Trading activity and bid–ask spreads of individual equity options

We empirically examine the impact of trading activities on the liquidity of individual equity options measured by the proportional bid–ask spread. There are three main findings. First, the option return volatility, defined as the option price elasticity times the stock return volatility, has a much higher power in explaining the spread variations than the commonly considered liquidity determinants such as the stock return volatility and option trading volume. Second, after controlling for all the liquidity determinants, we find a maturity-substitution effect due to expiration cycles. When medium-term options (60–90 days maturity) are not available, traders use short-term options as substitutes whose higher volume leads to a smaller bid–ask spread or better liquidity. Third, we also find a moneyness-substitution effect induced by the stock return volatility. When the stock return volatility goes up, trading shifts from in-the-money options to out-of-the-money options, causing the latter’s spread to narrow.     

Global industry portfolios and short-term predictability of returns: Is it there?

This paper explores the degree of success of a large set of active trading rules that have been popularized in the literature on the short-term predictability of returns in equity and foreign exchange markets by extending the scope of research in three dimensions: global portfolios, industry portfolios, and exclusive versus inclusive portfolios. Our results show that after adjusting for (1) the impact of nonsynchronous prices in the reported closing index levels which causes spurious autocorrelations in returns, (2) data snooping bias caused by searching through a large number of possible trading strategies in order to find a few that yield superior in-sample performance, and (3) transaction costs that reduce any profits from active trading, the risk-adjusted profits generated by short-term trend chasing trading rules are generally not statistically significant and the hypothesis of no outperformance of trading rules over either buy-and-hold or risk-free benchmark return cannot be rejected in most industries. Such findings favor short-term market efficiency and are hardly comforting for active traders.     

Are margin traders informed?

The literature offers contradictory views on the informativeness of margin trading using various measures of information content. Utilising data from a Chinese margin‐trading pilot programme initiated in 2010, this paper investigates whether margin traders are informed by directly examining the return predictability of margin‐trading activity. We find that margin‐trading activities cannot positively predict future stock returns. Moreover, we explore some non‐informational trading strategies used by margin traders, e.g., positive‐feedback strategies and moving‐average trading rules. These results suggest that margin traders are noise traders rather than informed traders, and margin trading conveys no new firm‐specific information.     

Trading frequency and the efficiency of price discovery in a non-dealer market

The increasing popularity of non-dealer security markets that offer automated, computer-based, continuous trading reflects a presumption that institutionally-set trading sessions are economically obsolete. This theoretical paper investigates the effect of the trading frequency, a key feature of the trading mechanism, on the efficiency of price discovery in a non-dealer market. By tracing the market pricing error to the correlation structures of arriving information and pricing errors of individual traders, the effect of diverging expectations on error-based and overall return volatility is isolated. The analysis reveals that, due to a portfolio effect, an increase in the trading time interval has contradictory effects on the portion of return volatility stemming from pricing errors. The greater accumulation of information increases error-based return volatility, but the greater volume and number of traders per session have the opposite effect. The net effect on overall return volatility can go either way. It is found that the return volatility of heavily traded securities is likely to be minimized under continuous trading, but that of thinly traded securities may be minimized under discrete trading at moderate time intervals. The latter is more likely to occur the greater is the divergence of expectations among traders. These findings challenge the presumption that automated continuous trading in a non-dealer market is more efficient than discrete trading for all securities, regardless of trading volume. The findings are applicable to all economies, but have special importance for developing countries where typically a single market is dominated by small issues and a low volume of trade. As a by-product of the analysis, it is shown how to correct the biased estimate of inter-session price volatility when observations are less frequent than the trading sessions themselves.     

