Horizon Bias and the Term Structure of Equity Returns

Cassella, Stefano; Golez, Benjamin; Gulen, Huseyin; Kelly, Peter

doi:10.2139/ssrn.3328970

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…See models byLettau and Wachter (2007) andHasler and Marfe (2016).6 Alternative explanations of why the premium on near-future cash flows exists include risk pricing(Eisenbach and Schmalz (2016), Lazarus (2022)), behavioral(Cassella et al (2019)), or institutional(Belo, Collin-Dufresne, and Goldstein (2015)) mechanisms.…”

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confidence: 99%

Duration‐Driven Returns

Gormsen

Lazarus²

2023

The Journal of Finance

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We propose a duration-based explanation for the premia on major equity factors, including value, profitability, investment, low-risk, and payout factors. These factors invest in firms that earn most of their cash flows in the near future and could therefore be driven by a premium on near-future cash flows. We test this hypothesis using a novel data set of single-stock dividend futures, which are claims on dividends of individual firms. Consistent with our hypothesis, the expected Capital Asset Pricing Model alpha on individual cash flows decreases in maturity within a firm, and the alpha is not related to the above characteristics when controlling for maturity.IN THIS PAPER, WE PROVIDE a simple framework for understanding the major equity risk factors in asset pricing. We focus our analysis on value, profit, investment, low-risk, and payout factors. These five categories of risk factors have a large impact on stock prices given their high persistence, and they form the basis of leading factor models such as the Fama and French models. 1 Yet, the economics behind these factors are not well understood because the factors are hard to relate to common economic fundamentals. We relate the risk factors back to economic fundamentals, and identify the source of their high

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confidence: 99%

Duration‐Driven Returns

Gormsen

Lazarus²

2023

The Journal of Finance

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“…According to Kahneman (2011, p. 255), optimism bias could be the most significant cog-nitive bias. 16 Cassella et al (2022) documents optimism bias in GDP and unemployment forecasts, and Cassella et al (2021) show that optimism bias can explain time-variation in the equity term structure. I focus on the interaction between stock risk and optimism bias.…”

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confidence: 99%

Essays on Empirical Asset Pricing

Jensen¹

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This thesis investigates the relationship between risk and return in the cross-section of stocks. The thesis consists of three papers that can be read independently. These papers are the result of my Ph.D. studies at the Department of Finance, the Center of Financial Frictions (FRIC), and the Center of Big Data in Finance (BIGFI) at CBS. The ﬁrst paper, Subjective Risk and Return, investigate the subjective risk-return tradeoﬀ. To do so, I introduce a novel data set of subjective risk and return expectations at the individual stock level. I ﬁnd that the required compensation for risk is high, but nevertheless, the realized compensation for risk is low. I show that this diﬀerence arises because cash ﬂow expectations are systematically too high for risky stocks, which can be explained by investors suﬀering from optimism bias. As a result of the low realized compensation for risk, I show that risk cannot explain the realized return of most equity factors, and that the best asset pricing models for explaining realized returns are the worst ones for explaining subjective risk compensation. The second paper, Is There a Replication Crisis in Finance (co-authored with Bryan Kelly and Lasse Heje Pedersen), tests whether equity factor research in ﬁnance is robust to scientiﬁc replication. In particular, we build a global data set of stock returns and characteristics to replicate 153 equity factors in 93 diﬀerent countries. Further, we develop and estimate a Bayesian model of factor replication, which can handle the multiple testing of many hypotheses, and the issue of publication bias. Our main result is that most equity factors can be replicated. Further, we show that most factors work well out-of-sample, that is, in time periods and countries diﬀerent from the ones studied in the original paper. We also show that the 153 factors can be grouped into 13 themes, most of which matter for the tangency portfolio. The third paper, Machine Learning and the Implementable Eﬃcient Frontier (co-authored with Bryan Kelly, Semyon Malamud, and Lasse Heje Pedersen), develops a framework that integrates trading-cost-aware portfolio optimization with machine learn-ing (ML). We show theoretically how to solve the optimal portfolio problem for an investor that faces trading costs when returns are predictable by a general function of security characteristics. In addition, we show to implement this solution via a machine learning methodology that learns directly about portfolio weight (rather than returns). Empirically, we ﬁnd that our method leads to signiﬁcant out-of-sample gains relative to various sophisticated benchmarks. Finally, our method gives a novel view of which security char-acteristics are economically important.

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Term Structure of Equity and Bond Yields over Business Cycles

Zeng

Zhao

2021

SSRN Journal

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Horizon Bias and the Term Structure of Equity Returns

Cited by 4 publications

References 30 publications

Duration‐Driven Returns

Duration‐Driven Returns

Essays on Empirical Asset Pricing

Term Structure of Equity and Bond Yields over Business Cycles

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