We quantify the effects of closing price manipulation on trading characteristics and stock price accuracy using a unique sample of prosecuted manipulation cases. Based on these findings we construct an index of the probability and intensity of closing price manipulation. As well as having regulatory applications, this index can be used to study manipulation in the large number of markets and time periods in which prosecution data are not readily available.

We empirically analyze the prevalence and economic underpinnings of closing price manipulation and its detection. We estimate that ∼1% of closing prices are manipulated, of which only a small fraction is detected and prosecuted. We find that stocks with high levels of information asymmetry and mid to low levels of liquidity are most likely to be manipulated. A significant proportion of manipulation occurs on month/quarter-end days. Manipulation on these days is more likely in stocks with high levels of institutional ownership. Government regulatory budget has a strong effect on both manipulation and detection.

Regulators globally are concerned that dark trading harms price discovery. We show that dark trades are less informed than lit trades. High levels of dark trading increase adverse selection risk on the lit exchange by increasing the concentration of informed traders. Using both high- and low-frequency measures of informational efficiency we find that low levels of non-block dark trading are benign or even beneficial for informational efficiency, but high levels are harmful. In contrast, we find no evidence that block trades in the dark impede price discovery.

We propose that the arrival of new information is a source of systematic risk for the holder of a financial security. Using several measures of information flows, we demonstrate that a stock’s sensitivity to market-wide information flow is associated with a robust cross-sectional return premium that is distinct from other return premia. We find that the amount of information impounded in prices through trading has increased in recent years consistent with declining trading costs and the rise of algorithmic trading. We show that the information flows risk premium is increasing through time.

Using the staggered entry of Chi-X in 12 European equity markets as a source of exogenous variation in high frequency trading (HFT), we find that HFT causes significant increases in comovement in returns and in liquidity. About one-third of the increase in return comovement is due to faster diffusion of market-wide information. We attribute the remaining two-thirds to correlated trading strategies of HFTs. The increase in liquidity comovement is consistent with HFT liquidity providers being better able to monitor other stocks and adjust their liquidity provision accordingly. Our findings suggest a channel by which HFT impacts the cost of capital.

We investigate the puzzle of widespread participation in cryptocurrency pump-and-dump manipulation schemes. Unlike stock market manipulators, cryptocurrency manipulators openly declare their intentions to pump specific coins, rather than trying to deceive investors. Puzzlingly, people join in despite negative expected returns. In a simple framework, we demonstrate how overconfidence and gambling preferences can explain participation in these schemes. Analyzing a sample of 355 cases in 6 months, we find strong empirical support for both mechanisms. Pumps generate extreme price distortions of 65% on average, abnormal trading volumes in the millions of dollars, and large wealth transfers between participants.

This paper examines the use, determinants, and impact of anonymous orders in a market where disclosure of broker identity in the trading screen is voluntary. We find that most trading occurs nonanonymously, contrary to prior literature that suggests liquidity gravitates to anonymous markets. By strategically using anonymity when it is beneficial, traders reduce their execution costs. Traders select anonymity based on various factors including order source, order size and aggressiveness, time of day, liquidity, and expected execution costs. Finally, we report how anonymous orders affect market quality and discuss implications for market design.

We exploit a unique natural experiment—recent restrictions of dark trading in Canada and Australia—and proprietary trade-level data to analyze the effects of dark trading. Disaggregating two types of dark trading, we find that dark limit order markets are beneficial to market quality, reducing quoted, effective, and realized spreads and increasing informational efficiency. In contrast, we do not find consistent evidence that dark midpoint crossing systems significantly affect market quality. Our results support recent theory that dark limit order markets encourage aggressive competition in liquidity provision. We discuss implications for the regulation of dark trading and tick sizes.

97% of orders in US stock markets are cancelled before they trade, straining market infrastructure and raising concerns about predatory or manipulative trading. To understand the drivers of these extreme cancellation rates, we develop a simple model of liquidity provision and find that growth in order-to-trade ratios (OTTRs) is driven by fragmentation of trading and technological improvements that lower monitoring costs. High OTTRs occur legitimately in stocks with high volatility, fragmented trading, small tick sizes, and low volume. OTTRs are usually within levels consistent with market making, but occasionally spike to levels that may indicate illegitimate trading such as spoofing.

Almost half of US share trading volume occurs in dark markets, prompting regulatory concerns. We examine the effects of dark trading on issuers and show that, at moderate levels, dark trading improves the quality of corporate investment decisions by increasing the amount of information in prices that is new to managers. Consistent with this mechanism, higher dark trading is associated with greater investment–price sensitivity, improved managerial forecast accuracy, stronger M&A-price sensitivity, and superior future operating performance. These benefits diminish, and can reverse, at high levels of dark trading. We establish causality using exogenous changes in dark trading.