Doron Avramov
Professor of Finance
Ph.D., Wharton School of the University of Pennsylvania



Curriculum Vitae

CV as a PDF file



Teaching:

Lecture Notes: Machine learning methods in the cross-section of asset returns

Lecture Notes: Asset Pricing


Research:


Working papers (comments welcome):


Active Fund Management when ESG Matters: An Equilibrium Perspective  (with Si Cheng and Andrea Tarelli)

Abstract:

This paper proposes an information acquisition model to analyze active management when ESG matters. In equilibrium, more information is purchased when the asset sustainability profile departs from green neutrality, the fund ESG preference departs from the aggregate, or cross-fund heterogeneities in ESG preferences widen. Sustainability-based information decisions increase the dispersion in stock holdings and tracking error, amplify the scope of the active management industry, reduce the cost of capital for greener assets, and improve price informativeness. Finally, enforcing ESG-perceptive funds to follow the optimal policies of ESG-indifferent funds leads to significant utility losses. Empirical evidence and calibration support the model predictions.


Dynamic ESG Equilibrium  (with Abraham Lioui,  Yang Liu, and Andrea Tarelli)

Abstract:

This paper develops and applies an equilibrium model that accounts for ESG demand and supply dynamics. In equilibrium, ESG preference shocks represent a novel risk source characterized by diminishing marginal utility and positive premium. Expected green asset returns are negatively associated with time-varying convenience yield, while exposures to ESG preference shocks lead to positive green premium. Augmenting these conflicting forces with positive contemporaneous effects of preference shocks on realized returns, the green-minus-brown portfolio delivers large positive payoffs for reasonably long horizons. Nonpecuniary benefits from ESG investing account for a nontrivial and increasing fraction of total consumption.



Publications and papers accepted for publication


Integrating Factor Models (with Si Cheng, Stefan Voigt, and Lior Metzker)
Forthcoming Journal of Finance

Abstract:

This paper develops a comprehensive framework to address uncertainty about the correct factor model. Asset pricing inferences draw on a composite model that integrates over competing factor models weighted by posterior probabilities. Evidence shows that unconditional models record near-zero probabilities, while post-earnings announcement drift, quality-minus-junk, and intermediary capital are potent factors in conditional asset pricing. The integrated model tilts away from the subsequently underperforming factors, and delivers robust strategies. Model uncertainty makes equities appear considerably riskier, while model disagreement about expected returns spikes during crash episodes. Disagreement spans all return components involving mispricing, factor loadings, and risk premia.


Machine Learning versus Economic Restrictions: Evidence from Stock Return Predictability  (with Si Cheng  and Lior Metzker)  
Forthcoming Management Science  
Discussion of this paper in The Wall Street Journal, January 05 2020: "Use AI for Picking Stocks? Not So Fast"  by Mark Hulbert.

Abstract:

This paper shows that investments based on deep learning signals extract profitability from difficult-to-arbitrage stocks and during high limits-to-arbitrage market states. In particular, excluding microcaps, distressed stocks, or episodes of high market volatility considerably attenuates profitability. Machine learning-based performance further deteriorates in the presence of reasonable trading costs due to high turnover and extreme positions in the tangency portfolio implied by the pricing kernel. Despite their opaque nature, machine learning methods successfully identify mispriced stocks consistent with most anomalies. Beyond economic restrictions, deep learning signals are profitable in long positions and recent years and command low downside risk.


Post-Fundamentals Price Drift in Capital Markets: A Regression Regularization Perspective  (with Guy Kaplanski  and Avanidhar Subrahmanyam)
Forthcoming Management Science  

Abstract:

Regression regularization techniques show that deviations of accounting fundamentals from their preceding moving averages forecast drifts in equity market prices. The deviations-based predictability survives a comprehensive set of prominent anomalies. The profitability applies strongly to the long-leg and survives value-weighting and excluding microcaps. We provide evidence that the predictability arises because investors anchor to recent means of fundamentals. A factor based on our fundamentals-based index yields economically significant intercepts after controlling for a comprehensive set of other factors, including those based on profit margins and earnings drift.


