An analysis of machine learning risk factors and risk parity portfolio optimization

Abstract

Many academics and experts focus on portfolio optimization and risk budgeting as a topic of study. Streamlining a portfolio using machine learning methods and elements is examined, as well as a strategy for portfolio expansion that relies on the decay of a portfolio’s risk into risk factor commitments. There is a more vulnerable relationship between commonly used trademarked portfolios and neural organizations based on variables than famous dimensionality decrease strategies, as we have found. Machine learning methods also generate covariance and portfolio weight structures that are more difficult to assess. The least change portfolios outperform simpler benchmarks in minimizing risk. During periods of high instability, risk-adjusted returns are present, and these effects are amplified for investors with greater sensitivity to chance changes in returns R.

Utilizing the Mean-Variance Model to Optimize Pension Investment Portfolios: A Detailed Analysis of Key Industries

“…In financial conditions, neural networks outperformed sophisticated architectures, especially amid market volatility. [6] Modern financial tools changed conventional economies throughout the Fourth Industrial Revolution, according to Ma et al. Rapid financial and technology advances necessitate new investment approaches that embrace digital financial innovations, according to this research.At the same risk threshold, bitcoin portfolios beat traditional portfolios.…”