Conclusion

Llorente, Michaely, Saar, and Wang (2002) show that trading based on information can cause a positive autocorrelation of returns. We test this hypothesis using the identification condition that foreigners and mutual funds trade on information.

Consistent with the hypothesis, we find that the autocorrelation of returns is higher when foreigners and mutual funds trade more heavily. We also hypothesize that short sale constraints will reduce the information content of sell volume and reduce the autocorrelation accordingly. The second hypothesis is also supported by our evidence that the sell volume from mutual funds and foreigners has a smaller effect on the autocorrelation of returns than buy volume.

Our results can help to better understand the time-series behaviors concerning the autocorrelations of returns. Bessembinder and Hertzel (1993) find a pattern in the autocorrelation of security returns around non-trading days. This pattern may be related to the information trading around those days. Future investigations may be warranted.

Our results also suggest that institutional investors in Taiwan are heterogeneous. In particular, dealers have a different impact on the autocorrelation of returns compared with foreigners and mutual funds. This suggests that dealers have a different incentive that is worth pursuing in the future.

Appendix: Estimate the transaction cost in a call auction market

Trading on the stock exchanges in Taiwan involves two call auction mechanisms: a periodic call used to open trading and a batch call used throughout the day (the trading interval between each call is less than one minute). In both auction mechanisms, orders accumulate and the computer sets a single market-clearing price at which demand

equals supply and all executed orders transact. The priority of the order execution depends first on the price and then on the arrival time of orders.

In an auction market, investors cannot trade immediately. Both a market order to buy and a market order to sell have to wait and transact at the same market-clearing price. Therefore, the widely used measures in continuous auction markets, such as the quoted spreads or the effective spread, cannot be used to measure the transaction cost in call auction markets.

The concept of trading friction discussed in Stoll (2000) may play a role while measuring transaction costs in call auction markets. The trading friction, in Stoll’s words, “is the real resources used up or extracted as monopoly rents to accomplish trades”. Stoll suggests that one can measure dynamic trading friction with “the temporary price change associated with trading”.

In call auction markets, however, not every investor has to pay a transaction cost in Stoll (2000)'s sense. For a given order submitted, the existence of a transaction cost in terms of a temporary price change depends on whether it belongs to the more aggressive side of orders submitted for the same call. For example, if large aggressive buy orders arrive for a liquidity reason, the transaction price will be pushed up temporarily. The price will drop later given the fundamental value does not change. Therefore, investors who submit buy orders will pay a higher price, which is part of the transaction cost incurred. Investors who submit sell orders around the same time for whatever reasons do not have to sell at a lower price given their sell orders are less aggressive than buy orders. Instead, they can sell at a higher price to investors who demands liquidity the most. Therefore, the transaction cost for investors on the less aggressive side of the trade will actually be lower. In other words, in a call auction market, the expected transaction cost of an order depends on the probability regarding whether the order is on

the aggressive side or on the less aggressive side. For example, when the probability that an order is on the aggressive side is θ and the trading friction is a %, the order’s expected transaction cost would be θ*a %＋(1－θ)(－a)%＝(2θ－1) a %, which is positive only when θ is greater than 0.5.

For a small-size order, it is fair to assign an equal probability to each side.

Accordingly, the expected transaction cost for a small trade should be 0.

In Taiwan, investors have to pay commissions and the securities transaction tax.

The two-way commission is 0.285%. The securities transactions tax 0.3% is paid only by the seller. Adding commissions and the securities transaction tax gives us the two-way transaction cost for a small-size order in Taiwan: 0.87%.

In our sample, the median of the estimated trading friction across all stocks is 0.31%. Therefore, the estimated two-way transaction cost will exceed 1.17% only when θ is greater than 0.9839.

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Essay II

Does the Volatility-Volume Relation

Asymmetrically Depend on Institutional Purchases and Sales?

Abstract

When short selling is costly, sales tend to convey less information than buys. We propose hypotheses on how different information content changes the volatility-volume relationship. To test these hypotheses, we use a sample of institutional trading in the Taiwan stock market because these institutions cannot sell short owing to the regulations. Consistent with our hypotheses, the empirical findings show that expected institutional purchases have a less negative effect on volatility than expected institutional sales, and unexpected institutional purchases have a less positive effect on volatility than unexpected institutional sales.

Keywords: short-sale, volatility, volume, information trading, institutional investor

1. Introduction

While considerable attention has been paid to the contemporary relationship between price volatility and trading volume, the potentially different roles played by institutional purchases and sales in terms of the relationship remain unclear. Daigler and Wiley (1999) find that different types of traders may have different effects on the volatility-volume relationship, because some types of traders possess less precise information than other types of traders. We take the analysis a step further by distinguishing purchases from sales. Institutional purchases and sales may also have different effects on the volatility-volume relationship since sales tend to convey less information than purchases under short-sales constraints.

