Decoding Stock Market with Quant Alphas

Abstract

We give an explicit algorithm and source code for extracting expected returns for stocks from expected returns for alphas. Our algorithm altogether bypasses combining alphas with weights into "alpha combos". Simply put, we have developed a new method for trading alphas which does not involve combining them. This yields substantial cost savings as alpha combos cost hedge funds around 3% of the P&L, while alphas themselves cost around 10%. Also, the extra layer of alpha combos, which our new method avoids, adds noise and suboptimality. We also arrive at our algorithm independently by explicitly constructing alpha risk models based on position data. Forecasting stock returns with quant alphas has implications for the investment industry.

Keywords: forecasting, expected return, stock, equities, market, alpha, optimization, trading, quant, weights, asset allocation, portfolio, risk model, specific risk, regression, factor model, principal components, volatility, correlation, covariance, Sharpe ratio, position data, machine learning, source code

JEL Classification: G00