David Enke

TL;DR: An information gain technique used in machine learning for data mining to evaluate the predictive relationships of numerous financial and economic variables is introduced and shows that the trading strategies guided by the classification models generate higher risk-adjusted profits than the buy-and-hold strategy.

TL;DR: A group of hypothesis tests are performed to show that combining the ANNs with the PCA gives slightly higher classification accuracy than the other two combinations, and that the trading strategies guided by the comprehensive classification mining procedures based on PCA and ANNs gain significantly higher risk-adjusted profits than the comparison benchmarks.

TL;DR: Results show that redeveloped neural network models that use the recent relevant variables generate higher profits with lower risks than the buy-and-hold strategy, conventional linear regression, and the random walk model, as well as the neural networks that use constant relevant variables.

TL;DR: A comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF based on 60 financial and economic features and results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset, as well as several other hybrid machine learning algorithms.

TL;DR: A detailed trading model that provides a more effective and intelligent way for recognizing trading signals and assisting investors with trading decisions by utilizing a system that adapts both the inputs and the prediction model based on the desired output is led.