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EFFECTS OF STOCK SPLITS ON RETURN AND LIQUIDITYLIQUIDITY:: A STUDY ON TECHNOLOGICAL COMPANCOMPANIESIES
GEHINI JOSHI ANR: 178628 MSC in Finance
Supervisor: Alberto Manconi
- 2014 -
EFFECTS OF STOCK SPLSPLITSITS ON RETURN AND LIQUIDITYLIQUIDITY:: A STUDY ON TECHNOLOGICAL COMPANCOMPANIESIES
Master TThesishesis Finance
School of Economics and Management
Tilburg University
GEHINI JOSHI ANR: 178628
Supervisor: Alberto Manconi
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AbAbAbstractAb stract
This thesis examines the effects of stock splits made by technology companies listed on
AMEX/NYSE and NASDAQ. Prior studies suggest that managers use stock splits to convey favourable information, improve liquidity and bring price back to normal trading range, and to broaden the shareholder base. I analyse splitting companies before and after stock splits. The results show that an increase in return, profitability, and liquidity in the year after the split. These findings strongly support the signalling and liquidity hypotheses. However, in contrast to the previous studies, I do not find any support for the trading range or the shareholder base hypothesis.
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AAAcknowledgementsAcknowledgements
Along the journey of my studies in Tilburg University, I have been supported and inspired by many people. I would like to take this opportunity to express my thanks to all people who have helped me to complete this thesis. First of all, I would like to thank my thesis supervisor Alberto Manconi for his invaluable suggestions and insightful comments during the time of my thesis.
I would like to acknowledge my family in Nepal for their emotional support and motivation. Special thanks go to my friends for their inspirational words. Finally, I would like to express deepest gratitude to my husband Rameswor and son Ruben for their immense support, understanding and patience. Their continuous support in pursuing my study is invaluable to me, for which I am eternally grateful.
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TablTablee of Contents
111 Introduction ...... 666
222 Literature Review and Hypothesis Development ...... 101010
2.1 Trading Range Hypothesis ...... 10
2.2 Liquidity Hypothesis ...... 12
2.3 Signalling Hypothesis ...... 13
333 Methodology ...... 161616
3.1 Description of Data ...... 16
3.2 Research Method...... 17
444 Results ...... 232323
4.1 Descriptive Statistics ...... 23
4.2 Test of the Trading Range Hypothesis...... 24
4.3 Test of the Liquidity Hypothesis ...... 25
4.4 Test of the Signalling Hypothesis ...... 26
4.5 Market Reaction to Stock Splits ...... 27
4.6 Sensitivity Analysis ...... 28
555 Conclusion ...... 292929
666 References ...... 313131
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List of TTablesables
Table 1: Descriptive statistics ...... 34
Table 2: Stock split factor by year ...... 35
Table 3: Test of the trading (price) range hypothesis ...... 36
Table 4: Test of the liquidity hypothesis ...... 37
Table 5: Test of the signaling hypothesis ...... 38
Table 6: Market reaction to stock splits on the day of announcement and execution ...... 39
Table 7: Sensitivity analysis of announcement return on different event windows ...... 40
List of Figures
Fig 1: Time line for Event Study ...... 41
Fig 2: Histogram of stock splits of Technology Company stocks in the period of 1995 to
2013 ...... 41
Fig 3: Plot of average abnormal return around the event day ...... 42
Fig 4: Cumulative average abnormal return around the event day ...... 43
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1 Introduction
Recently, major technological companies such as Google Inc. (GOOG) and Mastercard
Inc. (MA) announced stock splits. Share prices were up 0.5 % and 2.5% respectively hours after the announcement 1, 2. Both companies completed stock splits for the first time in their company history. Apple Inc. (AAPL) also completed a seven for one stock split this year for the fourth time. This means that every Apple stockholder receives six additional shares for every share he owns. This distribution cut Apple’s share price down to $92 from $645, but increased the number of outstanding shares from 861 million to 6 billion. On the day of the split announcement, Apple shares went up by 8%. The Apple managers said they want to make the stock more accessible to the average investors, and the early months’ return is impressive. (Source: online.wsj.com)
Stock splits are “cosmetic” corporate events. The effect is to bring stock prices down and increase the number of outstanding shares, without changing the market capitalization. For example a company has 1 million shares outstanding which are trading for $200 each and the total market value is $200 million. If the company announces two-for-one stock splits, the outstanding number of shares will increase to 2 million while the par value of stock drops to $100. Thus, the stock split per se does not create intrinsic value to the company. Similarly, it does not affect the value of investors’ holdings. It merely cuts the “pie” into smaller slices.
