A TOPSIS Approach of Ranking Classifiers for Stock Index Price Movement Prediction

2018 ◽  
pp. 665-674 ◽  
Author(s):  
Rajashree Dash ◽  
Sidharth Samal ◽  
Rasmita Rautray ◽  
Rasmita Dash
Keyword(s):  
Stock Index ◽  
Price Movement ◽  
Movement Prediction ◽  
Topsis Approach

A TOPSIS-ELM framework for stock index price movement prediction

2021 ◽  
pp. 1-19
Author(s):  
Sidharth Samal ◽  
Rajashree Dash
Keyword(s):  
Network Architecture ◽  
Binary Classification ◽  
Evaluation Criteria ◽  
Multiple Criteria ◽  
Stock Index ◽  
Activation Functions ◽  
Price Movement ◽  
Movement Prediction ◽  
Learning Machine ◽  
Approximation Capability

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.

An integrated TOPSIS crow search based classifier ensemble: In application to stock index price movement prediction

2019 ◽  
Vol 85 ◽  
pp. 105784 ◽  
Author(s):  
Rajashree Dash ◽  
Sidharth Samal ◽  
Rasmita Dash ◽  
Rasmita Rautray
Keyword(s):  
Classifier Ensemble ◽  
Stock Index ◽  
Price Movement ◽  
Movement Prediction

Dynamic Business Network Analysis for Correlated Stock Price Movement Prediction

2015 ◽  
Vol 30 (2) ◽  
pp. 26-33 ◽  
Author(s):  
Wenping Zhang ◽  
Chunping Li ◽  
Yunming Ye ◽  
Wenjie Li ◽  
Eric W.T. Ngai
Keyword(s):  
Network Analysis ◽  
Stock Price ◽  
Business Network ◽  
Price Movement ◽  
Movement Prediction

scholarly journals Stock price movement prediction using representative prototypes of financial reports

2011 ◽  
Vol 2 (3) ◽  
pp. 1-18 ◽  
Author(s):  
Ming-Chih Lin ◽  
Anthony J. T. Lee ◽  
Rung-Tai Kao ◽  
Kuo-Tay Chen
Keyword(s):  
Stock Price ◽  
Price Movement ◽  
Financial Reports ◽  
Movement Prediction

scholarly journals Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction

2020 ◽  
Author(s):  
Wei Li ◽  
Ruihan Bao ◽  
Keiko Harimoto ◽  
Deli Chen ◽  
Jingjing Xu ◽  
...  
Keyword(s):  
Correlation Matrix ◽  
Stock Price ◽  
Daily Basis ◽  
Market Impact ◽  
Convolutional Network ◽  
Price Movement ◽  
Extensive Experiment ◽  
Exact Time ◽  
Financial News ◽  
Movement Prediction

Stock movement prediction is a hot topic in the Fintech area. Previous works usually predict the price movement in a daily basis, although the market impact of news can be absorbed much shorter, and the exact time is hard to estimate. In this work, we propose a more practical objective to predict the overnight stock movement between the previous close price and the open price. As no trading operation occurs after market close, the market impact of overnight news will be reflected by the overnight movement. One big obstacle for such task is the lacking of data, in this work we collect and publish the overnight stock price movement dataset of Reuters Financial News. Another challenge is that the stocks in the market are not independent, which is omitted by previous works. To make use of the connection among stocks, we propose a LSTM Relational Graph Convolutional Network (LSTM-RGCN) model, which models the connection among stocks with their correlation matrix. Extensive experiment results show that our model outperforms the baseline models. Further analysis shows that the introduction of the graph enables our model to predict the movement of stocks that are not directly associated with news as well as the whole market, which is not available in most previous methods.

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