# Investigating Limit Order Book Characteristics for Short Term Price Prediction: a Machine Learning Approach

## TL;DR

This paper uses machine learning to analyze Limit Order Book features for short-term price prediction, revealing new insights into LOB dynamics and achieving superior prediction accuracy over baseline models.

## Contribution

It introduces a machine learning framework to explore LOB features for price prediction and provides novel observations about LOB behavior.

## Key findings

- Prediction accuracy significantly exceeds baseline models
- Identifies promising features for short-term price movement
- Provides new insights into LOB dynamics

## Abstract

With the proliferation of algorithmic high-frequency trading in financial markets, the Limit Order Book has generated increased research interest. Research is still at an early stage and there is much we do not understand about the dynamics of Limit Order Books. In this paper, we employ a machine learning approach to investigate Limit Order Book features and their potential to predict short term price movements. This is an initial broad-based investigation that results in some novel observations about LOB dynamics and identifies several promising directions for further research. Furthermore, we obtain prediction results that are significantly superior to a baseline predictor.