Forecasting based on spectral time series analysis: prediction of the Aurubis stock price


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The attempt to predict stock price movements has occupied investors ever since. Reliable forecasts are a basis for investment management, and improved forecasting results lead to enhanced portfolio performance and sound risk management. While forecasting using the Wiener process has received great attention in the literature, spectral time series analysis has been disregarded in this respect. The paper’s main objective is to evaluate whether spectral time series analysis can produce reliable forecasts of the Aurubis stock price. Aurubis poses a suitable candidate for an investor’s portfolio due to its sound economic and financial situation and the steady dividend policy. Additionally, reliable management contributes to making Aurubis an investment opportunity. To judge if the achieved forecast results can be considered satisfactory, they are compared against the simulation results of a Wiener process. After de-trending the time series using an Augmented Dickey-Fuller test, the residuals were compartmentalized into sine and cosine functions. The frequencies, amplitude, and phase were obtained using the Fast Fourier transform. The mean absolute percentage error measured the accuracy of the stock price prediction, and the results showed that the spectral analysis was able to deliver superior results when comparing the simulation using a Wiener process. Hence, spectral time series can enhance stock price forecasts and consequently improve risk management.

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    • Figure 1. Decomposition of Aurubis stock price
    • Figure 2. Periodogram
    • Figure 3. Comparison of simulation results vs the realized stock price
    • Table 1. Fourier components
    • Table 2. Aurubis stock forecasts
    • Conceptualization
      Julia Babirath, Karel Malec, Mansoor Maitah
    • Data curation
      Julia Babirath, Kamil Maitah
    • Formal Analysis
      Julia Babirath
    • Investigation
      Julia Babirath, Karel Malec, Kamil Maitah
    • Methodology
      Julia Babirath, Karel Malec
    • Software
      Julia Babirath, Rainer Schmitl
    • Validation
      Julia Babirath, Kamil Maitah, Mansoor Maitah
    • Visualization
      Julia Babirath, Rainer Schmitl, Kamil Maitah
    • Writing – original draft
      Julia Babirath, Karel Malec
    • Writing – review & editing
      Julia Babirath, Karel Malec, Rainer Schmitl, Mansoor Maitah
    • Funding acquisition
      Karel Malec
    • Supervision
      Karel Malec, Mansoor Maitah
    • Resources
      Rainer Schmitl
    • Project administration
      Mansoor Maitah