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<p>This study aims to address the criticisms of traditional mean–variance optimization (MVO), which suffers from dependence on historical data, infeasibility due to transaction costs, and the assumption of stable covariances. We introduce an alternative to both MVO and hierarchical risk parity (HRP) by proposing a method known as text-based hierarchical risk parity (TB-HRP). This innovative approach leverages financial text data, specifically from 10-K filings, to capture complex economic relationships, reduce dependence on historical prices, and adapt dynamically to the changing nature of businesses. Unlike traditional techniques that rely on past returns, TB-HRP offers several advantages, including the ability to reflect operational similarities, reduce noise in portfolio optimization, reveal unexpected relationships between companies, and provide a forward-looking perspective on risk factors. Using 10-K statements of firms in the S&amp;P 500, we apply the framework to construct a distance matrix and follow the approach similar to De Prado (J Portfolio Manag 42(4):59-69, 2016) for portfolio formation. TB-HRP can be used to enhance diversification, improve robustness, and dynamically adapt to market changes, making it a valuable tool for portfolio managers seeking to overcome the limitations of traditional optimization techniques.</p>

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Text based hierarchical risk parity (TBHRP)

  • 期刊论文

  • 发表日期：

    2025年12月27日

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  • Blake Rayfield

摘要

This study aims to address the criticisms of traditional mean–variance optimization (MVO), which suffers from dependence on historical data, infeasibility due to transaction costs, and the assumption of stable covariances. We introduce an alternative to both MVO and hierarchical risk parity (HRP) by proposing a method known as text-based hierarchical risk parity (TB-HRP). This innovative approach leverages financial text data, specifically from 10-K filings, to capture complex economic relationships, reduce dependence on historical prices, and adapt dynamically to the changing nature of businesses. Unlike traditional techniques that rely on past returns, TB-HRP offers several advantages, including the ability to reflect operational similarities, reduce noise in portfolio optimization, reveal unexpected relationships between companies, and provide a forward-looking perspective on risk factors. Using 10-K statements of firms in the S&P 500, we apply the framework to construct a distance matrix and follow the approach similar to De Prado (J Portfolio Manag 42(4):59-69, 2016) for portfolio formation. TB-HRP can be used to enhance diversification, improve robustness, and dynamically adapt to market changes, making it a valuable tool for portfolio managers seeking to overcome the limitations of traditional optimization techniques.