AI to predict the market evolution

Order books: a source of data

To train their model, the researchers draw on the limit order book, which lists all the buy orders at a given time, i.e. several million orders, each of which contains information on the buy and sell price as well as a time stamp for the execution. By sifting through this liquidity book algorithmically, it is then possible to determine a trajectory for market movements in one direction or the other and, above all, the decisive moment to carry out a transaction.

Moving from a single horizon to a multiple horizon

To gain in accuracy, the Oxford team implemented a multi-horizon analysis, where this type of AI analysis has previously used a single-horizon forecast to correlate order prices and market prices in a defined and above all unique time window. Indeed, single-horizon supervised learning is limited by the multitude of factors to take into account and the signal-to-noise ratio to obtain a reliable forecast. In contrast, multihorizon forecasting studies price movements over a series of intervals, with the results of each horizon informing the next. By aggregating these windows, forecasts cover a longer time frame.

NLP to the rescue of multihorizon

To develop this multi-horizon approach, the research team drew inspiration from natural language processing by using Seq2seq and Attention models based on complex recurrent neural layers comprising an encoder and a decoder. The Seq2Seq encoder summarizes information from past time series and the decoder combines the hidden states with known future inputs to generate predictions. The Attention model, on the other hand, addresses the limitations of Seq2Seq models adapted to short sequence processing.

A gain of more than 30 seconds on predictions

In fact, the limit order book (LOB) data was used to train a number of models on the IPU, including one — DeepLOB — developed by the same IMO team (Zhang et al, 2019). In terms of multi-horizon prediction, the researchers tested two variants of DeepLOB, named DeepLOB-Seq2Seq, and DeepLOB-Attention, which use Seq2Seq and Attention models as decoders, respectively.



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David BECK

David BECK

David is a former entrepreneur — Teacher — Researcher — Contributor to government publications.