The authors, Petter N. Kolm, Jeremy Turiel and Nicholas Westray achieve high levels of forecasting accuracy by training relatively simple Artificial Neural Network (ANN) on order book data.

Using intuitive cross-sectional regressions, the authors correlate the forecasting performance of Long Short-Term Memory (LSTM) networks to stock characteristics at the market microstructure level to demonstrate that returns of information abundunt stocks can be predicted with more accuracy. Thus establishing the importance of having rich data training ML models.

Key takeaways for the audience

The key contributions made by this paper are:

It performs a comparative assessment of forecasting accuracy with common ANNs including MLP, LSTM, LSTM-MLP, stacked LSTM, and CNN LSTM.
It using granular limit order book data (LOBSTER) which records order book events at nanosecond precision.
Using simple cross sectional regressions forecasting performance of the ML models is correlated with stock data attributes to emprical demonstrate that having rich data results in better forecasting precision.

About the speaker

Rachna Maheshwari works as a Quantitative Finance, Risk Analytics and AI/ML VP at CRISIL (S&P) and is also Visiting Faculty in Financial Statistics.

About The Fifth Elephant monthly paper discussions

The Fifth Elephant member - Bharat Shetty Barkur - is the curator of the paper discussions. Bharat has worked across different organizations such as IBM India Software Labs, Aruba Networks, Fybr, Concerto HealthAI, and Airtel Labs. He has worked on products and platforms across diverse verticals such as retail, IoT, chat and voice bots, edtech, and healthcare leveraging AI, Machine Learning, NLP, and software engineering. His interests lie in AI, NLP research, and accessibility.

The goal is for the community to understand popular papers in Generative AI, DL, and ML domains. Bharat and other co-curators seek to put together papers that will benefit the community, and organize reading and learning sessions driven by experts and curious folks in GenerativeAI, Deep Learning, and Machine Learning.

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Zoom link for today - Deep order flow imbalance This update is for participants only

About the paper

This paper applies deep learning for forecasting high frequency returns for stocks using granular limit order book data.

The authors, Petter N. Kolm, Jeremy Turiel and Nicholas Westray achieve high levels of forecasting accuracy by training relatively simple Artificial Neural Network (ANN) on order book data.

Key takeaways for the audience

The key contributions made by this paper are:

It performs a comparative assessment of forecasting accuracy with common ANNs including MLP, LSTM, LSTM-MLP, stacked LSTM, and CNN LSTM.
It using granular limit order book data (LOBSTER) which records order book events at nanosecond precision.
Using simple cross sectional regressions forecasting performance of the ML models is correlated with stock data attributes to emprical demonstrate that having rich data results in better forecasting precision.