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 economy and market due to their relation to gold, crude oil, and the dollar. This study investigates the impact of correlated features on the interpretability of Long Short-Term Memory (LSTM) 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.

翻译：石油公司是全球最大的企业之一，其全球股市中的经济指标因与黄金、原油和美元的关联而对世界经济和市场产生重大影响。本研究探讨了相关特征对用于预测石油公司股票的长短期记忆网络模型可解释性的影响。为此，我们设计了一个标准的长短期记忆网络，并使用多种相关数据集对其进行训练。我们的方法旨在通过考虑影响市场的多重因素（如原油价格、黄金价格和美元）来提高股价预测的准确性。结果表明，添加与石油股票相关的特征并不能提高LSTM模型的可解释性。这些发现表明，尽管LSTM模型在预测股价方面可能有效，但其可解释性可能有限。在仅依赖LSTM模型进行股价预测时应保持谨慎，因为其缺乏可解释性可能使人们难以完全理解驱动股价变动的基本因素。