深層学習と高頻度データを用いた株式注文状況の推定

抄録

For algorithmic trading, it is important to reduce market impact and opportunity costs that closely related to market liquidity. In this work, we propose a tick-based approach to prediction of the liquidity. Our method utilizes order data encoded according to its exibility and a Long Short-Term Memory(LSTM) that predict a next order. Accuracy of the model outperforms by a large margin maximum occurrence ratio of order labels. Furthermore, we examine the embedding layer of the trained model and find out that it obtains difference and similarity between each order.