We propose hypotheses, which illustrate how the different information content of institutional purchases and sales translates into their asymmetric effects on the volatility-volume relationship. The hypotheses postulate two assumptions. First, institutional traders are informed, but they on average sell stocks for non-information purposes. Second, after observing daily data on institutional purchases and sales at the end of each trading day, uninformed traders form expectations and decide how much to trade on the next day. The hypotheses predict that institutional sales, while having been expected, can contribute to volatility reduction; on the other hand, those institutional sales exceeding the expected level will increase volatility. Compared to the relationships between volatility and institutional sales, the relationship for expected institutional purchases could be less negative, and the relationship for unexpected institutional purchases tends to be less positive. We use data from the Taiwan stock market to test the hypotheses. The empirical findings are consistent with these hypothesized relationships.

Taiwan’s stock market data provides good material for empirical examination of the hypotheses because of the following characteristics. First, Barber, Lee, Liu, and

Odean (forthcoming) indicate that institutional traders in Taiwan are likely to be informed traders because they usually demand liquidity and consistently make profits from trading. Second, during our sample period, regulations on the Taiwan stock market prohibit institutional traders from selling short and hence greatly restrain institutional traders from taking advantage of their private information through sales (Chan and Lakonishok, 1993). Moreover, parallel to the second assumption, the Taiwan Stock Exchange has disclosed the daily number of shares bought and sold by institutional traders at the end of each trading day since December 12, 2000.

Even though we make two specific assumptions and investigate data for a local market, this study sheds light on the topic of institutional buy-sell asymmetry in a non-local sense. Despite the fact that many markets do not disclose institutional trading data on a daily basis, investors can use other public information to infer the trends in institutional trading. For example, Chakravarty (2001) finds that medium-sized trades by institutional traders contribute to most cumulative changes in stock prices, which justifies the use of medium-sized trades as proxy informed institutional trades. In addition, Chan and Lakonishok (1993) find that stock prices continue to rise after institutional purchases, while they tend to revert to their prior levels after institutional sales, suggesting that institutional sales are more indicative of liquidity-related trades than institutional purchases. Therefore, our hypotheses and empirical implications regarding the institutional buy-sell asymmetry are potentially applicable to the U.S.

stock market.

2. Hypotheses

Given that institutional traders are informed but that they on average sell stocks for non-information purposes, and also given that the institutional trading data are disclosed

to the public at the end of each trading day, we propose three hypotheses to explore the contemporary relationship between volatility and institutional trading: the dispersion of beliefs hypothesis, which is applicable to institutional purchases; the discretionary liquidity hypothesis, which focuses on the relationships for expected institutional purchases and sales; and the liquidity demand surprise hypothesis, which provides predictions for unexpected institutional purchases and sales.

The dispersion of beliefs hypothesis gives rise to a negative relationship between volatility and institutional purchases. Daigler and Wiley (1999) suggest that informed traders have relatively homogeneous beliefs because these traders possess more resources that they can use to obtain and analyze private information; as a result, informed trades are executed at prices relatively close to the fair value of the asset, which helps stabilize prices. Accordingly, institutional purchases and volatility should be negatively related since institutional traders utilize their private information through purchases. The hypothesis, however, gives no prediction for institutional sales because selling overall does not convey much private information.

The discretionary liquidity hypothesis, enlightened by Admati and Pfleiderer (1988), interprets the relationship between volatility and expected institutional trading as a result of the optimizing behavior of discretionary liquidity traders. Discretionary liquidity traders are uninformed liquidity demanders who can choose when to trade within a given period of time. As Admati and Pfleiderer (1988) have indicated, these traders prefer to trade together in order to reduce their trading impact on prices. Since institutional trading data are disclosed to the public at the end of each trading day, discretionary liquidity traders utilize the data to decide whether to trade on the next day.

When an increase in institutional sales has been expected, this expected heavier uninformed trading will attract more discretionary liquidity traders to join the market,

and these new entries of discretionary liquidity traders will help stabilize prices.

Similarly, an expected decrease in institutional sales will reduce the discretionary liquidity traders’ incentive to participate in the market; accordingly, there will be fewer discretionary liquidity traders in the market, and prices will be more volatile. To sum up, the discretionary liquidity hypothesis predicts a negative relationship between volatility and expected institutional sales.

In contrast to the decisive relationship for expected institutional sales, the hypothesized relationship between volatility and expected institutional purchases depends on whether private information is diversified among institutional traders. As Admati and Pfleiderer (1988) have shown in their model, because informed traders who possess the same information will compete with each other, more entries by these informed traders will improve the welfare of discretionary liquidity traders; on the other hand, however, if private information is diversified among informed traders, more entries by informed traders will increase the total amount of private information, which worsens the terms of trade for discretionary liquidity traders. In other words, when private information is identical (complementary), discretionary liquidity traders will have greater (less) incentive to join the market once they have expected an increase in institutional purchases, which translates into a negative (positive) relationship between volatility and expected institutional purchases.

The liquidity demand surprise hypothesis suggests that a positive relationship exists between volatility and unexpected institutional trading. This hypothesis postulates that liquidity providers predetermine their daily liquidity supply before each trading day.

When institutional traders’ actual liquidity demand surprises liquidity providers and is greater than the expected level, there will be a shortage in liquidity supply. As a result, price will be pushed to an unusual level, and a greater degree of volatility will be

Figure 1. The Hypothesized Relationships between Volatility and Institutional Trading

Dispersion of Beliefs

Volatility ↓

Expected Purchases↑ Identical

Information

Discretionary Liquidity Volatility ↓

Volatility ↑

Complementary Information

Expected Sales↑ ^Volatility ↓

Volatility ↓

Unexpected Purchases↑

Liquidity Demand Surprise

Volatility ↑

Unexpected Sales↑ ^Volatility ↑

Discretionary Liquidity

Dispersion of Beliefs

Liquidity Demand Surprise

observed. Given that institutional traders are net liquidity demanders both in buying and selling, unexpected institutional purchases and sales respectively represent institutional traders’ excess liquidity demand in buying and selling and hence are both positively related to volatility. This hypothesis is parallel to Fagan and Gencay (2008)’s finding that the scarcity of counterparties occasionally occurs even in a large and active market;

the counterparty scarcity can exhaust liquidity, thereby causing an increase in volatility.

Figure 1 summarizes the hypothesized contemporary relationship between volatility and institutional trading. The hypotheses provide decisive relationships for institutional sales. Specifically, the discretionary liquidity hypothesis predicts a negative relationship between volatility and expected institutional sales, while the liquidity demand surprise hypothesis predicts a positive relationship between volatility and unexpected institutional sales.

Different information content for institutional purchases and sales translates into asymmetric effects on the volatility-volume relationship. The liquidity demand surprise hypothesis predicts a positive relationship for both unexpected purchases and sales, while the dispersion of beliefs hypothesis predicts a negative relationship only for unexpected purchases. As a result, unexpected institutional purchases tend to have a less positive effect on volatility than unexpected institutional sales.

In addition, as a result of different information content, the relationships for expected institutional purchases and sales are grounded in different scenarios and hence are unlikely to be symmetric. The relationship between volatility and expected institutional purchases is negative if the dispersion of beliefs hypothesis dominates or institutional traders possess identical private information.

3. Data

The Taiwan Stock Exchange is an order driven market where orders are automatically matched through a fully computerized order book system. Detailed order book information, however, is not publicly available. Despite the fact that the system only accepts limit orders, traders can submit aggressive price-limit orders to obtain matching priority. Since 2001, trading has taken place between 9:00 a.m. and 1:30 p.m.

Monday to Friday.

To test our hypotheses, we examine the trading from foreigners and mutual funds.

Previous research suggests that foreigners and mutual funds trade on information because they are aggressive traders and consistently make profits from trade (Barber, Lee, Liu, and Odean, forthcoming). Both groups of investors have also been prohibited

by regulations from selling short until June 25, 2005.¹ Starting from June 25, the Taiwan Stock Exchange has allowed institutional investors to borrow shares from a centralized system and sell them on the exchange.

Our sample period, extending from December 12, 2000 to June 24, 2005, spans the day on which the market began to disclose the daily number of shares bought and sold by institutional traders, and the day before on which short selling by institutional traders was prohibited. Since the objective is to investigate the effect of institutional trading on volatility, we restrict our sample to stocks that are heavily traded by institutional traders

— the Taiwan 50 index constituent stocks. In 2007, institutional traders traded a total of US$257 billion for all 698 stocks listed on the exchange, while 57% of the money was concentrated in these 50 stocks. We require each stock to have a minimum of 150 daily observations, with the result that 46 stocks remain in the sample. The data source is the Taiwan Economic Journal.

We measure trading activities by share volumes. Specifically, total volume is defined as the total number of shares of a stock traded on a particular day. Institutional purchases (sales) are defined as the total number of shares of a stock bought (sold) by institutional traders on a particular day. To check robustness, we also measure trading activities based on turnover instead of share volume, where turnover is defined as the

We measure trading activities by share volumes. Specifically, total volume is defined as the total number of shares of a stock traded on a particular day. Institutional purchases (sales) are defined as the total number of shares of a stock bought (sold) by institutional traders on a particular day. To check robustness, we also measure trading activities based on turnover instead of share volume, where turnover is defined as the