, 1) in aggregate and 2) across ETFs with stocks of varying market capitalization. Per Brown and Cliff (2004, 2005) the metric used for investor sentiment for the retail and INST. data is the difference between the percentage of investors who feel bullish and bearish about the market. This data is provided on a weekly basis. I not only examine the significance of individual and institutional investor sentiment as the difference between those bullish and bearish on the market, but abnormal investor sentiment is also examined. I define abnormal investor sentiment for both sentiment proxies, where abnormal investor sentiment is calculated by first, taking the difference between bullish and bearish investor sentiment for each measure at time (t), and then subtracting it from its’ trailing eight week moving average.
5.2 Sentiment Components: Risk and Non-Risk The purpose of this study is to test the effects of investor sentiment on ETF pricing and creation and deletion activity. Specifically, I wish to isolate the component of sentiment most likely unrelated to risk to determine whether factors orthogonal to risk have the ability to affect security fundamentals such as price. Accordingly, I regress both direct sentiment measures on five commonly accepted risk measures to isolate the risk component of each measure. I use the residuals from these regressions as my measure of sentiment in all empirical testing. The risk measures are the term spread and risk premia, as well as the returns on the Fama and French (1993) market, size and value factors. Chen, Roll and Ross (1986) examine which state variables most likely affect stock prices and show that the term spread, which represents the difference in yield between the 10 year Treasury bond and one month Treasury bill, and the risk premia, which represents the difference in yield between the Moody’s BAA and AAA bond ratings, are significant. The Fama and French (1993) three factors are also commonly accepted as three proxies for risk.
42 Moreover, these proxies are similar in spirit to those used by Lemmon and Portniguina (2006). All data on term spread and risk premia is from the Federal Reserve website29. All returns from the Fama and French (1993) three factors are from the Kenneth French website30. Term spread and risk premia data are available on a monthly basis and, therefore, are held constant for the weeks in each calendar month. Data on these five risk factors are collected from January 1995 to December 30, 2005. The daily Fama and French factor returns beginning in January of 1995 are compounded to form weekly factor returns.
5.3 “Pure” Sentiment Regression Analysis It is necessary that the measures of sentiment are orthogonal to risk. As a result, both direct sentiment proxies are regressed on five macroeconomic risk variables. These macroeconomic variables include the term and default spread on interest rates and the three Fama and French (1993) market, size and value factors. Weekly data are used and the time-series regression for each sentiment proxy is:
SENTt = α + β1 RMRFt + β2 SMBt + β3 HMLt + β4 TERMt + β5 DEFt + εt (9)
where SENT represents one of the two specific proxies of investor sentiment; either RETAIL or INST.. RMRF, SMB and HML, respectively, represent the return on the market, size and value portfolios of Fama and French (1993). TERM represents the spread between the 10 year and three month Treasury bill, and DEF is the difference in yield between Moody’s BAA and AAA rated bonds. When used in the individual ETF regressions, the equation is estimated from the date of inception for that fund through December 30, 200531. When used in the pooled regression analysis, equation (9) is estimated over September 2001 through December 30, 2005. The residual from each regression is then employed as the measure of sentiment which is orthogonal to risk.
29 The Federal Reserve website is www.federalreserve.gov 30 Kenneth French’s website is http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ 31 This regression is estimated from January 1995 to December 30, 2005 for the SPDR ETF due to lack of data availability from January 1993 through December 1994.
43 Abnormal sentiment is the residual in week t subtracted from the average of the prior eight weeks residuals. Because of this eight weeks of averaging, eight weeks of observations are lost at the beginning of the time period when abnormal regressions are estimated.
5.4 Sentiment and ETF Effects 5.4.1 Autoregressive Nature of Sentiment Measures It is likely that the sentiment measures exhibit autocorrelation and show persistence in the time series. Given this, it is important to know how many periods of these respective sentiment measures need be included and controlled for in each regression. Accordingly, an autoregressive (ARMA) model is estimated. The time series model with weekly data is as follows:
yt = μ + γ1yt-1 + γ2yt-2 + … + γpyt-p + εt-1 - θ1εt-1 - θ2εt-2 - … - θqεt-q (10)
where yt represents the time series data for each one of the two sentiment measures, μ is a constant, γ1 through γp and θ1 through θq are the parameters of the model and ε 2 represents the error terms with the following assumption: εt ~ N(0, σ ). Accordingly, yt ~ N(0, σ2). The autoregressive portion of the model is used to gauge the number of lags used in the regression estimations. Table 2 presents results from the ARMA model and shows that both sentiment measures, individual and institutional, should include one lag in the regression. The results show that the second lag for both the individual and institutional sentiment measures are not significant at the 5 percent level, therefore a second lag is not included in regression estimations. 5.4.2 Sentiment and ETF Pricing Deviations Hypothesis 1a tests whether the percentage change in the measures of orthogonalized sentiment (sentiment hereafter) have the ability to affect the difference between market price and NAV, market price deviations, of the ETF. The weekly pricing deviations are calculated by subtracting the closing Friday NAV from the closing market price on that same day, as reported by Bloomberg. Both values are measured at 4:00pm.
44 These pricing deviations (ETFP) are calculated by differencing the NAV from the market price as presented below:
ETFPi,t = Pi,t – NAVi,t (11)
where ETFPi,t is the pricing deviation of ETF i at time t, Pi,t is the market price of ETF i 32 at time t, and NAVi,t is the net asset value per share of ETF i at time t . Two-stage least square regressions (2SLS) with weekly data are estimated 1) for each of the 28 ETFs individually and 2) by pooling a sub-sample of 22 ETFs. This instrumental variable estimation technique is used to control for potential endogeneity and/or omitted covariates so that consistent estimates for the independent variables may still be obtained. In this analysis, I estimate regressions where variables that are likely the cause of changes for both the independent and dependent variable are omitted. As a result of this potential for omitted variable bias, coefficient estimates may be highly biased. I estimate 2SLS regressions throughout this analysis, which helps correct for such bias. The 22 ETFs used for the pooled sub-sample all have data available from September 2001 through December 2005 and estimations are accordingly conducted over this period. The independent variables are measured in levels, as represented by the difference between market price and NAV, and abnormal sentiment. I pool the sample of ETFs to analyze the aggregate effect of sentiment on the sample, while testing each ETF identifies the individual fund effects. The time-series individual ETF 2SLS regressions are as follows:
ETFPi,t = αi + β1,i RETAILt + β2,i RETAILt-1 + β3,i INSTt + β4,i INSTt-1 + εi,t (12)
ETFPi,t = αi + β1,i ABRETAILt + β2,i ABRETAILt-1 + β3,i ABINSTt + β4,i ABINSTt-1 + εi,t (13)
where ETFPi,t represents the price deviations for ETF i for week t and RETAIL and INST represent the orthogonalized measure of sentiment, in levels, for American Association of Individual Investors and Investor Intelligence. In equation (13), ABRETAIL and
32 Percentage change in ETFP is also calculated as ΔETFPi,t = (Pi,t – NAVi,t ) / NAVi,t and this variable is also used as a dependent variable in equations 12-15.
45 ABINST represent the measure of abnormal sentiment for RETAIL and INST. A one week lag of each sentiment measure is also included in each estimation33. The pooled 2SLS regression equations are defined similarly, with the ETF subscript i deleted from the estimated coefficients. These equations are:
ETFPi,t = α + β1 RETAILt + β2 RETAILt-1 + β3 INSTt + β4 INSTt-1 + εi,t (14)
ETFPi,t = α + β1 ABRETAILt + β2 ABRETAILt-1 + β3 ABINSTt + β4 ABINSTt-1 + εi,t (15)
These regressions are used to gauge the significance and effect of sentiment with respect to changes in pricing deviations. My expectation is that pricing deviations will be positively related to sentiment, or as sentiment levels are high we should expect to see higher pricing deviations. I have a similar expectation with respect to the measures of abnormal sentiment. If there is a positive relation between pricing deviations and sentiment or abnormal sentiment, then null hypothesis number 1a would be rejected.
5.4.2 Individual vs. institutional investor sentiment impact on pricing deviations Hypothesis 1b asks whether individual and institutional investor sentiment affects the pricing of ETFs to an equal extent. Specifically, this question attempts to determine whether individual or institutional investor sentiment generally has the greater impact on ETF pricing deviations. By constraining, or excluding a subset of independent variables in the pooled sample, as denoted by regressions (14) and (15), and estimating an F-test across these regressions, I am able to determine which of the measures, individual or institutional, have the greater impact on the pricing deviations of the ETFs in the sample. The following constrained and unrestrained regressions are estimated and compared using the F statistic:
ETFPi,t = α + β1 RETAILt + β2 RETAILt-1 + β3 INSTt + β4 INSTt-1 + εi,t (14)
ETFPi,t = α + β1 RETAILt + β2 RETAILt-1 + εi,t (14a)
33 Table 2 presents results from an ARMA model indicating that one lag for all sentiment variables be included in the 2SLS regression estimations. Accordingly, all regression estimation results presented in the tables will include estimations with one lag.
46 ETFPi,t = α + β1 INSTt + β2 INSTt-1 εi,t (14b)
ETFPi,t = α + β1 ABRETAILt + β2 ABRETAILt-1 + β3 ABINSTt + β4 ABINSTt-1 + εi,t (15)
ETFPi,t = α + β1 ABRETAILt + β2 ABRETAILt-1 + εi,t (15a)
ETFPi,t = α + β1 ABINSTt + β2 ABINSTt-1 εi,t (15b)
where (14a) and (15a) represent the constrained regression estimations for individual investor sentiment and equations (14b) and (15b) represent the constrained regression estimations for institutional investor sentiment. Regressions are estimated for (14a) and (14), (15a) and (15), (14b) and (14), and (15b) and (15), respectively, and an F-test is then calculated for each regression pair to test the change in predictive power given the inclusion of all variables to the model. The F-test identifies which estimation yields the better fit and has the most explanatory power. The F-test, which follows an F-distribution having k-1 and T-k degrees of freedom, is as follows:
F = ((SSRres – SSR)/(k-1)) / SSR/(n-k) (16)
where SSRres is the sum of squared residual for the constrained regression, SSR is the sum of squares for the unconstrained regression, n represents the number of observations and k is the number of unrestricted coefficients. It is important to note that this test will only be implemented in a situation where both the individual and institutional investor sentiment measures are significant at a similar probability rejection level.
5.4.3 Sentiment and market capitalization Hypothesis 2 asks whether there are differences in effects between measures of individual and institutional sentiment as it relates to ETFs holding stocks of various market capitalizations. Namely, RETAIL is considered a measure of individual investor sentiment while INST. is considered a measure of institutional investor sentiment. Lee,
47 Shleifer and Thaler (1991) show that investor sentiment may affect smaller stocks more than they affect larger stocks, while Brown and Cliff (2004) find that institutional investor sentiment has the ability to impact larger stocks. To examine whether the relative effects of individual and institutional sentiment differ across ETFs holding stocks of different capitalization, modified versions of equation (12) and (13) are estimated with alternate pooled samples. The 22 ETFs in this pooled sample are grouped into three capitalization categories based on the market capitalization of the stocks held by the ETF as classified by Bloomberg market capitalizations. Moreover, market capitalization is also generally identified in the name of the fund. There are ten large capitalization ETFs, seven medium capitalization ETFs, and five small capitalization ETFs. The pooled 2SLS models are:
ETFPi,t = α + β1RETAILt + β2 RETAILt-1 + β3(D1,iRETAILt) + β4(D1,iRETAILt-1) + β5
(D2,i RETAILt ) + β6 (D2,i RETAILt-1 ) + β7INST.t + β8 INSTt-1 + β9(D1,iINSTt ) +
β10(D1,iINSTt-1) + β11 (D2,iINSTt ) + β12(D1,iINSTt-1) + εi,t (17)
ETFPi,t = α + β1ABRETAILt + β2 ABRETAILt-1 + β3(D1,iABRETAILt) +
β4(D1,iABRETAILt-1) + β5 (D2,i ABRETAILt ) + β6 (D2,i ABRETAILt-1 ) + β7ABINSTt +
β8 ABINSTt-1 + β9(D1,iABINSTt ) + β10(D1,iABINSTt-) + β11 (D2,iABINSTt ) +
β12(D1,iABINSTt-1) + εi,t (18)
where the binary variable D1,i (D2,i) equals one if ETF i holds small (medium) capitalization stocks, and zero otherwise. Thus, tests of significance of coefficients
associated with D1,i (D2,i) address whether ETFs with small (medium) capitalization stocks behave differently than ETFs with large capitalization stocks. The coefficients from these estimations are tested for significance in difference in the same manner used for hypothesis 1b in the case that it is necessary. I expect to find a positive and significant relationship for the INST. coefficient for ETFs holding stocks with large market capitalization. I expect that this measure will prove highly sensitive to the large capitalization ETFs relative to the RETAIL measure. I
expect the coefficient for the D1,iRETAIL variable (B3) to be positive, significant and relatively more sensitive to the smaller capitalized stocks than the coefficient for the
48 D1,i.INSTt (B5) variable. Accordingly, I have similar expectations with respect to tests involving abnormal sentiment. I have no expectation with respect to the D2,i coefficients (B4 and B6) and the ETFs with a mid-capitalization classification as it relates to which sentiment measure will prove most sensitive.
