Optimizing Fixed-Income and Commodity Asset Allocation with Machine Learning
Open Access DepositedThis Praxis implemented and evaluated a machine-learning-based framework for dynamic asset allocation in fixed income and commodity markets. Typical models like the Capital Asset Pricing Model (CAPM) and the Markowitz Mean-Variance Theory are based on static assumptions which hinder their implementation when the market behavior is non-linear and when structural changes occur. To solve these problems, this praxis comes up with a model that utilizes LSTM networks to make asset price predictions and a walk-forward backtesting architecture that simulates real-world trading with constraints such as transaction costs, slippage, and position limits.Based on the historic data from 2015 to 2022, the LSTM-based strategy led to a Sharpe ratio of 1.07, a System Quality Number (SQN) of 3.94, an annualized return of 10.998%, and a maximum drawdown of 7.73%. Compared to this, the traditional ARIMA based strategy had a Sharpe ratio of 1.056 and an annualized return of 5.995%. And a transformer-based strategy yielded a slightly higher annualized return of 7.74% with a Sharpe ratio of 0.64. Also, the Mean Variance Optimization method gave a Sharpe ratio of 0.661, an SQN of 1.94, and a 20% drawdown. These results show that the model not only generates a high risk-adjusted return but also adapts to the dynamic market conditions which include 2019 trade conflicts, the 2020 COVID-19 pandemic, and the 2022 Russian invasion of Ukraine. Thus, the model can be asserted as a trustworthy tool for asset managers who are in search of asset allocation approaches that assure both higher return and effective risk management during dynamic market environments.
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