Log In Sign Up

Machine Learning Classification Methods and Portfolio Allocation: An Examination of Market Efficiency

by   Yang Bai, et al.

We design a novel framework to examine market efficiency through out-of-sample (OOS) predictability. We frame the asset pricing problem as a machine learning classification problem and construct classification models to predict return states. The prediction-based portfolios beat the market with significant OOS economic gains. We measure prediction accuracies directly. For each model, we introduce a novel application of binomial test to test the accuracy of 3.34 million return state predictions. The tests show that our models can extract useful contents from historical information to predict future return states. We provide unique economic insights about OOS predictability and machine learning models.


page 1

page 2

page 3

page 4


Economic Recession Prediction Using Deep Neural Network

We investigate the effectiveness of different machine learning methodolo...

AlphaMLDigger: A Novel Machine Learning Solution to Explore Excess Return on Investment

How to quickly and automatically mine effective information and serve in...

Quantum Machine Learning for Radio Astronomy

In this work we introduce a novel approach to the pulsar classification ...

Applying economic measures to lapse risk management with machine learning approaches

Modeling policyholders lapse behaviors is important to a life insurer si...

Return to Bali

This paper gives an overview of the project Return to Bali that seeks to...

Propensity-to-Pay: Machine Learning for Estimating Prediction Uncertainty

Predicting a customer's propensity-to-pay at an early point in the reven...

Machine Learning for Yield Curve Feature Extraction: Application to Illiquid Corporate Bonds (Preliminary Draft)

This paper studies the application of machine learning in extracting the...