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A Prediction Model for Taiwan Tourism Industry Stock Index


Affiliations
1 Yu Da University of Science and Technology, Taiwan, Province of China
 

Investors and scholars pay continuous attention to the stock market, as each day, many investors attempt to use different methods to predict stock price trends. However, as stock price is affected by economy, politics, domestic and foreign situations, emergency, human factor, and other unknown factors, it is difficult to establish an accurate prediction model. This study used a back-propagation neural network (BPN) as the research approach, and input 29 variables, such as international exchange rate, indices of international stock markets, Taiwan stock market analysis indicators, and overall economic indicators, to predict Taiwan's monthly tourism industry stock index. The empirical findings show that the BPN prediction model has better predictive accuracy, Absolute Relative Error is 0.090058, and correlation coefficient is 0.944263. The model has low error and high correlation, and can serve as reference for investors and relevant industries.

Keywords

Artificial Neural Network, Stock Market Analysis, Prediction Model.
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  • A Prediction Model for Taiwan Tourism Industry Stock Index

Abstract Views: 200  |  PDF Views: 130

Authors

Han-Chen Huang
Yu Da University of Science and Technology, Taiwan, Province of China
Fang-Wei Chang
Yu Da University of Science and Technology, Taiwan, Province of China

Abstract


Investors and scholars pay continuous attention to the stock market, as each day, many investors attempt to use different methods to predict stock price trends. However, as stock price is affected by economy, politics, domestic and foreign situations, emergency, human factor, and other unknown factors, it is difficult to establish an accurate prediction model. This study used a back-propagation neural network (BPN) as the research approach, and input 29 variables, such as international exchange rate, indices of international stock markets, Taiwan stock market analysis indicators, and overall economic indicators, to predict Taiwan's monthly tourism industry stock index. The empirical findings show that the BPN prediction model has better predictive accuracy, Absolute Relative Error is 0.090058, and correlation coefficient is 0.944263. The model has low error and high correlation, and can serve as reference for investors and relevant industries.

Keywords


Artificial Neural Network, Stock Market Analysis, Prediction Model.