Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets

TL;DR

This paper introduces a Bayesian bilinear neural network with temporal attention for predicting mid-price movements in high-frequency limit-order book markets, emphasizing uncertainty quantification and improved predictive performance.

Contribution

It presents a novel Bayesian neural network model tailored for ultra-high-frequency financial data, integrating temporal attention and second-order optimization for enhanced prediction and uncertainty estimation.

Findings

Bayesian model outperforms traditional ML methods in accuracy.

The approach effectively quantifies uncertainty in predictions.

Results demonstrate the model's suitability for complex financial time-series.

Abstract

The prediction of financial markets is a challenging yet important task. In modern electronically-driven markets, traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. While recent research has established the effectiveness of traditional machine learning (ML) models in financial applications, their intrinsic inability to deal with uncertainties, which is a great concern in econometrics research and real business applications, constitutes a major drawback. Bayesian methods naturally appear as a suitable remedy conveying the predictive ability of ML methods with the probabilistically-oriented practice of econometric research. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention, suitable for the challenging time-series task of predicting mid-price movements in ultra-high-frequency limit-order book markets. We thoroughly compare our Bayesian model with traditional ML alternatives by addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts. Our results underline the feasibility of the Bayesian deep-learning approach and its predictive and decisional advantages in complex econometric tasks, prompting future research in this direction.