(Un)skilled leveraged trading of retail investors

We study the trading behavior of retail investors in the market of leveraged bank-issued retail derivatives, which are designed to encourage excessive trading and speculation. We investigate whether retail investors have private information and benefit disproportionately or whether they gamble without private information. We answer this question along three dimensions: (i) profitability, (ii) news trading, and (iii) transaction costs. We distinguish between derivatives by the type of underlying (index vs. individual stocks). We find that raw returns are negative for derivatives with stocks as the underlying, and only partially positive for those with index as the underlying. Nevertheless, risk-adjusted returns show poor performance, with Sharpe ratios below 0.30. We show that retail investors are attracted by news, but do not have private information prior to news events.     

Does the location of stock exchange matter? A within-country analysis

The current study documents an interesting phenomenon that retail investors prefer to invest in stocks listed at the stock exchange that is geographically close to them in China. This pattern is robust when we control for the well-documented local bias within a country. Among companies with similar distances to both stock exchanges and companies headquartered locally, investors still display a strong tendency to invest in locally-listed stocks. Among stocks with similar distances to both stock exchanges, those listed in Shanghai (Shenzhen) co-move more in returns and trading volumes, with the benchmark at the Shanghai (Shenzhen) stock exchange. Such a preference for local exchange seems not to be motivated by information advantage, because investors do not obtain abnormal returns from their trades on stocks listed nearby. Our findings provide additional evidence that non-information-based familiarity bias induces investment and that such investor bias and exchange-level sentiment influence asset price formation.     

Individual Investors and Volatility

We show that retail trading activity has a positive effect on the volatility of stock returns, which suggests that retail investors behave as noise traders. To identify this effect, we use a reform of the French stock market that raises the relative cost of speculative trading for retail investors. The daily return volatility of the stocks affected by the reform falls by 20 basis points (a quarter of the sample standard deviation of the return volatility) relative to other stocks. For affected stocks, we also find a significant decrease in the magnitude of return reversals and the price impact of trades.     

Online Search Frequency,Retail Investor Overreaction,and the Cross-Section of Stock Returns: Evidence from the Chinese Stock Market

I find a strong negative relation between online search frequency and future returns on the Chinese stock market. I suggest that this effect captures retail investor overreaction to unexpected signals, because online search frequency reflects the efforts made by investors to obtain firm-specific knowledge. The effect is particularly strong in stocks with high information uncertainty (high analyst dispersion, big past earnings surprises, low analyst coverage, and large trading volume), whose intrinsic values are difficult or costly for investors to estimate. Online search frequency as a direct indicator of retail investors’ reaction to signals also sheds light on the idiosyncratic volatility (IVOL) puzzle. I find that this puzzle is more pronounced in high-search-frequency subsamples and disappears in low-search-frequency subsamples. Further evidence shows that high search frequency strengthens the negative IVOL effect in stocks with positive signals but weakens this effect in stocks with negative signals. I suggest that the IVOL puzzle in the Chinese market can be partially explained as a reversal following overreaction to positive signals by retail investors.     

Is Proprietary Trading Detrimental to Retail Investors?

We study the conflict of interest that arises when a universal bank conducts proprietary trading alongside its retail banking services. Our data set contains the stock holdings of every German bank and those of their corresponding retail clients. We investigate (i) whether banks sell stocks from their proprietary portfolios to their retail customers, (ii) whether those stocks subsequently underperform, and (iii) whether retail customers of banks engaging in proprietary trading earn lower portfolio returns than their peers. We present affirmative evidence for all three questions and conclude that proprietary trading can, in fact, be detrimental to retail investors.     

Twitter activity,investor attention,and the diffusion of information

We examine the impact of Twitter attention on stock prices by examining over 21 million company‐specific tweets over a 5‐year period. Through a quasi‐natural experiment identifying official Twitter outages, we find that Twitter influences stock trading, especially among small, less visible securities primarily traded by retail investors. In addition, we determine that Twitter activity is associated with positive abnormal returns and when tweets occur in conjunction with traditional news events, more information is spread to investors. Finally, we show that retail investor activity drives the Twitter effect as institutional investors less actively trade the affected stocks.     

Retail clientele and option returns

Does the retail clientele matter for option returns? By delta-hedging options and trading straddles, thus allowing a focus on volatility, this paper empirically shows that a higher retail trading proportion (RTP) is related to lower option returns. Long-short portfolios involving options on low and high RTP stocks generate significantly positive abnormal returns. The results suggest that retail investors speculate and pay a lottery premium on the expected future volatility, resulting in more expensive options with higher implied volatilities.     

