An Analysis On The Link Between Crude Oil And Technology

Year 2025, Volume: 2 Issue: 1, 59 - 68, 30.06.2025

Mustafa Berk Bacaksız , Şahin Üregil

Abstract

This paper examines the relationship between crude oil and technology stock prices particularly focusing on crudeoil’s predictive capabilities using the Long Short Term Memory(LSTM) models. At first we conduct a descriptive analysis byidentifying two distinct trends regarding the correlation between oil and tech stock prices: A strong negative correlation before2016 and a relatively weaker positive correlation after 2016. Wethen develop an LSTM model to forecast technology stock prices based on historical crude oil price data. The performance of the model is evaluated using the Mean Absolute Error (MAE), MeanAbsolute Percentage Error (MAPE) and R2 score. Although it generally shows good forecasting power, the LSTM model struggles during periods of high trade volume and volatility,which introduces outliers and reduces forecast accuracy. Despitethis, the model shows that oil prices can be used as a valuable indicator in predicting trends of the technology sector and possibly with additional enhancements such as more training data and computational power, the forecasting capabilities of such models can be improved even in volatile market conditions.This study also shows us that there is potential benefits in using alternative economic indicators such as oil prices to inform investment strategies in the technology sector.

Keywords

Time‑Series Analytics; , Feature Engineering , Dynamic Correlation Modeling , Volatility Spillover Algorithms , Big‑Data Financial Analytics , Regime‑Detection Techniques

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