Development of stock market trend prediction system using multiple regression

2019 ◽  
Vol 25 (3) ◽  
pp. 271-301 ◽  
Author(s):  
Muhammad Zubair Asghar ◽  
Fazal Rahman ◽  
Fazal Masud Kundi ◽  
Shakeel Ahmad
Keyword(s):  
Stock Market ◽  
Multiple Regression ◽  
Prediction System ◽  
Trend Prediction

NORN PREDICTOR — STOCK PREDICTION USING A NEURAL OSCILLATORY-BASED RECURRENT NETWORK

2001 ◽  
Vol 01 (04) ◽  
pp. 439-451 ◽  
Author(s):  
RAYMOND S. T. LEE ◽  
JAMES N. K. LIU
Keyword(s):  
Stock Price ◽  
Graph Matching ◽  
Network Models ◽  
Recurrent Network ◽  
Prediction System ◽  
Stock Prediction ◽  
Trend Prediction ◽  
Neural Network Models ◽  
Fully Integrated ◽  
Stock Price Prediction

Financial prediction is one of the most typical applications in contemporary scientific study. In this paper, we present a fully integrated stock prediction system – NORN Predictor – a Neural Oscillatory-based Recurrent Network for finance prediction system to provide both a) long-term trend prediction, and b) short-term stock price prediction. One of the major characteristics of the proposed system is the automation of the conventional financial technical analysis technique such as market pattern analysis via the NOEGM (Neural Oscillatory-based Elastic Graph Matching) model and its integration with the Time-difference recurrent neural network models. This will provide a fully integrated and automated tool for analysis and investigation of stock investment. From the implementation point of view, the stock pricing information of 33 major Hong Kong stocks in the period from 1990 to 1999 is being adopted for system training and evaluation. As compared with the contemporary neural prediction model, the proposed system has achieved challenging results in terms of efficiency and accuracy.

scholarly journals The "CAPS" Prediction System and Stock Market Returns

2011 ◽  
Author(s):  
Christopher Avery ◽  
Judith Chevalier ◽  
Richard Zeckhauser
Keyword(s):  
Stock Market ◽  
Prediction System ◽  
Market Returns ◽  
Stock Market Returns

Relations of Imagery, Creativity, and Socioeconomic Status with Performance on a Stock-Market e-Trading Game

2001 ◽  
Vol 88 (3) ◽  
pp. 734-740
Author(s):  
Daniel S. L. Roberts ◽  
Brenda E. MacDonald
Keyword(s):  
Socioeconomic Status ◽  
Regression Analysis ◽  
Problem Solving ◽  
Stock Market ◽  
Personality Traits ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Social Environment ◽  
Stock Trading ◽  
Stock Market Trading

The purpose of the present investigation was to examine how measures of imagery, creativity, and socioeconomic status relate to performance in a stock-market trading game. The 368 participants were students enrolled in an administration studies curriculum. A multiple regression analysis showed imaging scores to be a predictor of stock-trading performance as were creativity and socioeconomic status to a lesser extent. High imagers and high scorers on creativity and socioeconomic status made several times more profit with their portfolios. Results are discussed in terms of imagery having multiple repercussions on learning, e.g., memory and problem-solving. It is concluded that scores on imagery, creativity, and socioeconomic status, being weakly correlated, are interdependent and likely associated with personality traits shaped within a stimulating home or social environment.

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