Stock Index Movement Prediction: A Crow Search-ELM Approach

Author(s):  
Sidharth Samal ◽  
Rajashree Dash
2021 ◽  
pp. 1-19
Author(s):  
Sidharth Samal ◽  
Rajashree Dash

In recent years Extreme Learning Machine (ELM) has gained the interest of various researchers due to its superior generalization and approximation capability. The network architecture and type of activation functions are the two important factors that influence the performance of an ELM. Hence in this study, a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) oriented multi-criteria decision making (MCDM) framework is suggested for analyzing various ELM models developed with distinct activation functions with respect to sixteen evaluation criteria. Evaluating the performance of the ELM with respect to multiple criteria instead of single criterion can help in designing a more robust network. The proposed framework is used as a binary classification system for pursuing the problem of stock index price movement prediction. The model is empirically evaluated by using historical data of three stock indices such as BSE SENSEX, S&P 500 and NIFTY 50. The empirical study has disclosed promising results by evaluating ELM with different activation functions as well as multiple criteria.


2019 ◽  
Vol 85 ◽  
pp. 105784 ◽  
Author(s):  
Rajashree Dash ◽  
Sidharth Samal ◽  
Rasmita Dash ◽  
Rasmita Rautray

CFA Digest ◽  
2003 ◽  
Vol 33 (3) ◽  
pp. 101-102
Author(s):  
Frank T. Magiera

2020 ◽  
Vol 38 (1) ◽  
Author(s):  
Farhan Ahmed ◽  
Salman Bahoo ◽  
Sohail Aslam ◽  
Muhammad Asif Qureshi

This paper aims to analyze the efficient stock market hypothesis as responsive to American Presidential Election, 2016. The meta-analysis has been done combining content analysis and event study methodology. The all major newspapers, news channels, public polls, literature and five important indices as Dow Jones Industrial Average (DJIA), NASDAQ Stock Market Composit Indexe (NASDAQ-COMP), Standard & Poor's 500 Index (SPX-500), New York Stock Exchange Composite Index (NYSE-COMP) and Other U.S Indexes-Russell 2000 (RUT-2000) are critically examined and empirically analyzed. The findings from content analysis reflect that stunned winning of Mr Trump from Republican Party worked as shock for American stock market. From event study, findings confirmed that all the major indices reflected a decline on winning of Trump and losing of Ms. Clinton from Democratic. The results are supported empirically and practically through the political event like BREXIT that resulted in shock to Global stock index and loss of $2 Trillion.


2019 ◽  
Vol 6 (02) ◽  
Author(s):  
Rony Mahendra ◽  
Erwin Dyah Astawinetu

The research objective is to establish an optimal portfolio and know the difference between risk and return stock index portfolio candidates and non-candidates. Method used in the preparation of this research portfolio is the single index model, while the samples of this study are active world stock indices version of The Wall Street Journal during the period August 2012 - August 2016 and The Global Dow is used as the benchmark stock index. In establishing the optimal portfolio is used two perspectives: the Rupiah perspective and the U.S. Dollar perspective. The results showed there were three stock indices from the perspective of Rupiah and 8 share index menurutperspektif U.S. Dollar that make up the optimal portfolio, with the cut-of-pointsebesar 0,01393menurut Rupiah perspective and the perspective of 0.0078 US Dollars Based on the perspective of return expectations Rupiah obtained by 0.0258 with a risk of 0.06512. Berdarkan perspective of US Dollars, obtained return expectations at 0.0154 with a risk of 0.0292. From the test results showed that the hypothesis, the return on both perspectives there are significant differences between the index of the candidate, with a non-candidate. Then the risk of stock index, among the candidates, with a non-candidate, the Rupiah perspective there is no difference, but in the perspective of US Dollars, there are significant differences.Keywords: Single Index Model, candidate portfolio, optimal portfolio, expected return, excess return to beta, cut-off-point


Sign in / Sign up

Export Citation Format

Share Document