Journal of Financial Markets最新文献

OTC Markets Group organizes stocks that trade over-the-counter (OTC) into three marketplaces (OTCQX, OTCQB, and Pink) based on firm quality and disclosure practices. We examine trading within these tiers and find that stocks in higher tiers are more liquid than stocks in lower tiers. After a series of difference-in-differences tests comparing a matched sample of stocks that change tiers, we find that liquidity improves (deteriorates) for stocks moving up (down) the tiered market structure, suggesting that the tier designations resolve uncertainty and increase firm visibility. Our results show that liquidity differences between tiers is attributable to OTC market structure.

The job openings-to-employment ratio ($\mathrm{JOE}$), defined as the number of job postings divided by the employment level, is among the strongest known predictors of the equity premium. We find that $\mathrm{JOE}$ outperforms a broad set of over two dozen popular predictor variables in both in-sample and out-of-sample forecasting tests. Forecasts based on $\mathrm{JOE}$ also produce gains of 2.91% in annualized certainty equivalent return and 0.20 in annualized Sharpe ratio relative to forecasts based on the historical mean equity premium. The empirical results are consistent with a standard production-based asset pricing model with labor inputs and search frictions.

I consider the effects of quadratic transaction costs on stock prices. It is optimal for investors to trade frequently with relatively small amounts in the presence of such costs. Contrary to previous papers that report that the strongest effects that transaction costs can have on the risk premium are of the order of a few percent, I find that the effects could be of the order of tens of percent conditioned that investors are sufficiently heterogeneous. Frequent trading in the presence of transaction costs substantially changes heterogeneity in demands across investors, resulting in a significant liquidity premium.

We estimate the number of machines “covering” a firm by separating machine Internet protocols (IPs) from human IPs based on the intensity of information retrieval using the EDGAR web log dataset. We investigate the relationship of machine coverage and the cross-section of stock returns and find that stocks in the lowest quintile of machine coverage outperform those in the highest quintile by 6% annually after adjusting for risk. Our results indicate that automation in information processing has a significant impact on the cross-section of stock returns.

We compare the IPO issuing firms in India that disclose risk in their advertisements with the firms that do not disclose such risks and find 31% higher underpricing in firms that disclose risk. For the risk disclosing firms, we find a significantly higher subscription from institutional investors. The difference in the subscription from retail investors for the two groups of firms is insignificant. The firms that disclose risk in their ads have superior performance in the post IPO period as compared to the firms that do not disclose such risk.

I study how realized idiosyncratic jumps play a role in pricing individual stocks. I find that stocks with high variances associated with positive idiosyncratic jumps tend to have low subsequent returns. To explain the negative premium, I show that positive idiosyncratic jump variances are important predictors for future skewness. Thus, my finding is consistent with investors’ preference for unusually large gains over short horizons. I demonstrate the economic significance of my results by highlighting the superior performance of a strategy based on variances associated with positive idiosyncratic jumps compared to strategies based on other variance measures.

We examine time-varying jump risk for modeling stock price dynamics and cross-sectional option prices. We explore jump-diffusion specifications with two independently evolving processes for stochastic volatility and jump intensity, respectively. We explicitly impose time-series consistency in model estimation using a Markov Chain Monte Carlo (MCMC) method. We find that both the jump size and standard deviation of jump size premia are more prominent under time-varying jump risk. Simultaneous jumps in returns and volatility help reconcile the time series of returns, volatility, and jump intensities. Finally, independent time-varying jump intensities improve the cross-sectional fit of option prices, especially at longer maturities.

This paper shows that the COVID-19 pandemic is associated with a decrease in liquidity and increases in price efficiency and informed trading before the NYSE closed its trading floor. The closure of the trading floor led to reductions in liquidity, price efficiency, and informed trading on the NYSE, and its subsequent reopening led to increases in these variables. The effects of the pandemic and the trading floor on price efficiency can be explained, at least in part, by their impacts on liquidity and informed trading. The effects on liquidity and price efficiency are fully reversed after the NYSE reopened its trading floor.

We examine the role of information from the options market in forecasting the equity premium. We provide evidence that the equity premium is predictable out-of-sample using a set of CBOE strategy benchmark indices as predictors. We use a range of econometric approaches to generate point, quantile, and density forecasts of the equity premium. We find that models based on option variables consistently outperform the historical average benchmark. In addition to statistical gains, using option predictors results in substantial economic benefits for a mean–variance investor, delivering up to a fivefold increase in certainty equivalent returns over the benchmark during the 1996–2021 sample period.

We analyze the determinants of the trading volumes of different trading mechanisms in equity markets using an extensive panel data set from European markets comprising public limit order books, call auctions, dark pools, internalization platforms, and the over-the-counter market. Market shares, resulting from investors’ order routing decisions, are driven by the degree of immediacy and anonymity offered by the venues, their ability to offer off-tick executions, as well as the informational environment and conditions in the market. Findings for small and large trades are distinctly different, likely because traders jointly choose trade size and venue type.