Journal of Financial Markets最新文献

Are margin traders as well-informed as short sellers when it comes to leveraged investing? Our paper, utilizing a unique dataset on stock-level short selling and margin trading from three international stock markets, reveals that while short selling has cross-sectional return predictability, margin trading does not. In comparison to short selling, margin-trading activities demonstrate a stronger correlation across stocks and weakly predict firm fundamentals. This suggests that margin traders are less likely to possess a firm-specific information advantage. Our findings at the investor account level also indicate that margin traders are less sophisticated than short sellers.

We develop a model of temporary short-selling bans by extending the infinite-horizon model of Scheinkman and Xiong (2003). Our model predicts that a temporary short-selling ban leads to a speculative bubble that is the highest at the beginning of the ban and gradually converges to zero. Examining the 2008 short-selling ban in the U.S., we find evidence consistent with the model’s predictions. The innovation of our empirical design is to use the financial segments of non-banned stocks as a control group for the banned financial stocks. Our results are robust across different test specifications and different samples of stocks.

The investment fund sector has expanded dramatically since 2008, increasing the capacity for its risk-taking to generate negative spillovers. This paper provides empirical evidence for the existence of wide-spread risk-taking incentives in the investment fund sector, with a particular focus on incentives for synchronized, cyclical risk-taking which could have systemic risk implications. Incentives arise from the positive response of investors to returns achieved through cyclical risk-taking and non-linearities in the relationship between fund returns and fund flows. The fact that market discipline may not be sufficient to ensure prudent behavior among managers creates a clear case for macroprudential regulatory intervention.

I demonstrate empirically that corporate bond dealers mitigate adverse selection risk by passing potentially informed transactions to institutional investors. I contrast price reversals following days with abnormal trading volume across bonds with different information asymmetry. In informed trading, the part of reversal specific to high-volume days should increase with information asymmetry. In uninformed trading, there is no such effect. Following high-volume days when investors provide liquidity, the reversals are consistent with the former case. When dealers provide liquidity, I observe the latter. The results suggest that the informational content of bond prices is higher when dealers do not take inventory.

We investigate the role of investor sentiment in institutional herding behavior and its impact on stock prices. We find that institutional investors exhibit more herding behavior during periods of high sentiment, which has a significant impact on stock prices. Our results show that herding has a stabilizing effect on the stock market when investor sentiment is low, while it causes price distortions when sentiment is high. We also show that the impact of sentiment on price is particularly pronounced for small, non-profitable, low tangibility, high-growth firms.

Prediction markets are a successful information aggregation structure, however the exact mechanism by which private information is incorporated into the price remains poorly understood. We introduce a novel method based on the “Kyle model” to identify traders who contribute valuable information to the market price. Applied to a large field prediction market dataset, we identify traders whose trades have positive informational price impact. In contrast to others, these traders realize profit (on average) in excess of a theoretical expected informed lower bound. Results are replicated on other field prediction market datasets, providing strong evidence in favor of the Kyle model.

We investigate how comovement of S&P 500 stocks changes during a day using a large high-frequency dataset and estimators that are robust under microstructure noise and asynchronicity. We find that, in 2011 to 2021, the stock correlation increases substantially throughout the trading session, while the cross-sectional beta dispersion decreases concurrently. Thus, S&P 500 stocks exhibit stronger comovement near the market close. The time-varying comovement can be explained by the intraday variation in the composition of index-based and firm-based order flows. A cross-section market impact model with time-varying demand from single-stock and index investors generates the intraday patterns we observe.

This paper examines the incentives to voluntarily centrally-clear swaps. It exploits changes resulting from a regulation mandating collateral on uncleared swaps to analyze determinants of traders’ clearing decisions. The rule promoted voluntary clearing by decreasing the relative cost of clearing swaps. Using unique regulatory data, the paper finds that clearing more than quadrupled for exchange rate derivatives that were implicated by this regulation, while clearing for similar but exempt derivatives increased by about one-third. These changes were driven by traders who were already clearinghouse members, suggesting that clearing members have substantially lower marginal clearing costs.

Corporate bonds carry an extreme illiquidity (EIL) premium. This premium permeates all rating categories and heightens during financial crises and periods of high uncertainty. EIL has predictive power in the cross-section for future returns up to at least one year. Active investors like mutual funds prefer low EIL bonds that can be easily liquidated during times of stress, whereas passive institutional investors overweight high EIL bonds to receive the EIL premium. While adding an EIL factor constructed from portfolios to the factor model increases the explanatory power, its effect is largely subsumed by bond-level EIL in a horse race.

I study how crossing networks, a type of dark pool, affect price discovery and market liquidity in the presence of noisy and heterogeneous trader signals. I identify a buffer function of crossing networks that helps mitigate traders’ losses from false signals. Additionally, I uncover an amplification effect. That is, when signal precision is high, crossing networks enhance price discovery. By contrast, when signal precision is low, crossing networks impair price discovery. These insights reconcile conflicting empirical evidence, yielding novel predictions and regulatory recommendations for equity and emerging markets.