5.4.4 Investor sentiment and creation and deletion activity Hypothesis 3 asks whether investor sentiment affects the creation and deletion activity of ETFs. Specifically, I conduct an analysis to examine the relationship between investor sentiment and the change in daily creation/deletion activity and estimate whether the creation/deletion activity is significantly affected by pricing deviations (ETFP) which I use to proxy for arbitrage or whether liquidity (natural log of abnormal volume) is a motivating factor. Abnormal volume is defined as the volume for the ETF I on day t subtracted from the prior 2 week averaged daily volume, and is used to gauge the relationship between volume activity beyond average volume and creation/deletion activity. To examine these questions I use a pooled sample of 16 ETFs, which all have consistent daily creation and deletion data, and estimate 2SLS equations (20) through (23) as follows:
CDi,t = α + β1 RETAILt + β2 RETAILt-1 + β3 INSTt + β4 INST.t-1 + β5 ETFPi,t + β6
RETi,t-1 + β7 VOLt + εi,t (20)
CDi,t = α + β1 ABRETAILt + β2 ABRETAILt-1 + β3 ABINSTt + β4 ABINSTt-1 + β5
ETFPi,t + β6 RETi,t-1 + β7 VOLt + εi,t (21)
CDi,t = α + β1RETAILt + β2 RETAILt-1 + β3 INSTt + β4 INSTt-1 + β5 ETFPi,t + β6 RETi,t-1
+ β7 VOLt + β8 (D1,iRETAILt) + β9 (D1,iRETAILt-1) + β10(D2,i RETAILt ) +
β11(D2,i RETAILt-1 ) + β12 (D1,iINSTt ) + β13(D1,iINSTt-1 ) + β14 (D2,iINSTt ) +
β15(D2,iINSTt-1 ) + β16(D1,iETFPi,t ) + β17 (D2,iETFPi,t ) + β18 (D1,i RETi,t-1 ) +
β19(D2,iRETi,t-1 ) + β20(D1,i VOLt) + β21(D2,iVOLt ) + εi,t (22)
CDi,t = α + β1ABRETAILt + β2 ABRETAILt-1 + β3 ABINST.t + β4 ABINST.t-1 + β5
ETFPi,t + β6 RETi,t-1 + β7 VOLt + β8 (D1,iABRETAILt) + β9 (D1,iABRETAILt-1) +
49 β10(D2,i ABRETAILt ) + β11(D2,i ABRETAILt-1 ) + β12 (D1,iABINSTt ) + β13(D1,iABINSTt-
1 ) + β14 (D2,iABINSTt ) + β15(D2,iABINSTt-1 ) + β16(D1,iETFPi,t ) + β17 (D2,iETFPi,t ) +
β18 (D1,i RETi,t-1 ) + β19(D2,iRETi,t-1 ) + β20(D1,i VOLt) + β21(D2,iVOLt ) + εi,t (23)
where CDi,t represents the daily percentage change in net creation/deletion activity for ETF i in period t. Unlike previous regressions, here data observations are daily instead of weekly. The weekly investor sentiment measures used in equations (11) through (18) and (19) through (23) are held constant for the week days of the release of the Friday survey.
ETFPi,t represents the daily price deviation from NAV for ETF i on day t, and RETi,t-1 represents the previous days return for ETF i as reported by CRSP. The natural log of abnormal daily volume for ETF i on day t (VOL) represents abnormal volume which is defined as the averaged volume for the calendar week minus day t volume. Abnormal volume is included as a gauge for liquidity in order to determine whether liquidity needs significantly drive creation/deletion activity. Specifically, it may be possible that creation and deletion activity is driven by the increased need for either the underlying stocks the ETF holds or the ETF itself. The return variable is used to determine whether the individual ETFs return significantly affects the net creation/deletion activity. I do not expect a significant relationship between the sentiment proxies, abnormal volume, return and the percentage change in creation/deletion activity for the aggregate pooled sample. I do expect a positive and significant relationship between ETFP and creation/deletion activity which would indicate that as pricing deviations are higher we should expect creation activity, and as pricing deviations are lower (more negative) we should expect deletions. I do not expect a highly significant relationship between ETF creations and deletions and the return and volume variable given my overall hypothesis that creations and deletions activity is in response to arbitrage opportunities which should be dictated by the size of the market price deviations from NAV. My expectations with respect to the market capitalization of the securities held by the ETFs only differ given that I expect that small capitalized ETF creation/deletion activity may be positive and significantly impacted by abnormal volume given that Authorized Participants may use and find creation and deletion activity an efficient means of meeting liquidity (demand) needs in securities that they are less likely to
50 specialize and hold inventory in. As before, it is not clear which factors will significantly affect the medium capitalization ETFs. Moreover, if necessary I then test for a significant difference in the sensitivities of the coefficients between the individual and institutional sentiment measures.
6.0 Results: Hypothesis 1a asks whether measures of individual and/or institutional investor sentiment orthogonalized to risk significantly impacts the market price deviations from NAV of a sample of twenty eight broad based ETFs. This question is examined 1) at the individual fund level, 2) in aggregate level and 3) using measures of abnormal sentiment as indicated by equations 12-15. Results are presented in Tables 3 and 4. Table 3 presents the coefficients and t-statistics for the 2SLS estimations of the effect of investor sentiment on the market price deviations from NAV common to ETFs at the fund level (equations 12 and 13). T-statistics are reported below the coefficient estimates. Panel A of Table 3 presents results for sentiment measured as the difference (labeled Difference) between those bullish and those bearish on the market as specified in equation (11), while Panel B presents the results at the fund level using abnormal sentiment which is measured as orthogonalized sentiment at time t subtracted from the 8 week trailing average. Table 4 presents aggregate results for the effect of individual and institutional investor sentiment on market price deviations from NAV for the full sample as specified in equations 14 and 15. Similar to Table 3, Panel A presents results for sentiment as the difference between those bullish and bearish (Difference) while Panel B presents results for the effect of abnormal sentiment on the deviations. Of the 28 individual ETF estimations in Panel A of Table 3, 8 funds were found to have a significant relationship between their market price deviations and the measures of investor sentiment. Of the 8 funds, 3 are small capitalization, 3 are medium capitalization and 2 are large capitalization. The regression estimations do not seem to lead me to any immediate noticeable or meaningful interpretations. However, I note that for the 8 funds that show a significant relationship, either RETAIL or INST. is significant, not both. Moreover, when institutional sentiment (INST) and its lag are significant, the effect is always negative and positive, respectively. In additional, when the individual sentiment measure (RETAIL) and its lag are significant, the effect is
51 opposite that of the institutional (INST) measure. Namely, with one exception (ticker IWM), RETAIL and its lag have a positive and negative relationship, respectively. These findings partially support my initial expectation of a positive and significant effect for the contemporaneous sentiment measure. Namely, the individual measure (RETAIL) is positive in all but one case where it is significant. However, the institutional measure (INST) is negative in the instances when it is significant. I had no clear expectation with respect to the sign and significance of the lags. The results for abnormal sentiment as presented in panel B of Table 3 are similar. Here, 7 funds show a significant relationship. Of the 7 funds, 3 are medium capitalization, 2 are small capitalization and 2 are large capitalization. As in Panel A one, not both, sentiment measures are significant and, in general, the individual and institutional sentiment measures have opposing signs. Table 4 presents the results for the aggregate sample. Results are presented, both, where sentiment is measured as the difference between those bullish and those bearish on the market, and for measures of abnormal sentiment calculated in the same manner as explained for Table 3. In general, Table 4 shows that the contemporaneous measure of institutional sentiment (INST) has a negative and significant relationship on the market price and NAV deviations. This is the case when measuring sentiment in difference and as abnormal sentiment. Moreover, when examining the sentiment results in difference, the lagged individual sentiment is positive and significant. These results, overall, may indicate that institutional sentiment orthogonalized to common risk factors significantly causes smaller pricing deviations. In other words, as these investors are more bullish on the market, as it relates to decisions unrelated to common risk, pricing deviations for the sample of ETFs generally become smaller. The fact that the lagged individual sentiment measure is positive and significant for the measure of sentiment in difference may or may not be all that telling given that INST is negative and highly significant for both difference and abnormal sentiment, while the lagged individual sentiment measure is not34. These results lend support to the notion that institutional investors play a significant role in ETF pricing given their ability to implement creations and deletions, which can serve the purpose of arbitraging away pricing deviations for the ETF. This
34 For robustness I also estimate a scaled measure of ETFP as follows: ETFPi,t = (Pi,t – NAVi,t ) / NAVi,t and this variable is also used as a dependent variable in regressions 12-15. The results are not significantly different from those presented in the tables.
52 interpretation would support the general finding of a negative and significant relationship. Accordingly, I reject the hypothesis of no significance, although my initial expectation of a positive and significant relationship between the sentiment measures and the pricing deviations is rejected. Hypothesis 1b asks whether individual and institutional investor sentiment affect the pricing deviations of the sample to an equal extent. This test is only performed in the event that both measures of sentiment were significant in the same regression estimation and at the same probability rejection level. This was not the case in any of the regression estimations. Therefore, equations 14-16 are not necessary. Hypothesis 2 asks whether the impact of orthogonalized sentiment on the pricing deviations of ETFs differs given a specific market capitalization. As specified in equations 17 and 18, Table 5 presents the 2SLS estimation results using a binary variable to denote whether the ETF is classified as a small or medium capitalization. A finding of a significant relationship for the small and medium capitalization dummy variables would indicate that the impact of the sentiment measure significantly differs from the large capitalization ETFs (the base case). A finding of no significance would indicate that the relationship does not tend to vary by size market capitalization. Panel A presents the results for the sample where sentiment is measured in difference, while Panel B presents results for the sample where sentiment is measured as abnormal sentiment. Panels A and B indicate that institutional sentiment (INST) and its lag are significant and have a negative and positive relationship with large capitalization ETFs, respectively. No significant results are found for the small and medium capitalization groupings indicating that the effect of sentiment does not differ by market capitalization. The RETAIL measures are not significant. Moreover, while it is not clear how to interpret the positive and significant institutional lag variable, the negative and significant contemporaneous measure (INST) seems to indicate that the more bullish institutional investors are, unrelated to common risk, the smaller the pricing deviations, in general, of the sample of ETFs, and the more bearish they are the larger the pricing deviations. Again, the results differ from my initial expectations. I expected a positive and significant relationship for both the RETAIL and INST. measures, and I also expected the significance of the measure to differ by market capitalization. This was not the case.
