Leveraging Micro-Alphas for Predictive Edge in Equity Risk Premiums: A Machine Learning Approach

White paper authors: Blair Hull, Petra Bakosova, Francois Cocquemas, Euan Sinclair, Petri Fast

Individual market signals may offer limited information on their own. This white paper examines how combining many weak predictive signals, or micro-alphas, can inform research into the equity risk premium.

What the paper explores

The authors study an elastic net approach, which combines LASSO and ridge regularization. The paper describes how this framework addresses correlated predictors and variable selection when working with financial data.

Combining information from multiple weak predictive signals.
Using market, macroeconomic, sentiment, variance and seasonal inputs.
Evaluating a model with an expanding-window, walk-forward methodology.

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Download the white paper for the authors’ methodology, historical analysis, results and references. The findings describe the research presented in the paper and should be read in that context.

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This article introduces the linked research. Historical model results are not a guarantee of future outcomes and should not be interpreted as current HTUS fund performance.

Disclaimer

This document does not constitute advice or a recommendation or offer to sell or a solicitation to deal in any security or financial product. It is provided for information purposes only and on the understanding that the recipient has sufficient knowledge and experience to be able to understand and make their own evaluation of the proposals and services described herein, any risks associated therewith and any related legal, tax, accounting, or other material considerations. To the extent that the reader has any questions regarding the applicability of any specific issue discussed above to their specific portfolio or situation, prospective investors are encouraged to contact HTAA or consult with the professional advisor of their choosing.

Except where otherwise indicated, the information contained in this article is based on matters as they exist as of the date of preparation of such material and not as of the date of distribution of any future date. Recipients should not rely on this material in making any future investment decision.

Research

Leveraging Micro-Alphas for Predictive Edge in Equity Risk Premiums: A Machine Learning Approach

What the paper explores

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Disclaimer

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