Investment Portfolio with Convex Optimization and Risk Adjustment Using Multi-Factor Model and Multi-Armed Bandit Algorithm

This paper examines the creation of investment portfolios through convex optimization, multifactor models, and the multi-armed bandit (MAB) algorithms, focusing on the KL-UCB strategy to optimize decisions in uncertain settings. It explores the impact of systematic risk factors using the Fama-French three-factor model, estimating the influence of market, size, and value premiums via linear regression. The use of Monte Carlo simulation is detailed for generating potential asset allocations and calculating their expected returns, volatility, and Sharpe ratios. The optimize minimize function from the SciPy library is employed to construct an efficient frontier and determine optimal asset allocation, aiming to maximize returns or minimize volatility across various risk levels. The findings suggest that the strategy of dynamic weight adjustments combined with the KL-UCB algorithm enhances portfolio returns, particularly during market volatility. The research also reveals a portfolio inclination towards large-cap growth stocks due to the negative impacts of size and value premiums. It concludes that dynamic weight adjustment strategies offer significant potential in optimizing portfolio performance in complex market conditions, though leveraging increases risk and should be carefully managed according to investor risk tolerance.