Institutional investor’ proportions and inactive trading

We focus on a typical market anomaly-inactive trading: trading volume shrinks while stock price abnormally jumps. We calibrate a theoretical model with variance ambiguity heterogeneous among investors and illustrate that ambiguity averse investors' proportions enhance trading volume shrinkage and abnormal price jumps. We provide a cross-section analysis of stocks' inactive trading by introducing institutional investors' proportions to measure investor structures' differences among stocks. We also empirically measure relative inactive trading for constituent stocks in S&P 500 from 2014 to 2019 and demonstrate that institutional investors' proportion is negatively related to inactive trading. Finally, we demonstrate that higher proportions of institutional investors lead to less inactive trading anomalies.     

Daily Stock Trading by Investor Type and Information Asymmetry: Evidence from the Korean Market

We examine the influence of trading by heterogeneous investors on information asymmetry in the Korean stock market, which includes domestic and foreign institutional investors and individual investors. In particular, we examine the relationship between the daily trading volume and the level of information asymmetry reflected in the stock price. The results reveal that high-volume daily trading by domestic institutional and individual investors increases the degree of information asymmetry in the short term, but is more evident for individual investors. Foreign institutional investors tend to mitigate the information asymmetry. Finally, our findings are robust to an alternative measure of investor trading.     

交易所大宗交易流动性折价研究——来自沪深交易所的经验证据

随着证券市场规模的扩大及机构投资者规模的壮大，机构投资者对市场流动性的需求日益剧增，大宗交易制度是满足投资者流动性需求的制度性创新。由于大宗交易的数量较大，其交易价格有别于正常交易规模的价格。本文利用沪深交易所的大宗交易数据实证探讨大宗交易价格及其影响因素。研究结果表明，大部分大宗交易价格低于当日收盘价格，呈现流动性折价现象，折价率达到1．27％：研究还发现，折价水平还受交易数量、正常交易时间段股票流动性水平、市场流动性水平及股价波幅等因素影响。     

信息不确定、坏消息与投资者交易行为

本文以2000年至2010年的A股上市公司违法违规事件为样本,分析该类事件中信息不确定性的影响,以及市场反应中的投资者交易行为。研究发现:上市公司市场价值在事件日呈显著下跌;然而,与直觉有些相悖的是,信息不确定程度与超额累积收益呈显著正相关,这意味着在坏消息到来时,不确定性反而提高了股票的市场价值;最后,通过对各类投资者在此类事件中的净买入情况分析,我们发现不同投资者的交易行为有明显差异。机构投资者在坏消息中采用了反向交易策略,并且知情交易促进机构投资者的买入。     

Volume,volatility and information linkages in the stock and option markets

This paper examines the relationship between option trading activity and stock market volatility. Although the option market is uniquely suited for trading on volatility information, there is little analysis on how trading activity in this market is linked to stock price volatility. The bulk of the discussion tends to focus on whether trading activity in the stock market is informative about stock volatility. To analyze the information in option trading activity for stock market volatility, a sample of 15 stocks with the highest option trading volume is selected. For each stock, it is noted that the trading activities in the put and call option markets have significant explanatory power for stock market volatility. In addition, the results indicate that the call option trading activity has a stronger impact on stock volatility compared with that of the put options. Our results demonstrate that information and sentiment in the option market is useful for the estimation of stock market volatility. Also, the significance of the effects of option trading activity on stock price volatility is observed to be comparable to that of stock market trading activity. Furthermore, the persistence and asymmetric effects in the volatility of some stocks tend to disappear once option trading activity is taken into account.     

Daily institutional trades and stock price volatility in a retail investor dominated emerging market

We examine the short-run dynamic relation between daily institutional trading and stock price volatility in a retail investor-dominated emerging market. We find a significantly negative relation between volatility and institutional net trading that is mainly due to the unexpected institutional trading. The price volatility–institutional trade relation differs for institutional buys and institutional sells, and for small and large stocks. Institutional investors herd-trade in large stocks, but do not systematically engage in positive-feedback trading. We argue that the net impact of informational and noninformational institutional trades determines the relation between volatility and institutional trading, and that the relation is negative when informational trading by institutions prevails.     

基于佣金收入动机的机构投资者盈利预测偏离与股票交易量研究

本文基于信息反应模型研究了机构投资者盈利预测偏离与普通投资者交易需求之间的激励关系,并选取2006至2010年的相关盈利预测和股票交易数据进行实证分析。结果表明,机构投资者的盈利预测存在显著偏离前期市场平均预测值的倾向,而且这种偏离倾向会进一步诱发股票交易增量,从而增加机构投资者的佣金收入。这意味着普通投资者在参考机构投资者的盈利预测报告时,需要考虑其基于刺激交易量的自利因素。     

Does Shareholder Composition Matter? Evidence from the Market Reaction to Corporate Earnings Announcements

We examine whether institutional ownership composition is related to parameters of the market reaction to negative earnings announcements. When firms report earnings below analysts' expectations, the stock price response is more negative for firms with higher levels of ownership by momentum or aggressive growth investors. There is no evidence, however, that these institutions cause an “overreaction” to earnings news. Ownership structure is also related to trading volume and to stock price volatility on days around earnings announcements. Our findings are consistent with the idea that the composition of institutional shareholders effects stock price behavior around the release of corporate information.     

