Optimization of Portfolio Composition in Python

Abstract

The aim of the thesis is to find the optimal portfolio by different portfolio optimization models using Python in short term. In this thesis, we select 30 constituent stocks of the Dow Jones Industrial Average Index. We describe the Python basics, portfolio optimization and portfolio optimization models, including Naive strategy and Markowitz Mean-Variance model. Besides, we analyze the performance of the two models in in-sample period and out-of-sample period and compare the results.

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Subject(s)

portfolio optimization, naive strategy, efficient frontier, Markowitz mean-variance model, Sharpe ratio, maximum drawdown

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