李江远 LI Jiangyuan

In this paper, we show that only the systematic component of exchange rate expectations of professional investors is a strong predictor of the cross-section of currency returns. The predictability is strong in short and long horizons. The strategy offers significant Sharpe ratios for holding periods of 1 to 12 months, and it is unrelated to existing currency investment strategies, including risk-based currency momentum. The results hold for forecast horizons of 3, 12, and 24 months, and they are robust after accounting for transaction costs. The idiosyncratic component of currency expectations does not contain important information for the cross-section of currency returns. Our strategy is more significant for currencies with low sentiment and it is not driven by volatility and illiquidity. The results are robust when we extract the systematic component of the forecasts using a larger number of predictors.

This paper reconciles the debates on carbon return estimation by introducing the concept of equity duration. We demonstrate that emission level and emission intensity yield divergent results for green firms, driven by inherent data problems. Our findings reveal that equity duration effectively captures the multifaceted effects of carbon transition risks. Regardless of whether carbon transition risks are measured by emission level or emission intensity, brown firms earn lower returns than green firms when the equity duration is long. This relationship reverses for short-duration firms. Our analysis underscores the pivotal role of carbon transitions' multifaceted effects on cash flow structures in understanding the pricing of carbon emissions.

We study the comovements between stocks and bonds by focusing on Treasury bonds and corporate bonds separately. The stock-Treasury bond correlation transitions from positive to negative while the correlation between stocks and high-yield corporate bonds consistently remains positive displaying a notable increasing pattern. Employing machine learning techniques, we find that inflation and bond illiquidity contribute the most to the positive stock-Treasury correlation while the negative scenario is largely explained by the cross-market hedging phenomenon. Default risk and bond illiquidity emerge as crucial characteristics influencing the correlation between stocks and high-yield corporate bond returns. Utilizing machine learning approaches and an extensive panel of characteristics, we provide a comprehensive and objective assessment on the determinants of stock-bond correlation.

Disagreement measures are known to predict cross-sectional stock returns but fail to predict market returns. This paper proposes a partial least squares disagreement index by aggregating information across individual disagreement measures and shows that this index significantly predicts market returns both in- and out-of-sample. Consistent with the theory in Atmaz and Basak (2018), the disagreement index asymmetrically predicts market returns with greater power in high-sentiment periods, is positively associated with investor expectations of market returns, predicts market returns through a cash flow channel, and can explain the positive volume-volatility relationship.

Time series momentum (TSM) refers to the predictability of the past 12-month return on the next one-month return and is the focus of several recent influential studies. This paper shows that asset-by-asset time series regressions reveal little evidence of TSM, both in- and out-of-sample. While the t-statistic in a pooled regression appears large, it is not statistically reliable as it is less than the critical values of parametric and nonparametric bootstraps. From an investment perspective, the TSM strategy is profitable, but its performance is virtually the same as that of a similar strategy that is based on historical sample mean and does not require predictability. Overall, the evidence on TSM is weak, particularly for the large cross section of assets.

We extend the continuous-time hedge fund framework to model the dynamic leverage choice of a hedge fund manager with time-inconsistent preferences. While time-inconsistency discourages the manager from investing when facing high liquidation risk, the payment of incentive fees may induce a time-inconsistent manager to be more aggressive with leverage. For the special case with no management fees, we derive the closed-form solutions and find that a time-inconsistent manager always chooses higher leverage than a time-consistent manager. The impact on the dynamic leverage strategy also depends on such factors as whether managers are sophisticated or naive in their expectations regarding future time-inconsistent behavior.

We investigate an alternative economic channel of a positive relationship between risk and compensation, as documented by Cheng et al. (2015). We propose that when information asymmetry exists, firms generally seek to use compensation as a signal of their CEOs’ ability. The risks arising from information asymmetry tend to encourage firms to pay higher compensation to their CEOs in a pattern of financial incentives we call the “Lake Wobegon effect”. However, when individual firms pursue complete signaling, a higher equilibrium compensation level can be achieved. This paper explores the factors that give rise to the “Lake Wobegon effect” and the learning process by which this effect can be counterbalanced over time (Hayes and Schaefer, 2009).