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A Comparison on LSTM Deep Learning Method and Random Walk Model Used on Financial and Medical Applications: An

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This study models cryptocurrency price trends using Long Short-Term Memory (LSTM) networks and Random Walk models, finding limitations in predicting Bitcoin, Ethereum, and COVID-19 impacts. Both models show distinct characteristics with varying parameters.

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Area of Science:

  • * Computational Finance
  • * Econometrics
  • * Data Science

Background:

  • * Analyzing cryptocurrency price trends is crucial for financial markets.
  • * The intersection of cryptocurrency markets and global health events (e.g., COVID-19) presents unique challenges.
  • * Existing financial theories and computational models are being adapted to understand these complex dynamics.

Purpose of the Study:

  • * To develop and evaluate a Long Short-Term Memory (LSTM) network model for cryptocurrency price trend prediction.
  • * To compare the performance of LSTM networks against the Random Walk model.
  • * To investigate the influence of different parameter settings (window length, prediction horizons) on model accuracy and practicality.

Main Methods:

  • * Quantitative analysis using Python programming language.
  • * Implementation of Long Short-Term Memory (LSTM) networks via the TensorFlow package.
  • * Application of the Random Walk model with varied parameter settings.
  • * Focus on Bitcoin, Ethereum, and COVID-19 related data.

Main Results:

  • * Both LSTM and Random Walk models demonstrated limitations in precise cryptocurrency price prediction.
  • * Different parameter settings significantly impacted the performance characteristics of each model.
  • * A trade-off was observed between model accuracy and practical applicability.

Conclusions:

  • * The study highlights the inherent difficulties in accurately forecasting cryptocurrency prices, even with advanced models.
  • * Understanding the distinct behaviors of different predictive models under various configurations is essential.
  • * Findings suggest a need for further research into more robust modeling techniques for volatile financial markets influenced by external factors.