53 Hypothesis 3 examines the motivation for creation and deletion activity common to ETFs. The 2SLS regression estimations using daily creation and deletion data examine whether the net, daily percentage change in creation and deletion activity is significantly affected by individual and/or institutional investor sentiment (RETAIL and INST), the contemporaneous daily pricing deviations (ETFP), and return (Return), as well as a daily measure of abnormal volume (ABVOL) calculated as volume for ETF I on day t subtracted from the prior 2 week averaged daily volume. Volume is used to gauge liquidity activity. The question is examined 1) for the aggregate sample using differenced and abnormal sentiment, and 2) by market capitalization as presented in equations 20 and 21. Panels A and B of Table 6 present results for the aggregate sample with sentiment calculated as a difference and abnormal, respectively. Both Panels indicate a negative and significant relationship between volume and creations and deletion activity. Namely, as abnormal volume, which is a gauge for liquidity, is high, there tends to be a negative net percentage change in ETF shares outstanding. This indicates that as volume is high, Authorized Participants tend to implement more ETF deletions (ETFs are undervalued). My expectation here was primarily for a positive and significant sign for the (ETFP) variable. While it does have the predicted sign, it is not significant. No other variable is significant. Here, I must reject my initial expectation of a positive and significant (ETFP) which would support an arbitrage story for creation and deletion activity. However, this preliminary result may point to a liquidity driven explanation given the sign and significance of the volume measure. Panel C of Table 6 presents results for the 2SLS estimations of equations 22 and 23. These estimation results are similar to Panels A and B, however similar to Table 5, Panel C examines the impact of the same variables used in Panels A and B of Table 6 by market capitalization. Here we see that the only significant variable, as I initially predicted, is ETFP, or the difference between market price and NAV. However, it is only significant under the abnormal sentiment results for the ETFs. Moreover, because the small and medium capitalization groupings are not significant, I assume that the incremental impact of (ETFP) does not differ by market capitalization. This indicates that, in general, as pricing deviations are higher for the sample, and when volume is abnormally high, Authorized Participants tend to create more units of the ETFs in the sample. In general, this is the result I expected, however, I did not expect the result to be
54 confined to abnormal volume and I also expected the result to differ by market capitalization. Hypothesis 1 tests the null that measures of investor sentiment do not significantly affect the pricing of exchange traded funds. This null is rejected given the significance of these measures in individual as well as aggregate estimations. Moreover, it appears that, in general, institutional sentiment has the greater impact on these pricing deviations. Hypothesis 2 tests the null that the relationship between the sentiment measures and pricing deviations do not vary given the market capitalization of the ETF. I fail to reject this null given the binary estimations yield no significant results. Hypothesis 3 tests the null that daily creation and deletion activity is not significantly affected by investor sentiment measures. I fail to reject this hypothesis as well given the investor sentiment measures are not significant in any regression estimation.
7.0 Conclusions: All of these results present a fairly consistent picture which seems to indicate that institutional investor sentiment, as opposed to individual investor sentiment, significantly and negatively impacts the price and the net percentage change in creation and deletion activity of the sample of ETFs. Results do not appear to differ by market capitalization. In general, individual sentiment does not affect price and creation and deletion activity. Specifically, as institutional investor sentiment is higher (or more bullish) there tends to smaller pricing deviations, which is likely caused by the implementation of ETF deletions. The opposite should also hold with respect to more bearish institutional sentiment. Ironically, the results from Panel C of Table 6 indicate that when the market price of the ETF is higher than NAV, more shares tend to be created. This finding is somewhat surprising as it seems to be counter-intuitive to an arbitrage explanation. I would intuitively expect a negative relationship. Namely, if ETFs are selling at a price greater than NAV (positive ETFP) I would expect that Authorized Participants would want to create more shares (positive CD). This is not the case and is left unexplained here to be examined in future research.
55
Table 6 Descriptive Statistics
This Table offers the descriptive statistics for the sample of broad based exchanged traded funds (ETFs) used in the analysis from January 1997 through August 2006. The values are calculated by taking the end of week value as reported by Bloomberg for all funds in a given week, and then taking the average through time. Panel A includes all ETFs in the sample grouped by market capitalization. There are 3872 weekly observations for ETFs that are classified as large capitalization funds, 2491 weekly observations for ETFs that are classified as medium capitalization funds and 1737 weekly observations for ETFs that are classified as small capitalization funds. The “Price Difference” variable represents the difference between market value and net asset value on a weekly basis for the sample. Here a positive number should signify that, on average, the ETFs sell at a premium to net asset value. The “Absolute Value” variable is the calculation of the absolute value of the difference between market price and net asset value, on a weekly basis, over the sample period. This variable shows the magnitude of the average weekly deviation for the funds. Volume is the measure of weekly volume. Panel B presents the occurrences of price deviations where “Premium” represents occurrences when market price is greater than NAV, “Discount” represents occurrences when market price is less than NAV and “Net” is when the two are equal.
PANEL A Standard Large Capitalization Observations Mean Deviation Minimum Maximum Price Difference 3872 0.007 0.359 -9.128 3.540 Absolute Value 3872 0.149 0.327 0 9.128 Volume 3872 17.813 18.667 4.605 20.590
Standard Mid Capitalization Observations Mean Deviation Minimum Maximum Price Difference 2491 0.011 0.197 -3.153 2.430 Absolute Value 2491 0.114 0.162 0 3.153 Volume 2491 14.217 14.799 5.991 17.191
Standard Small Capitalization Observations Mean Deviation Minimum Maximum Price Difference 1737 0.004 0.317 -5.485 4.150 Absolute Value 1737 0.139 0.285 0 5.485 Volume 1737 13.675 14.559 5.298 16.917
PANEL B Discount Net Premium % Deviation Small Cap 784 42 911 97.58 Medium Cap 992 54 1445 97.83 Large Cap 1580 140 2152 96.38
56 Table 7 ARMA Results
This table presents results of an ARMA model indicating the autoregressive nature of the weekly individual and institutional investor sentiment measures as compiled by the American Association of Individual Investors (RETAIL) and Investor Intelligence (INST). A significant autoregressive nature indicates how many lags of individual and/or institutional sentiment are to be included in regression estimations utilizing the sentiment measures.
yt = μ + γ1yt-1 + γ2yt-2 + … + γpyt-p + εt-1 - θ1εt-1 - θ2εt-2 - … - θqεt-q (10)
RETAIL Coefficient t Value AR1,1 0.49765 40.28*** AR2,1 0.22061 1.86 INST Coefficient t Value AR1,1 1.06768 85.35*** AR2,1 -0.15747 1.56
*** Significance at the 1% level ** Significance at the 5% level
^Results for abnormal sentiment were not significantly different
57 Table 8 ETFP Results By Ticker
This table presents results for Hypothesis 1a which uses 2 stage least squares regression to examine whether direct measures of individual and/or institutional investor sentiment significantly impact the market price deviations from net asset value for the sample of ETFs by ticker. Weekly data from the American Association of Individual Investors (RETAIL) and Investor Intelligence (INST) whom survey and compile data on individual and institutional investor sentiment, respectively, is regressed on the weekly market price deviation as of the market close of 4pm on Fridays as represented by market price minus Fridays 4pm net asset value. Results are presented for estimates where orthogonalized sentiment is represented as the difference (Difference) between the residuals of the percentage of those investors bullish on the market minus the percentage of investors that are bearish on the market (Panel A). Results are also presented in abnormal sentiment (Abnormal) where abnormal sentiment is defined as sentiment in week t subtracted from the prior 8 week moving average (Panel B). T-statistics are shown under coefficient estimates.
58 Table 8 Continued PANEL A Difference
Ticker Intercept RETAIL INST RETAILLAG INST LAG Obs R-Sq
DIA 0.598 0.272 0.029 -1.432 0.003 231 0.008 1.070 0.200 0.510 -0.920 0.050 DSG 3.722 1.197 -0.255 1.407 0.184 231 0.0253 2.59** 0.430 -1.68* 0.710 1.460 DSV 2.427 1.068 -0.177 0.699 0.163 231 0.014 2.04** 0.500 -1.560 0.320 1.89* ELG 2.166 0.133 0.055 1.140 -0.092 231 0.0129 3.11*** 0.110 0.870 0.700 -1.340 ELV 2.163 -0.177 -0.096 -0.148 0.076 231 0.0088 2.2** -0.080 -0.870 -0.090 0.800 IJH 5.856 8.012 -0.097 -1.906 -0.048 231 0.0086 1.74* 1.170 -0.600 -0.460 -0.210 IJJ 1.526 2.784 -0.035 -0.218 0.021 231 0.0151 2.41** 2.09** -0.670 -0.180 0.440 IJK 0.966 0.467 -0.001 -0.512 0.010 231 0.0014 2.01** 0.240 -0.020 -0.280 0.270 IJR 6.642 -8.321 -0.248 7.260 0.134 231 0.0103 1.91* -0.620 -1.140 0.810 0.550 IJT 0.323 2.366 -0.054 -4.195 0.100 231 0.0276 0.440 0.960 -0.830 -1.590 1.340 IWB 3.432 1.998 -0.311 2.688 0.232 231 0.0454 2.3** 1.090 -2.39** 0.950 2.35** IWD 1.074 -1.425 -0.063 0.683 0.059 231 0.0137 1.85* -0.750 -1.190 0.370 1.000 IWF 0.846 0.784 -0.033 -1.156 0.030 231 0.0111 2.18** 0.740 -1.060 -1.020 0.980 IWM 0.715 -5.684 0.046 3.070 0.008 231 0.0237 1.470 -1.99** 0.630 1.190 0.100 IWN 0.024 -9.943 0.038 -7.953 0.158 230 0.0141 0.010 -1.330 0.410 -0.960 1.190 IWO 2.810 0.911 -0.029 3.635 -0.005 230 0.0138 1.640 0.240 -0.410 1.460 -0.050 IWP 1.829 3.032 -0.002 -0.532 -0.037 230 0.0425 3.01*** 2.35** -0.080 -0.650 -1.010 IWR 0.844 -0.030 0.047 -0.031 -0.052 224 0.0093 1.79* -0.020 1.330 -0.030 -1.630 IWS 1.114 1.500 0.003 -2.908 0.010 230 0.0089 1.460 0.980 0.050 -1.550 0.150 IWV 2.453 3.477 -0.080 -0.751 0.049 231 0.0106 3.38*** 1.070 -0.650 -0.490 0.400 IWW 2.418 -4.262 0.108 1.658 -0.094 231 0.0053 2.31** -0.840 0.640 0.490 -0.530 IWZ 4.430 9.062 -0.171 -13.081 0.098 231 0.076 2.02** 1.89* -0.900 -2.64*** 0.710 IYY 0.609 -0.355 0.028 1.029 -0.017 231 0.0101 1.68* -0.320 0.680 0.910 -0.400 MDY 0.028 1.298 0.005 -2.601 0.040 231 0.0241 0.070 1.150 0.110 -1.88* 1.100 QQQQ 0.253 -3.944 0.059 1.642 -0.019 231 0.0193 0.280 -1.240 1.070 0.750 -0.320 59 Table 8 Continued Panel A Difference
SPY 1.620 0.648 0.110 -0.996 -0.116 231 0.0178 1.94* 0.550 0.950 -0.500 -0.900 VTI 1.403 1.418 0.115 -1.028 -0.124 223 0.0252 1.89* 0.530 1.620 -0.380 -1.520
60 Table 8 Continued PANEL B Abnormal
Ticker Intercept RETAIL INST RETAILLAG INST LAG Obs R-Sq
DIA 0.125 0.060 -0.002 -0.073 -0.001 231 0.0191 10.34*** 1.060 -0.770 -1.140 -0.290 DSG 0.173 -0.104 -0.001 0.009 0.003 231 0.0094 10.110 -1.110 -0.210 0.140 0.790 DSV 0.118 -0.031 -0.001 0.085 0.000 231 0.0158 17.87*** -0.650 -0.440 1.84* 0.080 ELG 0.144 0.016 -0.001 0.055 -0.002 231 0.018 11.12*** 0.300 -0.460 0.760 -0.750 ELV 0.154 -0.052 -0.002 -0.112 0.003 231 0.0238 11.12*** -0.840 -0.540 -1.71* 0.880 IJH 0.080 0.048 -0.001 0.024 0.000 231 0.0095 12.22*** 1.460 -0.460 0.750 -0.120 IJJ 0.062 0.030 -0.002 -0.006 0.001 231 0.0284 19.52*** 1.560 -2.320 -0.290 2.26** IJK 0.066 -0.001 -0.001 -0.006 0.000 231 0.0068 16.03*** -0.050 -0.740 -0.230 0.090 IJR 0.074 -0.041 0.001 0.050 -0.001 231 0.0134 11.41*** -1.220 0.540 1.240 -0.740 IJT 0.133 0.039 -0.001 0.040 0.001 231 0.011 18.24*** 1.000 -1.030 0.770 0.650 IWB 0.080 0.014 -0.002 0.000 0.000 231 0.0247 9.45*** 0.380 -1.270 0.000 -0.260 IWD 0.088 0.060 -0.002 -0.010 0.000 231 0.0211 10.41*** 1.73* -1.170 -0.210 0.050 IWF 0.077 0.064 -0.001 -0.030 0.001 231 0.0151 11.88*** 1.360 -0.970 -0.870 0.490 IWM 0.472 -0.215 -0.008 0.032 0.004 231 0.0333 21.15*** -1.350 -1.340 0.200 0.800 IWN 0.426 -0.158 -0.009 -0.011 0.004 231 0.0382 20.12*** -1.020 -1.550 -0.070 0.890 IWO 0.527 -0.129 -0.010 -0.012 0.004 231 0.0349 21.19*** -0.760 -1.71* -0.070 0.840 IWP 0.122 0.087 0.005 0.058 -0.006 230 0.041 9.63*** 0.890 1.540 1.300 -1.620 IWR 0.101 0.026 0.000 0.001 -0.001 230 0.016 15.7*** 0.670 0.010 0.010 -0.950 IWS 0.120 0.122 0.002 -0.018 -0.004 231 0.0602 13.53*** 2.59** 0.820 -0.320 -1.210 IWV 0.418 -0.054 -0.005 -0.012 -0.005 231 0.0536 17.45*** -0.360 -0.730 -0.070 -0.920 IWW 0.475 -0.089 -0.004 0.113 -0.007 231 0.0386 17.31*** -0.520 -0.440 0.640 -1.080 IWZ 0.268 0.038 -0.002 -0.067 -0.004 231 0.0407 18.52*** 0.360 -0.400 -0.690 -1.030 IYY 0.075 -0.047 -0.001 -0.017 0.001 231 0.0174 13.18*** -1.100 -0.370 -0.560 0.520 MDY 0.160 -0.004 -0.003 0.059 0.004 231 0.017 13.9*** -0.050 -1.290 0.790 1.76* QQQQ 0.114 -0.117 -0.001 0.051 0.000 231 0.0151 8.41*** -1.510 -0.260 0.640 -0.080 61 Table 8 Continued Panel B Abnormal
SPY 0.157 0.019 -0.003 0.053 0.002 231 0.0059 11.46*** 0.360 -0.920 0.520 1.050 VTI 0.104 0.091 0.001 0.008 -0.001 231 0.0133 9.49*** 1.040 0.420 0.190 -0.270
62 Table 9 ETFP Aggregate Results
This table presents results for Hypothesis 1a which uses 2 stage least squares regression to examine whether direct measures of individual and/or institutional investor sentiment significantly impact the market price deviations from net asset value for the aggregate sample of ETFs. Weekly data from the American Association of Individual Investors (RETAIL) and Investor Intelligence (INST) whom survey and compile data on individual and institutional investor sentiment, respectively, is regressed on the weekly market price deviation as of the market close of 4pm on Fridays as represented by market price minus Fridays 4pm net asset value. Results are presented for estimates where orthogonalized sentiment is represented as the difference (Difference) between the residuals of the percentage of those investors bullish on the market minus the percentage of investors that are bearish on the market. Results are also presented in abnormal sentiment (Abnormal) where abnormal sentiment is defined as sentiment in week t subtracted from the prior 8 week moving average.
