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    Home»Machine Learning»Kelly Criterion vs. Mean-Variance Optimization: A Practical Portfolio Allocation Study | by Farid Soroush, Ph.D. | May, 2025
    Machine Learning

    Kelly Criterion vs. Mean-Variance Optimization: A Practical Portfolio Allocation Study | by Farid Soroush, Ph.D. | May, 2025

    Team_AIBS NewsBy Team_AIBS NewsMay 8, 2025No Comments1 Min Read
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    Three property have been chosen as representatives of diversified exposures:

    • SPY (S&P 500 ETF)
    • QQQ (Nasdaq 100 ETF)
    • AAPL (Apple Inc.)

    Day by day adjusted shut costs have been collected, and log-returns have been computed. The next strategies have been carried out:

    1. Kelly Criterion:

    Allocates weights based mostly on the inverse covariance matrix of log returns and their anticipated imply:

    This method maximizes geometric development however doesn’t account for short-term volatility.

    2. Fractional Kelly:

    To mitigate the volatility and overfitting threat of full Kelly, we simulate a 50% scaled model — a typical follow in hedge fund threat administration.

    3. Imply-Variance Optimization:

    Traditional Markowitz framework, fixing for the portfolio that maximizes the Sharpe ratio below long-only constraints.

    4. Equal Weight:

    A naive however strong benchmark, allocating equally throughout all property.



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