I made a project on finding the optimal weight allocation for a portfolio based on Bayesian Approach.
Most of the approaches to do so are very vague in nature. They simply plug in the data into the frequentist estimators which sometimes lead to unbounded risks.
Here I tried to use Bayesian Approach called MELO which has bounded risks and maximizes Sharpe Ratios on both Global Minimum Variance and Tangency Portfolio Problems.
Little bit tweaking like changing the portfolio stock tickers and the data collection and testing timeframe in the code, you can maybe apply it into your own portfolio stocks.
I made a project on finding the optimal weight allocation for a portfolio based on Bayesian Approach. Most of the approaches to do so are very vague in nature. They simply plug in the data into the frequentist estimators which sometimes lead to unbounded risks.
Here I tried to use Bayesian Approach called MELO which has bounded risks and maximizes Sharpe Ratios on both Global Minimum Variance and Tangency Portfolio Problems.
Little bit tweaking like changing the portfolio stock tickers and the data collection and testing timeframe in the code, you can maybe apply it into your own portfolio stocks.