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Mid-Price Stock Prediction with Deep Learning

EasyChair Preprint no. 2424

5 pagesDate: January 20, 2020


Mid-price movement prediction based on limit order book data and historical data is a challenging task due to the complexity and dynamics of the limit order book and historical movements of stock data. So far, there have been very limited attempts for extracting relevant features based on limit order book data. In this paper, we address this problem by designing a new set of handcrafted features and performing an extensive experimental evaluation on both liquid stocks. More specifically, we present an extensive set of econometric features that capture the statistical properties of the underlying securities for the task of mid-price prediction. The experimental evaluation consists of a head-to-head comparison with other handcrafted features from the literature and with features extracted from a long short-term memory autoencoder by means of a fully automated process. Moreover, we develop a new experimental protocol for online learning that treats the task above as a multi-objective optimization problem and predicts i) the direction of the next price movement and ii) the number of order book events that occur until the change takes place. In order to predict the mid-price movement, features are fed into seven different deep learning models based on multi-layer perceptrons, convolutional neural networks, and long short-term memory neural networks. The performance of the proposed method is then evaluated on liquid stocks. For some stocks, results suggest that the correct choice of a feature set and a model can lead to the successful prediction of how long it takes to have a stock price movement.

Keyphrases: deep learning, Econometrics, limit order book, mid price, NSE stock data, Stock Trading

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Sushilkumar Deshmukh},
  title = {Mid-Price Stock Prediction with Deep Learning},
  howpublished = {EasyChair Preprint no. 2424},

  year = {EasyChair, 2020}}
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