Implementation and Evaluation of Historical Consistent Neural Networks Using Parallel Computing

Sammanfattning: Forecasting the stock market is well-known to be a very complex and difficult task, and even by many considered to be impossible. The new model, emph{Historical Consistent Neural Networks} (HCNN), has recently been successfully applied for prediction and risk estimation on the energy markets. HCNN is developed by Dr. Hans Georg Zimmermann, Siemens AG, Corporate Technology Dpt., Munich, and implemented in the SENN (Simulation Environment for Neural Network) package, distributed by Siemens. The evalution is made by tests on a large database of historical price data for global indicies, currencies, commodities and interest rates. Tests have been done, using the Linux version of the SENN package, provided by Dr. Zimmermann and his research team. This thesis takes on the task given by Eturn Fonder AB, to develop a sound basis for evaluating and using HCNN, in a fast and easy manner. An important part of our work has been to develop a rapid and improved implementation of HCNN, as an interactive software package. Our approach has been to take advantage of the parallelization capabilities of the graphics card, using the CUDA library together with an intuitive and flexible interface for HCNN built in MATLAB. We can show that the computational power of our CUDA implementation (using a cheap graphics device), compared to SENN, is about 33 times faster. With our new optimized implementation of HCNN, we have been able to test the model on large data sets, consisting of multidimensional financial time series. We present the results with respect to some common statistical measures, evaluates the prediction qualities and performance of HCNN, and give our analysis of how to move forward and do further testing.