In theory, stock splits should have no impact, but in practice corporate managers view stock splits to be more than cosmetic accounting. When companies do split their stocks, the interesting question may arise: why? The literature has put forward three
1 http://www.businessinsider.com/5-things-to-know-about-apples-stock-split-2014-6 2http://www.forbes.com/sites/samanthasharf/2013/12/10/mastercard-announces-10-for-1-stock- split-plans-to-return-cash-to-shareholders/
6 possible explanations, the trading range hypothesis, the liquidity hypothesis and the signalling hypothesis. The trading range hypothesis suggests that firms use stock splits to realign the share price to a preferred price range, so that it is more affordable to individuals as well as institutional investors. Keeping the stock within the normal price range attracts a larger ownership base, improves liquidity and reduces trading cost of the stock. The liquidity hypothesis argues that firms tend to split shares to increase the liquidity of their shares. A lower share price is attractive to the investors, which creates more buying and selling activity. The signalling hypothesis suggests that managers use stock splits to signal positive information about a firm’s future expectations. Abnormal returns observed around the split announcements provide better estimates of company prospects.
Prior studies conclude that splitting firms do better compared to non-splitting firms. The market generally reacts to stock splits as good news. Stock splits are a signal from management that their company’s share price continues to appreciate and improve profitability. If managers believe that there is a significant probability of a price increase they will split the shares and keep price within the trading range. Previous studies have found a significant positive market reaction to stock split announcements. Grinblatt,
Masulis, Titman, (1984) argue that stock splits convey information about the current value and future prospects of the splitting firms. There is information asymmetry between managers and shareholders. By splitting shares, managers lower the information asymmetry. Ikenberry, Rankine and Stice (1996) claim that stock splits send positive signals to the market that the firms are confident in the growth of their future earnings. Desai and Jain (1997) conclude that splitting firms experience long runs of excess returns. Copeland (1979) argues that firms prefer to keep their stock price in an optimal trading range to enlarge the ownership base and increase the number of retail
7 investors. Lakonishok and Lev (1987) show that splits increase trading volume in the period around the splits.
Academic research on the theory of stock splits is mainly focused on US based markets. Only few studies have examined stock splits in other countries, industries and funds. Stock splits in the technology sector are neglected in literature. However, if stock splits have an effect, they should be examined in a setting or context where they are more likely to be present, such as technology stocks. Recent stock splits in technological companies like Google Inc. (GOOG), Apple Inc. (AAPL) and Mastercard Inc. (MA) were extensively discussed and covered in the media. It is known that technology based companies have uncertain earnings and cash flow, as well as large investments in intangible assets such as research and development and human resources, as the companies’ success depends on the outcome of these investments. Investment in intangible assets creates an information asymmetry problem, since corporate managers can continually observe changes in investment productivity for individual assets and hence have more information about the company’s future prospects than the investors
(Himmelberg and Petersen, 1994). Furthermore, those firms that primarily hold intangible assets need to maintain more liquid stock as they rely more on equity markets for capital. Thus firms take actions such as stock splits that will lower the information asymmetry in the market, as well as enhance liquidity. This suggests that effects of stock splits should be more pronounced in technology stocks and it should be the most logical sector to test the stock split theory. Furthermore, the effect of stock splits on technology companies has not been investigated in literature.
Based on the above arguments and recent news on stock splits, my research question is:
“Why do technology firms announce stock splits?”
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I provide evidence based on detailed data analysis to answer the research question. Since the investors want to know their future earnings and prospects where they are investing, companies also want to know their performance after the splits. This thesis aims to provide a clear picture on whether or not stock splits improve the returns, liquidity and ownership base of companies that split their stock.
Using the CRSP/Compustat merged quarterly data of technology companies for the period 1995 to 2013, this thesis finds that there is a statistically significant improvement in liquidity and positive returns after a split. Using the CRSP daily data of technology companies set for the period 1995 to 2013, there is abnormal return on the day of the stock split announcements, measured by using the event study methodology.