Sustainable Investing with ESG Rating Uncertainty  (with Si Cheng,  Abraham Lioui,  and Andrea Tarelli) Journal of Financial Economics  145 (2022), 642-664.

Abstract:

This paper analyzes the asset pricing and portfolio implications of an important barrier to sustainable investing---uncertainty about the corporate ESG profile. In equilibrium, the market premium increases and demand for stocks declines under ESG uncertainty. In addition, the CAPM alpha and effective beta both rise with ESG uncertainty and the negative ESG-alpha relation weakens. Employing the standard deviation of ESG ratings from six major providers as a proxy for ESG uncertainty, we provide supporting evidence for the model predictions. Our findings help reconcile the mixed evidence on the cross-sectional ESG-alpha relation and suggest that ESG uncertainty affects the risk-return trade-off, social impact, and economic welfare.


The Distress Anomaly is Deeper than you Think: Evidence from Stocks and Bonds  (with Tarun Chordia,  Gergana Jostova,  and Alexander Philipov) Review of Finance  26 (2022), 355-405.

Abstract:

The distress anomaly reflects the abnormally low returns of high credit risk stocks during financial distress. Evidence from stocks and corporate bonds reinforces the anomaly and challenges rationales based on shareholders' ability to extract value from bondholders, time-varying betas, lottery-type preferences, biased earnings expectations, and limits-to-arbitrage. Moreover, mispricing of distressed stocks and bonds is associated with excess investment and excess external financing. Potential real distortions are materially understated when assessed based only on equity mispricing. We emphasize the important role of corporate bonds in dissecting the distress anomaly, and show that the anomaly is an unresolved puzzle.


Predicting Corporate Policies Using Downside Risk: A Machine Learning Approach  (with Minwen Li  and Hao Wang) Journal of Empirical Finance  63 (2021), 1-26.
Winner of the Best Paper Award at the 2016 CICF China

Abstract:

This paper develops a text-based downside risk measure using corporate annual reports and assesses its ability to forecast future corporate policies. The forward-looking measure dynamically captures adverse firm conditions evolving from economic fundamentals. When the measure is below its sample average, leverage, investment, R&D, employment, and dividends consistently fall. When the measure rises, firms increase cash holdings. The proposed measure also delivers robust and persistent forecasts based on in-sample and out-of-sample LASSO regressions.


Anomalies and Financial Distress  (with Tarun Chordia,  Gergana Jostova,  and Alexander Philipov) Journal of Financial Economics  108 (2013), 139-159.
Winner of the Fama-DFA Best Paper Award (second prize).
Winner of the Best Paper Award: FMA Asia.

Abstract:

This paper explores commonalities across asset-pricing anomalies. In particular, we assess implications of financial distress for the profitability of anomaly-based trading strategies. Strategies based on price momentum, earnings momentum, credit risk, dispersion, idiosyncratic volatility, and capital investments derive their profitability from taking short positions in high credit risk firms that experience deteriorating credit conditions. Such distressed firms are highly illiquid and hard to short sell, which could establish nontrivial hurdles for exploiting anomalies in real time. The value effect emerges from taking long positions in high credit risk firms that survive financial distress and subsequently realize high returns. The accruals anomaly is an exception - it is robust amongst high and low credit risk firms as well as during periods of deteriorating, stable, and improving credit conditions.


The World Price of Credit Risk  (with Tarun Chordia,  Gergana Jostova,  and Alexander Philipov) The Review of Asset Pricing Studies  2 (2012), 112-152.
Winner of the Best Paper Award for 2012.

Abstract:

Global asset-pricing models have failed to capture the cross section of average country equity returns. Emerging markets have displayed strikingly large and robust positive pricing errors. Country-level characteristics have played a significant role in pricing international equities, suggesting that financial markets may not be fully integrated. This paper offers a risk-based explanation that resolves such deviations from global asset pricing. A world credit risk factor fully explains the positive pricing errors in emerging market equities. Moreover, in the presence of this credit risk factor, country-level characteristics no longer play a role in pricing global equities. Factor models that account for the world credit risk factor uniformly outperform competing specifications, in both the time-series and cross-section, which exclude this factor. Over the 1989-2009 period, the risk premium for systematic credit risk exposure is 83 basis points per month and its importance has increased dramatically in recent years.


