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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The growth of machine-readable data in finance, such as alternative data,requires new modeling techniques that can handle non-stationary andnon-parametric data. Due to the underlying causal dependence and the size andcomplexity of the data, we propose a new modeling approach for financial timeseries data, the $\alpha_{t}$-RIM (recurrent independent mechanism). Thisarchitecture makes use of key-value attention to integrate top-down andbottom-up information in a context-dependent and dynamic way. To model the datain such a dynamic manner, the $\alpha_{t}$-RIM utilizes an exponentiallysmoothed recurrent neural network, which can model non-stationary times seriesdata, combined with a modular and independent recurrent structure. We apply ourapproach to the closing prices of three selected stocks of the S\&P 500universe as well as their news sentiment score. The results suggest that the$\alpha_{t}$-RIM is capable of reflecting the causal structure between stockprices and news sentiment, as well as the seasonality and trends. Consequently,this modeling approach markedly improves the generalization performance, thatis, the prediction of unseen data, and outperforms state-of-the-art networkssuch as long short-term memory models.

Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment