A decision-tree model trained on efficient-frontier polynomial coefficients produces monthly market-direction forecasts that are converted, via inverse Mills ratio and CAPM, into conditional asset-return estimates for portfolio optimization and shown to beat baselines on sector ETFs.
Forecasting Tangency Portfolios and Investing in the Minimum Euclidean Distance Portfolio to Maximize Out-of-Sample Sharpe Ratios
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Using Machine Learning to Forecast Market Direction with Efficient Frontier Coefficients
A decision-tree model trained on efficient-frontier polynomial coefficients produces monthly market-direction forecasts that are converted, via inverse Mills ratio and CAPM, into conditional asset-return estimates for portfolio optimization and shown to beat baselines on sector ETFs.