Artificial Intelligence applied to the Stock Market: AI for Portfolio Optimization
How about optimizing your portfolio with AI? But wait, hasn’t there been a mathematical method for optimizing portfolios around for some years?
Right, it’s called the Modern portfolio theory (MPT) by economist Harry Markowitz, introduced in a 1952 essay, for which he was later awarded a Nobel Memorial Prize in Economic Sciences.
The simple idea of the model is diversification in investing: owning different kinds of financial assets is less risky than owning only one type. Its key insight is that an asset's risk and return should not be assessed by itself, but by how it contributes to a portfolio's overall risk and return.
So, what’s new in the field? And how can we make it AI?
As the MPT and its model of financial markets does not match the real world in many ways, there have been many critics about it:
The risk, return, and correlation measures used by MPT all are statistical statements about the future.
Such mathematical risk measurements often cannot capture the true statistical features of the risk and return and fail to take account of new circumstances that did not exist when the historical data were generated.
Financial AI methods make it possible to evaluate both historical and new data and improve theories about risk, return, investor behavior, and asset allocation.
As machine learning can process a huge amount of data and extract patterns from the data, it greatly exceeds the limits of a traditional mathematical approach and can spot the nuances a human might miss.
Happy investing!