Abstract Using marketable order flow data, we analyze key characteristics of aggregate retail exchange‐traded fund (ETF) investing from 2010 to 2021, including allocations, holding period and investment performance. Retail traders allocate 12% more dollar volume to leveraged and inverse ETFs versus nonretail traders. Retail ETF trades distinctly increase with prior ETF returns, in contrast to contrarian stock trading. Estimated ETF holding periods are longer for retail investors versus nonretail. Finally, retail and nonretail ETF trades perform similarly over hypothetical holding periods up to one quarter. Overall, we provide policy‐relevant insights into retail investing behaviours, which have been the subject of recent concern.
Abstract One of the main explanations for the idiosyncratic volatility (IVOL) puzzle (i.e., the negative relation between lagged IVOL and returns) is a missing risk factor. We show, analytically, that if IVOL proxies for a missing risk factor, then the negative relation between IVOL and returns should persist at the portfolio level. Empirically, we find that the IVOL puzzle disappears when we use well‐diversified portfolios as test assets. The IVOL puzzle also weakens after controlling for additional risk factors. Overall, our results suggest that both diversifiable (i.e., true idiosyncratic risk) and nondiversifiable risk play a role in explaining the IVOL puzzle.
Abstract We investigate how information choices affect equity returns and risk. Building on an existing theoretical model of information and investment choice, we estimate a learning index that reflects the expected benefits of learning about an asset. High learning index stocks have lower future returns and risk compared to low learning index stocks. Analysis of a conditional asset pricing model, long-run patterns in returns and volatilities, other measures of information flow, and the information environment surrounding earnings announcements reinforce our interpretation of the learning index. Our findings support the model’s predictions and illustrate a novel empirical measure of investor learning.
Reliable excess returns from active portfolio management derive from informed trading. This article investigates the information content of informed trading in the equity market and the options market. The authors find that informed equity trading and options trading are positively correlated in the time series but virtually uncorrelated cross sectionally. Portfolio-level and stock-level analyses provide robust evidence that the cross-sectional return predictive power of informed trading in each market is distinct. Time-series analyses indicate that aggregate informed options trading is useful for predicting market returns but that the amount of informed trading has declined significantly in more recent years. The time-series patterns of both informed trading measures coincide closely with the decline in equity hedge fund excess returns. TOPICS:Security analysis and valuation, options, statistical methods, performance measurement
We conduct the first analysis of FinTech Retail Access programs for IPO share allocations. Issued largely by consumer-facing companies, these “Retail IPO” stocks underperform contemporaneous IPO stocks by an average of 17 percentage points over the first 9 months, even after adjusting for issuer size, quality, valuation, and industry. Retail IPO stocks exhibit much higher fractional trade volume after the IPO, consistent with greater retail trading, and this trade volume is a significant negative predictor of longer-run post-IPO stock returns, on average. Consistent with attention-induced trading by retail investors, we find that pre-IPO Retail Access notifications are associated with spikes in Google search interest for the IPO stock. This attention-induced trading offers a partial explanation for the underperformance of Retail IPO stocks while raising policy-relevant questions about the design of these programs and the welfare of retail investors.
Reliable excess returns from active portfolio management derive from informed trading. We investigate the information content of informed trading in the equity market and the options market. We find that informed equity trading and options trading are positively correlated in the time-series, but virtually uncorrelated cross-sectionally. Portfolio-level and stock-level analyses provide robust evidence that the cross-sectional return predictive power of informed trading in each market is distinct. Time-series analyses indicate that aggregate informed options trading is useful for predicting market returns, but that the amount of informed trading has declined significantly in more recent years. The time-series patterns of both our informed trading measures coincide closely with the decline in equity hedge fund excess returns.
We investigate how information choices impact equity returns and risk. Building on an existing theoretical model of information and investment choice, we estimate a learning index that reflects the expected benefits of learning about an asset. High learning index stocks have lower future returns and risk compared to low learning index stocks. Analysis of a conditional asset pricing model, long run patterns in returns and volatilities, other measures of information flow, and the information environment surrounding earnings announcements reinforce our interpretation of the learning index. Our findings support the model's predictions and illustrate a novel empirical measure of investor learning.
One of the main explanations for the idiosyncratic volatility (IVOL) puzzle (i.e., the negative relation between lagged IVOL and returns) is a missing risk factor. We show analytically that if IVOL proxies for a missing risk factor, then the negative relation between IVOL and returns should persist at the portfolio level. Empirically, we find that the IVOL puzzle disappears when we use well-diversified portfolios as test assets. The IVOL puzzle also weakens after controlling for additional risk factors. Overall, our results suggest that both diversifiable (i.e., true idiosyncratic risk) and non-diversifiable risk play a role in explaining the IVOL puzzle.
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