Hedge Funds, Managerial Skills, and Macroeconomic Variables  (with Robert Kosowski, Narayan Naik,  and Melvyn Teo) Journal of Financial Economics 99 (2011), 672-692.
Winner of the Best Paper Award at the 2007 European Finance Association.
Winner of the Best Paper Award at the 2008 Inquire Europe paper competition.
Discussion of this paper in The Well Street Journal, April 05 2007: "Yes, Virginia, There Is Hope"

Abstract:

This paper evaluates hedge fund performance through portfolio strategies that incorporate predictability based on macroeconomic variables. Incorporating predictability substantially improves out-of-sample performance for the entire universe of hedge funds as well as for various investment styles. While we also allow for predictability in fund risk loadings and benchmark returns, the major source of investment profitability is predictability in managerial skills. In particular, long-only strategies that incorporate predictability in managerial skills outperform their Fung and Hsieh (2004) benchmarks by over 17 percent per year. The economic value of predictability obtains for different rebalancing horizons and alternative benchmark models. It is also robust to adjustments for backfill bias, incubation bias, illiquidity, fund termination, and style composition.


Stock Return Predictability and Model Uncertainty Journal of Financial Economics  64 (2002), 423-458.

Abstract:

We use Bayesian model averaging to analyze the sample evidence on return predictability in the presence of model uncertainty. The analysis reveals in-sample and out-of-sample predictability, and shows that the out-of-sample performance of the Bayesian approach is superior to that of model selection criteria. We find that term and market premia are robust predictors. Moreover, small-cap value stocks appear more predictable than large-cap growth stocks. We also investigate the implications of model uncertainty from investment management perspectives. We show that model uncertainty is more important than estimation risk, and investors who discard model uncertainty face large utility losses.


Stock Return Predictability and Asset Pricing Models Review of Financial Studies  17 (2004), 699-738.

Abstract:

This paper develops an asset allocation framework that incorporates prior beliefs about the extent of stock return predictability explained by asset pricing models. We find that when prior beliefs allow even minor deviations from pricing model implications, the resulting asset allocations depart considerably from and substantially outperform allocations dictated by either the underlying models or the sample evidence on return predictability. Under a wide range of beliefs about model pricing abilities, asset allocations based on conditional models outperform their unconditional counterparts that exclude return predictability.


Investing in Mutual Funds when Returns are Predictable  (with Russ Wermers) Journal of Financial Economics  81 (2006), 339-377.
Discussion of this paper in The New York Times, November 20 2005: "The Manager Is in a Slump (or Maybe It's Just a Phase)"  by Mark Hulbert.
Discussion of this paper also appears in " Haaretz " an Israeli-based newspaper (for Hebrew readers).

Abstract:

This paper analyzes investments in U.S. domestic equity mutual funds, incorporating predictability in (i) manager skills, (ii) fund risk loadings, and (iii) benchmark returns. We find that predictability in manager skills is the dominant source of investment profitability -- long-only strategies that incorporate such predictability outperform their Fama-French and momentum benchmarks by 2 to 4% per year by timing industries over the business cycle, and by an additional 3 to 6% per year by choosing funds that outperform their industry benchmarks. Our findings indicate that active management adds significant value, and that industries are important in locating outperforming mutual funds.


Asset Pricing Models and Financial Market Anomalies  (with Tarun Chordia) Review of Financial Studies  19 (2006), 1001-1040.

Abstract:

This paper derives and implements a framework in which to test whether conditional asset pricing models, applied to single securities, can explain the size, value, turnover, and momentum effects in expected stock returns. In this framework individual stock betas vary with firm level size and book-to-market as well as with macroeconomic variables. The evidence shows that under the extensively studied constant beta framework, none of the models under consideration capture any of the size, value, turnover, and past return effects, even when returns are risk-adjusted by size, value, liquidity, and momentum factors. In contrast, when beta is allowed to vary, the size and book to market effects are often explained, but the explanatory power of turnover and past return remains robust. The past return or momentum effect is related to model mispricing that varies with macroeconomic variables, whereas turnover shows no business cycle patterns.


