Bellwether Trades: Characteristics of Trades influential in Predicting Future Price Movements in Markets

TL;DR

This paper uses advanced neural networks to identify influential trades that significantly impact future market price predictions, revealing heterogeneity in informational content across different trade types and contexts.

Contribution

It introduces a novel method combining neural networks and interpretability techniques to pinpoint impactful trades in market data, enhancing understanding of trade influence on price movements.

Findings

Neural network predictor accurately forecasts future market movements.

Identifies specific trades with high informational impact.

Reveals heterogeneity in trade informativeness across various trade characteristics.

Abstract

In this study, we leverage powerful non-linear machine learning methods to identify the characteristics of trades that contain valuable information. First, we demonstrate the effectiveness of our optimized neural network predictor in accurately predicting future market movements. Then, we utilize the information from this successful neural network predictor to pinpoint the individual trades within each data point (trading window) that had the most impact on the optimized neural network's prediction of future price movements. This approach helps us uncover important insights about the heterogeneity in information content provided by trades of different sizes, venues, trading contexts, and over time.