Heterogeneous preferences and equilibrium trading volume

The representative-agent Lucas model stresses aggregate risk and hence does not allow us to study the impact of agents’ heterogeneity on the dynamics of equilibrium trading volume. In this paper, we investigate under what conditions non-informational heterogeneity, i.e., differences in preferences and endowments, leads to nontrivial trading volume in equilibrium. We present a non-informational no-trade theorem that provides necessary and sufficient conditions for zero equilibrium trading volume in a continuous-time Lucas market model with heterogeneous agents, multiple goods, and multiple securities. We explain in detail how no-trade equilibria are related to autarky equilibria, portfolio autarky equilibria, and peculiar financial market equilibria, which play an important role in the literature on international risk sharing.     

Options trading activity and firm valuation

Options may have an effect on firm value because they help complete markets and stimulate informed trades. However, these benefits are likely to manifest themselves in active, rather than inactive, options markets. Supporting this observation, we find that firms with more options trading have higher values of Tobin's q, after accounting for other determinants of value. Corporate investment in firms with greater options trading is more sensitive to stock prices. Options trading affects firm valuation more strongly in stocks with greater information asymmetry. These results indicate that options trading is positively associated with firm values as well as information production.     

Momentum,contrarian, and the January seasonality

This paper reexamines the apparent success of two prominent stock trading strategies: long-term contrarian and intermediate-term momentum. The paper demonstrates that long-term contrarian is entirely attributable to the classic January size effect, rather than to investor overreaction, as argued by De Bondt and Thaler (1985). Further, the paper also resolves the Novy-Marx (2011) concern about whether return autocorrelation “is really momentum” by demonstrating that the superior performance of intermediate-term momentum is due to strong January seasonality in the cross-section of returns. The implications are that long-term contrarian must be considered largely illusory, and intermediate-term momentum must take account of annual seasonalities in returns.     

Informed trading,information uncertainty,and price momentum

In this paper we investigate the effects of informed trading (PIN) and information uncertainty in determining price momentum. We find that trading strategies based on buying high-uncertainty good-news stocks and shorting high-uncertainty bad-news stocks work well when limited to high-PIN stocks, while stocks with low-PIN do not exhibit price continuations, even when the uncertainty level of those stocks is high. In contrast, momentum returns are always significant for high-PIN stocks, irrespective of information uncertainty. Overall, we show that the informed trading effect is both independent of and stronger than that of information uncertainty in determining price momentum.     

Weather and intraday patterns in stock returns and trading activity

We examine the relation between weather in New York City and intraday returns and trading patterns of NYSE stocks. While stock returns are found to be generally lower on cloudier days, cloud cover has a significant influence on stock returns only at the market open. There are significantly more seller-initiated trades when there is more cloud cover at the market open, which is consistent with the return results. Cloudy skies are associated with higher volatility and less market depth over the entire trading day. Finally, cloud cover is not significantly correlated with spread measures and turnover ratios. The findings overall suggest that weather has a significant influence on investors’ intraday trading behavior.     

Risk and the January effect

We use a time-series GARCH framework with the conditional variance/covariance as proxies for systematic risk to reexamine the proposition by Rozeff and Kinney (1976) and Rogalski and Tinic (1986) that the January effect may be a phenomenon of risk compensation in the month. We find no clear evidence that either conditional volatility or unconditional volatility in January is predominantly higher across the sampling years. Hence, against the proposition, the January effect is not due to risk per se. Rather, we find strong evidence that the January effect is due to higher compensation for risk in the month. This may be possible if investors have an increasing RRA utility function. Although many studies find that volatility tends to be higher in January, we find it to be period-specific and mostly in value-weighted return series, but not in equal-weighted return series. This is true both for the unconditional and conditional return volatility.     

Mispricing of dual-class shares: Profit opportunities,arbitrage, and trading

This is the first paper to examine the microstructure of how mispricing is created and resolved. We study dual-class shares with equal cash flow rights and show that a simple trading strategy exploiting gaps between their prices appears to create abnormal profits after transactions costs. Trade and quote data show that investors shift their trading patterns to take advantage of gaps. Contrary to common perception, long–short arbitrage plays a minor part in eliminating gaps, and one-sided trades correct most of them. We also show that the more liquid share class is usually responsible for the price discrepancies.     

