MACROECONOMIC NEWS,STOCK TURNOVER,AND VOLATILITY CLUSTERING IN DAILY STOCK RETURNS

We study volatility clustering in daily stock returns at both the index and firm levels from 1985 to 2000. We find that the relation between today's index return shock and the next period's volatility decreases when important macroeconomic news is released today and increases with the shock in today's stock market turnover. Collectively, our results suggest that volatility clustering tends to be stronger when there is more uncertainty and disperse beliefs about the market's information signal. Our findings also contribute to a better understanding of the joint dynamics of stock returns and trading volume.     

Trading behavior in S&P 500 index futures

This article examines the determinants of trading decisions and the performance of trader types, in the context of the E-Mini S&;P 500 futures and S&;P 500 futures markets. Speculators and small traders tend to follow positive feedback strategies while hedgers dynamically adjust positions in response to market returns. Such strategies apparently reverse during the 2008–09 financial crisis. Investor sentiment and market volatility play an important role in determining the net trading position of traders across the sample period. While all trader types are better at foreseeing market upturns, an out-of-sample test suggests that speculators and small traders have some predictive ability for short-term market returns.     

Trading Patterns and Prices in the Interbank Foreign Exchange Market

The behavior of quote arrivals and bid-ask spreads is examined for continuously recorded deutsche mark-dollar exchange rate data over time, across locations, and by market participants. A pattern in the intraday spread and intensity of market activity over time is uncovered and related to theories of trading patterns. Models for the conditional mean and variance of returns and bid-ask spreads indicate volatility clustering at high frequencies. The proposition that trading intensity has an independent effect on returns volatility is rejected, but holds for spread volatility. Conditional returns volatility is increasing in the size of the spread.     

The pricing and performance of leveraged exchange-traded funds

Leveraged ETFs are a recent and very successful financial innovation. They provide daily returns that are in a multiple or a negative multiple of the daily returns on a market benchmark. In this paper, we examine the characteristics, trading statistics, pricing efficiency and tracking errors of a sample of leveraged ETFs. We find that these ETFs are traded mainly by retail traders with very short holding periods. Price deviations (from NAV) are small on average, but large premiums and discounts are prone to occur. More interestingly, the behavior of premiums is different between bull (i.e., those with a positive multiple) and bear ETFs (i.e., those with a negative multiple). Our findings are consistent with the argument that the end-of-day rebalancing of the funds’ exposures increases market volatility at the close of a trading day. As for tracking errors, they are small for holding periods of up to a week, but become increasingly larger for longer horizons.     

Investor Sentiment,Trading Behavior and Informational Efficiency in Index Futures Markets

This paper shows that traders in index futures markets are positive feedback traders—they buy when prices increase and sell when prices decline. Positive feedback trading appears to be more active in periods of high investor sentiment. This finding is consistent with the notion that feedback trading is driven by expectations of noise traders. Consistent with the noise trading hypothesis, order flow in index futures markets is less informative when investors are optimistic. Transitory volatility measured at high frequencies also appears to decline in periods of bullish sentiment, suggesting that sentiment‐driven trading increases market liquidity.     

Does information drive trading in option strategies?

We study trading in option strategies in the FTSE-100 index market. Trades in option strategies represent around 37% of the total number of trades and over 75% of the total trading volume in our sample. We find some evidence that order flow in volatility–sensitive option strategies contains information about future realized volatility. We do not find evidence that order flow in directionally–sensitive option strategies contains information about future returns. Overall, our evidence suggests that option strategies are used both by traders who possess non-public information about future volatility and by uninformed speculators who appear to follow unprofitable trend chasing strategies.     

WHEN‐ISSUED SHARES,SMALL TRADES,AND THE VARIANCE OF RETURNS AROUND STOCK SPLITS

The increases in volatility after stock splits have long puzzled researchers. The usual suspects of discreteness and bid‐ask spread do not provide a complete explanation. We provide new clues to solve this mystery by examining the trading of when‐issued shares that are available before the split. When‐issued trading permits noise traders to compete with a more homogenous set of traders, decreasing the volatility of the stock before the split. Following the split, these noise traders reunite in one market and volatility increases. Thus, the higher volatility after the ex date of a stock split is a function of the introduction of when‐issued trading, the new lower price level after the split date, and the increased activity of small‐volume traders around a stock split.     

