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101 Formulaic Alphas

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  • Zura Kakushadze

Abstract

We present explicit formulas - that are also computer code - for 101 real-life quantitative trading alphas. Their average holding period approximately ranges 0.6-6.4 days. The average pair-wise correlation of these alphas is low, 15.9%. The returns are strongly correlated with volatility, but have no significant dependence on turnover, directly confirming an earlier result based on a more indirect empirical analysis. We further find empirically that turnover has poor explanatory power for alpha correlations.

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  • Zura Kakushadze, 2016. "101 Formulaic Alphas," Papers 1601.00991, arXiv.org, revised Mar 2016.
    • Handle: RePEc:arx:papers:1601.00991
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    References listed on IDEAS

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    1. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
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    Cited by:

    1. Zura Kakushadze & Willie Yu, 2017. "Dead Alphas as Risk Factors," Papers 1709.06641, arXiv.org.
    2. Jie Fang & Jianwu Lin & Shutao Xia & Yong Jiang & Zhikang Xia & Xiang Liu, 2020. "Neural Network-based Automatic Factor Construction," Papers 2008.06225, arXiv.org, revised Oct 2020.
    3. Jie Fang & Shutao Xia & Jianwu Lin & Yong Jiang, 2019. "Automatic Financial Feature Construction," Papers 1912.06236, arXiv.org, revised Oct 2020.
    4. Zura Kakushadze & Willie Yu, 2017. "Notes on Fano Ratio and Portfolio Optimization," Papers 1711.10640, arXiv.org, revised Apr 2018.
    5. Shuo Yu & Hongyan Xue & Xiang Ao & Feiyang Pan & Jia He & Dandan Tu & Qing He, 2023. "Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning," Papers 2306.12964, arXiv.org.
    6. Chi Chen & Li Zhao & Wei Cao & Jiang Bian & Chunxiao Xing, 2020. "Trimming the Sail: A Second-order Learning Paradigm for Stock Prediction," Papers 2002.06878, arXiv.org.
    7. Zura Kakushadze & Willie Yu, 2017. "Open Source Fundamental Industry Classification," Papers 1706.04210, arXiv.org, revised Dec 2017.
    8. Xiao Yang & Weiqing Liu & Dong Zhou & Jiang Bian & Tie-Yan Liu, 2020. "Qlib: An AI-oriented Quantitative Investment Platform," Papers 2009.11189, arXiv.org.
    9. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    10. Yongli Li & Tianchen Wang & Baiqing Sun & Chao Liu, 2022. "Detecting the lead–lag effect in stock markets: definition, patterns, and investment strategies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-36, December.
    11. Zura Kakushadze & Willie Yu, 2018. "Dead alphas as risk factors," Journal of Asset Management, Palgrave Macmillan, vol. 19(2), pages 110-115, March.
    12. Ahmed M. Khedr & Ifra Arif & Pravija Raj P V & Magdi El‐Bannany & Saadat M. Alhashmi & Meenu Sreedharan, 2021. "Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(1), pages 3-34, January.
    13. Jun Lu & Joerg Osterrieder, 2022. "Feature Selection via the Intervened Interpolative Decomposition and its Application in Diversifying Quantitative Strategies," Papers 2209.14532, arXiv.org.
    14. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.

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