Predicting Stock Returns  (with Tarun Chordia) Journal of Financial Economics  82 (2006), 387-415.

Abstract:

This paper studies whether incorporating business cycle predictors benefits a real time optimizing investor who must allocate funds across 3,123 NYSE-AMEX stocks and cash. Realized returns are positive when adjusted by the Fama-French and momentum factors as well as by the size, book-to-market, and past return characteristics. The investor optimally holds small-cap, growth, and momentum stocks and loads less (more) heavily on momentum (small-cap) stocks during recessions. Returns on individual stocks are predictable out-of-sample due to alpha variation, whereas the equity premium predictability, the major focus of previous work, is questionable.


An Exact Bayes Test of Asset Pricing Models with Application to International Markets  (with John Chao) Journal of Business 79 (2006), 293-323.

Abstract:

This paper develops and implements an exact finite-sample test of asset pricing models with time varying risk premia using posterior probabilities. The strength of our approach is that it allows multiple conditional asset pricing specifications, both nested and non-nested, to be tested and compared simultaneously. We apply our procedure to international equity markets by testing and comparing the international CAPM and conditional ICAPM versions of Fama and French (1998). The empirical evidence suggests that the best performing model is the ICAPM with the value premium constructed based on global earnings-to-price ratio.


Liquidity and Autocorrelation in Individual Stock Returns  (with Tarun Chordia and Amit Goyal) Journal of Finance  61 (2006), 2365-2394.

Abstract:

This paper documents a strong relationship between short-run reversals and stock return illiquidity, even after controlling for trading volume. The largest reversals and the potential contrarian trading strategy profits occur in the high turnover, low liquidity stocks, as the price pressures caused by non-informational demands for immediacy are accommodated. Thus, the high frequency negative autocorrelations are more likely to result from stresses in the market for liquidity. The contrarian trading strategy profits are smaller than the likely transactions costs because the high turnover, low liquidity stocks face high transaction and large market impact costs. This lack of profitability and the fact that the overall findings are consistent with rational equilibrium paradigms suggest that the violation of the efficient market hypothesis due to short-term reversals is not so egregious after all.


The Impact of Trades on Daily Volatility  (with Tarun Chordia  and Amit Goyal) Review of Financial Studies  19 (2006), 1241-1277.

Abstract:

This paper proposes a trading-based explanation for the asymmetric effect in daily volatility of individual stock returns. Previous studies propose two major hypotheses for this phenomenon: leverage effect and time varying expected returns. However, leverage has no impact on asymmetric volatility at the daily frequency and, moreover, we observe asymmetric volatility for stocks with no leverage. Also, expected returns may vary with the business cycle, i.e., at a lower than daily frequency. Trading activity of contrarian and herding investors has a robust effect on the relationship between daily volatility and lagged return. Consistent with the predictions of the rational expectations models, non-informational liquidity driven (herding) trades increase volatility following stock price declines and informed (contrarian) trades reduce volatility following stock price increases. The results are robust to different measures of volatility and trading activity.


Understanding Changes in Corporate Credit Risk  (with Gergana Jostova  and Alexander Philipov) Financial Analysts Journal  63 (2007), 90-105.

Abstract:

This paper provides new evidence on the empirical success of structural models in explaining corporate credit risk changes. A parsimonious set of common factors and firm-level fundamentals, inspired by structural models, explains more than 54% (67%) of the variation in credit spread changes for medium (low) grade bonds. No dominant latent factor is present in the unexplained variation. While our set of variables has lower explanatory power among high-grade bonds, it does capture most of the systematic variation of credit spread changes in that category as well. It also subsumes the explanatory power of the Fama and French (1993) factors among all grade classes.


Momentum and credit rating  (with Tarun Chordia,  Gergana Jostova,  and Alexander Philipov) Journal of Finance  62 (2007), 2503-2520.

Abstract:

This paper establishes a robust link between momentum and credit rating. Momentum profitability is large and significant among low-grade firms, but it is nonexistent among high-grade firms. The momentum payoffs documented in the literature are generated by low-grade firms that account for less than four percent of the overall market capitalization of rated firms. The momentum payoff differential across credit rating groups is unexplained by firm size, firm age, analyst forecast dispersion, leverage, return volatility, and cash flow volatility.


