Journal of Financial Markets最新文献

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.

We develop a model of government intervention with information disclosure in which a government with two private signals trades directly in financial markets to stabilize asset prices. Government intervention through informed trading stabilizes financial markets and affects market quality through a noise channel and an information channel. Information disclosure negatively affects financial stability by deteriorating the information advantages of the government, while its final effects on market quality hinge on the relative sizes of the noise effect and the information effect. Under different information disclosure scenarios, there are potential tradeoffs between financial stability and price efficiency.

Does media coverage of a firm have a causal effect on the volatility of its stock price and, if so, is this of aggregate importance? I identify a robust link between coverage in the Financial Times and a firm’s intraday stock price volatility. This effect is not driven by persistence in volatility or anticipation of future newsworthy events, but is explained by an increase in trading volume, supporting a salience interpretation. The effect spills over into firms related by the structure of the production network, but does not affect the aggregate level of volatility.

We analyze the lead–lag relation between VIX futures and SPX futures. The two futures markets are weakly connected when market volatility is low. By contrast, when volatility is high, their prices are highly negatively correlated, with VIX futures leading SPX futures. However, the tightness of the lead–lag relation prevents the formation of profitable trading strategies in a setup that includes transaction costs. An analysis of the time variation in the lead–lag relation finds that an improvement in the relative liquidity of one market strengthens the lead of that market. Moreover, the hedging activities of market makers influence the lead–lag relation.