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<p>This paper explores the use of Second-Order Stochastic Dominance and Robust Stochastic Dominance models in dynamic portfolio optimization, focusing on how investment frequency and bankroll management affect portfolio performance. A simulation based on historical data of the S&amp;P&#xa0;500 index and its stocks evaluates several strategies by comparing end budget, average daily return, standard deviation, and Value at Risk. The results show that investment frequency is the key factor: more frequent rebalancing leads to higher returns and lower risk. Bankroll management thresholds, on the other hand, were not found to have a statistically significant effect, and no major differences were found between the SSD and RSSD models. A limitation of this study is that transaction costs such as taxes and trading fees are not included, which could change the real-world results, especially for strategies with very frequent rebalancing. Still, the findings give a clear overview of how dynamic strategies behave compared to passive investing, and suggest that frequent portfolio restructuring can be promising. Future work should include transaction costs and other risk management tools to better guide practical investment decisions.</p>

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Dynamic portfolio optimization under stochastic dominance: impact of investment frequency and bankroll management

  • 期刊论文

  • 发表日期：

    2025年10月7日

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  • Jakub Neugebauer

摘要

This paper explores the use of Second-Order Stochastic Dominance and Robust Stochastic Dominance models in dynamic portfolio optimization, focusing on how investment frequency and bankroll management affect portfolio performance. A simulation based on historical data of the S&P 500 index and its stocks evaluates several strategies by comparing end budget, average daily return, standard deviation, and Value at Risk. The results show that investment frequency is the key factor: more frequent rebalancing leads to higher returns and lower risk. Bankroll management thresholds, on the other hand, were not found to have a statistically significant effect, and no major differences were found between the SSD and RSSD models. A limitation of this study is that transaction costs such as taxes and trading fees are not included, which could change the real-world results, especially for strategies with very frequent rebalancing. Still, the findings give a clear overview of how dynamic strategies behave compared to passive investing, and suggest that frequent portfolio restructuring can be promising. Future work should include transaction costs and other risk management tools to better guide practical investment decisions.