Dispersion in analyst's earnings forecasts and credit rating  (with Tarun Chordia,  Gergana Jostova,  and Alexander Philipov) Journal of Financial Economics  91 (2009), 83-101.

Abstract:

This paper shows that the puzzling negative cross-sectional relation between dispersion in analysts' earnings forecasts and future stock returns is a manifestation of the credit risk effect. In particular, the profitability of dispersion based trading strategies is concentrated in a small number of the worst-rated firms and is significant only during periods of deteriorating credit conditions. In such periods, the negative dispersion-return relation emerges as low-rated firms experience substantial price drop along with considerable increase in forecast dispersion. Moreover, even for this small universe of worst-rated firms, the dispersion-return relation is nonexistent when either the dispersion measure or return is adjusted by credit risk. The results are robust to previously proposed explanations for the dispersion effect such as short-sale constraints, illiquidity, and leverage.


Credit ratings and the cross-section of stock returns  (with Tarun Chordia,  Gergana Jostova,  and Alexander Philipov) Journal of Financial Markets  12 (2009), 469-499.

Abstract:

Low credit risk firms realize higher returns than high credit risk firms. This effect is puzzling because investors pay a premium for bearing credit risk. This paper shows that the credit risk effect exists only in periods around credit rating downgrades. Around downgrades, low rated firms experience considerable negative returns, precipitated by substantial deterioration in their operating and financial performance, large negative earnings surprises and analyst forecast revisions, and strong institutional selling. In contrast, returns do not differ across credit risk groups in stable or improving credit conditions. Remarkably, the credit risk effect is driven by the lowest rated stocks which account for less than 4% of the total market cap, suggesting that there is no pervasive distress factor in the cross section of returns.


Bayesian Portfolio Analysis  (with Guofu Zhou) Annual Review of Financial Economics  2 (2010), 25-47.

Abstract:

This paper reviews the literature on Bayesian portfolio analysis. Information about events, macro conditions, asset pricing theories, and security-driving forces can serve as useful priors in selecting optimal portfolios. Moreover, parameter uncertainty and model uncertainty are practical problems encountered by all investors. The Bayesian framework neatly accounts for these uncertainties, whereas standard statistical models often ignore them. We review Bayesian portfolio studies when asset returns are assumed both independently and identically distributed as well as predictable through time. We cover a range of applications, from investing in single assets and equity portfolios to mutual and hedge funds. We also outline existing challenges for future work.


Hedge Fund Predictability Under the Magnifying Glass: The Economic Value of Forecasting Individual Fund Returns  (with Laurent Barras  and Robert Kosowski) Journal of Financial and Quantitative Analysis  48 (2013), 1057-1083.

Abstract:

The recent financial crises has highlighted the need to search for suitable models forecasting hedge fund performance. This paper develops and applies a framework in which to assess return predictability on a fund by fund basis. Using a comprehensive sample of hedge funds during the 1994-2008 period, we identify the fraction of funds in each style that are truly predictable, positively or negatively, by macro variables. Out-of-sample, exploring predictability can be difficult as estimation risk and model uncertainty lead to imprecise fund forecast. Moreover, in our multi-fund setting, investors face tradeoff between unconditional and predictable performance, as strongly predictable funds may exhibit low unconditional mean. Nevertheless, a strategy that combines forecasts across predictors circumvents all these challenges and delivers superior performance. We highlight the statistical and economic drivers of this performance, especially in periods when predictor values strongly depart from their long run means. Finally, we use such period - the 2008 crises - as a natural out of sample experiment to validate the robustness of our findings.


Time-Varying Liquidity and Momentum Profits  (with Si Cheng  and Allaudeen Hameed) Journal of Financial and Quantitative Analysis   51(2016),1897-1923.

Abstract:

A basic intuition is that arbitrage is easier when markets are most liquid. Surprisingly, we find that momentum profits are markedly larger in liquid market states. This finding is not explained by variation in liquidity risk, time-varying exposure to risk factors, or changes in macroeconomic condition, cross-sectional return dispersion, and investor sentiment. The predictive performance of aggregate market illiquidity for momentum profits uniformly exceed that of market return and market volatility states. While momentum strategies are unconditionally unprofitable in US, Japan, and Eurozone countries in the last decade, they are substantial following liquid market states.