The generality of spurious predictability

Previous empirical work has documented significant predictability (non-zero cross autocorrelations) in short-term security returns. Extant theoretical papers have shown that these cross autocorrelations can arise due to partial impounding of information in securities whose returns are driven by a common factor. In this paper, we show that non-zero cross autocorrelation in security returns can arise under weaker conditions than is generally known. We demonstrate that the existence of cross autocorrelations crucially depends on the information structure of informed traders. Thus, a common factor in security returns is neither sufficient nor necessary. Any one of the following conditions on the information structure can generate non-zero cross autocorrelations:

(1) existence of an informed trader with information relevant to two securities;

(2) correlation in the signal of informed traders with information relevant to different securities; or

(3) correlation in uninformed trading.

These cross autocorrelations are then shown to be spurious. That is, traders without any private information cannot make positive trading profit by exploiting cross autocorrelations.     

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.     

Converse trading strategies,intrinsic noise and the stylized facts of financial markets

This paper proposes a simple asset pricing model with three groups of traders: chartists who believe in the persistence of bull and bear markets, fundamentalists who bet on a reduction of the observed mispricing, and investors who follow a buy-and-hold strategy. The innovative feature of the model concerns the frequency of trading: rather than remaining constant over time, each agent in a group is only assumed to become active with a certain probability over a given market period. Depending on the trading strategy, part of this elementary kind of intrinsic noise is additive and another part is multiplicative. Using bootstrap and Monte Carlo methods, it is demonstrated that this combination can contribute to explaining the stylized facts of the daily returns on financial markets, such as volatility clustering, fat tails, and the autocorrelation patterns.     

The Determinants of Conditional Autocorrelation in Stock Returns

We investigate whether return volatility, trading volume, return asymmetry, business cycles, and day‐of‐the‐week are potential determinants of conditional autocorrelation in stock returns. Our primary focus is on the role of feedback trading and the interplay of return volatility. We present empirical evidence using conditional autocorrelation estimates generated from multivariate generalized autoregressive conditional heteroskedasticity (M‐GARCH) models for individual U.S. stock and index data. In addition to return volatility, we find that trading volume and market returns are important in explaining the time‐varying patterns of return autocorrelation.     

Intraday trading volume and international spillover effects

The objective of this paper is to explore whether lagged trading activity in one market contributes to the return and volatility process in other markets, using 5-min concurrent data from German and British equity market. Our results lend support to our initial premise that if international investors have access to the same information set as domestic traders, then after observing foreign trading activity, market makers adjust prices to reflect their expectation of the security value, conditional upon all available information, including prior trades. Our findings clearly indicate that intraday trading volume contains predictive power for cross-border return and volatility processes. Moreover, these volume effects are found to be asymmetric in the sense that the impact of positive volume changes upon foreign stock market volatility is greater than is the impact of negative changes.     

Volatility and dark trading: Evidence from the Covid-19 pandemic

We study the effect(s) of volatility on the share of trading in dark pools by exploiting the exogenous shock of the Covid-19 pandemic on financial markets and regulatory restrictions on dark trading. We find that high levels of volatility in lit exchanges is linked to an economically significant loss of market share by dark pools to lit exchanges. In line with the theory, the loss appears to be driven by informed traders’ migration from lit to dark markets during high volatility periods. The market quality implications of the trading dynamics are mixed: while it tempers liquidity decline in the lit market, it exacerbates the loss of informational efficiency.     

Option trading and individual investor performance

This paper examines the impact of option trading on individual investor performance. The results show that most investors incur substantial losses on their option investments, which are much larger than the losses from equity trading. We attribute the detrimental impact of option trading on investor performance to poor market timing that results from overreaction to past stock market returns. High trading costs further contribute to the poor returns on option investments. Gambling and entertainment appear to be the most important motivations for trading options while hedging motives only play a minor role. We also provide strong evidence of performance persistence among option traders.     

