References for the March Paper Discussion Prediction: Time Series Forecasting vs Regression Forecasting: Principles and Practice more
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.
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.
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.
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.
The paper discussions will be conducted every month - online and in person.
- Suggest a paper to discuss here - https://hasgeek.com/fifthelephant/call-for-papers/sub
This should involve slides, and code samples to make parts of the paper simpler and more understandable.
- Moderate/discuss a paper someone else is proposing.
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