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Related Experiment Video

Updated: Jul 11, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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Long- and Short-Term Memory Model of Cotton Price Index Volatility Risk Based on Explainable Artificial Intelligence.

Huosong Xia1,2,3, Xiaoyu Hou1, Justin Zuopeng Zhang4

  • 1School of Management, Wuhan Textile University, Wuhan, China.

Big Data
|November 17, 2023
PubMed
Summary

Market uncertainty impacts decisions, increasing risk. This study uses a Long Short-Term Memory (LSTM) model to analyze cotton price volatility, finding it accurately reflects trends but not exact prices.

Keywords:
LSTM modelXAIprice volatility risktransaction data plus interaction data

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Area of Science:

  • Agricultural Economics
  • Data Science
  • Time Series Analysis

Background:

  • Market uncertainty significantly impacts decision-making and increases risk for participants.
  • Cotton price volatility is complex, influenced by supply, demand, climate, and policy factors.

Purpose of the Study:

  • To reduce decision risk and support policymakers by analyzing cotton price index volatility.
  • To integrate multiple factors influencing cotton prices and develop a predictive model.

Main Methods:

  • Integration of 13 factors affecting cotton price index volatility, categorized into transaction and interaction data.
  • Construction and implementation of a Long Short-Term Memory (LSTM) model for volatility analysis.
  • Application of Explainable Artificial Intelligence (XAI) techniques for statistical analysis of input features.

Main Results:

  • The LSTM model accurately analyzes cotton price index fluctuation trends but does not predict exact prices.
  • Combined transaction and interaction data are more sensitive and beneficial for analyzing cotton price trends than transaction data alone.
  • Explainable AI enhances decision-maker confidence in the model's analysis.

Conclusions:

  • The study provides an accurate reflection of cotton market fluctuations, aiding state, enterprise, and farmer decision-making.
  • The model helps mitigate risks associated with volatile cotton prices.
  • XAI integration builds trust and facilitates the adoption of data-driven insights in market analysis.