Assessing the Effectiveness of Machine Learning Models in Predicting Stock Price Movements During Energy Crisis: Insights from Shell's Market Dynamics

The global energy crisis has presented an unprecedented degree of volatility and uncertainty in financial markets, specifically impacting the stock prices of energy sector organizations. Accurate forecasting of stock price patterns during such turbulent periods is essential for informed decision-making by investors, policymakers, and industry stakeholders. The main purpose of this study was to assess the effectiveness of different machine learning models in predicting stock price movements during an energy crisis. This research investigated the stock price fluctuations of Shell during the energy crisis, considering historical data and machine learning techniques to identify patterns and trends. The dataset for this study was sourced from accredited and credible sources providing a more detailed view of how the variation in stock prices of major energy firms was influenced by different energy crises that occurred during the period 2021-2024. The proposed three big energy companies listed under their abbreviations for convenience comprise ExxonMobil (XOM), Shelll-SHEL, and BP-BP; after which historical data was gathered using y-finance. This dataset was of great help to analysts interested in financial analysis, market behavior, and the impact of global events on the energy sector. The data consisted of the daily adjusted closing prices of the selected companies from January 2021 to date. Models like Logistic Regression, Random Forest classifiers, and Support Vector Machines classifiers were deployed since they offered distinct strengths and were capable of offering the right potential. The proven performance metric used encompassed Precision, Recall, Accuracy, and F1-Score. The Random Forest model has the highest accuracy at 0.52, followed by Logistic Regression with an accuracy of 0.51, and then the Support Vector Classifier with an accuracy of 0.50. There are great opportunities in the integration of machine learning and financial forecasting to improve predictive accuracy, especially in these volatile markets where prices fluctuate rapidly with immense uncertainty. Predictive models might also be put into practice by decision-makers within several finance-related spheres to great avail. In this regard, investment firms could practice machine learning for portfolio management by way of automated trading based on market signals in real-time. Predictive modeling of the energy crisis brings huge dividends for investors and analysts. The first among the main recommendations is to take advantage of the model predictions within a diversified investment strategy. In this direction, the investors must use the output of those different predictive models not as an isolated lead but as a complementary tool enhancing the traditional analysis techniques.