ETFPi,t = α + β1 RETAILt + β2 RETAILt-1 + β3 INSTt + β4 INSTt-1 + εi,t (14)
ETFPi,t = α + β1 ABRETAILt + β2 ABRETAILt-1 + β3 ABINSTt + β4 ABINSTt-1 + εi,t (15)
Difference Coefficient t Value Intercept 0.263557 15.68*** RETAIL -0.01085 -0.51 INST -0.00462 -4.57*** RETAIL LAG 0.048478 2.17** INST LAG 0.001375 1.82*
Number of Obs 6234 R Square 0.0192
Abnormal Coefficient t Value Intercept 0.181814 40.36*** RETAIL -0.01218 -0.6 INST -0.00191 -2.35** RETAIL LAG 0.009591 0.49 INST LAG -0.00014 -0.21
Number of Obs 6028 R Square 0.0042
*** Significance at the 1% level ** Significance at the 5% level * Significance at the 10% level
^ Results using the deviation as a percentage of NAV are not significantly different
63 Table 10 ETFP By Size
This table presents results for hypothesis 2 using 2 stage least square regression to determine whether the impact of the American Association of Individual Investors (RETAIL) and Investor Intelligence (INST.) surveys for individual and institutional investor sentiment, respectively, differ according to the market capitalization of the securities held by the ETF. The weekly RETAIL and INST. sentiment measures are expressed as the difference between market price and net asset value per share (NAV) as of the 4pm Friday close. A regression using a binary variable D1 (small) and D2 (medium) is estimated to examine the difference of the impact of the sentimen measures grouped by market capitalization. The sample includes 10 large cap, 7 medium cap and 5 small cap ETFs. Panel A presents results for differenced (Difference) data where the percentage of those bearish on the market at time t is subtracted from the percentage of investors that are bullish on the market. Panel B presents results for abnormal sentiment where abnormal sentiment is sentiment in week t subtracted from the prior 8 week moving average. ETFPi,t = α + β1RETAILt + β2 RETAILt-1 + β3(D1,iRETAILt) + β4(D1,iRETAILt-1) + β5 (D2,i RETAILt ) + β6 (D2,i RETAILt-1 ) + β7INST.t + β8 INST.t-1 + β9(D1,,iINSTt ) + β10(D1,,iINSTt-1) + β11 (D2,I,iINSTt ) + β12(D1,iINSTt-1) + εi,t (17) Panel A: Difference
Variable Coefficient t-Value
Intercept 0.003 0.49
RETAIL Large -0.008 -0.37 INST Large -0.002 -2.35** RETAIL LAG Large -0.008 -0.35 INST LAG Large 0.003 2.83*** RETAIL Small 0.037 0.95 INST Small 0.000 -0.02 RETAIL LAG Small -0.001 -0.02 INST LAG Small -0.001 -0.30 RETAIL Medium 0.030 0.96 INST Medium 0.002 1.19 RETAIL LAG Medium -0.009 -0.27 INST LAG Medium -0.001 -1.16 Observations: 6234 R Squared: 0.0025
Panel B: Abnormal Variable Coefficient t-Value
Intercept 0.008 5.33***
RETAIL Large -0.008 -0.37 INST Large -0.002 -3.42*** RETAIL LAG Large -0.024 -1.11 INST LAG Large 0.003 4.1*** RETAIL Small 0.014 0.38 INST Small 0.002 1.58 RETAIL Medium 0.030 0.86 INST Medium 0.000 0.97 RETAIL LAG Small 0.004 0.10 INST LAG Small -0.001 -0.99 RETAIL LAG Medium 0.001 0.08 INST LAG Medium 0.000 0.62 Observations: 6028 R Squared: 0.0019 *** Significance at the 1% level ** Significance at the 5% level * Significance at the 10% level 64 Table 11 Creation and Deletion Aggregate Results
This table presents results for Hypothesis 3 which examines the significance of individual and institutional investor sentiment as compiled by the American Association of Individual Investors (RETAIL) and Investor Intelligence (INST), respectively, market price minus net asset value per share (ETFP), individual ETF daily return (Return), and the natural log of daily abnormal volume for each ETF (Volume), on the daily percentage change in creation and deletion activity of the aggregate Sample of ETFs. The 2 stage least square regressions use daily data where weekly investor sentiment measures are held constant for the days of the week of its Friday survey release. Panel A presents results for the difference (Difference) between the percentage of those that are bullish and bearish on the market. Panel B presents result for abnormal sentiment where abnormal sentiment is defined as sentiment in week t subtracted from the prior 8 week average. Panel C presents results determining whether the market capitalization of the ETFs in the sample significantly affect the impact of the sentiment, market price deviation, return and volume measures on the daily percentage change in creation and deletion activity.
CDi,t = α + β1 RETAILt + β2 RETAILt-1 + β3 INST.t + β4 INST.t-1 + β5 ETFPi,t + β6 RETi,t-1 + β7 VOLt + εi,t (20)
Panel A: Difference Standard Variable Coefficient Error t-Value
INTERCEPT 0.015 0.007 2.12** RETAIL 0.003 0.005 0.74 RETAIL LAG 0.000 0.000 0.28 INST 0.009 0.005 1.63 INST LAG -0.001 0.000 -1.17 ETFP 0.005 0.006 0.82 RETURN 0.057 0.047 1.2 VOLUME 0.000 0.000 -1.95*
Observations: 17696 R-Square: 0.0013
Panel B: Abnormal Sentiment Standard Variable Coefficient Error t-Value
INTERCEPT 0.007 0.002 2.73*** RETAIL 0.003 0.004 0.67 RETAIL LAG 0.000 0.000 0.29 INST 0.007 0.006 1.23 INST LAG 0.000 0.000 -1.13 ETFP 0.005 0.006 0.83 RETURN 0.056 0.046 1.21 VOLUME 0.000 0.000 -2.03**
Observations: 17696 R Squared: 0.0009
65 Table 11 Continued
Panel C: Difference Market Capitalization Variable Coefficient t-Value INTERCEPT 0.017 1.92*
LARGE RETAIL 0.002 1.09 RETAIL LAG 0.005 1.42 INST 0.000 -1.00 INST LAG 0.000 -1.50 ETFP 0.011 0.95 RETURN 0.052 0.70 LOG VOLUME 0.000 -1.21
MEDIUM RETAIL 0.007 0.57 RETAIL LAG -0.012 -0.90 INST 0.002 1.06 INST LAG -0.002 -0.90 ETFP -0.012 -1.01 RETURN 0.068 0.50 LOG VOLUME -0.001 -1.00
SMALL RETAIL -0.001 -0.08 RETAIL LAG 0.021 1.58 INST 0.000 -0.85 INST LAG 0.000 0.77 ETFP -0.012 -0.94 RETURN -0.023 -0.23 LOG VOLUME 0.000 0.34 Observations: 17728 R Squared: 0.0021
Abnormal Sentiment Market Capitalization Variable Coefficient t-Value
INTERCEPT 0.007 1.49
LARGE RETAIL -0.001 -0.10 RETAIL LAG 0.015 0.7752 INST 0.000 0.97 INST LAG 0.000 0.1221 ETFP 0.011 1.66* RETURN 0.046 0.47 LOG VOLUME 0.000 -0.91
MEDIUM RETAIL 0.018 0.64 RETAIL LAG 0.027 0.7087 INST 0.001 0.8491 INST LAG 0.001 0.6386 ETFP -0.012 -0.67 RETURN 0.091 0.45 LOG VOLUME 0.000 -0.28
66 Table 11 Continued
SMALL RETAIL 0.003 0.14 RETAIL LAG 0.024 0.519 INST -0.001 -1.19 INST LAG 0.001 0.3669 ETFP -0.011 -1.17 RETURN -0.015 -0.09 LOG VOLUME 0.000 0.01 Observations: 17728 R Squared: 0.0010
*** Significance at the 1% level ** Significance at the 5% level * Significance at the 10% level
67
CHAPTER 4: REIT CEF RETURNS: THE IMPACT OF IRRATIONAL INVESTOR SENTIMENT AND CHANGES IN THE FEDERAL FUNDS INTEREST RATE
1. Introduction With assets in over 630 funds that increased from over $156 billion at the end of December 2002 to over 298 billion by the end of 200635, closed-end funds (CEFs) represent an expanding category of exchange traded products. Unlike open-end funds that are directly purchased from and redeemed to the mutual fund company at net asset value (NAV) and Exchange Traded Funds (ETFs) such as Spiders and DIAMONDS that generally represent a passive indexing strategy and trade relatively close to NAV, CEFs are actively managed shares that trade in the secondary market at prices that are frequently at a significant premium or discount to NAV. It is widely accepted that a general rule of thumb is to purchase CEFs selling at “large” discounts. However, the true benefit of this strategy should only be significant if the discount is mean reverting or if the CEF liquidates. Likely of greater interest to a CEF investor is risk-adjusted return and how susceptible these investors are to uncompensated risk. This should be of particular interest to CEF investors, given that CEFs tend to trade primarily among retail investors and are said to have pricing that is susceptible to factors unrelated to risk such as investor sentiment or investor bias. Accordingly, this study examines whether and how REIT CEF returns are impacted by uncompensated (in the form of return) investor sentiment. Using a sample of 16 Real Estate Investment Trust (REIT) CEFs, this study examines the relationship between investor sentiment, premium/discount levels and REIT CEF return. The study will test whether REIT CEF returns are significantly impacted by direct measures of retail and institutional investor sentiment orthogonalized to common market and macroeconomic risk factors, and/or whether these returns are impacted by how far they trade away from NAV. The study also tests whether the federal fund interest rate environment significantly impacts this relationship. This analysis should
35 Total asset values are as of the last business day of dates reported as provided by The Investment Company Institute. 68 increase understanding of the return risks associated with REIT CEF investing and whether certain market conditions make them more or less risky. Within the emerging category of exchanged traded products, REIT CEFs are those funds that specialize in actively managing a significant portion of their portfolio in REITs. There are currently 26 actively traded real estate CEFs with over $14.7 billion in assets36. Of these 26, twenty-three hold over 90% of their portfolio in REITs37,38. Given that they are exchange traded at market prices frequently different from fundamental value, CEFs allow the unique opportunity to examine how REIT investors perceive and react to various market conditions and situations and, more importantly, what implications this may have to an investor’s portfolio return.
