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Deep Learning Cocoa Price Prediction with Weather Data

Abstract
Climate-induced volatility in global cocoa markets poses significant challenges to producers and stakeholders, notably evidenced by the severe price surge in late 2024 following adverse weather events. This thesis investigates whether integrating weather variables, specifically temperature and precipitation, with historical cocoa futures prices can enhance predictive accuracy using Long Short-Term Memory (LSTM) neural networks. Leveraging daily price data from the ICE Futures U.S. exchange (1980–2025) and comprehensive meteorological data from the ERA5 dataset across key cocoa-producing regions, multiple LSTM models, including global and localised scales, were developed and evaluated. This deep learning approach to cocoa price prediction, incorporating meteorological inputs, addresses a gap in existing forecasting literature. Contrary to prior studies and expectations, models incorporating detailed weather indicators did not improve forecasting accuracy over a baseline model relying solely on historical prices, which achieved a notably high predictive performance (R² ≈ 0.998). A global-scale model using average climate indicators matched the baseline model's predictive power (R² ≈ 0.986), while more localised models underperformed significantly. Robustness tests, including permutation importance analyses, confirmed that historical price data predominantly drove the levels of predictive power, respectively, with weather variables providing minimal incremental value. This lack of improvement suggests that weather impacts may already be priced into market trends due to market efficiency or were too complex to capture with the utilised naive LSTM model. These results highlight methodological limitations, particularly the absence of cocoa yield data, which likely restricted the ability to capture the indirect economic impacts of weather conditions accurately. Future research incorporating this cocoa yield data within a structured causal framework, such as the DeepIV method, has the potential to more precisely model the economic transmission from climate impacts to the global cocoa market pricing.
Degree
Master 2-years
Other description
MSc in Economics
URI
https://hdl.handle.net/2077/88748
Collections
  • Master theses
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ECO 2025-2.pdf (1.004Mb)
Date
2025-07-06
Author
Bergander, William
Series/Report no.
2025:2
Language
eng
Metadata
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