State-Varying Factor Models of Large Dimensions

Pelger, Markus; Xiong, Ruoxuan

doi:10.2139/ssrn.3109314

Cited by 12 publications

(6 citation statements)

References 57 publications

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“…Pelger (2019) combines highfrequency data with PCA to capture non-parametrically the time-variation in factor risk. Pelger and Xiong (2018b) show that macroeconomic states are relevant to capture time-variation in PCAbased factors. Freyberger et al (2017) use Lasso selection methods to approximate the SDF as a non-linear function of characteristics but rule out interaction effects.…”

Section: Introductionmentioning

confidence: 95%

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: Introductionmentioning

confidence: 95%

Deep Learning in Asset Pricing

Chen¹,

2019

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“…3 Generalized correlation (also called canonical correlation) measures how close two vector spaces are. It has been studied by Anderson (1958) and applied in large-dimensional factor models (Bai and Ng, 2006;Pelger, 2019;Andreou et al, 2017;Pelger and Xiong, 2018). 4 In simulations, we verify that the lower bound has good finite sample properties.…”

Section: Introductionmentioning

confidence: 63%

Interpretable Proximate Factors for Large Dimensions

Pelger

Xiong

2018

SSRN Journal

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“…It is an open question whether we can extend this estimation approach to accommodate time-variation in the characteristics. Pelger and Xiong (2019) instead let the factor loadings be functions of an observable state variable. They consider the model:…”

Section: Testing Asset Pricing Restrictionsmentioning

confidence: 99%

Estimation of large dimensional conditional factor models in finance

Gagliardini

Ossola

Scaillet

2020

Handbook of Econometrics

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This chapter provides an econometric methodology for inference in large-dimensional conditional factor models in finance. Changes in the business cycle and asset characteristics induce time variation in factor loadings and risk premia to be accounted for. The growing trend in the use of disaggregated data for individual securities motivates our focus on methodologies for a large number of assets. The beginning of the chapter outlines the concept of approximate factor structure in the presence of conditional information, and develops an arbitrage pricing theory for large-dimensional factor models in this framework. Then we distinguish between two different cases for inference depending on whether factors are observable or not. We focus on diagnosing model specification, estimating conditional risk premia, and testing asset pricing restrictions under increasing cross-sectional and time series dimensions. At the end of the chapter, we review some of the empirical findings and contrast analysis based on individual stocks and standard sets of portfolios. We also discuss the impact on computing time-varying cost of equity for a firm, and summarize differences between results for developed and emerging markets in an international setting.

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State-Varying Factor Models of Large Dimensions

Cited by 12 publications

References 57 publications

Deep Learning in Asset Pricing

Deep Learning in Asset Pricing

Interpretable Proximate Factors for Large Dimensions

Estimation of large dimensional conditional factor models in finance

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