2.0 Closed-End Funds (CEFs) Closed-end funds represent one of the three basic types of investment companies39. Unlike open-end mutual funds, CEFs are exchanged listed; therefore, after an initial offering shareholders buy and sell shares in the secondary market at prices that are frequently at a premium or discount to NAV. Moreover, closed-end funds are permitted to invest a greater portion of their assets in illiquid securities. Closed-end funds also differentiate themselves from other exchanged traded products such as Unit Investment Trusts (UITs). UITs are structured to have a Trust termination date and typically have a mechanism in place where shares are redeemable. Closed-end funds are also generally actively managed, which is typically not the case with UITs. Moreover, the Investment Company Institute (ICI) reports that over the 2002-2004 time frame the number of mutual funds and UITs fell by 2.4 and 22%, respectively, while CEFs grew in number by 13.8%40.
3.0 REITs With assets of more than $475 billion41, publicly traded Real Estate Investment Trusts (REITs) in the US trade on all major stock exchanges and offer large and small
36 Total asset value and number of fund data as of 2/8/2007 as provided by The Closed-End Fund Association. 37 For a brief discussion on the use of REIT CEF as the sample versus REITs in general see Appendix. 38 Data on closed-end fund REIT investment is from Bloomberg. 39 Mutal funds and Unit Investment Trusts (UITs) are the other two forms of investment companies. 40 UIT numbers do not include ETFs. 41 Asset total is from www.nareit.com as of December 31, 2005. 69 investors the opportunity to diversify their portfolio’s with securitized assets that offer high levels of current income42, and historically have low correlation with the general stock market (see Ziering, Liang and McIntosh, 1999, Lin and Yung, 2006 and Maroney and Naka, 2006). REITs are companies that either own, and generally manage the operations of income producing real estate, or they supply the financing to real estate owners. These REITs are, respectively, known as equity and mortgage REITS. A third REIT classification includes REITs that actively invest in properties and provide financing to real estate investors. These REITS are known as hybrids. With nearly 200 publicly traded in the US markets today, REITs have been actively trading since 1960. In that year, Congress created REITS so that all investors, particularly individual (retail) investors, would have access and the ability to invest in large commercial real estate. Today, institutional and retail investors alike are attracted to REITs for their diversification qualities. It has been shown that most investors stand to earn higher risk adjusted returns by adding real estate to their portfolio than by investing solely in common stocks (see Bruggeman, Chen and Thibodeau, 1984, Firstenburg, Ross and Zisler, 1988, Grauer and Hakansson, 1995). Moreover, retail investors might be particularly attracted to these securities given the handsome levels of current income that REITs are required to distribute. Accordingly, it is reasonable to suspect that the perceptions of both retail and institutional investors might affect the pricing and return of REIT CEFs. However, of primary interest is whether and under what circumstances the portion of these market participants’ perception that is unrelated to risk, which serves as a proxy for irrational trader demand, has the ability to significantly affect REIT CEF returns. In addition, after controlling for market risk, it is of interest to examine whether the contemporaneous discount level of REIT CEFs actually impact return. If irrational trader demand, as proxied by investor sentiment orthogonal to risk, significantly affects price, it would be important to learn whether certain market conditions can exacerbate the effect. One such market condition is a change in the federal funds rate. It has been shown that REIT returns are negatively related to the federal funds rate (see Glascock, Lu and So, 2002). Accordingly, it is reasonable to
42 REITs are required to distribute at least 90% of their taxable income to shareholders and generally distribute all of this income given the preferential tax treatment the REIT itself is given on its corporate taxes. 70 suspect that this relationship should also be reflected in REIT CEF return levels, which may potentially change the premium/discount levels of these funds and/or the perception, thus impact, of the irrational traders on the returns of these funds. The effect of changes in the federal funds should be most detectable given a period of frequent changes to this rate. The frequency in level changes and distinct period of increasing and decreasing federal funds rates over the 2002-2005 period offer an unique opportunity to examine these questions.
4.0 Investor sentiment and sentiment proxies Using direct measures of retail and institutional investor sentiment, as provided by survey data of the American Association of Individual Investors (AAII/RETAIL) and Investor Intelligence (II/INST), respectively, this study examines whether and how investor biases, controlling for systematic and idiosyncratic characteristics, significantly impact the returns of REIT CEFs. In addition, I examine the role the contemporaneous magnitude of REIT CEF discount levels play and whether a pending change in the direction of the Federal Funds interest rate potentially intensifies the impact that irrational traders have on the returns of REIT CEFs. These issues are of interest given that a significant relationship may help to isolate circumstances under which REIT CEF investors may be more prone to uncompensated risk.
5.0 The Literature 5.1 Investor Sentiment Literature The literature often uses measures of investor sentiment as a gauge of investor optimism or a gauge of irrational investor perceptions of the market, independent of fundamental risk. This literature generally supports or refutes whether investor sentiment actually has the ability to affect some fundamental aspect of a security, such as price or return, or it disputes which measures are appropriate proxies. A series of papers has examined the effect investor sentiment on financial assets. Many of these papers examine its effects on closed-end mutual funds and other general equity properties such as stock returns. Gemmill and Thomas (2002) use a general sample of U.K. closed-end funds to examine what drives closed-end fund discounts. Using retail investor flows as a proxy for sentiment, they find that fluctuations in discounts are a function of irrational trader demand. However they also conclude that
71 noise-trader risk does not explain the long run persistence in CEF discounts. Shleifer and Summers (1990), DeLong, Shleifer, Summers and Waldman (1990), and Barber, Odean and Zhu (2005) all show that noise traders, or irrational investors, play a significant role in our financial markets given their ability to affect market pricing, thus presenting a challenge to the efficient market framework. Lee, Shleifer and Thaler (1991) examine the relationship between closed-end fund discounts and retail investor sentiment and conclude that changes in sentiment can explain the level of discount in closed-end funds, as well as smaller capitalized stocks, that are primarily traded by individual investors. Accordingly, they find that CEF discounts serve as a proxy for investor sentiment and that high levels of investor sentiment are marked by higher premiums (smaller discounts), thus indicating investor optimism, and that lower levels of sentiment are marked by lower premiums (deeper discounts), thus indicating investor pessimism. They also find that subsequent changes in investor sentiment make closed-end funds riskier than the underlying assets held by the fund. By contrast, Chen, Kan and Miller (1993) and Elton, Gruber and Busse (1998) refute the findings of Lee et al. Chen et al conclude that the Lee et al results are not supported by the data and that the Lee et al results are not robust to alternative sampling periods. Moreover, Elton et al test whether changes in closed-end fund discounts can be explained by changes in retail investor sentiment and find that closed-end fund discounts can be explained by factors unrelated to sentiment. Neal and Wheatley (1998) test whether three indirect measures of investor sentiment, closed-end fund discounts, mutual fund redemptions and odd-lot ratios, can predict returns. They conclude that closed-end fund discounts and mutual fund net redemptions have predictive power in determining the premium in returns between small and large firms, but they fail to find evidence that the odd-lot ratios have such power. Lemmon and Portniagina (2006) use the monthly University of Michigan Index of Consumer Sentiment as a proxy for investor sentiment. Further, Brown and Cliff (2004, 2005) use investor sentiment measures and investigate the effect these measures have on stock market returns and asset valuation, respectively. Brown and Cliff (2004) test whether direct measures of sentiment, as provided by AAII and II, have the ability to predict short-run market returns and find that market returns cause changes to future investor sentiment, but do not find that the inverse holds. They also find that changes in
72 sentiment measures are highly correlated with contemporaneous returns. Brown and Cliff (2005), similar to Brown and Cliff (2004), use the same measures of investor sentiment to test whether sentiment serves as a proxy for what Alan Greenspan coined “irrational exuberance,” or investor over-optimism in the market. Subsequently, they test whether such price run-ups exhibit mean reversion. They conclude that sentiment can affect the valuation of assets and where there is price run-up returns mean revert in the long run. Finally, Lemmon and Portniagina (2006), using the University of Michigan Index of Consumer Sentiment, conclude that after controlling for time varying market risk, this direct measure of sentiment forecasts returns for low institutional ownership and small capitalization stocks. Further, the literature has yet to explore the specific question detailing the significance of irrational trader perceptions, as proxied by direct measures of investor sentiment, given a change in direction of the federal funds rate on the returns of REIT CEFs. Specifically, this paper seeks to explore under what circumstances irrational retail and institutional investor trading and premium/discount levels significantly impact REIT CEF returns and what potential implications this may have for the risk associated with investing in REIT CEFs.
5.2 REIT literature The literature on REITs examines a plethora of issues related to real estate securities. The relevant literature to this analysis includes Barkham and Ward (1999) who examine investor sentiment and UK property companies and find that irrational investor sentiment is a significant cause of property company discounts. This essay differs from Barkham and Ward (1999) because I focus entirely on US REITs while they have an international focus. Moreover, their data and analysis covers the 1993-1995 time period. Clayton and MacKinnon (2001) examine what causes changes in the premiums and discounts to NAVs of REITs over the four year period of 1996-1999. They develop a model where short run fluctuations in REIT premiums are a function of liquidity, growth opportunities in the REIT sector and sentiment. Their findings suggest that observed fluctuations are related to changes in real estate fundamentals and cycles and that the magnitude of these changes are related to noise traders. By contrast, Gentry, Jones and Mayer (2004) suggest that it is unlikely that REIT premiums and discounts are
73 the result of investor sentiment or other explanations commonly offered to explain closed end fund discounts (see Lee, Shleifer and Thaler (1991), Chen, Kan and Miller (1993) and Elton, Gruber and Busse (1998)). Grullon and Wang (2001) posit that discounts and premiums are best explained by examining the relative difference in the amount of shares owned by individual versus institutional investors, not simply the ownership of individual investors. By examining investor behavior, Ling and Naranjo (2006) find that REIT mutual fund flows are positively related to past returns, and that the inverse does not hold. This study differs from previous studies in that it is the first to examine whether direct measures of investor sentiment which proxy for irrational investor demand, impact the return of REIT CEFs, what role discount levels play, and to what extent a change in the direction of the federal funds rate may improve or exacerbate these relationships.
5.3 Interest Rates and the Real Estate Cycle Previous research has shown that interest rates are a key component in determining the state of the real estate market as it relates to demand and, subsequently, prices. Cheong, Gerlach, Stevenson, Wilson and Zurbruegg (2000) examine the importance of interest rates and stock market behavior on the sensitivity of the securitized property market. They decompose the price behavior of securitized properties into these two components by testing the transitory and permanent nature of these components. They conclude that interest rates significantly influence the REIT market cycle, as reflected in prices. Moreover, they find that this relationship is greatest when credit spread risk is higher; however, they also suggest that interest rates should have a significant impact on the REIT market over the long term, even if short term importance is slightly negated in times of smaller credit spreads. Swanson, Theis and Casey (2002) examine the impact interest rates have on the risk premium to REITs and suggest that interest rates significantly affect REIT returns. Specifically, they find that REIT returns are more sensitive to the spread between short and long-term treasury rates (term spread) than the spread between commercial bonds and treasury bonds (credit spread). In addition, Jacob and Zisler (1994) find that real estate returns are positively related to interest rates. By contrast, Mueller and Pauley (1995) examine the impact interest rates have on REITs and find that there is little correlation between the price movements of REITs and
74 changes in interest rates. They also suggest that this correlation is even lower than that found between REIT prices and movements in the general stock market. Moreover, Chen and Tzang (1988) use the intertemporal asset pricing model to study how sensitive mortgage and equity REITs are to changes in interest rates and expected inflation. They conclude that mortgage REITs are rather sensitive to both changes in interest rates as well as to changes in inflation, while equity REITs seemed to only be sensitive to only changes in inflation.