Cross-Sectional Factor Dynamics and Momentum Returns  (with Satadru Hore) Journal of Financial Markets   38 (2017), 69-96.
Technical appendix.

Abstract:

This paper proposes and implements an inter-temporal model wherein aggregate consumption and asset-specific dividend growths jointly move with two mean-reverting state variables. Consumption beta varies through time and cross sectionally due to variation in half-lives and stationary volatilities of the dividend signals. Winner (Loser) stocks exhibit high (low) half-lives and stationary volatilities, and thus exhibit high (low) consumption beta commanding high (low) risk-premium. The model also rationalizes the "momentum crashes" phenomenon discussed in Daniel and Moskowitz (2014). High half-lives of dividend signals in Winners keep their consumption betas low long after recovering from a prolonged economic downturn, while low half-lives in Losers make their consumption betas grow rather quickly. Thus, coming out of a recession, the long Winner/short Loser strategy reduces in consumption beta and, hence, risk-premia.


Scaling Up Market Anomalies  (with Si Cheng,  and Amnon Schreiber) The Journal of Investing   26(2017),89-105.

Abstract:

This paper implements momentum among a host of market anomalies. Our investment universe consists of the 15 top (long-leg) and 15 bottom (short-leg) anomaly portfolios. The proposed active strategy buys (sells short) a subset of the top (bottom) anomaly portfolios based on past one-month return. The evidence shows statistically strong and economically meaningful persistence in anomaly payoffs. Our strategy consistently outperforms a naive benchmark that equal weights anomalies and yields an abnormal monthly return ranging between 1.273% and 1.471%. The persistence is robust to the post-2000 period, and various other considerations, and is stronger following episodes of high investor sentiment.


Talking Numbers: Technical versus Fundamental Investment Recommendations  (with Guy Kaplanski  and Haim Levy) The Journal of Banking and Finance   92 (2018), 100-114.

Coverage of the paper in marketwatch.com
Talking Number Broadcast: Has Apple Bottomed?

Abstract:

Market efficiency is often evaluated through the ability of fundamental analysis or technical trading rules to exploit predictable patterns in asset prices. The evidence following decades of empirical research is mixed. This paper reexamines the evidence using a novel database from the TV show “Talking Numbers.” We assess the performance of 1,599 investment recommendations, where each recommendation features a fundamental and a technical forecast. We show that technicians are able to predict individual stock returns to economically significant degrees up to a one-year horizon. Beyond that, the null hypothesis of market efficiency is not rejected for market-wide indices, equity sectors, bonds, or commodities.


Are Stocks Riskier Over the Long Run? Taking Cues from Economic Theory  (with Scott Cederburg  and Katarina Lucivjanska) The Review of Financial Studies   31 (2018), 556-594.

Abstract:

We study whether stocks are riskier or safer in the long run from the perspective of Bayesian investors who employ the long-run risk, habit formation, or prospect theory models to form prior beliefs about return dynamics. Economic theory delivers important guidance for long-run investment opportunities. Specifically, incorporating prior information from the habit formation or prospect theory models reinforces beliefs in mean reversion and inferences that stocks are safer over longer horizons. Conversely, investors with long-run risk priors perceive weaker mean reversion and riskier equities. Model-based information is particularly important for inferences about uncertainty in the dividend growth component of returns.


Mutual Funds and Mispriced Stocks  (with Si Cheng  and Allaudeen Hameed) Management Science 66(6):2372-2395.
Winner of the Best Paper Award at the 2016 FMA Europe.

Abstract:

We propose a new measure of fund investment skill, Active Fund Overpricing (AFO), encapsulating the fund’s active share of investments, the direction of fund active bets with regard to mispriced stocks, and the dispersion of mispriced stocks in the fund’s investment opportunity set. We find that fund activeness is not sufficient for outperformance: high (low) AFO funds take active bets on the wrong (right) side of stock mispricing achieve inferior (superior) fund performance. However, high AFO funds receive higher flows during periods of high investor sentiment, when performance-flow relation becomes weaker.


