Decoding stock market with quant alphas

Zura Kakushadze () and Willie Yu ()
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Zura Kakushadze: Quantigic® Solutions LLC
Willie Yu: Duke-NUS Medical School

Journal of Asset Management, 2018, vol. 19, issue 1, No 5, 38-48

Abstract: 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 [This is the last paper in the trilogy, which contains “Factor Models for Alpha Streams” (Kakushadze in J Invest Strateg 4(1): 83–109, 2014) and “How to Combine a Billion Alphas” (Kakushadze and Yu in J Asset Manag 18(1): 1–49, 2017a)]. Forecasting stock returns with quant alphas has implications for the investment industry.

Keywords: Expected return; Alpha; Stock; Risk model; Machine learning; Source code (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1057/s41260-017-0059-2

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