This implies that managers convey private information to the market about the firm’s current value. However, using mutual fund holding data from the year 2000 to 2013, the analysis does not provide strong evidence of increase in breadth of ownership, implying that bringing the price down to trading range does not necessarily increase ownership breadth.
The rest of the thesis is organised as follows: Chapter 2 provides a review of the related literature on stock splits and hypothesis development. Chapter 3 describes research method and the data. Chapter 4 analyses the results and Chapter 5 states concluding remarks and recommendation for further studies.
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2 Literature Review and Hypothesis Development
This chapter reviews the relevant literature for this thesis. Section 2.1 provides a literature review on the signalling hypothesis. Section 2.2 and section 2.3 provide a literature review on the liquidity and trading range hypotheses.
2.12.12.1 Trading Range Hypothesis
The trading (price) range hypothesis presumes that shareholders prefer to purchase
“round lots” of 100 shares but cannot afford to buy when the price is high. Stock splits realign per share prices to a desired price range, making the shares more affordable for small or uninformed investors.
A survey for financial executives conducted by Baker and Gallagher (1980) found that lowering price per share through stock splits brings the stock within the reach of more investors. Stock splits are a useful device to bring the stock into an optimal trading range. Another reason for management’s desire to increase the number of shares outstanding is to broaden the ownership base and enhance trading liquidity. Maloney and Mulheran (1992) investigated samples of NASDAQ firms and found evidence that stock splits lead to a greater number of shareholders, higher dollar volume, a larger number of trades and narrower absolute bid-ask spreads while returning the share price to a target range. They also found increased institutional ownership after the split. Stock splits allow pre-existing shareholders to sell in round lots, a portion of their holdings in a firm that has experienced substantial equity appreciation. Baker and Powell (1993) found improved trading liquidity, stock ownership and a number of transactions after splits. Lakonishok and Lev (1987) suggest that the main objective of the split is to return the price to a normal range after unusual growth in earnings and stock prices. They link splits more to past performance than to future performance. The normal range is based
10 on market and industry-wide price average, and firm specific prices. Mcnicholas and
Dravid (1990) found that the split factors are increasing the function of presplit share prices, which implies that managers have preferred the trading range when issuing stock splits. They also find an inverse relationship between split factors and the market value of a firm’s equity. Lamoureux and Poon (1987), Desai, Nimalendran and
Venkataraman ( 1998) find that stock splits enlarge the ownership base and the number of small trades, particularly by small investors. O’Hara and Saar (2000) studies also support the trading range hypothesis, i.e. that there is an increase in the number of uninformed trades and a slight of uninformed buyers to execute their trades by using market orders and entering the stock market. Dennis and Strickland (2003) find that institutional ownership increases post-splits for firms with low institutional ownership before the split announcement. Ikenberry, Rankine and Stice (1996) examine a sample of
1,275 two-for-one stock split announcements by NYSE and ASE firms between 1975 and
1990, and provide evidence that stock splits generally occur when trading at high prices.
Lin, Singh and Yu (2009) argue that firms use stock splits to attract more uninformed traders to participate in trading. Schultz (2000) finds that splits are used to increase the shareholder base for a stock. Rozeff (1998) analyse on mutual fund splits and find no support for trading range hypothesis.
Above all, theory suggests that stock splits make the price per share cheaper and attracts more investors to trade. Murkherji, Kim and Walker (1997) argue that stock splits increase the numbers of both institutional and individual investors. Lakonishok and Lev (1987) found that subsequent to a split, the ratio of individual investors increased in relation to the institutional investors. If corporate managers split stocks to realign prices with the market, or industry stock splits should increase the number of institutional investors. Based on this theory, the testable hypothesis is:
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H1H1H1:H1 Technology companies have greater breadth of ownership in a year after the stock splits.
Breadth of ownership is the ratio of mutual funds holding stock to the total number of mutual funds in the quarter as mentioned by Chen, Hong and Stein (2002).