Trading volume and short-horizon contrarian profits: Evidence from the Malaysian market

We provide evidence on short-term predictability of stock returns on the Malaysian stock market. We examine the relation between return predictability and the level of trading activity. This is particularly relevant in emerging stock markets, where thin trading is more pervasive. We find that the returns from a contrarian portfolio strategy are positively related to the level of trading activity in the securities. Specifically, the contrarian profits on actively and frequently traded securities are significantly higher than that generated from the low trading activity securities. We find that the differential behavior of high- and low-volume securities is not subsumed by the size effect, although for the small firms, the volume–predictability relation is most pronounced. We also suggest that the price patterns may be related to the institutional arrangement in the Malaysian stock market.     

Abnormal trading volume,news and market efficiency: Evidence from the Jamaica Stock Exchange

This paper investigates market efficiency of the Jamaica Stock Exchange (JSE). Together, weak and semi-strong form efficiency claim that historical and newly released public information do not predict future stock price movement. We test both forms of market efficiency by analyzing stock price behavior during times of abnormal trading volume and around the release dates of earnings information. Abnormal trading volume may be driven by liquidity demand or reflect new or private information flow to the market. Using JSE data over the period 2000 to 2021, we find price dynamics consistent with price pressure as firms experience negative abnormal returns on the day of abnormal trading activity but offsetting positive abnormal stock returns on the following day. Further findings show post earnings announcement drift on the JSE. Taken as a whole, the evidence suggests violations of market efficiency and has implications for capital allocation in this emerging market.     

短期羊群行为的影响因素与价格效应——基于高频数据的实证检验

通过创新性地使用日内高频交易数据对A股市场中的羊群行为进行研究,本文发现:(1)羊群行为具有短期脆弱性特征,随着度量频率的提高,羊群行为的程度严格递增。(2)信息不对称程度、机构投资者比例、股票规模等因素,会显著影响短期羊群行为程度。(3)短期羊群行为会伴随着明显的价格反转:短期买入(卖出)羊群行为后,股票的超额收益显著为负(正),并且短期羊群行为越显著,价格反转的程度越大。(4)价格反转效应存在不对称性:规模越大、交易越活跃的股票,短期买入羊群行为的价格反转越明显,而短期卖出羊群行为的价格反转越不明显。     

Volume autocorrelation,information, and investor trading

This study investigates whether the widely documented daily correlated trading volume of stocks is driven by individual investor trading, institutional trading, or both. We find that at least 95% of NYSE and AMEX stocks exhibit statistically significant, positive serial correlation. Volume autocorrelation decreases with the level of institutional ownership of a stock. We also show that the rate of arrivals of new information to the market contributes to the clustering of trades. When there is high information flow to the market, institutional trading generates a more pronounced effect on volume autocorrelation than individual investor trading. Our results are broadly consistent with the predictions of trading volume patterns suggested by most theoretical models of stock trading and by empirical research on investor trading.     

Intraday Price Change and Trading Volume Relations in the Stock and Stock Option Markets

This study investigates intraday relations between price changes and trading volume of options and stocks for a sample of firms whose options traded on the CBOE during the first quarter of 1986. After purging the price change series of the effects of bid/ask spreads, multivariate time-series analysis is used to estimate the lead/lag relation between the price changes in the option and stock markets. The results indicate that price changes in the stock market lead the option market by as much as fifteen minutes. The analysis of trading volume indicates that the stock market lead may be even longer.     

金融生态环境、股价波动同步性与上市企业融资约束

我国股票市场股价波动同步性是否正向地反映了股价的信息吸收效率?本文将股价波动同步性作为衡量信息不对称的指标,以2003~2006年我国上市企业为样本,检验在不同金融生态环境下股价波动同步性对企业融资约束的影响。研究发现在交易噪声广泛存在的我国股票市场,股价波动同步性与上市企业融资约束呈负相关关系,且对那些处于发达金融生态环境中的企业而言,这一负相关关系表现得更为明显。研究表明在控制企业规模等因素后,股价包含更多的市场和行业信息本身正向地反映了股价的信息吸收效率,而发达的金融生态环境有助于提高股价对市场和行业信息的吸收。     

Stock market automation and the transmission of information between spot and futures markets

This paper examines the impact of automated trading in the stock market on the information transmission between the stock and futures markets. This issue is of particular relevance given the trend of exchanges to introduce automated trading. We focus on the Australian market as its institutional features and recent changes in trading systems have created an ideal environment for examining this issue. We initially find evidence of a substantial bidirectional information flow between the stock and futures markets. The paper then focuses on the period surrounding the move by the Australian stock exchange to automated trading. After the introduction of automated trading, we find a significant change in the information transfer process between the two markets. The findings are consistent with the hypothesis that automated trading results in a richer and more timely information set which accelerates the price discovery process. However, the evidence is not overwhelming and alternative explanations exist.     

