Information Fusion and S&P500 Trend Prediction

The purpose of this study is the prediction of Standard & Poor's (S&P500) trends (ups and downs) with macroeconomic variables, technical indicators, and investor moods using k-NN algorithm and probabilistic neural networks. More precisely, eleven economic factors, twelve technical indicators and four measures of investor's mood were selected as potential predictive variables. Then, the Granger causality test was performed to identify among them the predictive variables that show a strong relationship with the stock market. Finally, the identified inputs are fed to k-NN and PNN separately and the correct detection of stock market ups (+0.5%) -aggressive investment strategy-is computed using the obtained hit ratios. The simulations results from 10-fold experiments show that the average detection rate of k-NN and PNN are respectively 93.45% (+/- 0.0019, standard deviation) and 92.4% (+/- 0.006, standard deviation). The results suggest that aggregating the three categories of information (economic, technical, and psychological information) along with k-NN as classifier leads to high detection accuracy of future stock market ups and downs.