1. On the Robustness of Idiosyncratic Volatility Effect

    [PDF]  [Online Appendix]

    Management Science, forthcoming

    The idiosyncratic volatility effect of Ang et al. (2006) is robust to restricting the sample to NYSE firms (once proper listing indicator is used) and to excluding from the sample small, illiquid, and low-price stocks. The IVol effect is also unlikely to stem from the short-run reversal of Jegadeesh (1990), as the IVol effect stays significant for about six months and seems stronger for high turnover firms, which, as Medhat and Schmeling (2022) find, do not exhibit short-term reversal. The IVol effect also does not seem to weaken post-publication.

  2. Firm Complexity and Post-Earnings-Announcement Drift (with Shawn Park and Celim Yildizhan)

    Review of Accounting Studies, 2024, v. 29 (1), pp. 527-579

    [PDF]   [Slides]

    We show that the post earnings announcement drift (PEAD) is stronger for conglomerates than single-segment firms. Conglomerates, on average, are larger than single segment firms, so it is unlikely that limits-to-arbitrage drive the difference in PEAD. Rather, we hypothesize that market participants find it more costly and difficult to understand firm-specific earnings information regarding conglomerates as they have more complicated business models than single-segment firms. This in turn slows information processing about them. In support of our hypothesis, we find that, compared to single-segment firms with similar size, conglomerates have relatively low institutional ownership and short interest, are covered by fewer analysts, these analysts have less industry expertise and also make larger forecast errors. Finally, we find that an increase in firm complexity leads to larger PEAD and document that more complicated conglomerates have greater PEADs. Our results are robust to a long list of alternative explanations of PEAD as well as alternative measures of firm complexity.

  3. Idiosyncratic Volatility, Growth Options, and the Cross-Section of Returns

    (with Georgy Chabakauri)

    Review of Asset Pricing Studies, 2023, v. 13 (4), pp. 653-690

    [PDF]   [Slides]   [Robustness]

    The paper shows that the value effect and the idiosyncratic volatility (IVol) discount (Ang et al., 2006) arise because growth firms and high IVol firms beat the CAPM during the periods of increasing aggregate volatility, which makes their risk low. All else equal, growth options' value increases with volatility, and this effect is stronger for high IVol firms, for which growth options take a larger fraction of the firm value and firm volatility responds more to aggregate volatility changes. The empirical volatility factor model with the market factor, the market volatility risk factor (FVIX) and the average IVol factor (FIVol) explains the value effect and the IVol discount and why those anomalies are stronger for firms with high short sale constraints.

  4. Profitability Anomaly and Aggregate Volatility Risk

    Journal of Financial Markets, 2023, v. 64, Article 100782

    [PDF]   [Slides]   [Theory Appendix]   [Robustness]   [Data Appendix]

    Firms with lower profitability have lower expected returns because such firms perform better than expected when market volatility increases. The better-than-expected performance arises because unprofitable firms are distressed and volatile, their equity resembles a call option on the assets, and call options value increases with volatility, all else fixed. Consistent with this hypothesis, the profitability anomaly and its exposure to aggregate volatility risk are stronger for distressed and volatile firms; for such firms, aggregate volatility risk explains roughly half of the profitability anomaly, while in single sorts on profitability about 70% of the anomaly is explained.

  5. Stock Liquidity and Issuing Activity

    Quarterly Journal of Finance, 2022, v. 12 (3), Article 2250010

    Top-5 most downloaded papers in QJF in 2022

    [PDF]   [Robustness]   [Slides]   [Data Appendix]

    The paper shows that issuing activity does not result in superior liquidity. Even the kinds of new issues that are supposed to be more liquid than others (IPOs backed by venture capital, new issues with high-prestige underwriters, severely underpriced IPOs) are just as liquid as their peer non-issuers or other similar issuing companies. The paper thus refutes the existing liquidity-based explanations of the new issues puzzle. The paper also shows that the low-minus-high turnover factor seems to explain the new issues puzzle and related anomalies only because it picks up volatility risk.

