Relative Movement to Support Stock Forecasting of the Thai Market using the Neural Network
|Paper File||Download Paper File|
|Appear In||1st Conference on Application Research and Development (Download Proceeding) (ECTI-CARD 2009)|
|Publication Date||04/05/2009 - 06/05/2009|
|Pages||325 - 330|
|Author 1||Pongsak Srithongnopawong|
|Author 2||Vatcharaporn Esichaikul|
Nowadays, stock analysis is a challenging task in stock prediction. Stock analysis methods including fundamental analysis and technical analysis are commonly used among financial professionals to help them on investment decisions. In recent days, AI-based system becomes a common tool to predict stock price. Among AI-based models for stock forecasting, Artificial Neural Network (ANN) is the most popular and accurate model. This research proposes data preprocessing using relative movement to improve performance of ANN-based stock forecasting. Both fundamental and technical indicators are chosen as input to the system. The common preprocessing including Principal Component Analysis (PCA) and Z-Scaling is also applied. The evaluation metrics include Root Mean Squared Error (RMSE), hit ratio, and total return. The k-fold cross validation is used to utilize the dataset of stocks in banking sector. The significance of those three metrics is determined through t-test over cross validation. The experiments show that the proposed model outperforms a traditional model, random walk model, and buy & hold strategy for all evaluation metrics.