The evolution of the January effect

This paper extends recent studies of the January effect by investigating the evolution of the daily pattern of the effect across size deciles. Our evidence documents a sizable mean reverting component beginning in the latter part of January and a shorter duration of the seasonal effect. Despite lower abnormal returns in the second part of January, higher abnormal returns in the first part of January keep the magnitude of the January effect unchanged. Further analysis of daily trading volumes suggests a stable trading volume intensity in the first part of January and a substantial decline in trading volume intensity in the second part of January.     

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.     

Recent trends in trading activity and market quality

We explore the sharp uptrend in recent trading activity and accompanying changes in market efficiency. Higher turnover has been associated with more frequent smaller trades, which have progressively formed a larger fraction of trading volume over time. Evidence indicates that secular decreases in trading costs have influenced the turnover trend. Turnover has increased the most for stocks with the greatest level of institutional holdings, suggesting professional investing as a key contributor to the turnover trend. Variance ratio tests suggest that more institutional trading has increased information-based trading. Intraday volatility has decreased and prices conform more closely to random walk in recent years. The sensitivity of turnover to past returns has increased and cross-sectional predictability of returns has decreased significantly, revealing a more widespread use of quantitative trading strategies that allow for more efficient securities prices.     

Exchange trading rules and stock market liquidity

We examine stock exchange trading rules for market manipulation, insider trading, and broker–agency conflict, across countries and over time, in 42 stock exchanges around the world. Some stock exchanges have extremely detailed rules that explicitly prohibit specific manipulative practices, but others use less precise and broadly framed rules. We create new indices for market manipulation, insider trading, and broker–agency conflict based on the specific provisions in the trading rules of each stock exchange. We show that differences in exchange trading rules, over time and across markets, significantly affect liquidity.     

An analysis of trade-size clustering and its relation to stealth trading

NYSE and Nasdaq trades increasingly cluster on multiples of 500, 1,000, and 5,000 shares. Such clustering varies over time and across stocks, and tends to increase with the level of trading activity. Furthermore, rounded trades tend to have more persistence both in occurrence and in trade initiation. Finally, medium-sized rounded trades tend to have greater relative price impact than large rounded trades. From these observations we surmise that trade-size clustering is consistent, at least in part, with the actions of stealth traders who tend to use medium-sized rounded transactions in an attempt to disguise their trades.     

O/S: The relative trading activity in options and stock

Relatively little is known about the trading volume in derivatives relative to the volume in underlying stocks. We study the time-series properties and the determinants of the options/stock trading volume ratio (O/S) using a comprehensive cross-section and time-series of data on equities and their listed options. O/S is related to many intuitive determinants such as delta and trading costs, and it also varies with institutional holdings, analyst following, and analyst forecast dispersion. O/S is higher around earnings announcements, suggesting increased trading in the options market. Further, post-announcement absolute returns are positively related to pre-announcement O/S, which suggests that at least part of the pre-announcement options trading is informed.     

Insider trading in credit derivatives

Insider trading in the credit derivatives market has become a significant concern for regulators and participants. This paper attempts to quantify the problem. Using news reflected in the stock market as a benchmark for public information, we find significant incremental information revelation in the credit default swap market under circumstances consistent with the use of non-public information by informed banks. The information revelation occurs only for negative credit news and for entities that subsequently experience adverse shocks, and increases with the number of a firm's relationship banks. We find no evidence, however, that the degree of asymmetric information adversely affects prices or liquidity in either the equity or credit markets.     

Disentangling the effect of jumps on systematic risk using a new estimator of integrated co-volatility

We propose a new threshold–pre-averaging realized estimator for the integrated co-volatility of two assets using non-synchronous observations with the simultaneous presence of microstructure noise and jumps. We derive a noise-robust Hayashi–Yoshida estimator that allows for very general structure of jumps in the underlying process. Based on the new estimator, different aspects and components of co-volatility are compared to examine the effect of jumps on systematic risk using tick-by-tick data from the Chinese stock market during 2009–2011. We find controlling for jumps contributes significantly to the beta estimation and common jumps mostly dominate the jump’s effect, but there is also evidence that idiosyncratic jumps may lead to significant deviation. We also find that not controlling for noise and jumps in previous realized beta estimations tend to considerably underestimate the systematic risk.     

