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

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  • Tejas Ramdas
  • Martin T. Wells

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.

Suggested Citation

  • Tejas Ramdas & Martin T. Wells, 2024. "Bellwether Trades: Characteristics of Trades influential in Predicting Future Price Movements in Markets," Papers 2409.05192, arXiv.org.
    • Handle: RePEc:arx:papers:2409.05192
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    References listed on IDEAS

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