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.