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Forecasting Quoted Depth With the Limit Order Book.

Daniel Libman¹, Simi Haber¹, Mary Schaps¹

¹Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel.

Frontiers in Artificial Intelligence

|May 28, 2021

Summary

This summary is machine-generated.

Deeper order book data significantly improves the forecasting of stock market liquidity (quoted depth). This study demonstrates that utilizing deeper order book layers provides valuable insights beyond traditional upper-layer analysis.

Keywords:

deep feed forward neural network deep feedforward deep learning deep learning—artificial neural network feed forward feed forward algorithm limit order book quoted depth

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Area of Science:

Financial markets
Computational finance
Market microstructure

Background:

Liquidity is crucial in financial markets, influencing stock prices, returns, and risk.
The stock market order book traditionally measures liquidity, detailing buy and sell orders.
Electronic trading has increased accessibility to deeper order book layers, enhancing research potential.

Purpose of the Study:

To evaluate the effectiveness of using deeper order book layers for liquidity forecasting.
To determine if information from deeper order book layers enhances the prediction of quoted depth.
To assess the value of deeper order book data in high-frequency trading contexts.

Main Methods:

Utilized Deep Feed Forward Neural Networks for predictive modeling.
Analyzed minute-by-minute quoted depth data.
Compared the predictive power of deeper order book layers against upper layers alone.

Main Results:

Deeper order book layers contain significant information for forecasting quoted depth.
The inclusion of deeper layers demonstrably improved liquidity forecasting accuracy.
Deep Feed Forward Neural Networks effectively leveraged this deeper information.

Conclusions:

Deeper order book layers offer valuable, previously underutilized information for liquidity analysis.
Forecasting stock market liquidity can be enhanced by incorporating data from the full depth of the order book.
This research supports the integration of advanced machine learning techniques with comprehensive order book data for improved financial market insights.