Information demand and stock market volatility

We study information demand and supply at the firm and market level using data for 30 of the largest stocks traded on NYSE and NASDAQ. Demand is approximated in a novel manner from weekly internet search volume time series drawn from the recently released Google Trends database. Our paper makes contributions in four main directions. First, although information demand and supply tend to be positively correlated, their dynamic interactions do not allow conclusive inferences about the information discovery process. Second, demand for information at the market level is significantly positively related to historical and implied measures of volatility and to trading volume, even after controlling for market return and information supply. Third, information demand increases significantly during periods of higher returns. Fourth, analysis of the expected variance risk premium confirms for the first time empirically the hypothesis that investors demand more information as their level of risk aversion increases.     

The Intraday Relation between NYSE and CBOE Prices

I extend the literature regarding price discovery across stock and option markets through an empirical model that allows information to flow through an error‐correction term and volatility. NYSE prices tend to lead CBOE prices by at least thirty minutes over the entire six‐year sample period. In addition, informed trading in the options market is revealed more strongly through persistence in volatility and the spillover of volatility to the stock market than it is through returns.     

Disagreement and asset prices

How do differences of opinion affect asset prices? Do investors earn a risk premium when disagreement arises in the market? Despite their fundamental importance, these questions are among the most controversial issues in finance. In this paper, we use a novel data set that allows us to directly measure the level of disagreement among Wall Street mortgage dealers about prepayment speeds. We examine how disagreement evolves over time and study its effects on expected returns, return volatility, and trading volume in the mortgage-backed security market. We find that increased disagreement is associated with higher expected returns, higher return volatility, and larger trading volume. These results imply that there is a positive risk premium for disagreement in asset prices. We also show that volatility in and of itself does not lead to higher trading volume. Instead, only when disagreement arises in the market is higher uncertainty associated with more trading. Finally, we are able to distinguish empirically between two competing hypotheses regarding how information in markets gets incorporated into asset prices. We find that sophisticated investors appear to update their beliefs through a rational expectations mechanism when disagreement arises.     

Do Proprietary Algorithmic Traders Withdraw Liquidity during Market Stress?

We investigate the role of proprietary algorithmic traders in facilitating liquidity in a limit order market. Using order‐level data from the National Stock Exchange of India, we find that proprietary algorithmic traders increase limit order supply following periods of both high short‐term stock‐specific volatility and extreme stock price movement. Even following periods of high marketwide volatility, they do not decrease their supply of liquidity. We define orders from high‐frequency traders as a subclass of orders from proprietary algorithmic traders that are revised in less than three milliseconds. The behavior of high‐frequency trading mimics the behavior of its parent class. This is inconsistent with the theory that fast traders leave the market when stress situations arise, although their limit‐order‐supplying behavior becomes weaker when the increase in short‐term volatility is more informational than transitory. Agency algorithmic traders and nonalgorithmic traders behave opposite to proprietary algorithmic traders by reducing the supply of liquidity during stress situations. The presence of faster traders in the market possibly instills the fear of adverse selection in them. We document that the order imbalance of agency algorithmic traders is positively related to future short‐term returns, whereas the order imbalance of proprietary algorithmic traders is negatively related to future short‐term returns.     

The day of the week effect on stock market volatility and volume: International evidence

This study investigates the day of the week effect on the volatility of major stock market indexes for the period of 1988 through 2002. Using a conditional variance framework, we find that the day of the week effect is present in both return and volatility equations. The highest volatility occurs on Mondays for Germany and Japan, on Fridays for Canada and the United States, and on Thursdays for the United Kingdom. For most of the markets, the days with the highest volatility also coincide with that market's lowest trading volume. Thus, this paper supports the argument made by Foster and Viswanathan [Rev. Financ. Stud. 3 (1990) 593] that high volatility would be accompanied by low trading volume because of the unwillingness of liquidity traders to trade in periods of high stock market volatility.     

Financial crises in efficient markets: How fundamentalists fuel volatility

When a financial crisis breaks out, speculators typically get the blame whereas fundamentalists are presented as the safeguard against excessive volatility. This paper proposes an asset pricing model where two types of rational traders coexist: short-term speculators and long-term fundamentalists, both sharing the same information set. In this framework, excess volatility not only exists, but is actually fueled by fundamental trading. Consequently, efficient markets are more volatile with a few speculators than with many speculators. Regulators should therefore be aware that efforts to limit rational speculation might, surprisingly, end up increasing volatility.