Journal of Financial Markets最新文献

The abnormal decline in the number of U.S. public firms is often blamed on merger activity, private equity investments, and stock market regulations. We compare the effects of these channels in a unified framework. In the U.S., an extra 100 mergers is associated with 22.01 additional missing public firms, whereas an extra 100 PE deals is associated with 3.62 fewer missing public firms. Regulatory changes contribute to the decline of U.S. listings too. We also specify the types of deals that most strongly affect listings. Finally, we document that similar listing gaps emerge in other developed economies.

Our evidence suggests that the profitability of volatility timing strategies applied to equity factor portfolios disappeared when changes in the trading and information environment in the U.S. in the early 2000s made arbitrage less costly. The reduction of volatility timing alphas is greater for factor portfolios based on small-capitalization stocks, which are less liquid, more costly to trade, and more expensive to short than portfolios based on large-capitalization stocks. The evidence holds for 11 factor portfolios and a broader sample of 110 anomaly portfolios in the U.S. Our research highlights the importance of frictions in the profitability of investment strategies.

For a sample of 356 LSE stocks from the period 2013–2019, we find that common short sold capital is positively and significantly associated with one-month ahead four-factor residual return correlation, controlling for many pair characteristics, including similarities in size, book-to-market, and momentum. The relation weakens with stock illiquidity, whereas it strengthens when short positions originate from informed agents, such as hedge funds, active investors, and short sellers with high past performance. This supports our hypothesis that the relation is driven by information, not price pressure. We show that these results can be used to obtain diversification benefits.

During China's stock market crash in 2015, the government purchased the stocks of over 1000 firms. We investigate how this government rescue affects investment efficiency and show that rescued firms experience a significant decrease in investment-q sensitivity. This rescue has an adverse impact on price efficiency and impedes managers from learning information for investment decisions. The learning channel is the main mechanism through which the rescue has a real effect. Our findings indicate that programs intended to stabilize the stock market could adversely affect the real efficiency, providing new insight into the consequences of government purchases.

We analyze the relation between transaction costs and venue choice using proprietary transaction-level data from institutional trade executions in the U.K. equity market. We show that, for a given investor, a higher share of dark trading (midpoint dark pools) is associated with lower execution costs. In the context of a recent ban on dark trading, we provide evidence consistent with migration towards substitute trading venues such as periodic auctions, which has mitigated adverse effects on trading costs for large investors. We also provide micro-evidence on Menkveld et al.’s (2017) pecking order theory of venue choice over the life-cycle of large parent orders.

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 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.

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.