Factors That Fit the Time Series and Cross-Section of Stock Returns

Lettau, Martin; Pelger, Markus

doi:10.1093/rfs/hhaa020

Cited by 239 publications

(61 citation statements)

References 23 publications

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“…However, our empirical results suggest that the long and short leg have different weights in the SDF and this additional flexibility improves the performance of the linear model. These findings are also in line with Lettau and Pelger (2018) who extract linear factors from the extreme deciles of single sorted portfolios and show that they are not spanned by long-short factors that put equal weight on the extreme deciles of each characteristic.…”

Section: B Cross Section Of Individual Stock Returnssupporting

confidence: 88%

“…Recently, new methods have been developed to study the cross-section of returns in the linear framework but accounting for the large amount of conditioning information. Lettau and Pelger (2018) extend principal component analysis to account for no-arbitrage. They show that a no-arbitrage penalty term makes it possible to overcome the low signal-to-noise ratio problem in financial data and find the information that is relevant for the pricing kernel.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning in Asset Pricing

Chen¹,

2019

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We propose a novel approach to estimate asset pricing models for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. Our general non-linear asset pricing model is estimated with deep neural networks applied to all U.S. equity data combined with a substantial set of macroeconomic and firm-specific information. We estimate the stochastic discount factor that explains all asset returns from the conditional moment constraints implied by no-arbitrage. Our asset pricing model outperforms out-of-sample all other benchmark approaches in terms of Sharpe ratio, explained variation and pricing errors. We trace its superior performance to including the no-arbitrage constraint in the estimation and to accounting for macroeconomic conditions and non-linear interactions between firm-specific characteristics. Our generative adversarial network enforces no-arbitrage by identifying the portfolio strategies with the most pricing information. Our recurrent Long-Short-Term-Memory network finds a small set of hidden economic state processes. A feedforward network captures the non-linear effects of the conditioning variables. Our model allows us to identify the key factors that drive asset prices and generate profitable investment strategies.

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Section: B Cross Section Of Individual Stock Returnssupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Deep Learning in Asset Pricing

Chen¹,

2019

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“…Figure 16 shows the test results for the generalized correlation test for the combination of any two state outcomes of the VIX. We use a five factor model motivated by the five factors of Fama and French (2015) and Lettau and Pelger (2018). The span of the loadings drastically changes with the realization of the VIX, which confirms the previous results.…”

Section: Empirical Application To Sandp500 Stockssupporting

confidence: 83%

“…It seems that a one-factor model with state-varying loadings captures most of the pricing information, while adding more factors distorts the model. 35 See Lettau and Pelger (2018) for a discussion. 36 The Sharpe ratio is the expected return of an asset in excess of the risk-free rate normalized by its standard deviation.…”

Section: Empirical Application To Sandp500 Stocksmentioning

confidence: 99%

State-Varying Factor Models of Large Dimensions

Pelger

Xiong

2018

SSRN Journal

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This paper develops an inferential theory for state-varying factor models of large dimensions. Unlike constant factor models, loadings are general functions of some recurrent state process. We develop an estimator for the latent factors and state-varying loadings under a large cross-section and time dimension. Our estimator combines nonparametric methods with principal component analysis. We derive the rate of convergence and limiting normal distribution for the factors, loadings and common components. In addition, we develop a statistical test for a change in the factor structure in different states. We apply the estimator to U.S. Treasury yields and S&P500 stock returns. The systematic factor structure in treasury yields differs in times of booms and recessions as well as in periods of high market volatility. State-varying factors based on the VIX capture significantly more variation and pricing information in individual stocks than constant factor models.

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“…These factors are usually estimated by Principal Component Analysis (PCA). Latent PCA factors have been successfully used in economics and finance, for example for prediction and forecasting (Stock and Watson, 2002a,b), asset pricing of many assets (Lettau and Pelger, 2018b;Kelly et al, 2018), high-frequency asset return modeling (Aït-Sahalia and Xiu, 2018;Pelger, 2019), conditional risk-return and term structure analysis Ng, 2007, 2009) and optimal portfolio construction (Fan et al, 2013). 1 However, as the latent PCA factors are linear combinations of all cross-sectional units, they are usually hard to interpret.…”

Section: Introductionmentioning

confidence: 99%