Asset pricing with complexity

71 Pages Posted: 6 Apr 2022 Last revised: 18 Mar 2023

See all articles by Mads Nielsen

Mads Nielsen

Utrecht University - School of Economics

Date Written: April 6, 2022

Abstract

Machine learning methods for big data trade off bias for precision in prediction. To understand the implications for financial markets, I formulate a trading model with a prediction technology where investors optimally choose a biased estimator. The model identifies a novel cost of complexity that arises endogenously. This effect makes it optimal to ignore costless signals and introduces in- and out-of-sample return predictability that is not driven by priced risk or behavioral biases. Empirically, the model can explain patterns of vanishing predictability of the equity risk premium. The model calibration is consistent with a technological shift following the rise of private computers and the invention of the internet. When allowing for heterogeneity in information between agents, complexity drives a wedge between the private and social value of data and lowers price informativeness. Estimation errors generate short-term price reversals similar to liquidity demand.

Keywords: Asset pricing, Asymmetric information, Cost of complexity, High-dimensional inference, Return predictability

JEL Classification: G11, G12, G14

Suggested Citation

Nielsen, Mads Bibow Busborg, Asset pricing with complexity (April 6, 2022). Available at SSRN: https://ssrn.com/abstract=4063721 or http://dx.doi.org/10.2139/ssrn.4063721

Mads Bibow Busborg Nielsen (Contact Author)

Utrecht University - School of Economics ( email )

Kriekenpitplein 21-22
Adam Smith Building
Utrecht, +31 30 253 7373 3584 EC
Netherlands

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