A simulation analysis of the microstructure of an order driven financial market with multiple securities and portfolio choices

In this paper we propose an artificial market where multiple risky assets are exchanged. Agents are constrained by the availability of resources and trade to adjust their portfolio according to an exogenously given target portfolio. We model the trading mechanism as a continuous auction order-driven market. Agents are heterogeneous in terms of desired target portfolio allocations, but they are homogeneous in terms of trading strategies. We investigate the role played by the trading mechanism in affecting the dynamics of prices, trading volume and volatility. We show that the institutional setting of a double auction market is sufficient to generate a non-normal distribution of price changes and temporal patterns that resemble those observed in real markets. Moreover, we highlight the role played by the interaction between individual wealth constraints and the market frictions associated with a double auction system to determine the negative asymmetry of the stock returns distribution.     

Noise trading and the price formation process

This study proposes the dispersion in daily net initiated order flow across brokers as a proxy for the level of noise trading in a stock, and applies this proxy to test some basic implications of market microstructure theory. We use data from the Australian Stock Exchange, a computerized limit order market where price, quantity, and broker identity for each incoming order are shown on broker screens. We find daily movements in our noise measure are positively associated with trading volume and market depth, and negatively related to the bid-ask spread. We find monthly movements in our noise measure are negatively associated with the probability of informed trading, and positively correlated with the arrival rate of uninformed traders. We also find the sensitivity of stock prices to net initiated order flow decreases in the level of noise trading. In addition we find that, after controlling for noise trading, the sensitivity of stock prices to net initiated order flow is significantly greater on Mondays. These empirical results consistently support the implications of various models of market microstructure, suggesting that our proxy provides useful information as a daily measure of noise trading.     

Empirical Identification of Non-Informational Trades Using Trading Volume Data

This paper empirically identifies non-informational and informational trades using stock returns and trading volume data of the U.S., Japanese, and U.K. stock markets and five individual firms. We achieve the identification by imposing a restriction from theoretical considerations. Our results show that trading volume is mainly driven by non-informational trades, while stock price movements are primarily driven by informational trades. We also find that, around the 1987 stock market crash, trading volumes due to non-informational trades increased dramatically, while the decline in stock market prices was due mainly to informational trades. Increases in volatilities both in returns and in trading volumes during and after the crash are mainly due to non-informational trades. Regarding the trading volume-serial correlation in the stock returns relationship, we find evidence that is consistent with theoretical predictions that non-informational components can account for high trading volume accompanied by a low serial correlation of stock returns.     

MACROECONOMIC NEWS AND STOCK MARKET CALENDAR AND WEATHER ANOMALIES

An analysis of six stock market calendar and weather anomalies from 1980 to 2003 shows that (1) returns on trading days in which macroeconomic announcements were made generate the anomalies and (2) five of the six anomalies are not present at all on the trading days in which such announcements were not made (more than 60% of the sample). The results suggest that the market response to macroeconomic news, not psychological or institutional factors, is the main source of calendar and weather anomalies.     

Price limit performance: New evidence from a quasi-natural experiment in China's ChiNext market

This study examines the impact of daily price limits on market performance and trading activity by using a quasi-natural experiment in China. It focuses on the case of the ChiNext market, where the daily price limits of stocks increased from 10% to 20% in 2020. The results show that, initially, the stock prices and occurrences of 10% price limit hits increase, but then decline after the new price limits have been implemented. The level of trading liquidity and volatility increases significantly, with a greater impact on the short term than the long term. These price limit performances are more pronounced for stocks with additional retail interest. The analysis of detailed trading data reveals that institutional investors initially purchase ChiNext stocks in large quantities, followed by retail investors who purchase smaller quantities. In the long run, institutional investors tend to increase their holdings, while retail investors tend to sell their holdings. Additionally, there is a temporary increase in investor attention, price synchronicity, and stock risks, followed by a decline. The findings suggest that wider price limits increase trading volumes and enhance long-term market efficiency, but encourage immediate price manipulation, causing short-term overreactions and long-term reversals. This study provides valuable insights for building an effective price limit system.