Option trading: Information or differences of opinion?

This paper investigates the motive of option trading. We show that option trading is mostly driven by differences of opinion, a finding different from the current literature that attempts to attribute option trading to information asymmetry. Our conclusion is based on three pieces of empirical evidence. First, option trading around earnings announcements is speculative in nature and mostly dominated by small, retail investors. Second, around earnings announcements, the pre-announcement abnormal turnovers of options seem to predict the post-announcement abnormal stock returns. However, once we control for the pre-announcement stock returns, the predictability completely disappears, implying that option traders simply take cues from the stock market and turn around to speculate in the options market. Third, cross-section and time-series regressions reveal that option trading is also significantly explained by differences of opinion. While informed trading is present in stocks, it is not detected in options.     

An encyclopedia for stock markets? Wikipedia searches and stock returns

We present empirical evidence that collective investor behavior can be inferred from large-scale Wikipedia search data for individual-level stocks. Drawing upon Shannon transfer entropy, a model-free measure that considers any kind of statistical dependence between two time series, we quantify the statistical information flow between daily company-specific Wikipedia searches and stock returns for a sample of 447 stocks from 2008 to 2017. The resulting stock-wise measures on information transmission are then used as a signal within a hypothetical trading strategy. The results evidence the predictive power of Wikipedia searches and are in line with the previously documented notion of buying pressure revealed by online investor attention and the trading patterns of retail investors.     

Do Retail Trades Move Markets?

We study the trading of individual investors using transactiondata and identifying buyer- or seller-initiated trades. We documentfour results: (1) Small trade order imbalance correlates wellwith order imbalance based on trades from retail brokers. (2)Individual investors herd. (3) When measured annually, smalltrade order imbalance forecasts future returns; stocks heavilybought underperform stocks heavily sold by 4.4 percentage pointsthe following year. (4) Over a weekly horizon, small trade orderimbalance reliably predicts returns, but in the opposite direction;stocks heavily bought one week earn strong returns the subsequentweek, while stocks heavily sold earn poor returns.     

隐性杠杆约束、流动性风险和投资者情绪

本文以2003—2019年间开放式主动管理型的股票型和偏股型基金为样本,以持仓占比为权重估算基金投组中A股的总市场风险暴露,检验结果表明,该序列上升反映了基金面临的隐性杠杆约束收紧,刻画了市场的弱流动性。内在逻辑在于,流动性收紧时,投资者难以通过融资直接增加杠杆,更倾向于重仓持有高市场风险头寸的股票而间接实现杠杆。本文发现隐性杠杆约束所刻画的风险在股票或基金收益截面上的无条件定价基本失效,而条件定价则依赖于低市场情绪与弱流动性。分解基金持股的敞口,进一步发现,因中小盘基金在流动性收紧时具有更强的流动性偏好,其持股的市场风险头寸能够更敏锐地捕捉到弱流动性风险。     

Left-tail momentum: Underreaction to bad news,costly arbitrage and equity returns

This paper documents a significantly negative cross-sectional relation between left-tail risk and future returns on individual stocks trading in the US and international countries. We provide a behavioral explanation to this anomaly based on the idea that investors underestimate the persistence in left-tail risk and overprice stocks with large recent losses. Thus, low returns in the left-tail of the distribution persist into the future causing left-tail return momentum. We find that the left-tail risk anomaly is stronger for stocks that are more likely to be held by retail investors, that receive less investor attention, and that are costlier to arbitrage.     

名义价格幻觉——基于证券分析师目标价格预测的经验证据

本文利用证券分析师发布的股票目标价格预测,为名义价格幻觉提供了能够直接反映心理预期的经验证据。研究发现,证券分析师对低价股未来收益的心理预期显著高于高价股,该行为偏误在规模小、上市时间短、股票波动性大、财务透明度低和无形资产占比高等估值难度更大的股票中表现得更加明显。我们还利用股票送转,对证券分析师是否受到名义价格幻觉的影响做进一步验证,发现由送转引起的与基本面无关的名义价格下降显著提升了证券分析师对股票未来回报的心理预期。进一步研究表明,上述发现并不是因为证券分析师准确预见了低价股和高价股未来有不同的投资机会,也不是为了最大化其供职证券公司的利益而有意迎合投资者。     

Price limit performance: New evidence from a quasi-natural experiment in China's ChiNext market