Positive Feedback Trading and Investor Sentiment

This article examines how investor sentiment affects positive feedback trading behavior. By analyzing the daily closing total return of CSI 300 index and its individual returns of stocks, we find that relatively high or low sentiment induces active positive feedback trading. With a specific indicator of sentiment, we explain the microstructure setting of the relationship between positive feedback trading and sentiment. We adopt the classical feedback model from Sentana and Wadhwani (1992) to measure positive feedback trading behavior. By adding sentiment factor to the model, we successfully explain how sentiment influences the behavior of both feedback traders and rational investors. The empirical findings suggest that positive feedback traders are more likely to trade when the prices of most securities move forward together. When the sentiment of feedback traders is at an intermediate level, the feedback trading behavior is insignificant.     

Prospect theory and trading patterns

Reference dependence, loss aversion, and risk seeking for losses together comprise the preference-based component of prospect theory that sets its value function apart from the standard risk-aversion model. Using an elasticity analysis, we show that this distinctive preference component serves to underpin negative-feedback trading propensities, but cannot manifest itself in behavior directly or holistically at the individual-choice level. We then propose and demonstrate that the market interaction between prospect-theory investors and regular CRRA investors allows this preference component to dominate in equilibrium behavior and hence helps to reestablish the intuitive link between prospect-theory preferences and negative-feedback trading patterns. In the model, the interaction also reconciles the contrarian behavior of prospect-theory investors with asymmetric volatility and short-term return reversal. The results suggest that prospect-theory preferences can lead investors to behave endogenously as contrarian noise traders in the market interaction process.     

Trading performance and market efficiency: Evidence from algorithmic trading

In India, National Stock Exchange directly identifies algorithmic trading participation. Algorithmic traders possess intraday market timing skills. Results are not motivated by extreme short-term signals or transitory price trading. Magnitude of market timing performance in cross-sectional group of traders shows that they earn profit across all the cases, and maximize while providing liquidity. Volume-weighted-average-price decomposition analysis reports algorithmic traders earn profits through intraday market timing performance for five-minute and one-minute intervals, and it is higher compared to short-term market timing performance across all trader groups. Order imbalance and price delay regressions show that algorithmic trading significantly improves price efficiency.     

Index futures and positive feedback trading: evidence from major stock exchanges

This paper tests the hypothesis that the introduction of index futures has increased positive feedback trading in the spot markets of six industrialized nations. The analysis is based on a model that assumes two different groups of investors, i.e., risk averse expected utility maximizing investors and positive feedback traders. There is evidence consistent with positive feedback trading before the introduction of index futures across all markets under investigation. In the period following the introduction of index futures, there is no evidence supporting the hypothesis that positive feedback trading drives short-term dynamics of stock returns. The possibility that this is due to possible migration of feedback traders from the spot to the futures markets is also tested. The results show no evidence of positive feedback trading in the futures markets. Overall, the findings support the view that futures markets help stabilize the underlying spot markets by reducing the impact of feedback traders and thus attracting more rational investors who make the markets more informationally efficient and thus providing investors with superior ways of managing risk.     

并购重组前的知情交易行为研究

并购重组中基于内幕信息的知情交易行为既是监管重点,也是学术界关注的热点问题。本文以2006—2020年我国上市公司并购重组事件为样本,考察并购重组前的知情交易行为对并购公告收益的影响。研究发现：并购重组前的知情交易行为引发了主并公司股价的提前反应,从而降低了并购公告时的市场反应,这一现象是由内幕信息泄露引起,且内幕信息主要来源于包括员工在内的公司内部人,而非机构投资者。进一步分析表明,改善信息环境可以有效缓解并购重组的信息泄露问题,体现为知情购买交易的信息泄露效应受到分析师跟踪、审计质量和问询函制度的有效制约。本文研究深化了现有的并购重组内幕交易行为研究,为实施精准监管和防范内幕交易等政策提供了一定参考和依据。     

A new paradigm for forecasting security returns in a market regulated by price limits

Numerous futures markets in the US and many stock markets around the world set a “limit” price before each trading session, based on the settlement price at the end of the previous trading day. Price limits are boundaries set by market regulators to restrict large daily fluctuations in the price of securities. Once the return limit is triggered, traders cannot observe the equilibrium return that would have prevailed in the absence of such regulation. We develop an innovative approach for forecasting security returns (and prices) in a market regulated by price limits. Our forecasting model allows for multiple limit-hits. The model is robust, straightforward and easy for practitioners to use. A few numerical predictions are provided for hypothetical securities, and for seven traded futures contracts.     