  6. Estimating the Cost of Equity Capital for Insurance Firms with Multi-period Asset Pricing Models (with Jianren Xu and Steven Pottier)

    Journal of Risk and Insurance, 2020, v. 87 (1), pp. 213-245

    2021 Casualty Actuarial Society Award for the best paper on casualty actuarial science published in Journal of Risk and Insurance

    [PDF]    [Online Appendix]

    Previous research on insurer cost of equity (COE) focuses on single-period asset pricing models. In reality, however, investment and consumption decisions are made over multiple periods, exposing firms to time-varying risks related to economic cycles and market volatility. We extend the literature by examining two multi-period modelsóthe conditional CAPM (CCAPM) and the intertemporal CAPM (ICAPM). Using 29 years of data, we find that macroeconomic factors significantly influence and explain insurer stock returns. Insurers have countercyclical beta implying that their market risk increases during recessions. Further, insurers are sensitive to volatility risk (the risk of losses when volatility goes up), but not to insurance-specific risks, financial industry risks, liquidity risk, or coskewness after controlling for other economy-wide factors.

  7. Stocks with Extreme Past Returns: Lotteries or Insurance?

    Journal of Financial Economics, 2018, v. 129 (3), pp. 458-478

    Outstanding Paper in Investments Award, 2013 Southern Finance Association (SFA) meetings

    [PDF]   [Slides]

    The paper shows that lottery-like stocks are hedges against unexpected increases in market volatility. The loading on the aggregate volatility risk factor explains the majority of low abnormal returns to stocks with high maximum returns in the past month (Bali, Cakici, and Whitelaw, 2011) and high expected skewness (Boyer, Mitton, and Vorkink, 2010). Aggregate volatility risk also explains the new evidence that the maximum effect and the skewness effect are stronger for firms with high market-to-book or high expected probability of bankruptcy.

  8. Institutional Ownership and Aggregate Volatility Risk

    Journal of Empirical Finance, 2017, v. 40, pp. 20-38

    Finalist, Best Paper Award, 2013 French Finance Association (AFFI) meetings

    [PDF]   [Slides]

    The paper shows that the difference in aggregate volatility risk can explain why several anomalies are stronger among the stocks with low institutional ownership (IO). Institutions tend to stay away from the stocks with extremely low and extremely high levels of firm-specific uncertainty because of their desire to hedge against aggregate volatility risk or exploit their competitive advantage in obtaining and processing information, coupled with the dislike of idiosyncratic risk. Thus, the spread in uncertainty measures is wider for low IO stocks, and the same is true about the differential in aggregate volatility risk.

  9. Why Does Higher Variability of Trading Activity Predict Lower Expected Returns?

    Journal of Banking and Finance, 2015, v. 58, pp. 457-470

    [PDF]   [Slides]   [Data Appendix]   [Theory Appendix]   [Older version with Volume Variability]

    The paper shows that controlling for the aggregate volatility risk factor eliminates the puzzling negative relation between variability of trading activity and future abnormal returns. I also find that variability of other measures of liquidity and liquidity risk is largely unrelated to expected returns. Lastly, I show that the low returns to the firms with high variability of trading activity are not explained by liquidity risk and mispricing stories.

  10. High Short Interest Effect and Aggregate Volatility Risk (with Julie Wu)

    Journal of Financial Markets, 2014, v. 21, pp. 98-122

    [PDF]   [Slides]   [Older Version with CAPM Benchmark]   [Theory Appendix]

    We propose a risk-based explanation on why stocks of firms with high relative short interest (RSI) have lower future returns. We argue that these firms have negative alphas because they are a hedge against expected aggregate volatility. Consistent with this argument, we show that these firms have high firm-specific uncertainty and real options, and the ICAPM with the aggregate volatility risk factor can explain the high RSI effect. The key mechanism is that high RSI firms have abundant growth options and, all else equal, growth options become less sensitive to the underlying asset value and more valuable as idiosyncratic volatility goes up. Idiosyncratic volatility usually increases together with aggregate volatility, i.e., in recessions.

  11. Turnover: Liquidity or Uncertainty?

    Management Science, 2014, v. 60 (10), pp. 2478Ė2495

    Runner-up for the Best Paper in Market Microstructure Award, 2009 Financial Management Association (FMA) meetings

    [PDF]   [Slides]   [Theory Appendix]   [Data Appendix]   [Volatility Appendix]

    I show that turnover is unrelated to several alternative measures of liquidity and liquidity risk and that liquidity risk factors cannot explain why higher turnover predicts lower future returns. I find that the aggregate volatility risk factor explains why higher turnover predicts lower future returns. I also find that the negative relation between turnover and future returns is stronger for firms with high market-to-book or bad credit rating and these regularities are also explained by the aggregate volatility risk factor.