2.22.22.2 Liquidity HypothesHypothesisisisis
The liquidity hypothesis advocates that stock splits enhance liquidity by increasing the proportion of shares traded. By bringing the stock price down, more investors find it affordable and buy it which increases liquidity. Previous studies have reported that the impact of stock splits on liquidity is mixed. Anshuman and Kalay (2002) documented an increase in the number of shareholders after stock splits. By splitting, a firm lowers its stock thereby increasing the incentives of liquidity traders to time their trades. The resulting concentration of trades reduces overall transaction costs incurred by liquidity traders. Their findings of positive cumulative abnormal returns and increased raw volume after splits also support the liquidity hypothesis. Desai, Nimalendran and
Vekantaraman (1998) found increases the number of trades and a decrease in the average turnover per trade after the split. Muscarella and Vetsuypens (1996) investigated non-U.S companies whose shares trade in American Depository Receipts
(ADRs) and found that stock splits result in improved liquidity. Dennis and Strickland
(2003) found evidence that supports the existence of liquidity gains for firms that split their stock. Their evidence leads to the conclusion that stock splits result in excess return as improved liquidity. Dennis (2003) investigated NASDAQ-100 index tracking stock and found liquidity is improved for smaller trades. The post-split lower share price of the Index Tracking Stock helps smaller investors trade in smaller lot sizes and improves liquidity. Schultz (2000) examined intraday trades and quotes around splits and found that there are many small orders subsequent to splits, as well as an increased
12 number of buying orders. Thus splits are used to increase the shareholders’ base for a stock. Goyenko, Holden and Ukhov (2006) found that firms that split experience gain in liquidity over longer periods. However, Conroy, Harris and Benet (1990) measured stock split and shareholders’ liquidity by bid-ask spreads and found liquidity worsened after splits. Copeland (1979) reported significant decreases in trading volume after splits.
Technology companies’ products are unique and intangible because of innovation and brand value. In case of bankruptcy there will be ripple effects on their customers, supplier and workers, so they maintain lower leverage and heavily depend on the equity market. As a result, technology companies are more likely to split their shares to make their firms more transparent, and to lower the information asymmetry between managers and investors which leads them to improve liquidity (Aboody and Lev, 2000),
(Gopalen, Kadan and Pevzner, 2011). To evaluate whether stock split improves liquidity the following hypothesis is tested:
H2H2H2:H2 Technology companies that split their common stock improve liquidity in a year after the stock splits.
2.32.32.3 SignalSignallingling Hypothesis
According to the signalling hypothesis, managers declare stock splits to convey favourable private information to the market about a firm’s value and positive future performance. Thus, excess returns are observed around split announcement. The signalling theory was first suggested by Fama, Fisher, Jensen and Roll (1969). They investigated the information content of stock splits and analysed how share prices adjusted to the new information. They used a sample of 940 stock splits from the NYSE over the period 1927-1959 and found that stock splits tend to occur during boom periods, and the particular stock will tend to be that which performed unusually well during the period of general price increase. This finding is explained by the information asymmetry
13 which exists between managers and investors. Stock splits reduce the informational asymmetry between a firm and outsiders by sending information about the level of return. They also found abnormal return around the split months, suggesting that the market considers stock splits to be good news. Grinblatt, Masulis and Titman (1984) examined the valuation effect of stock splits using 1762 announcements and 1740 ex- date events for proposed splits and stock dividends from NYSE and AMEX over the period 1967-1979. They found that splitting firms experience abnormal returns during the announcement period. Asquith, Healy and Palepu (1989) concluded that firms have significant earnings increase four years before the stock split, and these earnings appear to continue for up to five years. So a stock split conveys earning information.
The split announcement leads investors to increase their expectations that earnings will increase permanently. Desai and Jain (1997) argue that the abnormal returns following stock split announcements are positively associated with the increase in dividends. Brennan and Copeland’s (1988) studies provide evidence that stock splits are costly due to trading costs depending on stock price, as stock price decreases, brokerage commission increases. Stock splits have signalling value because they have associated costs, like execution costs, higher listing fees and trading costs which drop price off the stock. Only firms with positive private information can afford to signal through a stock split. Therefore the number of shares that will be outstanding after the split signals private information about the company to the investors. Nayak and
Prabhala (2001) found 70% of splits are associated with positive stock price reactions.
