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