The determinants of quantile autocorrelations: Evidence from the UK

We empirically analyse the cross-sectional determinants of stock return autocorrelations in the UK in different quantiles of conditional return distributions. Autocorrelations in low quantiles are predominantly positive, whereas those in the remaining quantiles are negative. Autocorrelations in different quantiles depend on different sets of firm and trading characteristics: when returns are normal or high, prices react quickly to information, are driven by positive feedback traders, instantaneous news arrivals, and overshoot, trades are predominantly motivated by hedging/liquidity needs, and measured autocorrelations can be biassed by the bid–ask bounce effect and nonsynchronous trading. However, when returns are unusually low, prices are driven by information arriving sequentially and react sluggishly to it, and are influenced by trading on private information and/or negative feedback traders.     

HOURLY RETURNS,VOLUME, TRADE SIZE,AND NUMBER OF TRADES

Previous research identifies a causal relationship between returns and trading volume. But volume has two components: number of trades and number of shares per trade. In this paper the relationship between each of these volume components and returns is examined and the number of trades is found to be the dominant component. Results show that return activity in a period is associated with the level of trading frequency in a subsequent period and also with the number of shares in a subsequent period. This is consistent with small traders reacting to returns while professional traders largely ignore previous returns in their trading.     

A Characterization of the Daily and Intraday Behavior of Returns on Options

The daily and intraday behavior of returns on Chicago Board Options Exchange options is examined. Option returns contain systematic patterns even after adjusting for patterns in the means and variances of the underlying assets. This is consistent with the hypothesis that informed trading in options can make the order flow in the options market informative about the value of the underlying asset, making options nonredundant. The intraday patterns in adjusted option return variances are further consistent with a model of strategic trading by informed and discretionary liquidity traders.     

Information percolation,momentum and reversal

We propose a joint theory of time-series momentum and reversal based on a rational-expectations model. We show that a necessary condition for momentum to arise in this framework is that information flows at an increasing rate. We focus on word-of-mouth communication as a mechanism that enforces this condition and generates short-term momentum and long-term reversal. Investors with heterogeneous trading strategies—contrarian and momentum traders—coexist in the marketplace. Although a significant proportion of investors are momentum traders, momentum is not completely eliminated. Word-of-mouth communication spreads rumors and generates price run-ups and reversals. Our theoretical predictions are in line with empirical findings.     

The effect of public information and competition on trading volume and price volatility

In a one-period model of market making with many exogenouslyinformed traders, we first show that the variance of pricesand expected trading volume depend on the public informationreleased at the start of trading. This is accomplished by representingbeliefs with elliptically contoured distributions, for whichthe form of optimal decision rules does not depend on the specificdistribution used. Second, if the model is altered so that thedecision to become informed is made endogenous, then the decisionrules of the market-maker and informed traders depend on thepublic information. Third, in a multiperiod model with manyinformed traders and long-lived private information, recursionformulas similar to those of Kyle (1985) hold for all ellipticallycontoured distributions, trading volume is autocorrelated and,unless per period liquidity trading is bounded away from zeroas new trading periods are added, informed traders' profitsvanish.     

Convergence of trading strategies in continuous double auction markets with boundedly-rational networked traders

This paper considers the convergence of trading strategies among artificial traders connected to one another in a social network and trading in a continuous double auction financial marketplace. Convergence is studied by means of an agent-based simulation model called the Social Network Artificial stoCk marKet model. Six different canonical network topologies (including no-network) are used to represent the possible connections between artificial traders. Traders learn from the trading experiences of their connected neighbours by means of reinforcement learning. The results show that the proportions of traders using particular trading strategies are eventually stable. Which strategies dominate in these stable states depends to some extent on the particular network topology of trader connections and the types of traders.     

