We'll discuss how applying deep learning to identifying market regimes can be valuable in helping anticipate and position a portfolio for significant structural shifts in the market. We will explain how we develop deep neural networks, including time delay and recurrent neural networks, and train them to identify and target intervals that delineate market state changes such as factor-based trends (e.g. growth vs. value), volatility regimes, and economic cycles. We'll also cover how the optimization algorithm used to drive the training relies on CUDA for high-performance computations on the GPU.

We'll examine an innovative approach using an optimized algorithm to create a decision tree for the basis of regime dependent and pattern classification of financial and macroeconomic time-series data. Implemented in a supervised and unsupervised learning framework, the algorithm relies on the GPU for high performance computing and the host processor to further integrate the results in a deep learning framework. Also, we implement random number generation, in part, using a hardware quantum based true random number generator, balanced with the pseudo-random number generator in CUDA, so as to optimize overall speed where an exhaustive search is not feasible.

Our presentation this year will provide an update on the signal processing aspect of the presentation we gave last year - An Approach to Parallel Processing of Big Data in Finance for Alpha Generation and Risk Management. We will demonstrate the use of signal processing on financial time-series data to inform us of market patterns and signals that may be evolving in real time. We will implement a signal filtering algorithm on a real time basis on securities price time-series data and will develop a cluster chart organizing these patterns visually.