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\cite{ec00} and market due to their relation to gold\cite{ec01}, crude oil\cite{ec02}, and the dollar\cite{ec03}. This study investigates the impact of correlated features on the interpretability of Long Short-Term Memory(LSTM)\cite{ec04} 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 system\cite{ec05}. One of the fundamental challenges faced in utilizing LSTM models for financial markets lies in interpreting the unexpected feedback dynamics within them.

翻译：石油公司是全球规模最大的企业之一，其在全球股市中的经济指标因与黄金、原油及美元密切相关，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基于长短期记忆（LSTM）模型预测石油公司股票可解释性的影响。为此，我们设计了一个标准长短期记忆网络，并使用多种相关数据集进行训练。该方法通过考虑影响市场的多重因素（如原油价格、黄金价格和美元汇率），旨在提升股价预测的准确性。结果表明，添加与石油股票相关的特征并不能改善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，但其可解释性有限。在仅依赖LSTM模型进行股价预测时应保持谨慎，因为其可解释性不足可能导致难以全面理解驱动股价变动的潜在因素。我们采用复杂度分析来支撑论点，认为金融市场包含一种物理复杂系统。利用LSTM模型应对金融市场所面临的基本挑战之一在于解释其内部的意外反馈动态。