Dissecting Characteristics Nonparametrically

Freyberger, Joachim; Neuhierl, Andreas; Weber, Michael

doi:10.2139/ssrn.3254246

Cited by 26 publications

(40 citation statements)

References 40 publications

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“…Many models predict either the time series (Cochrane (2017)) or the crosssection (McLean and Pontiff ( 2016)) of returns. The standard approach to disentangling these models is to use advanced econometrics (Bryzgalova (2017), Chinco, Clark-Joseph, andYe (2019), Feng, Giglio, andXiu (2020), Freyberger, Neuhierl, and Weber (2020), Harvey and Liu (2020), Kozak, Nagel, and Santosh (2020), Chinco, Neuhierl, and Weber (2021)). Instead, we propose a survey-based framework to test whether investors follow the core economic logic behind a given factor model.…”

Section: Contribution To the Literaturementioning

confidence: 99%

A New Test of Risk Factor Relevance

Chinco¹,

Sussman

2022

The Journal of Finance

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Textbook models assume that investors try to insure against bad states of the world associated with specific risk factors when investing. This is a testable assumption and we develop a survey framework for doing so. Our framework can be applied to any risk factor. We demonstrate the approach using consumption growth, which makes our results applicable to most modern asset-pricing models. Participants respond to changes in the mean and volatility of stock returns consistent with textbook models, but we find no evidence that they view an asset's correlation with consumption growth as relevant to investment decisions.THE TEXTBOOK EXPLANATION FOR WHY some assets have higher expected returns than others involves "extending the principles behind fire and casualty insurance to investment portfolios (Cochrane (1999))." While investors in

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Section: Contribution To the Literaturementioning

confidence: 99%

A New Test of Risk Factor Relevance

Chinco¹,

Sussman

2022

The Journal of Finance

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“…Penalized Regressions. Many recent papers have used penalized-regression procedures to solve asset-pricing problems (Pástor, 2000;DeMiguel et al, 2009;Bryzgalova, 2017;Feng et al, 2017;Freyberger et al, 2017;Ledoit and Wolf, 2017;Chinco et al, 2018;Kozak et al, 2018b). We are using these same techniques with an entirely different goal in mind: estimating the anomaly base rate.…”

Section: Data Miningmentioning

confidence: 99%

Estimating The Anomaly Base Rate

Chinco¹,

Neuhierl²,

Weber³

2019

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The academic literature literally contains hundreds of variables that seem to predict the crosssection of expected returns. This so-called "anomaly zoo" has caused many to question whether researchers are using the right tests of statistical significance. But, here's the thing: even if researchers use the right tests, they will still draw the wrong conclusions from their econometric analyses if they start out with the wrong priors---i.e., if they start out with incorrect beliefs about the ex ante probability of encountering a tradable anomaly.So, what are the right priors? What is the correct anomaly base rate?We develop a first way to estimate the anomaly base rate by combining two key insights: 1) Empirical-Bayes methods capture the implicit process by which researchers form priors based on their past experience with other variables in the anomaly zoo. 2) Under certain conditions, there is a one-to-one mapping between these prior beliefs and the best-fit tuning parameter in a penalized regression. We study trading-strategy performance to verify our estimation results. If you trade on two variables with similar one-month-ahead return forecasts in different anomaly-base-rate regimes (low vs. high), the variable in the low base-rate regime consistently underperforms the otherwise identical variable in the high base-rate regime.

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“…Other examples includeFreyberger et al (2017),Kozak et al (2017),Kelly et al (2017), andFeng et al (2017).…”

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confidence: 99%