Trends in Sunspots & Agriculture Stock Prices - Finding Correlations and Predicting Trends Using Machine Learning and Deep Neural Networks

Abstract

According to previous research, sunspots and weather conditions have both direct and latent economic impacts, such as human financial activities. The goal of this project was to see if machine learning and deep neural network methods could reveal a link between natural phenomena, specifically sunspots, weather, and agricultural stock price trends. I suggested that some of these natural events could be related to the price trends of individual equities. Using machine learning and deep neural network methods, I analysed at both the general Dow Jones index level and the particular agriculture stock level. Outperforming other models, the LSTM (Long-Short-Term Memory) model produced an MSE (Mean Squared Error) error of 9.91 between the sunspot number and various agricultural price patterns, which was far lower than my hypothesis. The outcome shifts from standard algorithm trading to a completely new aspect, with (space) meteorological factors playing critical roles for the first time. The implications of these results extended far beyond commercial advantages. The findings provided unique proof that not only our commercial world is impacted by space weather, the impact of which can also be digitally recorded and anticipated. This preliminary but effective study established a computer link between space weather and human business behavior, sparking one's vivid imagination of the forces at work.