Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models Evidence from European Financial Markets and Bitcoins

ArXiv ID: 2307.08853 “View on arXiv”

Authors: Unknown

Abstract

This study analyzes the transmission of market uncertainty on key European financial markets and the cryptocurrency market over an extended period, encompassing the pre, during, and post-pandemic periods. Daily financial market indices and price observations are used to assess the forecasting models. We compare statistical, machine learning, and deep learning forecasting models to evaluate the financial markets, such as the ARIMA, hybrid ETS-ANN, and kNN predictive models. The study results indicate that predicting financial market fluctuations is challenging, and the accuracy levels are generally low in several instances. ARIMA and hybrid ETS-ANN models perform better over extended periods compared to the kNN model, with ARIMA being the best-performing model in 2018-2021 and the hybrid ETS-ANN model being the best-performing model in most of the other subperiods. Still, the kNN model outperforms the others in several periods, depending on the observed accuracy measure. Researchers have advocated using parametric and non-parametric modeling combinations to generate better results. In this study, the results suggest that the hybrid ETS-ANN model is the best-performing model despite its moderate level of accuracy. Thus, the hybrid ETS-ANN model is a promising financial time series forecasting approach. The findings offer financial analysts an additional source that can provide valuable insights for investment decisions.

Keywords: ARIMA, Hybrid ETS-ANN, kNN, Time series forecasting, Machine learning, Multi-Asset

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper applies established statistical and machine learning models (ARIMA, ETS-ANN, kNN) with standard accuracy metrics, requiring significant data processing and backtesting, but involves minimal novel mathematical derivations or complex theoretical proofs.

  flowchart TD
    A["Research Goal:<br>Analyze market uncertainty transmission & compare forecasting models"] --> B["Data Collection<br>European financial markets & Bitcoin indices<br>Pre/During/Post-pandemic periods"]
    B --> C["Methodology: Comparative Analysis<br>Parametric vs Non-parametric vs Hybrid"]
    C --> D["Modeling & Computation"]
    subgraph D [" "]
        D1["ARIMA"]
        D2["Hybrid ETS-ANN"]
        D3["k-NN"]
    end
    D --> E["Performance Evaluation<br>Forecast Accuracy Metrics"]
    E --> F["Key Findings/Outcomes"]
    F --> G["ARIMA best in 2018-2021"]
    F --> H["Hybrid ETS-ANN best overall<br>Promising for time series"]
    F --> I["k-NN outperforms in specific periods<br>Dependent on metric"]