6.0 Data The data used for this analysis include the NAV, market price, volume, and return for 16 REIT CEFs. Descriptive statistics are presented in Table 1. Data are collected on a daily interval for NAV, market price, return and volume. Data on each CEF are obtained from Bloomberg and the Center for Research in Securities Prices (CRSP) over the December 2, 2002 to December 31, 2005 time period. There are a total of 11,255 observations. Data on the measures of investor sentiment are from American Association of Individual Investors (AAII) and Investor Intelligence (II) representing retail and institutional investor sentiment, respectively. AAII provides a direct measure of retail investor sentiment by conducting a weekly random sentiment survey of its members to “measure the percentage of individual investors who are bullish, bearish, and neutral on the stock market short term.43” The sentiment measure provided by II is a compilation of analyzed surveys of more than 120 independent financial advisory newsletters. II classifies the attitudes of these newsletters toward market conditions. Specifically, they compile the number of newsletters that are bullish, bearish and neutral to market conditions on a weekly basis. Focusing on direct measures of investor sentiment as opposed to indirect measures, such as closed-end fund discounts and odd-lot sales, may have greater appeal since direct measures may offer the most efficient means of testing the feelings and expectations of investors. Moreover, the literature continues to question the validity of many of the indirect measures often used as proxies for investor sentiment; therefore, I rely on direct measures. This analysis uses the percentage change in the
43 Quote is obtained from the American Association of Individual Investors website. 75 difference between those that are bullish and bearish on the market. This method is used for both retail and institutional measures. Changes in the federal funds interest rates and data obtained for the calculation of the credit and term spread is from Federal Reserve Bank website. The credit spread is the difference between Moody’s Baa and Aaa rating yields and the term spread is the difference between the one month Treasury bill and the 10 year Treasury bond. The credit and term spread data are both collected over the December 2, 2002 to December 31, 2005 time period at a daily frequency. Dates where changes to the federal funds rate were announced by the Federal Reserve Bank during the November 2002 through the December 2005 time period are also collected. Given the frequency at which the Federal Reserve has changed the federal funds interest rate over the November 2002 through December 31, 2005 time period44, the sample period presents a unique opportunity to examine the impact irrational traders have on REIT CEF discount levels given a changing interest rate environment. The sample of REIT closed-end funds includes all funds classified by the Closed End Fund Association (CEFA) as real estate funds. Further, to be included in the analysis, the fund must invest 100% of its assets in domestic securities and the fund must invest at least 90% of its assets in REITs. Daily net asset values (NAV), return, market price, and volume must be available for at least two years to be included in the sample.
4.0 Methodology This study examines whether and under what circumstances irrational retail and institutional trading, as proxied by investor sentiment not attributable to risk, significantly impact the returns of REIT CEFs. Specifically, I examine how changes in the direction of the Federal Funds interest rate affects this relationship and what implications this may have on REIT CEF investor’s portfolio.
4.1 Formal Hypothesis The formal null hypotheses tested are as follows: H1a: Irrational investor trading, as proxied by direct measures of retail and institutional investor sentiment orthogonalized to risk, does not significantly affect the returns of
44 The federal Reserve Bank has changed interest rates 15 times over the November 2002 to December 31, 2005 period. 13 of these changes were rate increases and 2 were rate decreases. 76 REIT CEFs. H1b: When REIT CEFs are grouped by cumulative return relative to the return on the NAREIT all-equity REIT index, irrational investor trading, as proxied by direct measures of retail and institutional investor sentiment orthogonalized to risk, does not significantly affect the returns of REIT CEFs. H2: Changes in the direction of the federal funds interest rate have no significant effect on the relationship between irrational investor traders and REIT CEF returns.
My expectation is to reject hypothesis 1a. I expect that irrational trading in the prior week (time t -1), as proxied by investor sentiment measures, will have a negative and significant impact on the returns of these funds at time t, which suggests that when irrational investor sentiment is high, there should be a subsequent negative relation to returns. Accordingly, when irrational investor sentiment is lower in the prior week, I expect that returns should be relatively higher. Moreover, I expect the measure of retail sentiment (as opposed to the institutional measure), which proxies for irrational retail trading activity, to be the most significant sentiment measure, given the widely held belief that CEFs tend to primarily trade among retail investors. Hypothesis 1b is of particular interest given that the return level on the REIT CEF as compared to the NAREIT all-equity REIT index45, may significantly impact how active irrational traders are in the market for these CEFs. The expectation is to reject the null hypothesis and I expect that the investor sentiment measure, which proxies for the irrational investor trading in the market, will have a negative and significant effect on the group of low return (lower than the cumulative return on the NAREIT all-equity index over the same period) funds. The question then becomes whether the returns to the fund are negative because of the presence of these traders. In other words, do lower return funds cause increased activity by irrational traders or do irrational traders cause lower returns? This question will be examined later. No significance is expected for the sample of REIT CEFs with higher relative returns. Hypothesis two examines the impact that changes in the direction of the federal funds interest rate have on the relationship between irrational traders and REIT CEF discounts. Numerous studies have examined the impact interest rates have on REIT
45 Data on the NAREIT all-equity REIT index returns are from the National Association of Real Estate Investment Trust website www.nareit.com. 77 returns and demand (see Jacob and Zisler (1994), Mueller and Pauley (1995), Cheong, Gerlach , Stevenson, Wilson and Zurbruegg (2000), Glascock, et al (2002) and Swanson, Theis and Casey (2002) for discussions). Mueller and Pauley generally conclude that there is little relationship. By contrast, it has been shown that the magnitude of the credit and term spreads significantly influence the REIT market cycles as reflected in REIT market prices. Moreover, Glascock, Lu and So (2002) find a negative relationship exists between the federal funds rate and REIT returns. As a result, after controlling for the credit and term spreads, individual CEF premium/discount percentages, and volume, it is of interest to see what relationship holds between REIT CEF returns and irrational traders given the interest rate environment. The expectation is to reject the null of no significant difference. I expect that irrational investor trader reactions to the impact of an increasing versus a decreasing federal funds rate on their investment value will not be the same. Numerous studies have examined investor reactions to events. Chan (2003), finds that relative to positive news, bad news events are followed by a return drift indicating that investors may be slower to react to events that are perceived as bad. By contrast, Barberis, Shleifer and Vishny (1998) and Skinner and Sloan (1999) find that the market reacts strongly to bad news relative to good. Further, Glascock, et al. (2002) find a negative relationship between the federal funds rate and REIT returns. Accordingly, I expect that irrational traders will see the changing direction of interest rates as a news event for their REIT investment, however, it is not clear to me exactly how these irrational traders will react to this news.
4.2 “Pure” Sentiment Regression Analysis Fundamental to this study is the ability to isolate the portion of investor sentiment that is orthogonal to risk for each measure. To accomplish this, the RETAIL and INST. direct sentiment measures are regressed on six macroeconomic risk variables and the residuals of these estimations are used as the proxy for the irrational traders in the market. These macroeconomic variables include the term and default spread on interest rates and the four Fama and French (1993) and Carhart (1997) market, size, value, and momentum factors. Chen, Roll and Ross (1986) examine which state variables most likely affect stock prices and show that the term spread (the difference in yield between the 10 year Treasury bond and one month Treasury bill) and risk premium (the difference in yield
78 between the Moody’s BAA and AAA bond ratings) are significant. The Fama and French (1993) and Carhart (1997) market, size, value and momentum factors are also commonly accepted as four proxies for risk. These proxies are similar in spirit to those used by Lemmon and Portniguina (2006) and Baker and Wurgler (2006). I regress each of the six risk measures on the weekly measure of retail and institutional investor sentiment to isolate the risk component of each measure. This will serve as the proxy for the portion of investor sentiment not attributed to risk. The weekly residuals from these regressions are my “pure” measure of retail and institutional investor sentiment. The time-series regression with weekly data, is estimated separately for the two sentiment measures using the equation:
SENTt = α + β1 RMRFt + β2 SMBt + β3 HMLt + β4 UMDt + β5 TERMt + β6 DEFt + εt (1)
where SENT represents either the measure of retail (RETAIL) or institutional (INST) investor sentiment. RMRF, SMB, HML and UMD respectively, represent the return on the market minus the risk free rate, and the return on the size, value and momentum portfolios of Fama and French (1993) and Carhart (1997). TERM represents the spread between the 10 year and three month Treasury bill, and DEF is the difference in yield between Moody’s BAA and AAA rated bonds. The equation is estimated over the period December 2, 2002 through December 31, 2005, and the residual from each regression is then used as pure measures of sentiment.
4.3 Sentiment and REIT ETF Return Controlling for risk and general trading activity, hypothesis one tests whether the lagged “pure” sentiment, which proxies for the irrational traders in the market, has a significant impact on the return of the REIT CEF at time t. CEF returns (RET) are calculated as:
RETi,t = ( Pi,t – Pi,t-1 ) / Pi,t-1 (2)
Returns are calculated on a daily basis over the December 2, 2002 to December 31, 2005 time period for each CEF. Two-Stage Least Squares (2SLS) regression is then used for
79 the following time series regression with daily data estimated over the same time period46.
RETi,t = αi + β1 RMRFt + β2 SMBt + β3 HMLt + β4 UMDt + β5 TERMt + β6 DEFt + β7
SENTt + β8 PCTPMDTi,t + β9 VOLi,t + εi,t (3)
where VOLi,t controls for general trading activity and is the daily volume for fund i at time t. PCTPMDTi,t represents the daily percentage deviation from NAV for CEF i on day t as shown below:
PCTPMDTi,t = ( Pi,t – NAVi,t ) / NAVi,t (4)
A positive number signifies a premium and a negative number signifies a discount. RMRF, SMB, HML, UMD, TERM and DEF are as previously described and control for the portion of return attributable to risk. I expect a positive and significant sign for all risk variables. When the return on RMRF, SMB, HML and UMD are high, I also expect relatively higher returns for REIT CEFs as well. Moreover, it has been shown that REIT returns are positively related to the spread between Baa and Aaa bonds (see Swanson et al, 2002). While Swanson et al. also show that the REIT returns are more sensitive to the term spread than the credit spread, I am also expecting a positive and significant relation between REIT CEF returns and the credit spread. General volume levels (VOL) are expected to be positively and significantly related to REIT CEF returns as well.
While the SENTt variable is of primary interest, PCTDCTi,t is also of significant interest given that the literature often debates whether this measure serves as an (indirect) proxy for investor sentiment47. Accordingly, it is of interest to see what affect this variable has on return, if any, and whether the sign and significance of the SENT and PCTDCT variables differ. I do not have a clear expectation with respect to this variable. However, the results compared to the direct measures of investor sentiment may shed some light on the debate regarding premium and discounts and their appropriateness as a proxy for sentiment.
46 OLS regressions are also estimated on all 2SLS regressions and do not yield significantly different results. 47 Note that the correlation between the RETAIL and INST. SENTi,t measure and PCTDCTi,t is 1.7% and - 13.4%, respectively. 80
2SLS is used to account for possible correlation between the disturbance term of the dependent variable (RET) and one or more independent variables. Accordingly, instrumental variables are created in the first stage to replace the original endogenous variables in an attempt to estimate unbiased estimates in the second (OLS) stage. The 2SLS regression calculates potentially unbiased beta coefficients and in the second stage
uses these estimates in the standard yi = βxi + εi (OLS) model form. A 2SLS regression is estimated for each measure of sentiment since the original sentiment measures (before regressing them on risk factors to obtain the portion unrelated to risk) of retail and institutional sentiment had a correlation of .32. Accordingly, these variables are not used in the same regression estimation. This regression is used to gauge the significance and impact the respective measures of “pure” sentiment have on the return of the REIT CEFs. My expectation is that there will be a negative and significant relationship, or as “pure” sentiment levels are high, indicating irrational investor optimism and trading is high without regard to risk, we should expect to see lower returns. Moreover, I expect that the measure of “pure” retail investor sentiment will likely be the primary, if not sole contributing measure of significance.
4.3.1 REIT CEF Returns and the NAREIT All Equity REIT Index Hypothesis 1b asks whether irrational traders have the same effect on the group of REIT CEFs that have cumulative returns higher than the NAREIT all-equity index. Of the 16 CEFs in the sample, 5 have cumulative returns higher than the REIT index over the sample period. I estimate the 2SLS regression separately for the pooled time series returns of the sub-sample with lower returns and then again with the sample with higher cumulative returns.
