This paper examines the impact of Exchange-Traded Fund (ETF) ownership on corporate earnings management. We find that ETF ownership is associated with a significant reduction in earnings management, and this result remains robust across a wide range of endogeneity tests and robustness checks. Further analyses reveal that ETFs exert a pronounced mitigating effect on sales manipulation, production manipulation, and expense manipulation. Mechanism tests indicate that ETFs curb earnings management by improving stock liquidity and strengthening external monitoring. We also find that the influence of ETFs is stronger in private firms, in firms with lower information transparency, and in firms with CEO duality, suggesting that ETFs serve as a more prominent external governance force when internal governance mechanisms are relatively weak. Overall, this study enriches the literature on the economic consequences of ETFs and provides new empirical evidence that financial innovation in emerging markets can help alleviate the information risk faced by investors.

This study investigates the role of contextual cues in memory-based decision-making within high-stakes trading environments. Using trade records from a large Chinese brokerage firm, we provide evidence that both extreme events (COVID-19 quarantines) and everyday contexts (geographic locations) trigger the recall of previously traded stocks, increasing the likelihood of subsequent orders for those stocks. The observed patterns align more closely with similarity-based recall than with alternative channels. Welfare analysis reveals that these memory-induced trades lead to substantial losses for the representative investor's portfolio. We also find evidence at the market level: when the geographical distribution of quarantine risks is recalled, the probability of recalling the cross-sectional stock return-volume distribution from the same day increases by 1.6 percentage points. This study provides evidence from a real-world setting for memory-based theories, particularly similarity-based recall, and highlights a novel channel through which contextual cues affect financial markets.

We document a striking anomaly in international capital flows that we term "sanction arbitrage": U.S. investors exploited the 2014 sanctions on Russia by significantly increasing holdings in Russian equities while Rest-of-World (ROW) investors fled. We rationalize this behavior through a simple game-theoretic model where the sanctioning government faces a trade-off between geopolitical objectives and domestic welfare, effectively creating a protective shield for domestic investors and driving out ROW investors. Empirically, we confirm that pre-sanction U.S flows negatively predicted subsequent sanction designations. Consequently, U.S. investors internalized this protection to act as opportunistic buyers, absorbing fire-sale assets from exiting foreign investors and capturing significant excess returns from Russian stock holdings. These findings reveal that "smart" sanctions designed to preserve market access can inadvertently generate wealth transfers from foreign to domestic agents.

We study asset demand and price formation in a two-asset rational expectations equilibrium with narrow thinking, where traders imperfectly coordinate decisions across assets under non-nested price information. When the price of one asset increases, cross-asset inference from prices reduces expected demand for the other asset, which feeds back into the demand response for the original asset. Narrow thinking weakens internal coordination and amplifies reliance on price-based inference. As a result, more severe narrow thinking leads to higher own-price elasticities. The model delivers sharp implications for market liquidity and price informativeness in the presence of bounded rationality.

Serving on the official unsecured creditors' committee (UCC) of a bankrupt firm provides hedge funds with access to material nonpublic information (MNPI), which can facilitate their informed trading across firms and asset markets. We find that hedge funds increase equity turnover and execute more large trades in the quarters following UCC membership. In contrast, hedge funds do not exhibit such trading behavior after accessing public information about bankrupt firms or holding the bankrupt firm's debt without committee involvement. Importantly, these large trades often target firms with close economic ties to the bankrupt entity. Returns from these MNPI-driven trades are substantial.

This paper introduces reinforcement learning to examine the effect of trading on noise in a dynamic limit order market equilibrium. It shows that intensive noise liquidity provision (consumption) increases speculators' liquidity consumption (provision), improving (reducing) market liquidity. Channeled by uninformed chasing and informed aggressive liquidity provision, the increasing noise liquidity provision and consumption, respectively, improve price efficiency, generating a U-shaped price efficiency to the noise trading uncertainty on liquidity provision and consumption. Associated with a hump-shaped (U-shaped) profitability for the informed (uninformed) at a U-shaped noise trading cost in the noise trading uncertainty, this implies that, at increasing noise trading cost, intensive noise liquidity provision improves market liquidity, price efficiency, order profitability of informed traders, and reduces the loss, even makes profit, for uninformed traders.

We document a novel "news extrapolation" behavior among investors, which distorts the market reaction to corporate news. Specifically, investors tend to extrapolate the value of past news in the immediate reaction to the newly arrived news. News extrapolation generates a biased price reaction to news, which is completely reversed afterwards. Furthermore, the tendency of news extrapolation is related to the recency, consistency, and value uncertainty of news. Investors extrapolate not only from news of the same category but also from news of different categories. By analyzing the trading behavior and sentiment of different investor groups, we find that retail investors tend to be news extrapolators, while institutional investors trade against the news extrapolators.

This study examines how global turbulence factors shape volatility spillovers among emerging stock markets through the lens of risk cycles. We find that emerging market connectedness exhibits clear regime heterogeneity across risk cycles, while also preserving several persistent structural patterns. Specifically, trade policy uncertainty (TPU) and economic policy uncertainty (EPU) serve the dominant drivers during risk outbreak and risk accumulation periods, respectively. Meanwhile, sustainability uncertainty (ESGUI) consistently plays a leading driver role in both regimes, while physical climate risk plays a comparatively limited role. Furthermore, the effects of these core turbulence factors are nonlinear and threshold-dependent, highlighting the importance of accounting for risk cycle heterogeneity and nonlinear dynamics when assessing emerging market risk transmission.

This study investigates the role of contextual cues in memory-based decision-making within high-stakestrading environments. Using trade records from a large Chinese brokerage firm and a novel dataset on COVID-19 quarantines, we find that quarantine periods trigger the recall of previously traded stocks, increasing the likelihood of subsequent orders for those stocks. The observed patterns align more closely with similarity-based recall than with alternative channels. Welfare analysis reveals that these memory-induced trades lead to an annualized loss of approximately 70 percentage points for the representative investor’s portfolio. We also find evidence at the market level: when the geographical distribution of quarantine risks is recalled, the probability of recalling the cross-sectional stock return-volume distribution from the same day increases by 1.6 percentage points. This study provides causal evidence from a real-world setting for memory-based theories, particularly similarity-based recall, and highlights a novel channel through which COVID-19 policies affect financial markets.

This paper investigates how foreign institutional investors, specifically Qualified Foreign Institutional Investors (QFIIs), influence the investment strategies of Chinese mutual fund management companies (FMCs) in which they hold shares. By analysing panel data from 1,766 mutual funds managed by 44 foreign-invested FMCs in China between 2005 and 2021, we explore whether QFII-invested FMCs (Q-FMCs) learn more from their domestic counterparts (D-FMCs) than other foreign-invested FMCs (NQ-FMCs). Our findings show that Q-FMC-managed mutual funds exhibit portfolio allocations more closely aligned with local DFMCs than those managed by NQ-FMCs. This imitation is particularly pronounced when selecting new stocks, enhancing portfolio performance, but not when rebalancing existing positions. Additionally, Q-FMCs trade more actively than NQ-FMCs. Robustness checks confirm these results across various ownership structures, fund characteristics, market conditions, and regulatory changes. These findings highlight the dual role of QFIIs as both investors and learners in China’s evolving financial landscape, offering insights into how foreign capital integrates into emerging mutual fund markets, informing regulatory policy aimed at fostering cross-border financial development.