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Market Confidence Predicts Stock Price: Beyond Supply and Demand
Xiao-Qian Sun1,2, Hua-Wei Shen2, Xue-Qi Cheng2
1University of Chinese Academy of Sciences, Beijing, China.
Plos One
|July 9, 2016
Summary
This study introduces a new market confidence index derived from trader activity to improve stock price prediction. Incorporating this index significantly enhances prediction accuracy for financial markets.
Area of Science:
- Computational Finance
- Behavioral Finance
- Data Science
Background:
- Stock price prediction is a complex financial analysis task.
- Existing methods rely on historical price data or external factors.
- A novel approach is needed to capture market sentiment for better predictions.
Purpose of the Study:
- To introduce a new market confidence index based on trader activity.
- To assess the Granger causality between the market confidence index and stock prices.
- To improve stock price prediction accuracy by integrating this index into a neural network model.
Main Methods:
- Utilizing transaction records with trader identifiers to calculate the market confidence index.
- Applying Granger causality tests to establish the relationship between the index and stock prices.
- Developing and evaluating a neural network model incorporating the market confidence index for time series stock price prediction.
Main Results:
- A strong Granger causality was observed between the market confidence index and stock prices.
- The inclusion of the market confidence index significantly improved the accuracy of stock price predictions.
- Experimental results demonstrated enhanced prediction performance across 50 stocks on Chinese Stock Exchanges.
Conclusions:
- Cross-day trading behavior can effectively characterize market confidence.
- The developed market confidence index is a valuable tool for improving stock price prediction.
- This research offers a novel perspective on leveraging trader dynamics in financial forecasting.
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