Data mining and related topics
Saman Haratizadeh; Fatemeh Rezaee
Abstract
Purpose: Selection of the best stocks for the portfolio as well as allocating the optimal amount of capital per stock in the portfolio are serious challenges in investing in the stock market. The use of machine learning capacities in the process of optimal capital allocation among portfolio assets has ...
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Purpose: Selection of the best stocks for the portfolio as well as allocating the optimal amount of capital per stock in the portfolio are serious challenges in investing in the stock market. The use of machine learning capacities in the process of optimal capital allocation among portfolio assets has received less attention and usually, the same weight is assigned to portfolio stocks or traditional risk assessment methods are used to divide capital between portfolio stocks. The common disadvantage of these methods is that they all use simple and inflexible mechanisms to estimate the performance of a set. The purpose of this paper is to show for the first time, that machine learning can be used to create a more effective mechanism for estimating performance, which leads to a more efficient allocation of capital to portfolio stocks.Methodology: Our proposed framework, uses two predictive models based on machine learning. In the first step, stocks historical information is used in a return forecasting model, then based on the predicted returns, the appropriate stocks of the portfolio are selected. In the second step, a separate forecasting model predicts portfolio returns by taking into account both the forecasted returns in the first model and the expected risk of the stocks. At the end based on the predicted return of the numerous random portfolios, the appropriate weight for each asset is selected.Findings: Comparing the returns of adjusted portfolios with this model and adjusted portfolios with other portfolio optimization methods shows the superiority of the proposed model.Originality/Value: In this paper, by using machine learning models, the process of selecting the appropriate stock of the portfolio and allocating capital among the candidate stocks is done optimally.
Data Envelopment Analyses
Mohammad Reza Alirezaee; Fatemeh Rakhshan; Bahareh Banaye khoyi
Abstract
One of the problems in portfolio selection, is choosing a stock with conflicting and incomparable objectives such as return and risk. DEA cross efficiency is one of the most useful tools in assessing performance and prioritize a number of firms that makes it possible to determine efficient units in portfolio ...
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One of the problems in portfolio selection, is choosing a stock with conflicting and incomparable objectives such as return and risk. DEA cross efficiency is one of the most useful tools in assessing performance and prioritize a number of firms that makes it possible to determine efficient units in portfolio selection from different industries. Although cross efficiency is an approach for evaluating performance, it application is improved in portfolio selection. The method used in this research, calculates the (average) cross efficiency scores and considers its changes and then incorporates two statistics of cross efficiency into the mean-variance (MV) formulation of portfolio selection. This method has two advantages: One is selection of portfolios well-diversified in terms of their performance on multiple evaluation criteria, and the other is alleviation of the so-called ‘‘ganging together’’ phenomenon of DEA cross-efficiency evaluation in portfolio selection. This procedure is applied on stock portfolio selection in the Iranian stock market consist of 20 reputable companies and efficiency changes with causes over this period is examined. It is demonstrated in this paper that the selected portfolio yields higher risk-adjusted returns than two stock market index for a 9-year sample period.