Lakonishok and Lev (1987) consider that stock splits are credible signals for future performance. However, for signalling to be valid, there should be costs associated with sending false signals. Brennan and Hughes (1991) argue that managers with private good news announce a stock split. Investors interpret stock splits as a favourable signal, which explains the positive abnormal returns observed around split announcements.
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Mcnicholas and Dravid (1990) found a significant relation between excess announcement returns and forecast error previously, suggesting that announcement period returns can be explained by management’s private information sharing about future earnings. This empirical evidence suggests that corporate management is confident that their share prices will continue to appreciate and reflect optimism about the firm’s future prospects.
If it is true, this theory should be applicable to technology companies as well, because these companies’ products are unique and are based on intensive investment in intangible assets such as research and development and human resources. Aboody and
Lev (2000) argue that a firm’s future value depends on the success of its investment in intangible assets, and only corporate managers have information about products under development, marketing prospects and the likelihood of success. Therefore, managers reveal costly private information through stock splits. Ikanberry, Rankin and Stick
(1996) and Desai and Jain (1997) suggest that firms with better performance, as measured by return on assets (ROA), are more likely to split their shares. To evaluate whether splits convey information on announcement and earnings improvement, two hypotheses are tested:
H3H3H3 : Firms that split their common share experience an increased return on assets in the year subsequent to the split.
H4H4H4 : There is a positive abnormal return on the day of the stock split announcement.
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3 Methodology
This chapter outlines the research method of the thesis. Section 3.1 describes the data source. Section 3.2 illustrates the research method used in the thesis.
3.13.13.1 Description of Data
The thesis uses a data set of technology firms listed on the New York Stock Exchange
(NYSE), American Stock Exchange (AMEX) and the National Association of Securities
Dealers Automated Quotation System (NASDAQ) from January 1995 to December 2013.
Data are downloaded using Standard Industrial Classification codes (SIC) as defined by
Loughran and Ritter (2004). The thesis is focused on technology companies with SIC codes: 3571, 3572, 3575, 3577, 3578 (Computer hardware), 3661, 3663, 3669
(Communications equipment), 3671, 3672, 3674, 3675, 3677, 3678, 3679
(Electronics), 3812 (Navigation equipment), 3823, 3825, 3826, 3827, 3829
(Measuring and controlling devices), 3841, 3845 (Medical instruments), 4812,
4813 (Telephone equipment), 7371, 7372, 7373, 7374, 7375, 7378, 7379 (Software) and 4899 (Communications services). Daily data on returns, price of stock, return, number of shares outstanding, value-weighted market return, split factor, declaration and execution date are from the CRSP daily master file . The CRSP stock split code is 5523. The final sample consists of 227 stock splits for event study analysis. Quarterly data on s hares outstanding, volume, total liabilities, net income and total assets for panel data analysis are from the CRSP/Compustat merged quarterly data set. The final sample for panel data analysis consists of 135 stock split firms.
Breadth of ownership is computed based on the Thomson Reuters mutual fund holdings, following Chen, Hong, and Stein (2002) for the year 2000 to 2013.
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3.23.23.2 Research Method
3.2.1 The Regression Model and Control Variables
Trading range measure:
The impact of stock splits on the trading (price) range is measured using a panel data set of splitting firms. Specifically this section investigates whether breadth of ownership
(related to number of investors holding a given stock) increases over the one-year period following the stock split.
While prior research has mainly focused on retail investors to test the trading range hypothesis, we can argue breadth of ownership is a good proxy to measure this hypothesis for two reasons. First, in practice retail investors do not control large fraction of the US stock market. According to Blume and Keim, (2014) and Lewellen (2011) institutional investors control 68% of the market, and of which mutual funds in particular hold about 28% in 2010 3. So, if the trading range hypothesis matters at all, it should matter for institutional investors. Second, we might simply take breadth of ownership as a possible noisy proxy for the overall ownership dispersion. To the extent that we are able to obtain any results with a noisy proxy, a less noisy proxy should deliver stronger results. To sum up, the trading range hypothesis is
H1H1H1 : Technology companies have greater breadth of ownership in a year after the stock splits.
To test this hypothesis, I regressed breadth of ownership on an indicator variable
111{Aftersplit}.1 Thus the regression model is: