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EFFECTS OF 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 listed on

AMEX/NYSE and . 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 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 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 , 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 . For example a company has 1 million 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 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 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 . 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 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 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 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 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

(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:

(((111))) ℎ ℎ = + { } + +

3 2011 Investment Company Fact Book. http://www.ici.org/pdf/2011_factbook.pdf

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where is defined as the ratio of number of mutual funds holding of ℎℎ firm at quarter and total number of mutual funds active in the same quarter, and is a vector of control variables including size, book-to-market, leverage and .

Liquidity measure

This section tests the hypothesis by identifying liquidity improvement after stock splits using panel data. Dennis and Stickland, (1998), Maloney and Mulherin, (1992) studies show that stock splits enhance liquidity by increasing the proportion of share traded, number of shareholders and improving dollar volume. A lower share price attracts more investors so that it becomes easier for investors to buy and sell shares. Thus, the liquidity hypothesis is

H2H2H2:H2 Technology companies that split their common stock improve liquidity in a year after the stock split.

To examine whether stock splits improve liquidity, I regressed liquidity on an indicator variable 111{Aftersplit}.1 Thus the regression model is:

(((222))) = + { } + +

where is defined as the number of shares traded of firm in quarter , divided by the firm’s outstanding shares, and is a vector of control variables including size, book-to-market, leverage and momentum.

Signalling measure

This section tests the signalling hypothesis using panel data. According to the signalling hypothesis, managers convey favourable private information about future prospects and

18 improved profitability. If stock splits signal profitability, splitting firms’ return on assets

(ROA) should increase in a year. Thus the hypothesis is

H3H3H3 : Firms that split their common share experience increase in return on assets (ROA) in the year subsequent to the split.

The observed regression model is:

(((333))) = + { } + +

where is the return on asset, defined as the ratio of net income and total assets of firm at quarter , and is a vector of control variables including size, book-to-market, leverage and momentum.

In the above regression equations (1)-(3), dependent variables include breadth of ownership, liquidity and return on assets (ROA) respectively. Breadth of ownership is the number of mutual funds actually holding a given stock at a given quarter divided by the total number of mutual funds active in a given quarter. Breadth of ownership is computed based on the Thomson Reuters mutual fund holdings, following Chen, Hong, and Stein (2002) . The observation period for equation (1) is restricted from the year 2000 to 2013 due to lack of available data. Liquidity is the ratio of number of shares traded to number of outstanding shares. Return on Assets (ROA) is the net income divided by total assets. Independent variable { } is a dummy variable equal to 1 if a firm is observed for four quarters after the stock split quarter and zero otherwise. is a vector of control variables; size is a natural log of market capitalization, market capitalisation is define as product of stock price and number of outstanding shares. Book-to-market is the ratio of book value to market capitalization. Book value is calculated as the value of common stockholders’ equity plus deferred taxes and investment tax credit minus book value of . Leverage is total liabilities scaled by total assets. Momentum is

19 a cumulative return from -3 to 0 quarter. 0 is the stock splits’ execution quarter. The firm, year and industry dummies are included to control for potential year, firm and industry fixed effects.

3.2.2 Event Study Methodology

The event study methodology measures the valuation effects of stock splits around the day of announcement. According to the signalling hypothesis as well as the liquidity hypothesis, positive abnormal returns are observed because the market reacts to stock splits as to good news (Lakonishok and Lev, 1987), (Maloney and Mulherin, 1992),

(Wulff, 1999). If the signalling hypothesis holds, the market should react positively. Thus the hypothesis is

H4H4H4 : There is a positive abnormal return on the day of stock split announcement.

In this event study there are two events: the stock splits announcement day and the actual stock execution day (also called ex-day). The announcement day is defined as the first day the information about stock splits becomes public. The stock split execution day is the first day that the stocks are traded at the new split-adjusted price. In order to measure the market reaction to a stock split event, the abnormal return of the stock price around the announcement and execution is calculated following the procedure given by Brown and Warner (1985).

In Fig 1, the event day is defined as day 0. The event window is used to test the valuation effect of stock splits, both for the announcement and execution of splits. 11 trading days (-5, +5) surrounding the event are examined. The pre-event estimation period is defined as (-244, -6). Splitting firms’ normal performance or returns are estimated using a pre estimation period. The post event estimation period is (+6, +244).

The event window and estimation period is used as defined by Kenneth (2000).

The market and risk adjusted daily returns are calculated using

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(((444))) = − −

where is the abnormal return of firm i on day t, is the normal return and is the value weighted market return index for day t. are estimators estimated and from with the estimation window of 244 trading days. Where = + +

and . For each event date t, the averages of abnormal return = 0 = (AAR) for all firms are calculated using formula

(((555))) 1 =

For a p-days event window, cumulative abnormal return (CAR) is calculated as

(((666)))

=

Where t=1 is defined as the first day in the event window. The t-statistic for the and are calculated as follows:

(((777))) =

(((888))) =

Where and are standard deviations of event date and the event period.

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To be included in the sample for an event study analysis, a firm has to meet the following criteria (i) only common stock announcement made in the year between 1995 to

2013 (ii) missing announcement date and execution date are excluded (iii) combined announcement made with stock splits are not included (iv) firms with missing data in pre-estimation period of 244 trading days before the announcement are also excluded.

The original sample consists of 319 technology firms that made stock split announcements. Stock splits with a split factor less than 1.5 are dropped following

Mcnicholas and Dravid (1990). The final sample for analysis consists of 227 splitting firms.

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4 Results

This chapter presents the empirical results on the trading range hypothesis, liquidity hypothesis, signalling hypothesis and market reaction of stock splits hypothesis applying methods as mentioned in chapter 3. The section begins with results from descriptive statistics.

4.14.14.1 Descriptive Statistics

Table 1 shows the descriptive statistics of variables used for the analyses in the thesis.

The mean split factor is 2. Maximum split factor in the sample is 5. The minimum and maximum stock prices are $0.340 and $432.688 respectively. Mean price of stock is

$33.080. The liquidity ratio ranges from 0.005 to 6.387 and the mean ratio of liquidity is

0.497. Size is natural log of market capitalization. The average size of the firm is 21.118

($1484M). The data shows that a small firm has a size of 14.861 ($2.84M) and a large firm has a market cap of 26.272 ($256.91B). Book-to-Market ratio ranges from -3.364 to

4.352 and has a mean of 0.411. Leverage ratio ranges from 0.024 to 1.894 and the mean is 0.414. Momentum is a cumulative return from -3 to 0 quarter. Data shows a minimum momentum of -0.516 and maximum of 3.141. Return on assets (ROA) ratio varies from -

0.676 to 0.524 and mean value is 0.019. Breadth of ownership ratio has a minimum value of 0.019 and maximum value of 0.463. Mean is 0.131.

Table 2 and Fig 2 report the number of the stock splits and split factors by year.

Split factor means the number of new shares exchanged for old shares. The sample period is 1995-2013. The data begins with the year 1996 due to the restriction of the sample. The resulting sample consists of 227 stock splits made by technology companies.

The largest number of firms i.e. 160 chose split factor two for one. Stock splits were more frequent corporate events during the period of 1996 to 2000. It is well known that during

23 these years technology stock had a high price run up. 34 technology companies completed stock splits in the year 2000. After the year 2000, the number of stock splits dropped. In the year 2001, only 8 companies announced stock splits. This coincided with the burst of the ‘dot-com’ bubble. During the sub-prime mortgage crisis in the year 2008, the number of stock splits dropped significantly to 1. This fluctuating trend also shows that technology stock splits depend on the economic cycle.

4.24.24.2 Test of the Trading Range Hypothesis

Table 3 represents regression of breadth of ownership with indicator variable Aftersplit.

Model (1) includes only control variable. Firm fixed effect is added in model (2). Firm and year fixed effects are added in model (3). Model (4) includes firm, year and two digit industry fixed effect.

Model (1) of Table 3 shows the coefficient of indicator variable Aftersplit is positive but not statistically significant. Size (t=33.460) and Book-to-Market (t=4.340) are positively significant at one percent level, indicating that firms with large size and higher book-to-market have higher breadth of ownership. In contrast, momentum (t=-

11.680) is negatively significant it means that firms with bad past performance have lower breadth of ownership. Leverage (t=-1.040) is negative and insignificant which does not predict breadth of ownership. Adding firm fixed effect in Model (2) shows that the coefficient of indicator variable Aftersplit (t=2.860), is positive and highly significant which indicates that breadth of ownership increases in a year after stock splits. Size

(t=32.070) is positive and significant, this could be interpreted as large firm is likely to improve breadth of ownership. Book-to-market (t=-8.0) and leverage (t=-5.22) is negatively significant at one percent level, which mean that low risk and low leveraged firms improve breadth of ownership. After controlling year and industry fixed effect indicator variable, Aftersplit (t=0.070) does not give any meaningful value. Previous

24 studies conclude that stock splits bring prices into the trading range and attract more shareholders. However, these results, by testing breadth of ownership of mutual fund holdings, do not strongly support the trading range hypothesis. As mentioned earlier, the trading range hypothesis does not matter for institutional investors. However, this result is also consistent with Rozeff’s (1998) findings. His analysis on mutual funds splits provides no evidence for the trading range hypothesis. This test does not find effect on breadth of ownership perhaps we are looking at wrong proxy. Other reason for this inconsistency could be the available sample period (2000-2013) leading to a small sample size (number of observations=1311) for the study.

4.34.34.3 Test of the Liquidity Hypothesis

Table 4 presents regression of liquidity with indicator variable Aftersplit. Model (1) includes only control variables. Firm fixed effect is added in model (2). Firm and year fixed effects are added in model (3). Model (4) includes firm, year and two digit industry fixed effect.

The regression result using model (1)-(4) show that the coefficient of indicator variable Aftersplit is positive and highly significant. In model (1) Aftersplit coefficient is

0.097 (t=3.860) even adding firm, year and industry fixed effects in the model, the

Aftersplit coefficient remains consistent and significant in all models. This finding suggests that stock splits improve liquidity in the year following the split. Stock splits reduce the unit price of a share which increases or trader participation. In model (1) size (t=16.070) and momentum (t=15.610) is positively related with liquidity, which suggests that large firms with a better past performance improve liquidity.

The coefficient of Book-to-Market (t=-1.780) is negative and significant at ten percent level which implies that firms with low book-to-market or also improve liquidity. Leverage coefficient -0.179 is negatively significant, (t=-7.380)

25 indicating that low leveraged firms have significantly higher improvement in liquidity.

In model (2), (3) and (4), size and leverage remain positively significant, meanings that large firm and high leverage firm enjoy liquidity increment after the stock split, whereas book-to-market and momentum become statistically meaningless. The finding supports our hypothesis that splitting firms’ share price returns to a price range and improves trading liquidity.

4.44.44.4 Test of the Signalling Hypothesis

Table 5 presents regression of return on assets (ROA) with indicator variable Aftersplit.

Model (1) includes only control variables. Firm fixed effect is added in model (2). Firm and year fixed effects are added in model (3). Model (4) includes firm, year and two digit industry fixed effect.

The result using model (1)-(4) shows indicator variable Aftersplit are positive and highly significant. Coefficient of indicator variable, Aftersplit (t=4.620) in model (1) without any fixed effect is positive and statistically significant at one percent level.

Control variable size (t=8.380) is positive and significant, which shows that large firms improve return on assets. The coefficients for the variables such as book-to-market -

0.021 (t=-8.590), leverage -0.039 (t=17.760) and momentum -0.002 (-3.170) are negative and statistically significant which implies that low book-to-market firms or growth firms improve return on assets. Leverage is negatively related with return-on-assets which means that low leveraged firms have better operating performance. Firms with negative past returns or bad performers decrease return on assets. Including firm, year and industry fixed effect in model (2), (3) and (4), the coefficient of Aftersplit remains positive and significant. This indicates that there is increase in return on assets in the year following the stock split. This result strongly supports the signalling hypothesis that

26 stock splits send signals about future profitability. This result is consistent with previous findings by Ikenberry, Rankine and Stice (1996), Desai and Jain (1997).

4.54.54.5 Market Reaction to Stock Splits

The signalling hypothesis is further measured using announcement period returns. Prior studies have linked excess return around the split announcement with management optimism about future returns, reduced share price, increased ownership and liquidity.

In this analysis stock splits announcement and execution made by technology companies during the periods 1995 to 2013 is observed. Resulting sample includes 227 firms that had stock splits. The results of the event study regarding announcement and execution date of stock splits are presented in Table 6 and Fig 3.

On the day of the stock split announcement (day 0) the average abnormal return is 1.4%, which is positively significant (t=4.268) at one percent level. Average abnormal return remains positive and significant on the next day (+1) which yields 1%. It remains positive but insignificant up to three days after the announcement. This shows that the market reacts positively to stock split announcements. This result is consistent with previous studies made by (Grinblatt, Masulis and Titman, 1984), (Ikenberry, Rankine and Stice, 1996). The findings further support the signalling and liquidity hypothesis that the market receives stock split announcements as a positive signal for future profitability and returns.

However, abnormal returns on execution day (day 0) of stock splits are positive but not statistically significant. On the day of execution, the average abnormal return is

0.4% and on the day after the execution of the stock split it is zero. This result is consistent with Maloney and Mulherin (1992) and Conrad and Conroy (1994).

Fig 4 shows cumulative average abnormal return around the announcement and execution day of stock splits.

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4.64.64.6 Sensitivity Analysis

Table 7 shows a sensitivity analysis of abnormal return on the day of announcement and execution using different event windows.

For the robustness check of abnormal return, cumulative abnormal return (CAR) was measured using different event windows. Using 3 event windows (-1, +1) CAR is

(t=5.190) positive and highly significant at one percent level and 2.360%. 5 days (-2,

+2) event windows and 7 days (-3, +3) event window CAR remains positive statistically significant. Thus the results strongly support positive market reaction around the announcement day. However using different event windows on the execution day of stock splits, cumulative returns do not support the signalling hypothesis. This result is consistent with Fama, French and Roll (1969) who find that the market adjusts new information to the stock price.

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5 Conclusion

This thesis examines the drivers of stock splits made by technology companies during the period 1995-2013. The resulting sample contains 227 technology companies. The three hypotheses: signalling, liquidity and trading range were tested.

The trading range hypothesis was tested observing changes in breadth of ownership of splitting firms. It was observed that ownership is improved only after controlling time invariant firm fixed effects. When excluding the fixed effects and including year and industry fixed effects, the result is not statistically significant. Hence, this thesis does not strongly support the trading range hypothesis. One reason for this inconsistency vis-à-vis the earlier literature could be the shorter sample period (2000-

2013), leading to a small sample size (number of observations=1311) for the study.

The analysis of the liquidity hypothesis shows that splitting firms’ liquidity increases in the year following the splits. This finding is consistent with Dennis and

Stickland (1998), Maloney and Mulherin (1992), Goyenko, Holden and Uklov (2006), and

Muscarella and Vetsuypens (1996). The empirical results show that returns on assets improve after the splits. This result strongly supports the signalling hypothesis, i.e. that managers reveal private information about future value or returns by splitting shares

(Mcnicholas and Dravid, 1990), (Ikenberry, Rankine and Stice, 1996), (Lakonishok and

Lev, 1987).

The signalling hypothesis is further tested using the event study methodology for announcement period returns. On the day of announcement, positive, and statistically significant abnormal returns are found. The average abnormal return on that day is

1.4% (t=4.268). This validates the signalling hypothesis that the market reacts positively

29 to stock split announcements by technology companies. However, on the day of execution, abnormal return is positive but not statistically significant.

In conclusion, the regression and event study analysis presented in this thesis shows that there is increase in return on assets and liquidity in the year after splits.

Thus the results of the study support the signalling and liquidity hypothesis as a motivation for stock splits by technology firms. The market perceives stock split announcements as good news which reveals favourable information about future profitability and improves liquidity. These findings are consistent with previous studies.

The empirical results from this thesis have led to another interesting aspect of stock splits; do the stock splits affect investors through a “sentiment” channel?

Psychologically, stocks might appear cheaper to the investors after the split and are more attractive to invest in, thereby increasing trading. Further research is recommended to examine investors’ sentiment factor and duration of the market response following the stock split. The existing literature has used various proxies for investor sentiment index. Baker and Wurgler (2006) apply individual sentiment analysis using various investor sentiment proxies: monthly trading unbalance of individual investors, flows, customer expectation index for business cycle, relative equity issuance, and turnover ratio. Due to the limited availability of data, investors’ underlying sentiments and behaviour is left as the topic of future research.

