# The Interpretability of LSTM Models for Predicting Oil Company Stocks: Impact of Correlated Features

arXiv: https://arxiv.org/abs/2201.00350 · HTML: https://mlsanity.com/paper/2201.00350/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 2 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization](https://mlsanity.com/paper/1808.02941/) (2018)
- [Reinforcement Learning in Economics and Finance](https://mlsanity.com/paper/2003.10014/) (2020)
- [Deep Learning in Robotics: A Review of Recent Research](https://mlsanity.com/paper/1707.07217/) (2017)
- [Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs](https://mlsanity.com/paper/1603.00423/) (2016)

## Abstract (arXiv)

Oil companies are among the largest companies in the world whose economic indicators in the global stock market have a great impact on the world economyciteec00 and market due to their relation to goldciteec01, crude oilciteec02, and the dollarciteec03. This study investigates the impact of correlated features on the interpretability of Long Short-Term Memory(LSTM)citeec04 models for predicting oil company stocks. To achieve this, we designed a Standard Long Short-Term Memory (LSTM) network and trained it using various correlated datasets. Our approach aims to improve the accuracy of stock price prediction by considering the multiple factors affecting the market, such as crude oil prices, gold prices, and the US dollar. The results demonstrate that adding a feature correlated with oil stocks does not improve the interpretability of LSTM models. These findings suggest that while LSTM models may be effective in predicting stock prices, their interpretability may be limited. Caution should be exercised when relying solely on LSTM models for stock price prediction as their lack of interpretability may make it difficult to fully understand the underlying factors driving stock price movements. We have employed complexity analysis to support our argument, considering that financial markets encompass a form of physical complex systemciteec05. One of the fundamental challenges faced in utilizing LSTM models for financial markets lies in interpreting the unexpected feedback dynamics within them.