Neural networks, grid computing, parallelization, high-dimensional optimization, quantitative investment, decision support. - Neuronale Netze, Parallelisierung, hoch-dimensionale Optimierung, quantitatives Investment, Entscheidungsunterstützung

The study reports empirical evidence that artificial neural network based models are applicable to forecasting of stock market returns. The Nigerian stock market logarithmic returns time series was tested for the presence of memory using the Hurst coefficient before the models were trained. The...

Financial services institutions are adopting artificial intelligence and machine learning based solutions for accessing credit quality, market surveillance, fraud detection, and in many more areas. It enables institutions to make better decisions, better compliance management, better customer...

Traditional empirical finance research uses hand-engineering and trial-and-error to look for the anomalies in the cross-section of stock returns. In this paper, we take advantage of deep learning and utilize both the price and fundamental information to separate stocks' winners from losers. For...

One way of thinking about the popular momentum return factor is as a simple algorithm that predicts what percentile a stock's returns will fall into next month using the previous eleven months of return data. We want to determine if there is more information in those past returns that could be...

Based upon the recent work of Dacheng Xiu's team at the University of Chicago, we demonstrate that we can repurpose a consumer-level character recognition neural network to recognize price chart patterns in the Ethereum cryptocurrency for the purpose of forecasting short term (small N days)...