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Table 111:1: Descriptive statistics

This table reports descriptive statistic of stock split announcements of technology companies over the period 1995 to 2013. The data on stock splits declarations is from CRSP (distribution code 5523). Only technology companies listed on the , American Stock Exchange and NASDAQ recorded on CRSP SIC code (3571, 3572, 3575, 3577, 3578, 3661, 3663, 3669, 3671, 3672, 3674, 3675, 3677-3679, 3812, 3823, 3825-3827, 3829, 3841, 3845, 4812, 4813, 4899, 7371-7375, 7378, 7379 ) are used for analysis. Splits factor is referred as number of new shares exchanged for old shares. Stock price is defined as closing share price at trading day. Liquidity is the ratio of number of share traded to number of outstanding shares. Size is natural log of market capitalization: product of stock price and number of outstanding shares. Book- to-market is the ratio of book value to market capitalization. Book value is calculated as the value of common stockholders’ equity plus deferred taxes and investment tax credit minus book value of preferred stock. Leverage is total liabilities scaled by total assets. Momentum is a cumulative return from -3 to 0 quarter. Return on Assets is net income divided by total assets. 0 is the stock splits execution quarter. Breadth of ownership is the number of mutual funds actually holding a given stock at a given quarter divided by total number of mutual funds active at a given quarter. Split factor, share price, returns are from CRSP data set. Numbers of shares outstanding, volume, total liabilities, net income and total assets are from the CRSP/Compustat merged quarterly data set. Breadth of ownership is computed based on the Thomson Reuters mutual fund holdings, following Chen, Hong, and Stein (2002).

Standard Variable N Mean deviation Median Min Max Split factor 10941 2.001 0.486 1.667 1.5 00 5.000 Stock price 10941 33.080 26.003 28.375 0.340 432.688 LLLiquidity 10941 0.497 0.509 0.361 0.005 6. 387 Size 10941 21.118 2.101 21.214 14.861 26.272 Book ---tototo --- Market 10941 0.411 0.380 0.328 -3.364 4.352 Leverage 10941 0.414 0.234 0.401 0.024 1.894 Momentum 10941 0.729 0.576 0.553 -0.516 3.141 Return on assets 10941 0.019 0.036 0.0197 -0.676 0.524 Breadth of ownership 1311 0.131 0.066 0.126 0.019 0.463

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Table 222:2::: SSStockStock split factor by year

The table shows stock splits factor of technology companies by year in the period from 1995 to 2013. Split factor is defined as the number of new shares exchanged for the old shares. The data on splits size are from CRSP daily master file with distribution code 5523. Only selected technology companies reported on CRSP SIC code (3571, 3572, 3575, 3577, 3578, 3661, 3663, 3669, 3671, 3672, 3674, 3675, 3677-3679, 3812, 3823, 3825-3827, 3829, 3841, 3845, 4812, 4813, 4899, 7371-7375, 7378, 7379 ) is used for analysis. The sample consists of 227 stock splits of technology companies with split factor greater than 1.25. Firms with missing data in estimation and event windows, missing execution and announce date, and jointly made announcement with other corporate events are also excluded.

Split fa ctor year 1.5 1.67 2 2.5 3 4 Total 1996 3 0 14 0 0 0 17 1997 6 0 15 1 1 0 23 1998 1 0 14 0 0 0 15 1999 5 0 20 0 1 0 26 2000 5 0 28 0 1 0 34 2001 4 0 4 0 0 0 8 2002 1 0 4 0 1 0 6 2003 5 0 4 0 1 0 10 2004 4 0 12 0 2 1 19 2005 4 0 11 0 3 0 18 2006 1 0 7 1 0 0 9 2007 1 0 4 0 2 0 7 2008 1 0 4 0 1 0 6 2009 0 0 1 0 0 0 1 2010 1 1 5 1 1 0 9 2011 1 0 7 0 1 0 9 2012 1 0 2 0 0 0 3 2013 2 0 4 0 0 1 7 Total 46 1 160 3 15 2 227

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Table 333:3: Test of the trading (price) range hypothesis

The table reports the estimates of the model:

. ℎℎ = + { } + +

The dependent variable is defined as the ratio of number of ℎℎ mutual funds holding of firm at quarter and total number of mutual funds active at the same quarter. Independent variable { } is a dummy variable equal to 1 if firm is observed for four quarters after the stock split quarter and zero otherwise. is a vector of control variables: firm size (natural log of the market capitalization), book-to- market (book value divided by market capitalization), Leverage (total liabilities divided by total assets), Momentum (cumulative returns from the beginning of quarter -3 to end of stock split execution quarter 0 ). Column (1) does not include fixed effects. Column (2) includes firm fixed effects. Column (3) includes firm and year fixed effect and column (4) includes firm, year and industry (two-digit) fixed effects. Stock returns are from CRSP monthly tape. Breadth of ownership is computed based on the Thomson Reuters mutual fund holdings, following Chen, Hong, and Stein (2002). Numbers of outstanding shares, price of share, total liabilities, total assets, book value are from the CRSP/Compustat merged quarterly data set for the period 2000 to 2013. t-statistics are in parenthesis. The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels.