Moving Average Distance as a Predictor of Equity Returns  (with Guy Kaplanski  and Avanidhar Subrahmanyam) Review of Financial Economics, 39 (2), 127-145

Abstract:

The distance between short- and long-run moving averages of prices (MAD) predicts future equity returns in the cross-section. Annualized value-weighted alphas from the accompanying hedge portfolios are around 9%, and the predictability goes beyond momentum, 52-week highs, profitability, and other prominent anomalies. MAD-based investment payoffs survive reasonable trading costs faced by institutions, and are stronger on the long side relative to the short counterpart.



Permanent working papers:


Implications of Long Run Risk for Asset Allocation Decisions  (with Scott Cederburg)

Abstract:

This paper proposes a structural approach to long-horizon asset allocation. In particular, the investor draws inferences about asset returns from a vector auto-regression (VAR) with economic restrictions on the intercept, slope, and covariance matrix implied by the long-run risk model of Bansal and Yaron (2004). Comparing the optimal allocations of investors using the long run risk VAR versus an unrestricted reduced-form VAR reveals stark differences in portfolio strategies. Long-run risk investors are quite conservative relative to reduced-form investors due to inter-temporal hedging concerns. Despite the differing strategies, both investors achieve success in timing the market. The gains of the long-run risk investor appear to arise from his ability to avoid exposure to large negative events, while the reduced-form investor better capitalizes on periods of high average returns.


Cross-Sectional Asset Pricing Puzzles: An Equilibrium Perspective  (with Scott Cederburg  and Satadru Hore)

Abstract:

This paper proposes an inter-temporal asset pricing model that resolves the negative cross-sectional relations between expected stock return and dispersion, idiosyncratic volatility (IV), and credit risk. All three puzzling effects naturally emerge in the cross section of an economy characterized by recursive preferences and persistent dividend and consumption growth rates. The equilibrium cross section of expected return is driven by time-varying exposure to an economic growth factor. The three effects are emerge through the interaction of firm cash flow timing and investor aversion to shocks in economic growth. Specifically, low expected growth firms derive their values primarily from short-run cash flows. Such firms exhibit high dispersion, IV, and credit risk due to their high price sensitivity to idiosyncratic shocks. However, they have low exposure to economic growth shocks thereby exhibiting low growth beta and expected return. In contrast, firms with values weighted towards long-run cash flows have greater exposure to aggregate risk and are relatively insensitive to idiosyncratic cash flow shocks. Thus they are characterized by high expected return coupled with low dispersion, IV, and credit risk levels.


Momentum, Information Uncertainty, and Leverage - an Explanation Based on Recursive Preferences  (with Satadru Hore)

Abstract:

Momentum payoffs concentrate in high information uncertainty and high credit risk firms and are virtually nonexistent otherwise. This paper rationalizes such momentum concentrations in consumption based equilibrium asset pricing. In our paradigm, dividend growth is mean reverting, expected dividend growth is persistent, the representative agent is endowed with stochastic differential utility of Duffie and Epstein (1992), and dividend streams are used for both consumption and debt repayment per Abel (1999). Employing reasonable risk aversion levels we are able to produce the observational momentum effects. Momentum profitability is large in the interaction between high levered and risky cash flow firms. It rapidly deteriorates and ultimately disappears as leverage or cash flow risk diminishes.


Risk Shocks, Uncertainty Shocks, and Corporate Policies  (with Minwen Li  and Hao Wang)

Abstract:

We originate risk and uncertainty shock measures through textual analysis of corporate annual reports and assess their implications for corporate policies. Risk shocks are followed by long-lasting diminishing leverage, investment, employment, dividend payouts, stock repurchases, and increasing cash holdings, with small, high credit risk, and non-profitable firms displaying stronger effects. As risk diminishes, firms need not reverse cash holdings and payouts. Uncertainty shocks are followed by a short-term reduction in leverage, while other corporate policies remain unchanged. Overall, risk shocks trigger persistent policy adjustments, while managers adopt a "wait-and-see" strategy until uncertainty resolves. The evidence is robust to various considerations.