Bellwether Trades: Characteristics of Trades influential in Predicting Future Price Movements in Markets
This work addresses the problem of understanding market dynamics for traders and analysts by identifying informative trades, though it is incremental as it applies existing methods to financial data.
The study used non-linear machine learning to identify trade characteristics that predict future market movements, achieving accurate predictions with an optimized neural network and pinpointing influential trades based on size, venue, and context.
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.