Dependent Variable: Breadth of ownership Indep endent Variables (1) (2) (3) (4) After split 0.001 0.006 *** 0.000 -0.000 (0.42 0) (2.86 0) (0.07 0) (-0.01 0) Size 0.051 *** 0.030 *** 0.041 *** 0.041 *** (33.46 0) (32.07 0) (33.00 0) (31.55 0) Book to Market 0.0140 *** -0.027 *** -0.006 *** -0.008 *** (4.34 0) (-8.00 0) (-2.01 0) (-2.40 0) Leverage -0.004 -0.028 *** 0.012 ** 0.009 * (-1.04 0) (-5.22 0) (2.37 0) (1.72 0) Momentum -0.018 *** 0.000 0.0000 -0.000 (-11.68 0) (0.13 0) (0.00 0) (-0.00 0) Constant -1.010 *** -0.545 *** -0.800 *** -0.770 *** (-28.80 0) (-25.15 0) (-28.01 0) (-26.01 0) Firm f.e. No Yes Yes Yes Year f.e. No No Yes Yes Industry f.e. No No No Yes R square 0.820 0.960 0.972 0.973 No. Of Observations 1311 1311 1311 1311

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Table 444:4: Test of the liquidity hypothesis

The table reports the estimates of the model:

= + { } + +

The dependent variable is , representing the number of share traded of firm in quarter , divided by the firm’s outstanding shares. Independent variable { } is a dummy variable equal to 1 if firm is observed for four quarters after the stock split quarter and zero otherwise. is a vector of control variables: firm size (natural log of the market capitalization), book-to-market (book value divided by market capitalization), Leverage (total liabilities divided by total assets), momentum (cumulative returns from the beginning of quarter -3 to end of stock split execution quarter 0 ). Column (1) does not include fixed effects. Column (2) includes firm fixed effects. Column (3) includes firm and year fixed effect and column (4) includes firm, year and industry (two-digit) fixed effects. Stock returns are from CRSP monthly tape. Numbers of outstanding shares, price of share, total liabilities, total assets, book value are from the CRSP/Compustat merged quarterly data set for the period 1995 to 2013. t- statistics are in parenthesis. The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels..

Dependent Variable: Liquidity Independent Variables (1) (2) (3) (4)

After split 0.097 *** 0.060 *** 0.066 *** 0.069 ***

(3.86 0) (3.30 0) (3.61 0) (3.86 0) Size 0.037 *** 0.092 *** 0.088 *** 0.103 *** (16.07 0) (19.00 0) (12.98 0) (14.93 0) Book to Market -0.030 * 0.015 -0.003 0.006 (-1.78 0) (0.94 0) (-0.18 0) (0.37 0) Leverage -0.179 *** 0.109 *** 0.091 *** 0.119 *** (-7.38 0) (3.92 0) (3.19 0) (4.28 0) Momentum 0.182 *** 0.000 0.000 0.000 (15.61 0) (0.01 0) (0.01 0) (0.01 0) Constant -0.337 *** -1.263 *** -1.149 *** -1.354 *** (-6.44 0) (-11.24 0) (-7.85 0) (-9.09 0) Firm f.e. No Yes Yes Yes Year f.e. No No Yes Yes Industry f.e. No No No Yes R square 0.067 0.545 0.554 0.567 No. Of Observations 10941 10941 10941 10941

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Table 555:5: Test of thethethe sssignalingsignaling hypothesis

The table reports the estimates of the model:

. = + { } + +

The dependent variable is the return on asset, defined as the ratio of net income and total assets of firm at quarter . Independent variable { } is a dummy variable equal to 1 if firm is observed for four quarters after the stock split quarter and zero otherwise. is a vector of control variables: firm size (natural log of the market capitalization), book-to-market (book value divided by market capitalization), Leverage (total liabilities divided by total assets), Momentum (cumulative returns from the beginning of quarter -3 to end of stock split execution quarter 0). Column (1) does not include fixed effects. Column (2) includes firm fixed effects. Column (3) includes firm and year fixed effect and column (4) includes firm, year and industry (two-digit) fixed effects. Stock returns are from CRSP monthly tape. Numbers of outstanding shares, price of share, total liabilities, total assets, net income and book value are from the CRSP/Compustat merged quarterly data set for the period 2000 to 2013. t-statistics are in parenthesis. The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels.

Dependent Variable: Return on asset sss Independent Variables (1) (2) (3) (4) After split 0.006 *** 0.004 *** 0.004 *** 0.004 ***

(4.62 0) (4.04 0) (3.70 0) (3.82 0)

Size 0.002 *** 0.004 *** 0.006 *** 0.006 *** (8.38 0) (8.42 0) (7.21 0) (6.23 0)

Book to Market -0.021 *** -0.023 *** -0.018 *** -0.019 *** (-8.59 0) (-6.56 0) (-4.65 0) (-4.89 0)

Leverage -0.039 *** -0.044 *** -0.040 *** -0.040 ***

(-17.76 0) (-8.31 0) (-7.36 0) (-7.50 0) Momentum -0.002 *** 0.000 0.000 0.000

(-3.17 0) (0.01 0) (0.01 0) (0.01 0) Constant -0.006 -0.052 *** -0.087 *** -0.079 ***

(-0.97 0) (-4.38 0) (-4.53 0) (-4.09 0) Firm f.e. No Yes Yes Yes Year f.e. No No Yes Yes Industry f.e. No No No Yes

R square 0.110 0.230 0.238 0.245

No. Of Observations 10941 10941 10941 10941

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Table 666:6::: Market reareactionction to stock splits on the day of announcementannouncement anandd execution

The table reports the average abnormal returns (AAR) of firms around the stock splits announcement and execution day in the period of 1995 to 2013. Average abnormal returns (AAR) are calculated using OLS market model regression as suggested by Brown and Warner, 1985. Daily abnormal returns (AR) are defined as , = − − where is the abnormal return of firm i on day t, is the normal return and is the value weighted market return index for day t. Announcement date is the first stock splits declaration date. Ex-date is the execution date of stock splits. Eleven days event window is used. Day 0 is the day of the event. Data set are available on CRSP daily master tape. The symbol *, **, and *** denote statistical significance at the 10%, 5% and 1% levels.

Announce ---date ExExEx ---date Event day Avg Abnormal ttt---statistic AAAvg A bnormal ttt---statistic RRReturnReturn (%) RRReturnReturn (%) ---555 -0.300 -1.546 0.300 1.757 * ---444 0.300 1.309 -0.400 -1.941 * ---333 -0.100 -0.497 -0.100 -0.570 ---222 0.400 1.800 * -0.100 -0.751 ---111 0.000 -0.140 0.100 0.522 000 1.400 4.268 *** 0.400 1.290 111 1.000 3.206 *** 0.000 -0.070 222 0.100 0.387 -0.300 -1.519 333 0.000 0.217 -0.200 -0.782 444 0.000 -0.257 -0.400 -1.886 * 555 -0.100 -0.588 -0.600 -2.562 **

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Table 777:7: Sensitivity analysis of announcement return on differentdifferent event windows

The Table shows sensitivity analysis of abnormal return on the day of announcement and execution using different event windows. Cumulative abnormal return is calculated using OLS market model regression over 238 estimation days. The symbol *, **, and *** denote statistical significance at the 10%, 5% and 1% levels.

Return Announce ---date ExExEx ---date Event CAR (%) ttt---statistic ppp--- CAR (%) ttt--- ppp--- Window value statistic value ---1 to 1 2.3 60 5.190 *** 0.000 0.474 0.960 0.338 ---2 to 2 2.860 5.020 *** 0.000 0.0 19 0.030 0.974 ---3 to 3 2.8 00 4.520 *** 0.000 -0.292 -0. 460 0.645 ---5 to 5 2.557 3.6 50*** 0.000 -1.3 27 -1.8 70* 0.063

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Fig 111:1: Time line for Event Study

Fig 222:2: Histogram of stock splits of Technology Company sstockstocks in the period of 1995 to 2013

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Fig 333:3: Plot of average abnormal return around the event day The graph shows average abnormal returns calculated in percentage using market excess return model. Day 0 is the day of stock splits announcement or execution.

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Fig 444:4: Cumulative average abnormal return around the eveeventnt day The graph illustrates cumulative average abnormal return calculated in percentage using market excess return model. Day 0 is the day of stock splits announcement or execution.

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