首页|Order book mid-price movement inference by CatBoost classifier from convolutional feature maps
Order book mid-price movement inference by CatBoost classifier from convolutional feature maps
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NSTL
Elsevier
This paper presents the application of a hybrid model to predict the mid-price trend of assets in the Brazilian Stock Exchange (B3) from the market by order data. A Convolution Neural Network (CNN) is applied to extract spatial features from an order book aggregated by price and then a decision tree-based algorithm (CatBoost) combines these CNN features with events provided by Times and Trades information (TTinfo) to have the final prediction. Differently from most stock exchanges, TTinfo from B3 includes to which broker an order belongs, and in this work its impact in the final prediction is analysed as well. The proposed solution innovates by joining CNN with CatBoost, improving accuracy by 8% when compared to a common CNN, where 5% is only due to the adoption of CatBoost and another 3% is due to the combination of features from CNN with TTinfo. In addition, for training update, only CatBoost needs to be retrained, allowing learning transfer for the CNN, which reduces the overall updating time in at least one order of magnitude.
Brazilian stock marketDeep learningLimit order bookPrice trend forecasting
Bileki G.A.、Silva L.H.C.、Bonato V.、Barboza F.
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University of S?o Paulo (USP) Institute of Mathematical and Computer Sciences Campus S?o Carlos
Federal University of Uberlandia (UFU) School of Business and Management Campus Santa M?nica
NSTL
Elsevier
