TECHNIQUES TO FORECAST FUTURE ORDERS USING DEEP LEARNING

摘要

Techniques to use deep learning to forecast future orders for a financial service provider to execute by a future date. These techniques leverage a variety of features to predict an appropriate number of orders to execute, for example, on a next trading day. Some features correspond to market indices including their reconstitution schedule while others may correspond to historical orders by the financial service provider. By segregating the features and filtering individual features, these techniques are able to eliminate some noise and focus on a particular feature for insight into the future orders.