Concept drift mining of portfolio selection factors in stock market

2015 ◽  
Vol 14 (6) ◽  
pp. 444-455 ◽  
Author(s):  
Yong Hu ◽  
Kang Liu ◽  
Xiangzhou Zhang ◽  
Kang Xie ◽  
Weiqi Chen ◽  
...  
Keyword(s):  
Stock Market ◽  
Portfolio Selection ◽  
Concept Drift ◽  
Selection Factors

scholarly journals Decision Support System for Stock Portfolio Selection Using Artificial Intelligence and Machine Learning

2021 ◽  
Vol 26 (1) ◽  
pp. 87-93
Author(s):  
Sandeep Patalay ◽  
Madhusudhan Rao Bandlamudi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Support ◽  
Stock Market ◽  
Decision Support System ◽  
Portfolio Selection ◽  
Support System ◽  
Financial Analysis ◽  
Traditional Approach ◽  
Stock Portfolio

Investing in stock market requires in-depth knowledge of finance and stock market dynamics. Stock Portfolio Selection and management involve complex financial analysis and decision making policies. An Individual investor seeking to invest in stock portfolio is need of a support system which can guide him to create a portfolio of stocks based on sound financial analysis. In this paper the authors designed a Financial Decision Support System (DSS) for creating and managing a portfolio of stock which is based on Artificial Intelligence (AI) and Machine learning (ML) and combining the traditional approach of mathematical models. We believe this a unique approach to perform stock portfolio, the results of this study are quite encouraging as the stock portfolios created by the DSS are based on strong financial health indices which in turn are giving Return on Investment (ROI) in the range of more than 11% in the short term and more than 61% in the long term, therefore beating the market index by a factor of 15%. This system has the potential to help millions of Individual Investors who can make their financial decisions on stocks and may eventually contribute to a more efficient financial system.

DEA portfolio selection in Malaysian stock market

2012 ◽  
Author(s):  
Md Khairu Amin Ismail ◽  
Nik Muhammad Naziman Abd Rahman ◽  
Norhana Salamudin ◽  
Badrul Hisham Kamaruddin
Keyword(s):  
Stock Market ◽  
Portfolio Selection

scholarly journals Application of Markowitz and Sharpe Models in Nepalese Stock

2007 ◽  
Vol 3 (1) ◽  
pp. 18-35 ◽  
Author(s):  
Dr. Rajan Bahadur Paudel ◽  
Sujan Koirala
Keyword(s):  
Stock Market ◽  
Portfolio Selection ◽  
Optimal Portfolios ◽  
Half Century ◽  
Business Studies

ABSTRACT The purpose of this article is to test whether or not Markowitz and Sharpe models of portfolio selection offer better investment alternatives to Nepalese investors. It has been done by applying those models in a sample of 30 stocks traded in Nepalese stock market. The study finds that the application of these elementary models developed about a half century ago offer better options for making decision in the choice of optimal portfolios in Nepalese stock market. Journal of Nepalese Business Studies 2006/III/1 pp. 18-35

scholarly journals Seleção de carteiras ótimas sob restrições nas normas dos vetores de alocação: uma avaliação empírica com dados da BM&FBOVESPA

2015 ◽  
Vol 13 (3) ◽  
pp. 504
Author(s):  
Paulo Ferreira Naibert ◽  
João Caldeira
Keyword(s):  
Stock Market ◽  
Covariance Matrix ◽  
Portfolio Selection ◽  
Stock Exchange ◽  
Minimum Variance ◽  
Average Risk ◽  
Short Sale ◽  
Performance Indexes ◽  
Out Of Sample ◽  
Minimum Variance Portfolio

In this paper, we study the problem of minimum variance portfolio selection based on a recent methodology for portfolio optimization restricting the allocation vector proposed by Fan et al. (2012). To achieve this, we consider different conditional and unconditional covariance matrix estimators. The main contribution of this paper is one of empirical nature for the brazilian stock market. We evaluate out of sample performance indexes of the portfolios constructed for a set of 61 different stocks traded in the São Paulo stock exchange (BM&FBovespa). The results show that the restrictions on the norms of the allocation vector generate substantial gains in relation to the no short-sale portfolio, increasing the average risk-adjusted return (larger Sharpe Ratio) and lowering the portfolio turnover.

iTrade

2015 ◽  
Vol 11 (1) ◽  
pp. 66-83 ◽  
Author(s):  
Yong Hu ◽  
Xiangzhou Zhang ◽  
Bin Feng ◽  
Kang Xie ◽  
Mei Liu
Keyword(s):  
Stock Market ◽  
Large Scale ◽  
Concept Drift ◽  
Composite Index ◽  
Performance Comparison ◽  
Mobile App ◽  
Data Driven ◽  
Support Vector ◽  
Individual Investors ◽  
Chinese Stock Market

Among all investors in the Chinese stock market, more than 95% are non-professional individual investors. These individual investors are in great need of mobile apps that can provide professional and handy trading analysis and decision support everywhere. However, financial data is challenging to analyze because of its large-scale, non-linear and noisy characteristics in a varying stock environment. This paper develops a Mobile Data-Driven Stock Trading System (iTrade), which is a mobile app system based on Client-Server architecture and various data mining techniques. The iTrade is characterized by 1) a data-driven intelligent learning model, which can provide further insight compared to empirical technical analysis, 2) a concept drift adaptation process, which facilitates the model adaptation to market structure changes, and 3) a rigorous benchmark analysis, including the Buy-and-Hold strategy and the strategies of three world-famous master investors (e.g., Warren E. Buffett). Technologies used in iTrade include the Least Absolute Shrinkage and Selection Operator (Lasso) algorithm, Support Vector Machine (SVM) and risk-adjusted portfolio optimization. An application case of iTrade is presented, which is based on a seven-year (2005-2011) back-testing. Evaluation results indicated that iTrade could gain much higher cumulative return compared to the benchmark (Shanghai Composite Index). To the best of our knowledge, this is the first study and mobile app system that emphasizes and investigates the concept drift phenomenon in stock market, as well as the performance comparison between data-driven intelligent model and strategies of master investors.

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