42,956 research outputs found
Feature engineering for mid-price prediction with deep learning
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
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books
We develop a large-scale deep learning model to predict price movements from
limit order book (LOB) data of cash equities. The architecture utilises
convolutional filters to capture the spatial structure of the limit order books
as well as LSTM modules to capture longer time dependencies. The proposed
network outperforms all existing state-of-the-art algorithms on the benchmark
LOB dataset [1]. In a more realistic setting, we test our model by using one
year market quotes from the London Stock Exchange and the model delivers a
remarkably stable out-of-sample prediction accuracy for a variety of
instruments. Importantly, our model translates well to instruments which were
not part of the training set, indicating the model's ability to extract
universal features. In order to better understand these features and to go
beyond a "black box" model, we perform a sensitivity analysis to understand the
rationale behind the model predictions and reveal the components of LOBs that
are most relevant. The ability to extract robust features which translate well
to other instruments is an important property of our model which has many other
applications.Comment: 12 pages, 9 figure
Tensor Representation in High-Frequency Financial Data for Price Change Prediction
Nowadays, with the availability of massive amount of trade data collected,
the dynamics of the financial markets pose both a challenge and an opportunity
for high frequency traders. In order to take advantage of the rapid, subtle
movement of assets in High Frequency Trading (HFT), an automatic algorithm to
analyze and detect patterns of price change based on transaction records must
be available. The multichannel, time-series representation of financial data
naturally suggests tensor-based learning algorithms. In this work, we
investigate the effectiveness of two multilinear methods for the mid-price
prediction problem against other existing methods. The experiments in a large
scale dataset which contains more than 4 millions limit orders show that by
utilizing tensor representation, multilinear models outperform vector-based
approaches and other competing ones.Comment: accepted in SSCI 2017, typos fixe
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