[1A5-GS-2] Machine learning: stock price prediction

[1A5-GS-2-04] Evaluation of Market Impact based on Stock Price Prediction using Limit Order Book

〇kohei sugawara¹, jun hozumi¹, kiyoshi izumi¹ (1. University of Tokyo)

Keywords:MI, Limit Order Book, DeepLearning

Predicting market impact is important for all traders to formulate, evaluate and improve their trading strategies. In the past, there have been studies that approximated market impact from historical trading data. There have also been studies that calculate market impact indirectly by modeling limit order books(LOB). However, these models are extremely generalized and have difficulties in terms of practicality. On the other hand, in recent years, deep learning has been used to analyze LOB, which is an important factor in considering the market impact. However, the main purpose of these studies is mainly stock price prediction or optimization of the overall execution cost of trades. There is little research on the evaluation of market impact alone. In this paper, we propose a new model for evaluating market impact using deep learning. First, we predict stock prices based on the LOB information, and then evaluated the market impact using the prediction. Through experiments, we have found that the market impact calculated by the new model was consistent with that of existing studies, and LOB analysis by deep learning captured significant information in market impact calculation.