CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence
Modelwith Attention for Predicting Trends of Financial Markets
Abstract
Financial markets are a complex dynamical system. The complexity comes from the interaction
between a market and its participants, in other
words, the integrated outcome of activities of the
entire participants determines the markets trend,
while the markets trend affects activities of participants. These interwoven interactions make financial markets keep evolving. Inspired by stochastic recurrent models that successfully capture variability observed in natural sequential data such
as speech and video, we propose CLVSA, a hybrid model that consists of stochastic recurrent networks, the sequence-to-sequence architecture, the
self- and inter-attention mechanism, and convolutional LSTM units to capture variationally underlying features in raw financial trading data. Our
model outperforms basic models, such as convolutional neural network, vanilla LSTM network, and
sequence-to-sequence model with attention, based
on backtesting results of six futures from January
2010 to December 2017. Our experimental results
show that, by introducing an approximate posterior, CLVSA takes advantage of an extra regularizer based on the Kullback-Leibler divergence to
prevent itself from overfitting traps