Efficient Portfolio Management using TOPSIS and Ada-Boost

摘要

The nature of the stock market is random and uncertain and therefore it is difficult to make accurate decisions in stock trading. With this paper we propose a model which can select stocks effectively in the US stock market by feature extraction from data provided by the Quantopian platform. Our approach consisted of 17 features of 4 different domains. To determine the importance of each feature Ada-boost classifier was use. Then the topsis method was applied over 1500 stocks from the US stock market. After the applying the TOPSIS method Ideal solutions and Worst solutions were generated. Using those values all the stocks were given a performance score, which was used in selecting the stocks for the ideal portfolio. Our overall approach was to use Ada-boost to find the weights of each of the features and then apply TOPSIS to select the best stocks.