Strategic Trading When Agents Forecast the Forecasts of Others

We analyze a multi-period model of trading with differentially informed traders, liquidity traders, and a market maker. Each informed trader's initial information is a noisy estimate of the long-term value of the asset, and the different signals received by informed traders can have a variety of correlation structures. With this setup, informed traders not only compete with each other for trading profits, they also learn about other traders' signals from the observed order flow. Our work suggests that the initial correlation among the informed traders' signals has a significant effect on the informed traders' profits and the informativeness of prices.     

The perception of time,risk and return during periods of speculation

Abstract

What return should you expect when you take on a given amount of risk? How should that return depend upon other people's behaviour? What principles can you use to answer these questions? In this paper, I approach these topics by exploring the consequences of two simple hypotheses about risk.

The first is a common-sense invariance principle: assets with the same perceived risk must have the same expected return. It leads directly to the well known Sharpe ratio and the classic risk-return relationships of arbitrage pricing theory and the capital asset pricing model.

The second hypothesis concerns the perception of time. I conjecture that in times of speculative excitement, short-term investors may instinctively imagine stock prices to be evolving in a time measure different from that of calendar time. They may perceive and experience the risk and return of a stock in intrinsic time, a dimensionless time scale that counts the number of trading opportunities that occur, but pays no attention to the calendar time that passes between them.

Applying the first hypothesis in the intrinsic time measure suggested by the second, I derive an alternative set of relationships between risk and return. Its most noteworthy feature is that, in the short-term, a stock's trading frequency affects its expected return. I show that short-term stock speculators will expect returns proportional to the temperature of a stock, where temperature is defined as the product of the stock's traditional volatility and the square root of its trading frequency. Furthermore, I derive a modified version of the capital asset pricing model in which a stock's excess return relative to the market is proportional to its traditional beta multiplied by the square root of its trading frequency.

I also present a model for the joint interaction of long-term calendar-time investors and short-term intrinsic-time speculators that leads to market bubbles characterized by stock prices that grow super-exponentially with time.

Finally, I show that the same short-term approach to options speculation can lead to an implied volatility skew.

I hope that this model will have some relevance to the behaviour of investors expecting inordinate returns in highly speculative markets.     

Short-run Returns around the Trades of Corporate Insiders on the London Stock Exchange

Previous work examined the long-run profitability of strategies mimicking the trades company directors in the shares of their own company, as a way of testing for market efficiency. The current paper examines patterns in abnormal returns in the days around these trades on the London Stock Exchange.
We find movements in returns that are consistent with directors engaging in short-term market timing. We also report that some types of trades have superior predictive content over future returns. In particular, medium-sized trades are more informative for short-term returns than large ones, consistent with Barclay and Warner's (1993) 'stealth trading' hypothesis whereby informed traders avoid trading in blocks.
Another contribution of this study is to properly adjust the abnormal return estimates for microstructure (spread) transactions costs using daily bid-ask spread data. On a net basis, we find that abnormal returns all but disappear.     

Strategic trading, asymmetric information and heterogeneous prior beliefs

We develop a multi-period trading model in which traders face both asymmetric information and heterogeneous prior beliefs. Heterogeneity arises because traders agree to disagree on the precision of an informed trader's private signal. In equilibrium, the informed trader smooths out her trading on asymmetric information gradually over time, but concentrates her entire trading on heterogeneous beliefs toward the last few periods. As a result, the model's volume dynamics are consistent with the U-shaped intraday pattern at the close. Furthermore, the model predicts a positive autocorrelation in trading volume, and a positive correlation between trading volume and contemporaneous price volatility.