Stock Portfolio Optimization with Using a New Hybrid Evolutionary Algorithm Based on ICA and GA: Recursive-ICA-GA (Case Study of Tehran Stock Exchange)

2012 ◽  
Author(s):  
Mostafa Emami ◽  
Amrollah Amini ◽  
Alireza Emami
Keyword(s):  
Evolutionary Algorithm ◽  
Portfolio Optimization ◽  
Stock Exchange ◽  
Hybrid Evolutionary Algorithm ◽  
Stock Portfolio

US Financial Crisis Critique and the Statistical Predictability of a NYSE Portfolio

2012 ◽  
Vol 1 (3) ◽  
pp. 25-44
Author(s):  
Gerry Wymar
Keyword(s):  
New York ◽  
Financial Crisis ◽  
Stock Exchange ◽  
Complex Polynomial ◽  
Portfolio Performance ◽  
The Us ◽  
Market Indexes ◽  
Stock Portfolio ◽  
Us Financial Crisis

This study’s purpose is to review investment practitioner accounts describing the causes and effects of the global financial crises, with a focus of the US financial crisis. A critical gap in the literature was found: the lack of an independent indicator that could do forecast a market upturn or downturn at least a week in advance to provide sufficient lead time for hedging a stock portfolio before a crash. A sample of 95 high performing companies listed on the New York Stock Exchange (NYSE) was used as a multiyear case study. Publicly available market indexes such as Mood’s, Standards and Poor’s (S&P, and others, were tested as independent factors to explain the behavior of the case study stock portfolio performance. Correlation, regression (simple, multiple, stepwise, surface response) and ANOVA (with T-tests) were used to analyze 817 days of returns during the 2008-2011 period of the US financial crisis. A complex polynomial nonlinear equation was developed which could predict the behavior of the case study portfolio five days in advance.

US Financial Crisis Critique and the Statistical Predictability of a NYSE Portfolio

2013 ◽  
pp. 259-279
Author(s):  
Gerry Wymar
Keyword(s):  
Financial Crisis ◽  
Stock Exchange ◽  
Complex Polynomial ◽  
Portfolio Performance ◽  
High Performing ◽  
Critical Gap ◽  
The Us ◽  
Stock Portfolio ◽  
Us Financial Crisis

This study’s purpose is to review investment practitioner accounts describing the causes and effects of the global financial crises, with a focus of the US financial crisis. A critical gap in the literature was found: the lack of an independent indicator that could do forecast a market upturn or downturn at least a week in advance to provide sufficient lead time for hedging a stock portfolio before a crash. A sample of 95 high performing companies listed on the New York Stock Exchange (NYSE) was used as a multiyear case study. Publicly available market indexes such as Mood's, Standards and Poor's (S&P, and others, were tested as independent factors to explain the behavior of the case study stock portfolio performance. Correlation, regression (simple, multiple, stepwise, surface response) and ANOVA (with T-tests) were used to analyze 817 days of returns during the 2008-2011 period of the US financial crisis. A complex polynomial nonlinear equation was developed which could predict the behavior of the case study portfolio five days in advance.

scholarly journals Portfolio Optimization Based on Clustering in Indonesia Stock Exchange: A Case Study of The Index LQ45

2021 ◽  
Vol 1 (1) ◽  
pp. 59-70
Author(s):  
Bakti Siregar ◽  
F. Anthon Pangruruk
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Portfolio Optimization ◽  
Stock Exchange ◽  
Minimum Variance ◽  
Clustering Method ◽  
Expected Return

In general portfolio, optimization is a technique for selecting the proportion of assets to make a better portfolio by maximizing the expected return while also minimizing the risk. In this research, the k-means clustering method is used to classify stocks are listed on the LQ45 Index and select stocks whose has prices tend to be increased. Then the Markowitz approach is used to analyze the performance of optimization portfolio models that have a minimum variance in expected return and risk. After understanding the performance of this portfolio optimization, future works will be able to apply this model in cloud computing or artificial intelligence. In addition, investors will develop a better view of the latest performance of the stocks are listed in the LQ45 index and support them decide which stocks should be included in their portfolios, thus prevent wrong decisions.

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