4.4 Interest Rates, Sentiment and CEF Returns Hypothesis two examines the effect that interest rate changes have on the role of sentiment as a factor explaining the pricing deviations from NAV of REIT CEFs. This hypothesis is tested by examining the direction of change in the federal funds rate. Specifically, there are 15 interest rate changes over the November 2002 to December 2005 time period. Two of these changes are rate decreases and fall within the November
81 2002 to June 200448 time period, while 13 of these changes are rate increases within the June 30, 2004 and December 31, 2005 time period. This dynamic environment allows me to break the sample period into a pre and post June 30, 2004 time period in order to examine whether there is a significant difference in the irrational trading behavior during these respective time periods. A 2SLS regression is estimated over the partitioned data to determine whether changes to the federal fund interest rate significantly impact the relationship between irrational investor sentiment and REIT CEF returns. To test whether the impact of the SENT measures differ across interest rate environments I also test for the stability of coefficients across the respective 2SLS49 using a Chow Test (Chow 1960) for structural change. The Chow Test for structural breaks is as follows:
f = [((RSS-SSE1 - SSE2)/p)/((SSE1+SSE2)/(n-2p))] (6) where f represents the application of the F-Test, RSS represents the sum of squared errors from the full sample, and SSE1 and SSE2 represents the sum of squared errors for each of the two samples, respectively, n is the size of the sample, and p and n-2p are the degrees of freedom. This test examines whether the β’s across regressions for the independent variables are equal. This offers another means of examining irrational traders on REIT CEF returns across increasing and decreasing federal funds rates. Evidence of a structural break should further indicate a change in reaction by the irrational traders in the market given the federal funds environment.
5.0 Results Over the 2002-2004 time period the number of CEFs grew by almost 14% while mutual funds and UITs fell by 2.4 and 22%, respectively. Accordingly, it is of interest to determine whether and when investors in this growing CEF category may be subject to irrational trader demand and how this may subsequently affect the price of REIT CEFs and their subsequent returns.
48 June 29, 2004 is the date before the first federal fund interest rate increase. 49 The test is estimated between the correlation coefficients for the irrational sentiment measure for the period of increasing federal funds rates versus the period of decreasing federal funds rates. Evidence of a structural break between coefficients should indicate a different reaction between the two interest rate environments. 82 This analysis seeks to determine whether and under which circumstances REIT CEF returns are impacted by irrational investor sentiment, or investor biases unrelated to common risk factors. By examining direct measures of retail and institutional investor sentiment orthogonalized to these common risk factors, I seek to determine whether there is a relationship between investor sentiment which is not driven by market risk factors (irrational sentiment) and REIT CEF returns. Moreover, I seek to determine how this relationship is affected given REIT CEF return levels relative to the NAREIT all-equity REIT index (REIT index) and how the direction of changes in the federal fund interest rate affects this relationship. Hypothesis 1a asks the general question of whether there is a significant relationship between irrational investor sentiment and REIT returns. Hypothesis 1b asks whether this relationship is affected by the REIT CEF return level relative to the concurrent REIT index. Panels A and B of Table 2 present the results of two-stage least squares estimations for hypothesis 1a and 1b, respectively. Panel A presents results for hypothesis 1a and shows that both retail and institutional investor sentiment have a significant, negative effect on the return for the full sample of REIT CEFs. This seems to indicate that, in general, irrational investor trading impacts REIT CEF returns and as these traders are more bullish, REIT CEF returns are negatively impacted. Further, the market, size, value and term factors are positively and significantly related to the CEF sample returns. Results also seem to indicate that higher volume levels are associated with lower contemporaneous returns. No significant relationship is found with respect to the momentum, credit and percentage discount from NAV variables. Panel B of Table 2 presents the results of the sample of REIT CEFs grouped by those that had cumulative returns higher than the NAREIT index and those that had lower returns than the index over the sample period. Panel B presents evidence that the funds with lower cumulative returns than the REIT index are those funds significantly impacted by investor sentiment. These results seem to indicate that when irrational investors are active in the market REIT CEF returns are negatively impacted. The sign and significance of all other variables are generally the same as presented in Panel A with the exception of the term, credit and percentage discount from NAV variables. The sample of REIT CEFs with higher returns than the REIT index demonstrate a negative and significant relationship between REIT CEF returns and the credit spread indicating that returns tend to be higher when there is a wider spread between Moody’s Baa and Aaa
83 rated yields. Moreover, there is a positive and significant relationship between return and the percentage discount from NAV. The results in panel A were positive but insignificant. The term factor is insignificant. By contrast, the sample with returns lower than the REIT index show insignificant results for the percentage discount from NAV and credit variables. In general, the tests of hypothesis 1 seem to indicate that REIT CEF returns are negatively and significantly impacted by the contemporaneous irrational sentiment of investors. Hypothesis 2 examines the impact of the interest rate environment on the relationship between irrational investor sentiment and REIT CEF returns. The sample of REIT CEFs is further partitioned by both time and return level relative to the REIT index. Specifically, the period prior to June 30th, 2004 is characterized by consistent downward changes to the federal funds rate (decreasing interest rate environment), and the period following June 30th, 2004 is characterized by consistent increases in the federal funds rate. Further, as with Panel B in Table 2, funds are then separated by return level relative to the REIT index. This partitioning, results in 4 sub-samples grouped by an increasing or decreasing federal fund rate environment and by those funds with higher versus lower returns relative to the REIT index. These results for the sample given a falling interest rate environment are presented in Panel A of Table 3 and the results for the partitioned sample during an increasing interest rate period are presented in Panel B of Table 3. Panel A results indicate that during a period of falling federal fund interest rates, irrational investor sentiment seems to have a similar effect as previously reported. The sign remains negative for both, however retail investor sentiment is the only significant measure. Institutional sentiment is insignificant. Moreover, during a falling federal fund interest rate environment, those funds experiencing higher returns than the REIT index are not significantly impacted by either measures of investor sentiment. The sign and significance of the market, size, value and volume variables are as previously reported in Panels A and B of Table 2. However, it is of interest to note that the sign and/or significance for the term, credit and percentage discount variables has reversed relative to what was reported in panel B of Table 2. The term variable is significant indicating that the larger the spread between the 10 year bond and 1 year bill, when rates are falling, the higher the return on REIT CEFs. In addition, although still insignificant, the sign for the percentage discount from NAV and credit variables are now reversed. The momentum
84 variable is negative yet insignificant. Moreover, the initial expectation of a greater retail sentiment impact seems to hold true here, as retail sentiment is the sole measure significantly and negatively impacting return. Panel B results are of particular interest because they show that during periods of increasing federal fund interest rates irrational investor sentiment is not significantly related to the returns of REIT CEFs. This holds regardless of the return relative to the REIT index. Further, while the sign and significance of the market, size, value, institutional sentiment, volume and percentage discount from NAV variables are the same as reported in panel A of Table 3. However, the term and credit variables both reverse in sign yet generally remain significant. The momentum variable remains insignificant but the sign is reversed in all but one estimation. More importantly, although the sign is still negative, the individual sentiment measure is now insignificant given a period of increasing federal fund interest rates. In aggregate, Table 3 results across Panels A and B are of considerable interest since they may indicate that REIT CEF investor returns are more susceptible to irrational investor activity and lower returns during periods of falling federal funds interest rates relative to periods when this rate is increasing50. This result seems to hold regardless of the return level relative to the REIT index. A Chow test for a structural break in the coefficients across the increasing versus decreasing federal funds rate environment is presented in Table 4. The results confirm that a structural break occurs in the impact of irrational investor sentiment on the returns of REIT CEFs on July 20, 2004, which is approximately 3 weeks after the initial change in direction of the federal funds rate (rate increases) which occurred on June 30, 2004, and approximately 3 weeks prior to the next rate increase which occurred on August 10, 2004. This lends further evidence to the findings reported in Table 3 which show that the direction of federal fund interest rates may significantly affect the impact irrational investor sentiment has on the returns of REIT CEFs.
6.0 Conclusions
50 It is possible that the results found given the increasing and decreasing federal fund interest rate periods are affected by inflationary trends. However, inflation steadily increases over the sample period. Moreover, given the daily frequency of the analysis, inflation is less likely to be a significant factor. Inflation data are provided by Inflationdata.com. 85 This analysis seeks to determine whether irrational investor sentiment, as proxied by direct measures of investor sentiment orthogonalized to common risk factors, significantly impacts the return of REIT CEFs and whether relative return levels and the federal fund interest rate environment affect this relationship. I find that REIT CEF investors may be subjecting themselves to irrational investor risk which may adversely affect their investment return given periods of decreasing federal fund interest rates relative to a period of increasing interest rates. Results indicate that irrational retail activity, as opposed to irrational institutional trading, is a significant factor in lower REIT CEF returns.
86 Table 12 Descriptive Statistics
Descriptive statistics, by fund, are presented for each CEF used in the sample over the daily December 2, 2002 through December 30, 2005 time period. Net asset value (NAV), market price, return, percent deviation in market price from NAV and log of volume are reported .
