English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Financial markets are a complex dynamical system. The complexity comes fromthe interaction between a market and its participants, in other words, theintegrated outcome of activities of the entire participants determines themarkets trend, while the markets trend affects activities of participants.These interwoven interactions make financial markets keep evolving. Inspired bystochastic recurrent models that successfully capture variability observed innatural sequential data such as speech and video, we propose CLVSA, a hybridmodel that consists of stochastic recurrent networks, the sequence-to-sequencearchitecture, the self- and inter-attention mechanism, and convolutional LSTMunits to capture variationally underlying features in raw financial tradingdata. Our model outperforms basic models, such as convolutional neural network,vanilla LSTM network, and sequence-to-sequence model with attention, based onbacktesting results of six futures from January 2010 to December 2017. Ourexperimental results show that, by introducing an approximate posterior, CLVSAtakes advantage of an extra regularizer based on the Kullback-Leiblerdivergence to prevent itself from overfitting traps.

CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets