Introduction

In securities markets, trading volume is highly publicized information. There exists a lengthy list of papers that examine the relationship between volume and the return process.¹ Llorente, Michaely, Saar, and Wang (2002, LMSW hereafter) developed a model that examines how trading volume affects the autocorrelation of returns when investors may trade for an informational or hedging purpose. This paper builds on that literature and provides new tests for the LMSW model using data from the Taiwan Stock Exchange. The new tests are made possible because the data allows for the identification of two subgroups, namely, trading that is primarily information based and trading that is not.

LMSW’s research revealed that when investors trade on private information, price changes are likely to continue. Given the existence of positive private information, informed investors will buy and drive up the price. However, when the information is not perfect, there will be only a partial price increase that will continue into the future.

Therefore, returns are positively autocorrelated when investors trade on private information.

When investors trade for hedging (allocation) purposes, price changes tend to be temporary. For example, when investors buy stocks for hedging, the increase in buy orders pushes up the stock price in order to attract other investors to provide liquidity.

However, the higher price is only temporary because the fundamental value of the stock remains unchanged. The price reverses the next day; hence, the returns are negatively

1The literature studies either returns, their volatility, or their autocorrelation. Morse (1980) was one of the first to examine the relation between total trading volume and return autocorrelation, and later Avramov, Chordia, and Goyal (2006), Campbell, Grossman, and Wang (1993), Conrad, Hameed, and Niden (1994), and Stickel and Verrecchia (1994) also studied the issue for either whole markets or individual stocks.

autocorrelated.

LMSW test their prediction using its cross-sectional implication: the correlation between volume and return autocorrelation is more positive for stocks with a higher information asymmetry. Cross-sectional evidence, however, is susceptible to alternative interpretations because firm characteristics tend to be correlated. In contrast, this paper tests the time-series implications of the LMSW model by using subgroups of trading volume based on investor identity and trading direction.

There is both theoretical reasons and empirical evidence to assert that the volume subgroups we have chosen primarily reflect information trading. Institutional investors, a priori, are better informed than individual investors. On average, they are more sophisticated, better educated, and possess more resources to obtain and analyze private information. Consistent with the role of informed traders, Barber, Lee, Liu, and Odean, (forthcoming) found that both foreign investors and domestic mutual funds in Taiwan make profits from trading. Therefore, we have chosen foreign investors and domestic mutual funds as informed traders and use their trading volume to test the LMSW model.

According to the LMSW model, we should find that returns are more positively autocorrelated when the trading volumes of foreigners or mutual funds are high. Our evidence is consistent with the prediction of the LMSW model, particularly in the case of large firms.

In addition to investor identity, we also classify trading volume based on trade direction, and posit that buy volume should contain more information than sell volume.

When short selling is costly, investors with a piece of negative information are less likely to sell unless they already own the stock (Hong and Stein, 2002). In the most extreme case, short selling is prohibited outright, and the sell volume is less likely to convey information. Therefore, we expect to observe a less positive autocorrelation of

returns when the sell volume is high than when the buy volume is high.

To test for the implication of the short-sale restriction, we utilized the sell volumes of both foreigners and mutual funds in Taiwan. Both groups of investors are prohibited by regulations from selling short.² According to the LMSW model, we expected to observe a stronger positive autocorrelation when the buy volume of foreigners (or mutual funds) is high than when their sell volume is high. The evidence presented in this paper is consistent with this prediction.

The empirical findings on the difference between buy and sell volume contribute to the literature of short-sale constraints. Researchers have studied the various aspects of short-sale constraints such as the behavior of short sellers, the market response following short sale transactions, and the cross-sectional relation between overvaluation and short sale constraints.³ This paper examines a different issue. It identifies groups of investors who cannot sell short, and examines whether the market takes this into account and reacts differently to their buys and sales.

Our findings also have bearing on the literature concerning the role of order imbalance in asset markets (Brown, Walsh, and Yuen, 1997; Chan and Fong, 2000;

Chordia, Roll, and Subrahmanyam, 2002; Chordia and Subrahmanyam, 2004). We argue that when short sale is constrained, buy and sell volume can have different price impacts, and we find such evidence. Therefore, to examine buy and sell volume separately may provide more information than to limit our investigation to order imbalance.

2Article 10 of the Regulations Governing Securities Investment Trust Funds forbids mutual funds and Article 21 of the Regulations Governing Investment in Securities by Overseas Chinese and Foreign Nationals forbids foreigners from selling short.

3Altken, Frino, McCorry and Swan (1998), Chang, Cheng and Yu (2007), Chen, Hong and Stein (2002), Dechow, Hutton, Meulbroek and Sloan (2001), Figlewski (1981), Jones and Lamont (2002).

Our findings on the relationship between volume and return autocorrelation are related to Sias and Starks (1997). They find a positive cross-sectional relation between the autocorrelation of returns and institutional ownership. While the authors suggest that institutional trading is the underlying reason, they cannot test directly for this possibility due to the limited availability of data. In this paper, we go one step further to show that trading is directly responsible for such a positive relation.

Another difference between this paper and the literature is that we are able to reveal the heterogeneity of institutional investors while Sias and Starks (1997) look only at the aggregate institutional ownership.⁴ Ex ante information discussed in Section 2 suggests that, unlike foreigners and mutual funds, dealers may trade for reasons other than private information. Given a liquidity-based trading, LMSW will predict a negative autocorrelation when dealers trade. Our evidence is consistent with such a prediction.

Andrade, Chang, and Seasholes (2008) find that the imbalance of margin trading in Taiwan also creates price reversals. In their paper however, individuals are responsible for the margin trading and price reversals while dealers are responsible for our results.

The remainder of the paper is organized into five sections. Section 2 describes the trading mechanism in Taiwan and provides ex-ante information to identify the primary motivation of trading for different groups of institutional traders. Section 3 introduces the empirical method and data. Section 4 reports the main empirical results and supplementary results are provided in Section 5. Section 6 concludes.

4Yan and Zhang (forthcoming) use turnover to separate institutions into short-term and long-term investors and then separately examine the cross-sectional relation between their ownership and future stock returns.

2. The Trading Mechanism and Institutional Investors in Taiwan