Standard Ticker: IIA Variable Observations Mean Deviation Minimum Maximum NAV 545 16.54 1.28 13.48 19.45 Price 545 14.98 0.78 12.59 16.76 Return 545 0.00 0.01 -0.05 0.02 %Prem/Disc 545 -9.16 3.91 -15.91 2.06 Volume 545 10.58 10.02 7.44 12.18
Ticker: JDD NAV 522 16.08 0.81 14.15 17.49 Price 522 15.12 0.78 12.74 16.39 Return 522 0.00 0.01 -0.08 0.03 %Prem/Disc 522 -5.87 4.01 -13.45 4.49 Volume 522 10.89 10.23 9.32 12.56
Ticker: JRS NAV 777 18.99 2.85 13.61 23.95 Price 777 18.13 1.73 14.60 21.23 Return 777 0.00 0.01 -0.07 0.03 %Prem/Disc 777 -3.58 7.09 -14.57 13.67 Volume 777 11.01 10.56 8.79 13.02
Ticker: NRI NAV 676 19.08 2.42 14.30 23.89 Price 676 16.96 1.26 14.58 19.85 Return 676 0.00 0.01 -0.07 0.03 %Prem/Disc 676 -10.47 5.79 -17.92 7.27 Volume 676 11.22 10.93 9.49 13.67
Ticker: NRL NAV 776 20.41 3.70 13.13 27.22 Price 776 18.64 2.34 14.47 23.43 Return 776 0.00 0.01 -0.07 0.03 %Prem/Disc 776 -7.49 7.26 -15.44 17.67 Volume 776 9.62 9.67 4.61 12.17
Ticker: NRO NAV 527 15.82 1.26 12.59 18.65 Price 527 14.16 0.74 11.50 15.51 Return 527 0.00 0.01 -0.06 0.05 %Prem/Disc 527 -10.13 5.61 -18.36 2.28 Volume 527 11.36 10.94 9.95 13.77
Ticker: RFI NAV 776 17.18 2.19 12.86 21.01 Price 776 17.49 1.82 13.56 21.09 Return 776 0.00 0.01 -0.08 0.04 %Prem/Disc 776 2.13 3.72 -7.94 10.88 Volume 776 9.56 9.16 7.55 11.76
Ticker: RIT NAV 776 18.34 2.47 13.30 22.69 Price 776 16.67 1.50 13.40 19.56 Return 776 0.00 0.01 -0.08 0.04 %Prem/Disc 776 -8.44 5.38 -16.01 4.77 Volume 776 10.36 9.93 8.24 12.31 87 Table 12 Continued
Standard Ticker: RLF Variable Observations Mean Deviation Minimum Maximum NAV 776 19.16 3.38 12.65 25.39 Price 776 18.40 2.38 13.95 23.30 Return 776 0.00 0.01 -0.08 0.06 %Prem/Disc 776 -3.00 6.54 -14.14 14.86 Volume 776 10.99 10.66 8.88 13.21
Ticker: RNP NAV 634 27.53 2.07 23.50 31.76 Price 634 25.44 1.15 20.94 27.91 Return 634 0.00 0.01 -0.06 0.04 %Prem/Disc 634 -7.27 5.07 -14.84 6.99 Volume 634 11.59 10.96 10.08 13.19
Ticker: RPF NAV 776 19.70 3.66 12.41 26.49 Price 776 18.27 2.60 12.94 23.04 Return 776 0.00 0.01 -0.07 0.05 %Prem/Disc 776 -6.35 5.61 -15.95 11.54 Volume 776 11.42 10.97 9.83 13.59
Ticker: RQI NAV 776 18.72 3.35 12.13 24.99 Price 776 17.49 2.26 12.80 22.08 Return 776 0.00 0.01 -0.09 0.05 %Prem/Disc 776 -5.56 6.42 -15.43 13.96 Volume 776 11.48 11.07 9.81 13.64
Ticker: SRO NAV 590 17.17 1.87 13.47 21.19 Price 590 15.33 1.00 11.95 17.83 Return 590 0.00 0.01 -0.06 0.04 %Prem/Disc 590 -10.16 6.50 -18.57 7.32 Volume 590 11.59 11.05 9.95 13.42
Ticker: SRQ NAV 775 21.15 3.70 13.71 27.80 Price 775 18.94 2.45 13.80 23.41 Return 775 0.00 0.01 -0.08 0.04 %Prem/Disc 775 -9.62 5.15 -17.16 3.68 Volume 775 10.82 10.44 8.19 12.84
Ticker: RIF NAV 776 18.95 2.59 13.51 23.47 Price 776 17.03 1.40 13.75 19.79 Return 776 0.00 0.01 -0.06 0.04 %Prem/Disc 776 -9.35 6.25 -16.90 8.95 Volume 776 9.47 9.33 4.61 11.52
Ticker: RRE NAV 777 17.31 2.38 12.15 21.20 Price 777 15.49 1.35 12.00 17.79 Return 777 0.00 0.01 -0.09 0.03 %Prem/Disc 777 -9.80 6.11 -17.66 7.26 Volume 777 11.73 11.24 9.72 13.85
88 Table 13 Impact of Sentiment on REIT CEF Returns
Panel A presents the regression estimation results of the overall effect of investor sentiment on the returns of REIT CEFs. Panel B presents the results of the Two-stage least squares regression of the effect investors sentiment has given the level of cumulative return relative to the NAREIT equity REIT index. Results are estimated and presented separately for the measure of retail and institutional investors sentiment. RETi,t = αi + β1 RMRFt + β2 SMBt + β3 HMLt + β4 UMDt + β5 TERMt + β6 DEFt + β7 SENTt + β8 PCTPMDTi,t + β9 VOLi,t + εi,t (3) PANEL A Retail Institutional Intcp Mktrf SMB HML UMD Sent Sent Volume Term Credit Pctpmdc R-Sqr Obs Full Sample 0.001 0.344 0.177 0.427 0.021 -0.002 -3.56E-08 0.001 0.003 0.000 0.1285 11239 0.51 25.77*** 7.63*** 12.21*** 1.01 -4.06*** -21.13*** 2.53** 0.37 0.83
Full Sample 0.001 0.343 0.183 0.438 0.018 -0.00003 -3.60E-08 0.001 -0.0001 0.000 0.1279 11239 0.70 25.69*** 7.85*** 12.38*** 0.83 -2.90*** -21.40*** 2.71*** 0.14 0.71
PANEL B Retail Institutional Intcp Mktrf SMB HML UMD Sent Sent Volume Term Credit Pctpmdc R-Sqr Obs Higher Return 0.008 0.343 0.198 0.414 0.034 -0.001 -4.66E-08 -0.0003 -0.004 0.00003 0.1428 3879 vs. NAREIT 3.76*** 15.32*** 4.93*** 6.78*** 0.95 -1.31 -15.72*** -0.02 -2.97*** 4.57***
Higher Return vs. NAREIT 0.008 0.344 0.199 0.417 0.033 -0.00001 -4.70E-08 0.0002 -0.004 0.0001 0.1428 3879 3.87*** 15.34*** 4.95*** 6.76*** 0.92 -0.67 -15.88*** 0.05 -3.23*** 4.66***
Lower Return vs. NAREIT -0.001 0.349 0.171 0.441 0.005 -0.002 -3.05E-08 0.001 0.001 -0.00002 0.1257 7376 -0.91 20.91*** 6.01*** 10.37*** 0.20 -3.36*** -15.27*** 2.46** 1.41 -1.06
Lower Return vs. NAREIT -0.001 0.348 0.178 0.454 0.001 -0.00003 -3.09E-08 0.001 0.001 -0.0002 0.1249 7376 -0.75 20.79*** 6.25*** 10.55*** 0.03 -2.38** -15.51*** 2.62*** 0.97 -1.26
89 Table 14 Impact of Sentiment on REIT CEF Returns Given Relative Return to the NAREIT all equity REIT index and Interest Rate Environment
This Table presents regression estimations of the various sub-samples of REIT CEFs grouped by whether cumulative returns were higher or lower than the cumulative return on the NAREIT all-equity index over the same time period, then by the interest rate environment period. Panel A presents the results for the period of decreasing interest rates, further categorized by those funds with cumulative returns than were higher than the S&P 500 given the time period. Panel B presents these same results given an increasing interest rate environment. RETi,t = αi + β1 RMRFt + β2 SMBt + β3 HMLt + β4 UMDt + β5 TERMt + β6 DEFt + β7 SENTt + β8 PCTPMDTi,t + β9 VOLi,t + εi,t (3)
Retail Institutional Intcp Mktrf SMB HML UMD Sent Sent Volume Term Credit Pctpmdc R-Sqr Obs
PANEL A
Lower Return & 0.007 0.236 0.120 0.534 -0.053 -0.003 -4.55E-08 -0.001 -0.0002 0.0001 0.1277 3186 Falling Rates 3.76*** 10.49*** 2.81*** 7.93*** -1.42 -3.23*** -14.06*** -2.34** -0.16 1.12
Lower Return & 0.007 0.238 0.125 0.543 -0.053 -0.00002 -4.62E-08 -0.001 -0.005 0.0005 0.1251 3186 Falling Rates 3.53*** 10.51*** 2.92*** 8.01*** -1.41 -1.26 -14.27*** -1.94* -0.34 1.17
Higher Return & 0.017 0.247 0.173 0.447 -0.044 -0.001 -5.00E-08 -0.003 -0.005 0.0003 0.1367 1975 Falling Rates 5.83*** 8.79*** 3.26*** 5.21*** -0.98 -1.01 -12.34*** -3.39*** -2.36** 4.54***
Higher Return & 0.018 0.250 0.171 0.444 -0.042 0.00001 -5.02E-08 -0.003 -0.006 0.0003 0.1367 1975 Falling Rates 5.96*** 8.90*** 3.20*** 5.15*** -0.92 0.53 -12.40*** -3.00*** -2.74*** 4.86***
PANEL B
Lower Return & -0.011 0.519 0.160 0.451 -0.034 -0.002 -1.77E-08 0.002 0.008 0.0000 0.1690 4190 Rising Rates -5.57*** 19.27*** 3.91*** 7.60*** -0.88 -1.64 -6.95** 6.06*** 5.16*** 0.17
-0.011 0.515 0.169 0.459 0.030 -1.3E-05 -1.78E-08 0.002 0.007 0.0000 0.1682 4190 Lower Return & 0.75 Rising Rates -4.77*** 19.16*** 4.13*** 7.65*** -0.92 -7.00*** 5.61*** 4.32*** 0.17
Higher Return& -0.008 0.576 0.141 0.460 0.052 -0.002 -4.78E-08 0.002 0.007 0.0002 0.1938 1904 Rising Rates -2.22** 13.07*** 2.11** 4.74*** 0.80 -1.14 -9.29*** 4.27*** 2.93*** 3.06***
Higher Return & -0.008 0.575 0.142 0.451 0.054 0.00001 -4.88E-08 0.002 0.008 0.0002 0.1933 1904 Rising Rates -2.18** 13.05*** 2.13** 4.59*** 0.83 0.46 -9.46*** 4.25*** 2.86*** 3.43***
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Table 15 Chow Test Results This Table presents the results of a Chow structural change test between the effect of irrational investor sentiment given an increasing versus decreasing federal funds rate environment.
Chow Structural Change Test Break Date Point F-Stat Prob 8/20/2004 5372 2.32* 0.0981
*** 1percent significance level ** 5 percent significance level * 10 percent significance level
91 CHAPTER 5: DISSERTATION SUMMARY
This dissertation is composed of three essays that examine exchange traded securities. Specifically, Essays 1 and 2 examine the relatively new financial innovation the exchange traded fund (ETF). Essay 1 examines the growth of ETFs in order to determine whether their significant growth comes at the expenses of open-end mutual funds that track the same index. Specifically, this essay finds that the introduction of the SPDR has negatively and significantly impacted the dollar flows to open-end mutual funds that also track the S&P 500, after controlling for distinct traders. Essay 2 looks at how measures of individual and institutional investor sentiment affect the market price deviations from the NAV of a sample of broad based ETFs as well as the relationship between sentiment and the creation and deletion activity common to ETFs. In general, institutional investor sentiment has a negative relationship with both the market price deviations and daily net creation and deletion activity. Moreover, the effect does not seem to differ across the market capitalization of the ETFs. These results indicate that when ETFs are selling at a premium to NAV there tends to be smaller pricing deviations. Accordingly, we might expect to find a negative and significant relationship between net creation and deletion activity and a market price deviation variable. This is not the case, however, and remains unexplained in this essay and left for future research. Essay 3 examines REIT CEFs and how market price deviations from NAV are impacted by irrational investor bias as gauged by investor sentiment orthogonalized to common risk factors and how this relationship changes given an increasing and decreasing federal fund interest rate environment. The results show that REIT CEFs are most susceptible to the risk of irrational investor bias during periods of decreasing interest rates. This finding should be significant to investors that purchase REIT CEFs for the short term during a decreasing federal fund rate market given it may indicate a period with a higher probability of taking on uncompensated risk. Overall, this dissertation examines various issues relating to the increasing popularity of exchange traded fund products and highlights some of their basic characteristics, its effects on our market, and some risks that investors should consider when investing in these products.
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97 BIOGRAPHICAL SKETCH
Vaneesha Boney Curriculum Vitae
Education: Florida State University, Tallahassee, FL Ph.D., Finance Expected January 2007 Support Areas: Econometrics Real Estate
Loyola College, Baltimore, MD M.B.A. w/ concentration in Finance 2001
University of Maryland at College Park, College Park, MD Bachelor of Science, Finance 1998
Current Research:
“The Effect of the Spider Exchange Traded Fund on the Flow of Funds of S&P Index Mutual Funds” with James Doran and David Peterson. Dissertation Essay 1 of 3 Under Review: Journal of Investing
“Investor Sentiment: Its Effect on ETF Pricing and Creations and Deletions” Dissertation Essay 2 of 3
“Timing the Investment Grade Securities Market: Evidence From High Quality Bond Funds” with George Comer and Lynne Kelly First Round Revisions: Journal of Empirical Finance
“Behavioral Finance: Are the Disciples Profiting from the Doctrine?” with Prithviraj Banerjee and Colbrin Wright Under Review: Financial Review
“REIT CEF Returns: The Impact of Irrational Investor Sentiment and Changes In the Federal Funds Interest Rate” Dissertation Essay 3 of 3
“REIT Exchange Traded Funds and the Fund Flows of REIT Index Funds” with Stacy Sirmans
“Market Timing Ability of REIT Managers” with Lynne Kelly and Russell Price
98 Research Interests: Investments, Mutual Funds, Market Efficiency, REITS, Behavioral Finance
Teaching Interests: Investments, Real Estate Principles, Real Estate Finance
Presentations:
Boney, Vaneesha and G. Stacy Sirmans, “REIT Exchange Traded Funds and the Fund Flows of REIT Index Funds” American Real Estate Society, April 2006
Boney, Vaneesha, James Doran and David Peterson, “The Effect of the Spider Exchange Traded Fund on the Flow of Funds of S&P 500 Index Mutual Funds” Finance Workshop Series, Florida State University, March 2006
Boney, Vaneesha, George Comer and Lynne Kelly, “Timing the Investment Grade Securities Market: Evidence From High Quality Bond Funds” Georgetown University Summer Research Series, August 2004
Academic Discussant:
Financial Management Association Meeting, October 2005
Service Panelist:
KPMG PhD Project “How to Maintain a Balanced Life as a Doctoral Student” June 2005
KPMG PhD Project “The Finance Doctoral Student Experience: What You Need to Know” November 2004
Awards:
RERI Research Grant ($9000), 2007 Florida State University Doctoral Assistantship, 2003-present McKnight Doctoral Fellowship, 2003-present Russell Ewald Academic Excellence & Human Service Award, Nominated 2006
Professional Experience:
Cash Reserve Manager, Tower Federal Credit Union Laurel, MD 2001-2003
Mutual Fund Pricing Analyst, T Rowe Price Baltimore, MD 1999-2001
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