Decoding Stock Market with Quant Alphas

Journal of Asset Management 19(1) (2018) 38-48

20 Pages Posted: 10 May 2017 Last revised: 9 Feb 2018

See all articles by Zura Kakushadze

Zura Kakushadze

Quantigic Solutions LLC; Free University of Tbilisi

Willie Yu

Duke-NUS Medical School - Centre for Computational Biology

Date Written: April 25, 2017


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

Suggested Citation

Kakushadze, Zura and Yu, Willie, Decoding Stock Market with Quant Alphas (April 25, 2017). Journal of Asset Management 19(1) (2018) 38-48, Available at SSRN: or

Zura Kakushadze (Contact Author)

Quantigic Solutions LLC ( email )

680 E Main St #543
Stamford, CT 06901
United States
6462210440 (Phone)
6467923264 (Fax)


Free University of Tbilisi ( email )

Business School and School of Physics
240, David Agmashenebeli Alley
Tbilisi, 0159

Willie Yu

Duke-NUS Medical School - Centre for Computational Biology ( email )

8 College Road
Singapore, 169857

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