Embedding Technical Analysis into Neural Network Based Trading Systems

We have recently proposed a promising trading system for the S&P 500 index, which consists of a feature selection component and a simple filter for data preprocessing, two specialized neural networks for return prediction, and a rule base for prediction integration. The objective of this study is to explore if including additional knowledge for more sophisticated data filtering and return integration leads to further improvements in the system. The new system is using a well-known technical indicator to split the data, and an additional indicator for reducing the number of unprofitable trades. Several system combinations are explored and tested over a five year trading period. The most promising system yielded an annual rate of return (ARR) of 15.99% with 54 trades. This compares favorably to the ARR for the buy and hold strategy (11.05%) and the best results obtained using the system with no technical analysis knowledge embedded (13.35% with 126 trades). Research sponsored in part b..

Embedding Technical Analysis into Neural Networks Based Trading Systems

We have recently proposed a promising trading system for the S&P 500 index, which consists of a feature selection component and a simple lter for data preprocessing, two specialized neural networks for return prediction, and a rule base for prediction integration. The objective of this study is to explore if including additional knowledge for more sophisticated data ltering and return integration leads to further improvements in the system. The new system is using a well-known technical indicator to split the data, and an additional indicator for reducing the number of unpro table trades. Several system combinations are explored and tested overa ve year trading period. The most promising system yielded an annual rate of return (ARR) of 15.99 % with 54 trades. This compares favorably to the ARR for the buy and hold strategy (11.05%) and the best results obtained using the system with no technical analysis knowledge embedded (13.35 % with 126 trades)

Validation of an improved competitive enzyme-linked immunosorbent assay to detect Equine arteritis virus antibody

The objective of the present study was to validate a previously described competitive enzyme-linked immunosorbent assay (cELISA) to detect antibody to Equine arteritis virus (EAV) based on GP5-specific nonneutralizing monoclonal antibody (mAb) 17B79 using the World Organization for Animal Health (OIE)–recommended protocol, which includes the following 5 in-house analyses. 1) The assay was calibrated with the OIE-designated reference serum panel for EAV; 2) repeatability was evaluated within and between assay runs; 3) analytical specificity was evaluated using sera specific to related viruses; 4) analytical sensitivity was evaluated with sera from horses vaccinated with an EAV modified live virus (MLV) vaccine; and 5) the duration of cELISA antibody detection following EAV vaccination was determined. The positive cELISA cutoff of ≥35% inhibition (%I) was confirmed by receiver operating characteristic plot analysis. Analytical sensitivity of the cELISA was comparable to the serum neutralization (SN) assay in that it detected EAV-specific antibody as early as 8 days postvaccination. The duration of EAV-specific antibody detected by cELISA was over 5 years after the last vaccination. This cELISA could detect EAV-specific antibody in serum samples collected from horses infected with various EAV strains. In the field trial performed by American Association of Veterinary Laboratory Diagnosticians–accredited state laboratories and OIE laboratory, the diagnostic specificity of the cELISA was 99.5% and the diagnostic sensitivity was 98.2%. The data using various serum panels also had consistently significant positive correlation between SN titers and cELISA %I results. The results further confirm that the EAV antibody cELISA is a reliable, simple alternative to the SN assay for detecting EAV-specific antibodies in equine sera