Paper: arXiv 2409.14157

Abstract

This study explores the prediction of high-frequency price changes using deep learning models. Although state-of-the-art methods perform well, their complexity impedes the understanding of successful predictions. We found that an inadequately defined target price process may render predictions meaningless by incorporating past information. The commonly used three-class problem in asset price prediction can generally be divided into volatility and directional prediction. When relying solely on the price process, directional prediction performance is not substantial. However, volume imbalance improves directional prediction performance.

Complexity vs Empirical Score

  • Math Complexity: 4.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper employs standard statistical definitions and neural network architectures without deep theoretical derivations, but conducts a thorough empirical analysis using a large, recent dataset (AAPL Nasdaq ITCH) with detailed daily evaluation, cross-validation, and comparisons against naive baselines.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Predict High-Frequency Price Changes"] --> B["Methodology:<br>Deep Learning on Limit Order Book Data"]
  B --> C{"Key Inputs<br>Price Process vs. Volume Imbalance"}
  C --> D["Computational Process:<br>Three-Class Classification<br>Directional vs. Volatility Prediction"]
  D --> E{"Analysis of<br>Predictability Drivers"}
  E --> F["Outcome 1:<br>Price Process Alone<br>Lacks Directional Signal"]
  E --> G["Outcome 2:<br>Volume Imbalance<br>Significantly Improves Prediction"]

Cited References (26)

  1. Deeplob: D eep convolutional neural networks for limit order books (2019)
  2. Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods (2018)
  3. Forecasting stock prices from the limit order book using convolutional neural networks (2017)
  4. Efficient capital markets: A review of theory and empirical work (1970)
  5. Order imbalance and individual stock returns: T heory and evidence (2004)
  6. Deep learning with long short-term memory networks for financial market predictions (2018)
  7. Going Deeper with Convolutions (2014) — link
  8. Transformers for limit order books (2020) — link
and 18 more
  1. How informative is the order book beyond the best levels? Machine learning perspective (2022) — link
  2. Trading volume, bid-ask spread, and price volatility in futures markets (2000)
  3. Bid-ask spreads and volatility in the foreign exchange market: A n empirical analysis (1994)
  4. Transactions, volume, and volatility (1994)
  5. AE-ACG : A novel deep learning-based method for stock price movement prediction (2023)
  6. Forecasting directional movements of stock prices for intraday trading using LSTM and random forests (2022)
  7. A novel deep learning framework: Prediction and analysis of financial time series using CEEMD and LSTM (2020)
  8. Volatility Spillovers and Contagion During Major Crises: An Early Warning Approach Based on a Deep Learning Model (2023)
  9. Market volatility prediction and the efficiency of the S&P 100 index option market (1992)
  10. A comprehensive look at financial volatility prediction by economic variables (2012)
  11. Stock volatility prediction by hybrid neural network (2019)
  12. Stock returns, order imbalances, and commonality: E vidence on individual, institutional, and proprietary investors in China (2009)
  13. Recurrent neural network based parameter estimation of Hawkes model on high-frequency financial data — → 2304.11883
  14. Financial forecasting with machine learning: price vs return (2021)
  15. Predicting bitcoin price movements using sentiment analysis: a machine learning approach (2022)
  16. Forecasting cryptocurrencies’ price with the financial stress index: a graph neural network prediction strategy (2024)
  17. Reliability study of stock index forecasting in volatile and trending cities using public sentiment——based on word2 V ec and LSTM models (2023)
  18. A hybrid deep learning model for Bitcoin price prediction: data decomposition and feature selection (2023)

1 of 26 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.