The promising predictive power of machine learning (ML) models is encouraging a wide range of applications, but the weak interpretability of their black-box feature is a major obstacle to their application to finance. The large scale of limit order book (LOB) data provides fertile ground for ML...

This paper introduces a high frequency trade execution model to evaluate the economic impact of supervised machine learners. Extending the concept of a confusion matrix, we present a 'trade information matrix' to attribute the expected profit and loss of the high frequency strategy under...

This experiment examines forecasting behavior under varying information conditions to assess the extent to which traders in security markets incorporate information in trading activity to resolve fundamental uncertainty and to resolve higher-order uncertainty. Fundamental uncertainty refers to a...

The aim of our model is to leverage recurrent neural networks to predict the trends of stocks traded on India’s National Stock Exchange. By integrating an analysis of the stock’s historic price, and the contemporary market sentiment of the parent company, we endeavored to build a model that...