چکیده مقاله
Finding the best combination of buying weights of given portfolios at a certain time is the final goal in portfolio optimization problems Markowitz showed that diversifying investment on portfolios reaches to a higher amount of return rather than one source of investment This study focuses on predicting the future efficient pattern of portfolio and picking the final investment strategy Using machine learning algorithms for classifying the efficient and non efficient portfolios and finally using the mean variance model for obtaining the final weights of opted portfolios is the main purpose of this study The implementation results showed that taking advantages of classifiers has a bright role in making the old and popular economical models for investment work more efficient Two different classifiers called Support Vector Machine SVM and Extreme Learning Machine ELM are used and compared to each other The use of classification has two major benefits first it classifies stock with the ability to reach a certain gain in the next day and second it reduces the quadratic programming search area by removing non efficient stocks from available portfolios and leads the algorithm to find the global solution faster Moreover, a supervised dimension reduction method called Negative Matrix Factorization NMF is used to see if some features are redundant or causing any conflict in classification The results show that the model works better without NMF dimension reduction
کلیدواژهها
نویسندگان
Niloofar Jazayeri
School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran;
Hedieh Sajedi
School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran;
شیوه ارجاع
Jazayeri, Niloofar and Sajedi, Hedieh,1398,Portfolio Selection Using Machine Learning Algorithms,3rd International Conference on Soft Computing,Rudsar
ارائهشده در
مجموعه مقالات سومین کنفرانس بین المللی محاسبات نرم29 آبان 1398 · رودسر