Asset pricing with neural networks: Significance tests

Hasan Fallahgoul, Vincentius Franstianto and Xin Lin

Journal of Econometrics, 2024, vol. 238, issue 1

Abstract: This study proposes a novel hypothesis test for evaluating the statistical significance of input variables in multi-layer perceptron (MLP) regression models. Theoretical foundations are established through consistency results and estimation rate analysis using the sieves method. To validate the test’s performance in complex and realistic settings, an extensive Monte Carlo simulation is conducted. Results of the simulation reveal that the test has a high power and low rate of false positives, making it a powerful tool for detecting true effects in data. The test is further applied to identify the most influential predictors of equity risk premiums, with results indicating that only a small number of characteristics have statistical significance and all macroeconomic predictors are insignificant at the 1% level. These findings are consistent across a variety of neural network architectures.

Keywords: Asset Pricing; Risk Premium; Neural Networks; Variable Significance Test (search for similar items in EconPapers)
JEL-codes: C1 C5 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:238:y:2024:i:1:s0304407623002907

DOI: 10.1016/j.jeconom.2023.105574

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