Portfolio Optimization or the process of giving optimal weights to assets in a financial portfolio is a fundamental problem in Financial Engineering. There are many approaches one can follow — for passive investments the most common is liquidity based weighting or market capitalisation weighting. If one has no view on investment performance one follows equal weighting. Following the Capital Asset Pricing Model, the most elegant solution is the Markovitz Optimal portfolio — where risk-averse investors try to maximize return based on their level of risk .
Be it for the fundamental investor, or the quantitative trader, portfolio optimization is imperative for successful portfolio management
However there is no one solution to this problem. It is essentially a problem where an agent that can best learn and adapt to the market environment will deliver best results. This is the essence of any Reinforcement Learning problem. Reinforcement Learning has delivered excellent results in problems with similar premise like video games and board games where they have far outperformed humans. Talk to us about how we are using Deep Learning to optimise multi asset portfolios and create portfolios that outperform