An improved estimation method and empirical properties of the probability of informed trading

We report evidence that boundary solutions can cause a bias in the estimate of the probability of informed trading (PIN). We develop an algorithm to overcome this bias and use it to estimate PIN for nearly 80,000 stock-quarters between 1993 and 2004. We obtain two sets of PIN estimates by using the factorized likelihood functions in both [Easley et al., 2010] and [Lin and Ke, 2011], respectively. We find that the estimate based on the EHO factorization is systematically smaller than the estimate based on the LK factorization, meaning that there is a downward bias associated with the EHO factorization. In addition, we find that boundary solutions appear with a very high frequency when the LK factorization is used. Thus it is necessary to use the LK factorization together with the algorithm in this paper. At last, we document several interesting empirical properties of PIN.     

Institutional investors,intangible information,and the book-to-market effect

This paper establishes a robust link between the trading behavior of institutions and the book-to-market effect. Building on work by Daniel and Titman (2006), who argue that the book-to-market effect is driven by the reversal of intangible returns, I find that institutions tend to buy (sell) shares in response to positive (negative) intangible information and that the reversal of the intangible return is most pronounced among stocks for which a large proportion of active institutions trade in the direction of intangible information. Furthermore, the book-to-market effect is large and significant in stocks with intense past institutional trading but nonexistent in stocks with moderate institutional trading. This influence of institutional trading on the book-to-market effect is distinct from that of firm size. These results are consistent with the view that the tendency of institutions to trade in the direction of intangible information exacerbates price overreaction, thereby contributing to the value premium.     

Caught on tape: Institutional trading,stock returns,and earnings announcements

Many questions about institutional trading can only be answered if one tracks high-frequency changes in institutional ownership. In the United States, however, institutions are only required to report their ownership quarterly in 13-F filings. We infer daily institutional trading behavior from the “tape”, the Transactions and Quotes database of the New York Stock Exchange, using a sophisticated method that best predicts quarterly 13-F data from trades of different sizes. We find that daily institutional trades are highly persistent and respond positively to recent daily returns but negatively to longer-term past daily returns. Institutional trades, particularly sells, appear to generate short-term losses—possibly reflecting institutional demand for liquidity—but longer-term profits. One source of these profits is that institutions anticipate both earnings surprises and post-earnings announcement drift. These results are different from those obtained using a standard size cutoff rule for institutional trades.     

Size, book-to-market ratio and macroeconomic news

Little is known about the reactions of daily returns on portfolios with different characteristics to unexpected changes in macroeconomic conditions. This paper fills this void by analyzing the reactions of daily returns on portfolios formed on size and book-to-market ratio to news about a wide range of macroeconomic variables. Returns on different portfolios not only react to different news but also react differently to the same news. Reactions of portfolios to macroeconomic news also change over the business cycle. Results are strongest for news about Employees on Nonfarm Payrolls in expansions. Both at daily and monthly frequencies, large and growth firms react differently to employment news from small and value firms in expansions but not in recessions. Differences in the sensitivities of expected future cash flows to employment news in expansions can help explain differences in the observed reactions.     

Stock market crashes,firm characteristics,and stock returns

A number of studies have investigated the causes and effects of stock market crashes. These studies mainly focus on the factors leading to a crash and on the volatility and co-movements of stock market indexes during and after the crash. However, how a stock market crash affects individual stocks and if stocks with different financial characteristics are affected differently in a stock market crash is an issue that has not received sufficient attention. In this paper, we study this issue by using data for eight major stock market crashes that have taken place during the December 31, 1962–December 31, 2007 period with a large sample of US firms. We use the event-study methodology and multivariate regression analysis to study the determinants of stock returns in stock market crashes.