Using “the Measures for the Administration of Bank Wealth Management (henceforth BWM) Funds Sales” as an exogenous shock in fund distribution channels in Chinese BWM industry, we investigate the impact of non-affiliated distribution on fund performance. We find that the adoption of non-affiliated distribution brokers has a positive effect on BWM fund performance. We further find that the effect is more pronounced when the non-affiliated distribution broker has more market power and when the fund issuer has better governance. We interpret our findings to indicate that non-affiliated distribution brokers alleviate the agency problems of fund managers by introducing both ex-ante and ex-post monitoring, highlighting the role of non-affiliated distribution brokers as an external governance mechanism in wealth management industry.

Investors' previous experiences with a stock affect their willingness to repurchase it. Using Chinese investor-level brokerage data, we find that investors are less likely to repurchase stocks that have increased in value since they were sold. We then construct a novel measure of Regret to capture investors' repurchase regret and investigate its asset pricing implications. Stocks with higher Regret experience lower buying pressure from retail investors in the future, leading to lower future returns. In terms of economic magnitude, portfolios with low Regret generate 12% more annualized abnormal returns. Further analyses show that the pricing effect of Regret is more pronounced among lottery-like stocks and those in which investors have previously gained profit. The results are robust to alternative estimations.

We examine how stakes affect investor behaviors. In our unique setting, the same investors trade stocks in real accounts using their own money and, at the same time, trade in a simulated setting. Our real-world within-investor estimation produces strong evidence that investors exhibit stronger biases and perform worse in their higher-stakes real accounts than in their lower-stakes simulated accounts. Even with no monetary stakes, investors exhibit strong biases in their simulated accounts, and biases in the two types of accounts are strongly positively correlated. Such behavioral consistency suggests that low-stakes experimental methods, although imperfect, can be informative about real-world human behaviors. Using account data from two brokerage companies, we find that investors exhibit a stronger disposition effect on positions with greater portfolio weight. Hence, the finding that stakes-strengthening-biases may not be unique to the comparison between no-monetary and high-monetary stakes.

Using the regression discontinuity design, a quasi-experiment approach, this paper establishes a causal relationship between the down payment requirement and house prices by exploiting a unique institutional background in Shanghai. In the unique setting, the required minimal down payment ratio jumps at the Inner Ring, a circular elevated highway, from 50% to 70% for a large group of buyers. With transaction level data from the largest real estate broker in Shanghai, we find that a lower required down payment ratio increases the apartment price by 138.8 thousand RMB, around 3.71% of the average transaction price.

Fintech firms mobilize information technology to provide intermediation services using a broker methodology, whereas dealer banks intermediate using leveraged balance sheets. The integration of Fintech into banking may reduce the unit cost of intermediation by shifting the production function from dealer to broker. A “Fintech score” is derived using nonlinear and machine learning algorithms that show on-balance sheet lending for low Fintech score dealer banks versus securitization, brokered deposits, and non-interest income for high score, broker banks. Using Data Envelopment and Stochastic Cost Frontier Analyses, we find that banks with higher Fintech scores are more operationally efficient and resilient in crises.

The inelastic markets hypothesis states that the aggregate stock market price elasticity of demand is small, implying that flows have large impacts on prices. We exploit demand shocks created as investor funds are frozen and unfrozen during Chinese IPOs to estimate the impact of demand shocks on the Chinese stock market. Using brokerage account records, we observe the selling and buying as investors raise cash to subscribe for IPOs and then reinvest the funds that supported unsuccessful subscriptions. Using an instrumental variables estimator we find that a 10 bps demand shock increases stock prices by between 30 and 48 bps.

We provide a model of hedge fund securing financing from a prime broker where deterioration in collateral value exacerbates counterparty risk and liquidity risk for the prime broker due to strategic actions of hedge funds. Costs of liquidity insurance and enforcing contracts determine hedge fund leverage. The model provides several new insights. First, it uncovers a new channel for funding liquidity that can explain why illiquid funds fare worse in times of stress and why better governed funds fared better during the financial crisis. Second, the model provides a new testable hypothesis that systematic or idiosyncratic shocks to fundamentals of bank holding companies may spillover to connected hedge funds through internal capital markets. It also offers an identification strategy to distinguish between possible competing hypotheses. Third, strong governance at hedge funds may reduce incentives to invest in profitable opportunities. Fourth, banking reforms such as Supplementary Leverage Ratio, Liquidity Coverage Ratio and Standing Repo Facility intended to improve resilience of banks may also make hedge funds less vulnerable to shocks in the banking sector. Fifth, the model offers a possible reconciliation for the mixed evidence on the impact of leverage on hedge fund survival documented in the literature.

We provide direct evidence of leverage-induced fire sales contributing to a market crash using account-level trading data for brokerage- and shadow-financed margin accounts during the Chinese stock market crash of 2015. Margin investors heavily sell their holdings when their account-level leverage edges toward their maximum leverage limits, controlling for stock-date and account fixed effects. Stocks that are disproportionately held by accounts close to leverage limits experience high selling pressure and abnormal price declines which subsequently reverse. Unregulated shadow-financed margin accounts, facilitated by FinTech lending platforms, contributed more to the crash despite their smaller asset holdings relative to regulated brokerage accounts.

The cost of trading in securities markets is often estimated on the basis of: 1) a trade execution rather than an original order; and 2) a quote midpoint at the time of trade execution rather than at the time of order submission. In our paper, we obtain data from a U.S. brokerage firm to examine the severity of these two problems. We find that the quote midpoint and order size at submission differ from that at execution approximately 40% of the time. These differences are economically important and are more likely to occur when the market is less liquid. Our results highlight the need for caution when inferring trading costs from market center data sources.

This study examines for evidence of peer effects in the trading decisions of individual investors from Mainland China, a country whose cultural and social structures are vastly different from those of Western countries. Cultural differences, as widely documented, play a significant role in social interactions and word-of-mouth behavior. In contrast to U.S. studies, we find robust evidence that the trading decisions of Chinese investors are influenced, via word-ofmouth, by those of their peers who maintain brokerage accounts at the same branch, but not by those whose accounts are maintained at another branch located in the same city.