This study examines the impact of daily price limits on market performance and trading activity by using a quasi-natural experiment in China. It focuses on the case of the ChiNext market, where the daily price limits of stocks increased from 10% to 20% in 2020. The results show that, initially, the stock prices and occurrences of 10% price limit hits increase, but then decline after the new price limits have been implemented. The level of trading liquidity and volatility increases significantly, with a greater impact on the short term than the long term. These price limit performances are more pronounced for stocks with additional retail interest. The analysis of detailed trading data reveals that institutional investors initially purchase ChiNext stocks in large quantities, followed by retail investors who purchase smaller quantities. In the long run, institutional investors tend to increase their holdings, while retail investors tend to sell their holdings. Additionally, there is a temporary increase in investor attention, price synchronicity, and stock risks, followed by a decline. The findings suggest that wider price limits increase trading volumes and enhance long-term market efficiency, but encourage immediate price manipulation, causing short-term overreactions and long-term reversals. This study provides valuable insights for building an effective price limit system.     

Which past returns affect trading volume?

Anecdotal evidence suggests and recent theoretical models argue that past stock returns affect subsequent stock trading volume. We study 3,000 individual investors over a 51 month period to test this apparent link between past returns and volume using several different panel regression models (linear panel regressions, negative binomial panel regressions, Tobit panel regressions). We find that both past market returns as well as past portfolio returns affect trading activity of individual investors (as measured by stock portfolio turnover, the number of stock transactions, and the propensity to trade stocks in a given month). After high portfolio returns, investors buy high risk stocks and reduce the number of stocks in their portfolio. High past market returns do not lead to higher risk taking or underdiversification. We argue that the only explanations for our findings are overconfidence theories based on biased self-attribution and differences of opinion explanations for high levels of trading activity.     

Piecing together the extent of retail fractional trading

We examine the introduction of fractional trading and its impact on retail security ownership. Fractional trading aims to increase investor access to securities with high prices. Over the initial months of Robinhood’s fractional trading program, the number of unique owners increases approximately 53 percentage points more for stocks priced above $100 versus those priced below $50. Intraday, high-price stocks exhibit incremental ownership growth specifically during periods when fractional trading is permitted. Our results show that Robinhood investors make ample use of fractional trading to acquire previously inaccessible securities, indicating a substantial reduction in price-based investing frictions and carrying implications for retail portfolio management. In addition, we show that potential market impacts of fractional trading appear negligible based on share volume data from multiple brokers with fractional trading programs.     

Are investors consistent in their trading strategies? An examination of individual investor-level data

While it has been demonstrated that momentum or contrarian trading strategies can be profitable in a range of institutional settings, less evidence is available concerning the actual trading strategies investors adopt. Standard definitions of momentum or contrarian trading strategies imply that a given investor applies the same strategy to both their buy and sell trades, which need not be the case. Using investor-level, transaction-based data from China, where tax effects are neutral, we examine investors' buy-sell decisions separately to investigate how past returns impact differentially on the trading strategies investors adopt when buying and selling stock. After controlling for a wide range of stock characteristics, extreme price changes and portfolio value, a clear asymmetry in trading is observed; with investors displaying momentum behavior when buying stocks, but contrarian behavior when selling stocks. This asymmetry in behavior is not driven purely by reactions to stock characteristics or extreme stocks. We discuss behavioral and cultural explanations for our findings.     

Trading Volume: Implications of an Intertemporal Capital Asset Pricing Model

We derive an intertemporal asset pricing model and explore its implications for trading volume and asset returns. We show that investors trade in only two portfolios: the market portfolio, and a hedging portfolio that is used to hedge the risk of changing market conditions. We empirically identify the hedging portfolio using weekly volume and returns data for U.S. stocks, and then test two of its properties implied by the theory: Its return should be an additional risk factor in explaining the cross section of asset returns, and should also be the best predictor of future market returns.     

Implied cost of capital investment strategies: evidence from international stock markets

Investors can generate excess returns by implementing trading strategies based on publicly available equity analyst forecasts. This paper captures the information provided by analysts by the implied cost of capital (ICC), the internal rate of return that equates a firm’s share price to the present value of analysts’ earnings forecasts. We find that U.S. stocks with a high ICC outperform low ICC stocks on average by 6.0 % per year. This spread is significant when controlling the investment returns for their risk exposure as proxied by standard pricing models. Further analysis across the world’s largest equity markets validates these results.     

The Timing of Opening Trades and Pricing Errors

After demonstrating that a zero investment trading strategy that buys stocks with overnight returns below the market average and sells stocks with overnight returns above the market average earns more than 1% monthly profit, I demonstrate that this profit is greater for stocks that start trading more quickly than for other stocks. These results control for trading costs. The resulting pricing errors are a material portion of stock price volatility and suggest that a quick response to overnight information adds non‐information‐based stock volatility to stock prices.