  12. Analyst Disagreement and Aggregate Volatility Risk

    Journal of Financial and Quantitative Analysis, 2013, v. 48 (6), pp. 1877-1900

    [PDF]   [Slides]   [Theory Appendix]

    The paper explains why firms with high dispersion of analyst forecasts earn low future returns. These firms beat the CAPM in the periods of increasing aggregate volatility and thereby provide a hedge against aggregate volatility risk. The aggregate volatility risk factor can explain the abnormal return differential between high and low disagreement firms. This return differential is higher for the firms with abundant real options, and this fact can be explained by aggregate volatility risk. Aggregate volatility risk is also capable of explaining why the link between analyst disagreement and future returns is stronger for firms with high short-sale constraints.

  13. Aggregate Volatility Risk: Explaining the Small Growth Anomaly and the New Issues Puzzle

    Journal of Corporate Finance, 2012, v. 18 (4), pp. 763-781

    [PDF]   [Slides]   [Theory Appendix]

    The paper shows that small growth firms earn low returns because they tend to beat the CAPM when expected aggregate volatility increases. Consistent with that, the ICAPM with the aggregate volatility risk factor can explain the small growth anomaly, as well as the new issues puzzle and the cumulative issuance puzzle. The key mechanism is that, all else equal, growth options become less sensitive to the underlying asset value and more valuable as idiosyncratic volatility goes up, which usually happens when aggregate volatility also increases, that is, in recessions. Small growth stocks, which have high idiosyncratic volatility and abundant growth options, are therefore a natural hedge against aggregate volatility risk.


  1. The Bright Side of Distress Risk (November 2022)

    [PDF]   [Slides]   [Theory Appendix]   [Robustness]   [Data Appendix]

    Revise and Resubmit at Journal of Banking and Finance, 2nd round

    The paper shows that distressed firms have positive abnormal returns when aggregate volatility unexpectedly increases. This hedging property of distressed firms explains the puzzling negative relation between firm-specific distress risk and future alphas from benchmark asset-pricing models. Controlling for aggregate volatility risk exposure also explains why the negative relation is stronger for volatile firms and growth firms.

  2. Firm Complexity and Conglomerates Expected Returns (November 2021)

    [PDF]   [Slides]   [Robustness]   [Data Appendix]

    The paper discovers that firm complexity is negatively priced in cross-section. High/low-complexity conglomerates have 35-50/20-28 bp per month more negative five-factor Fama and French (2015) alphas than single-segment firms, and this effect is stronger in subsamples with low institutional ownership, higher idiosyncratic volatility, and around earnings announcements. The complexity effect is robust to controlling for a long list of pre-existing anomalies and seems to be generated by the interaction of higher uncertainty/disagreement about conglomerates (Barinov, Park, and Yildizhan, 2016) and short-sale constraints. The complexity effect seems to be contributing to the diversification discount by slowly eroding the valuations of conglomerates.

  3. Firm Complexity and Limits to Arbitrage (July 2020)

    [PDF]   [Slides]

    Several important anomalies are stronger for more complex firms. Despite conglomerates being on average larger and more liquid than single-segment firms, anomalies are stronger for conglomerates. In the conglomerates-only subsample, anomalies are stronger for conglomerates with more between-segments difference in market-to-book and operating leverage.

  4. Product Market Power and Technological Innovation (February 2022)

    (with Hyun 'Shana' Hong, Ji Woo 'Ian' Ryou, and Xiao-Jun Zhang)


    Product market power serves as a natural hedge against adverse shocks and competitive threats, thus increasing managerial risk tolerance of innovation investment. Consistent with that, we find that product market power is positively associated with firm innovation input and output. Additionally, consistent with learning from the leaderís market valuation, we find that firm innovation is positively and significantly sensitive to market valuation of its product market leader, especially if the stock price of the leader/followers is more/less informative. The follower firms alter their R\&D investments based on stock return around their leaderís patent grant dates. The followers mimic innovation investments of their product market leader and private information in leaderís prices is associated with improvement in their future profits. We find that liquidity shocks to leaderís stock price hamper the following firmsí learning. We conclude that product market power promotes innovation and firms learn from product market leaderís market valuation.

  5. Why Is Asymmetric Timeliness of Earnings Priced? (September 2022)

    [PDF]   [Data Appendix]

    Asymmetric timeliness (AT) measure from Basu (1997) regression is priced. Sorting firms on AT produces a 40 bp per month spread in six-factor alphas. The AT effect is driven almost exclusively by the bottom AT quintile, populated by aggressive firms that recognize gains more timely than losses. Investors seem to misinterpret aggressive accounting numbers and are ill-prepared for future negative events. The AT effect in returns in concentrated around earnings announcements, writedowns, and downgrades. The AT effect is also stronger for high limits to arbitrage firms and seems unrelated to liquidity and the business cycle.