  Mid-price movement prediction based on limit order book (LOB) data is a
challenging task due to the complexity and dynamics of the LOB. So far, there
have been very limited attempts for extracting relevant features based on LOB
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 and illiquid stocks. More specifically, we implement a new set of
econometrical features that capture statistical properties of the underlying
securities for the task of mid-price prediction. Moreover, we develop a new
experimental protocol for online learning that treats the task 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, the features
are fed into nine different deep learning models based on multi-layer
perceptrons (MLP), convolutional neural networks (CNN) and long short-term
memory (LSTM) neural networks. The performance of the proposed method is then
evaluated on liquid and illiquid stocks, which are based on TotalView-ITCH US
and Nordic stocks, respectively. 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.
