Dead Alphas as Risk Factors

Journal of Asset Management 19(2) (2018) 110-115, Invited Editorial

9 Pages Posted: 1 Aug 2017 Last revised: 26 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: July 31, 2017

Abstract

We give an explicit algorithm and source code for extracting equity risk factors from dead (a.k.a. "flatlined" or "hockey-stick") alphas and using them to improve performance characteristics of good (tradable) alphas. In a nutshell, we use dead alphas to extract directions in the space of stock returns along which there is no money to be made (and/or those bets are too volatile). In practice the number of dead alphas can be large compared with the number of underlying stocks and care is required in identifying the aforesaid directions.

Keywords: Expected Return, Stock, Equities, Market, Alpha, Optimization, Trading, Quant, Weights, Asset Allocation, Portfolio, Risk Model, Risk Factor, 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, Dead Alphas as Risk Factors (July 31, 2017). Journal of Asset Management 19(2) (2018) 110-115, Invited Editorial. Available at SSRN: https://ssrn.com/abstract=3011446 or http://dx.doi.org/10.2139/ssrn.3011446

Zura Kakushadze (Contact Author)

Quantigic Solutions LLC ( email )

1127 High Ridge Road #135
Stamford, CT 06905
United States
6462210440 (Phone)
6467923264 (Fax)

HOME PAGE: http://www.linkedin.com/in/zurakakushadze

Free University of Tbilisi ( email )

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

Willie Yu

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

8 College Road
Singapore, 169857
Singapore

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