A neural network based approach to support the Market Making strategies in High-Frequency Trading

Artificial Neural Networks (ANN) have been frequently applied to reduce risks and maximize the net returns in different types of algorithm trading. Using a real dataset, and aiming to support the Market Making process in High-Frequency Trading, this work investigates the use of a multilayer perceptron (MLP) to predict positive oscillations in short time periods (5, 10 or 15 minutes). The statistical analysis of our results showed that a neural network is more effective in short-term oscillations (5 minutes) when compared with the results obtained in longer periods (10 or 15 minutes). The result is important because it allows to insert a higher quantity of limit orders once they will be placed more frequently, which increases the market liquidity. It contextualizes a new contribution in the High-Frequency Trading field, where this work proposes a new trigger to start a market making process.