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Oil companies are among the largest companies in the world whose economicindicators in the global stock market have a great impact on the worldeconomy\cite{ec00} and market due to their relation to gold\cite{ec01}, crudeoil\cite{ec02}, and the dollar\cite{ec03}. This study investigates the impactof correlated features on the interpretability of Long Short-TermMemory(LSTM)\cite{ec04} models for predicting oil company stocks. To achievethis, we designed a Standard Long Short-Term Memory (LSTM) network and trainedit using various correlated datasets. Our approach aims to improve the accuracyof stock price prediction by considering the multiple factors affecting themarket, such as crude oil prices, gold prices, and the US dollar. The resultsdemonstrate that adding a feature correlated with oil stocks does not improvethe interpretability of LSTM models. These findings suggest that while LSTMmodels may be effective in predicting stock prices, their interpretability maybe limited. Caution should be exercised when relying solely on LSTM models forstock price prediction as their lack of interpretability may make it difficultto fully understand the underlying factors driving stock price movements. Wehave employed complexity analysis to support our argument, considering thatfinancial markets encompass a form of physical complex system\cite{ec05}. Oneof the fundamental challenges faced in utilizing LSTM models for financialmarkets lies in interpreting the unexpected feedback dynamics within them.

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