Forecasting based on spectral time series analysis: prediction of the Aurubis stock price
-
DOIhttp://dx.doi.org/10.21511/imfi.17(4).2020.20
-
Article InfoVolume 17 2020, Issue #4, pp. 215-227
- Cited by
- 676 Views
-
729 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
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.
- Keywords
-
JEL Classification (Paper profile tab)F17, G15, G17
-
References54
-
Tables2
-
Figures3
-
- 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
-
- Abidin, S. N. Z., & Jaffar, M. M. (2012). A Review on Geometric Brownian Motion in Forecasting the Share Prices in Bursa Malaysia. World Applied Sciences Journal Special Issue of Applied Math, 17, 87-93.
- Aurubis, A. G. (2020). Der Kupfermarkt.
- Azami, H., Mohammadi, K., & Bozorgtabar, B. (2012). An Improved Signal Segmentation Using Moving Average and Savitzky-Golay Filter. Journal of Signal and Information Processing, 3, 39-44.
- Azizah, M., Irawan, M. I., & Putri, E. R. M. (2020). Comparison of Stock Price Prediction Using Geometric Brownian Motion and Multilayer Perceptron. Institut Teknologi Sepuluh Nopember, Indonesia.
- Birr, S., Volgushev, S., Kley, T., Dette, H., & Hallin, M. (2016). Quantile Spectral Analysis for Locally Stationary Time Series.
- Brockwell, P. J., & Davis, R. A. (2016). Introduction to Time Series and Forecasting (2nd ed.). New York: Springer Verlag.
- Chan, N. H. (2011). Time Series: Applications to finance with R and S-Plus. New Jersey: John Wiley & Sons.
- Cheung, Y. W., & Kon, S. L. (1995). Lag Order and Critical Values of the Augmented Dickey-Fuller Test. Journal of Business & Economic Statistics, 13(3), 277-280.
- Cochrane, J. H. (1997). Time Series for Macroeconomics and Finance.
- Cooper, M. J., Gulen, H., & Rau, P. R. (2016). Performance for Pay? The Relation Between CEO Incentive Compensation and Future Stock Price Performance.
- Deutsches Kupferinstitut Copper Alliance. (2020). Die Struktur der europäischen und deutschen Kupferindustrie.
- Eiamkanitchat, N., Moontuy, T., & Ramingwong, S. (2016). Fundamental analysis and technical analysis integrated system for stock filtration. Cluster Computing, 20, 883-894.
- Fernandez, E., Navarro, J., Solares, E., & Coello, C. C. (2019). A novel approach to select the best portfolio considering the preferences of the decision maker. Swarm and Evolutionary Computation, 46, 140-153.
- Fujianti, L. (2018). Top management characteristics and company performance: An empirical analysis on public companies listed in the Indonesian stock exchange.
- Fumi, A., Pepe, A., Scarabotti, L., & Schiraldi, M. M. (2013). Fourier Analysis for Demand Forecasting in a Fashion. Company. International Journal of Engineering Business Management, Special Issue on Innovations in Fashion Industry, 5, 1-9.
- Granger, C. W. J. (1992). Forecasting Stock Market Prices: Lessons for Forecasters. International Journal of Forecasting, 8(1), 3-13.
- Grzesica, D., & Wiecek, P. (2016). Advanced Forecasting Methods Based on Spectral Analysis. Procedia Engineering, 161, 253-258.
- Hasuike, T., & Mehlawat, M. K. (2018). Investor-friendly and robust portfolio selection model integrating forecasts for financial tendency and risk-averse. Ann Oper Res, 269, 205-221.
- Hossein, H. (2007). Singular Spectrum Analysis: Methodology and Comparison. Journal of Data Science, 5, 239-257.
- Hull, J. C. (2009). Options, Futures, and other Derivatives (3rd ed.). New Jersey.
- Jenkins, G. M., & Priestley, M. B. (1957). The Spectral Analysis of Time-Series. The Journal of the Royal Statistical Society, 19(1), 1-12.
- Keim, D. A., Nietzschmann, T., Schelwies, N., Schneidewind, J., Schreck, T., & Ziegler, H. (2006). A Spectral Visualization System for Analyzing Financial Time Series Data. Eurographics/IEE-VGTC Symposium on Visualization.
- Khair, U., Fahmi, H., Al Hakim, S., & Rahim, R. (2017). Forecasting Error Calculation with Mean Absolute Deviation and Mean Absolute Percentage Error. Journal of Physics: Conference Series, 930(1).
- Kim, J. B., Wang, Z., & Zhang, L. (2016). CEO overconfidence and stock price crash risk. Contemporary Accounting Research, 33(4), 1720-1749.
- Kim, K-j. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55, 307-319.
- Kleiner, G., & Rybachuk, M. (2016). System Structure of the Economy: qualitative time-space analysis. Fronteiras: Journal of Social, Technological and Environmental Science, 5(2), 61-81.
- Kobelt, H., & Steinhausen, D. (2006). Wirtschaftsstatistik für Studium und Praxis. Stuttgart: Schäffer Poeschel.
- Koller, W. (2012). Prognose makroökonomischer Zeitreihen: Ein Vergleich linearer Modelle mit neuronalen Netzen (Dissertation an der Wirtschaftsuniversität Wien).
- Maitah, M., Saleem, N., Malec, K., & Gouda, S. (2014). Economic Value Added and Stock Market Development in Egypt. Asian Social Science, 11(3), 126-134.
- Marques, C. A. F., Ferreira, J. A., Rocha, A., Castanheira, J. M., Melo-Gonçalves, P. Vaz, N., & Dias, J. M. (2006). Singular spectrum analysis and forecasting of hydrological time series. Science Direct, Physics and Chemistry of the Earth, 31, 1172-1179.
- Meng, X., & Chen, X. (2018). The Investment Models Based on the Fundamental and Technical Indicators of Listed Companies. Paper presented at 37th Chinese Control Conference (CCC), Wuhan.
- Minelli, S. (2012). Analyse der Relation zwischen der Volatilität und dem Handelsvolumen von börsenkodierten Immobilienaktiengesellschaften im EPRA/NAREIT Europa Index und im SXI Real Estate Share Index.
- Moncrieff, J., Clement, R., Finnigan, J., & Meyers, T. (2004). Averaging, Detrending, and Filtering of Eddy Covariance Time Series. In X. Lee, M. Wassman & B. Law (Eds.), Handbook of Micrometeorology. Atmospheric and Oceanographic Sciences Library (Vol. 29). Springer, Dordrecht.
- Montgomery, D. C., Jennings, C. L., & Kuhlahci, M. (2015). Introduction to Time Series Analysis and Forecasting (2nd ed.). New Jersey: John Wiley & Sons.
- Oest, T. (2002). Zeitreihenanalyse von Finanzdaten.
- Östermark, R. (1991). Vector forecasting and dynamic portfolio selection: Empirical efficiency of recursive multiperiod strategies. European Journal of Operational Research, 55(1), 46-56.
- Picasso, A., Merello, S., Ma, Y., Oneto, L., & Cambria, E. (2019). Technical analysis and sentiment embeddings for market trend prediction. Expert Systems with Applications, 135, 60-70.
- Potirakis, S. M., Zitis, P. I., Balasis, G., & Eftaxias, K. (n.d.). On the use of financial analysis tools for the study of Dst time series in the frame of complex systems.
- Reddy, K., & Clinton, V. (2016). Simulating Stock Prices Using Geometric Brownian Motion: Evidence from Australian Companies. Australasian Accounting, Business and Finance Journal, 10(3), 23-47.
- Reschenhofer, E., Mangat, M. K., Zwatz, C., & Guzmics, S. (2020). Evaluation of current research on stock return predictability. Journal of Forecasting, 39, 334-351.
- Scargle, J. D. (1982). Studies in Astronomical Time Series Analysis II. Statistical Aspects of Spectral Analysis of Unevenly Spaced Data. Astrophysical Journal, Part I, 263, 835-853.
- Schmelzer, M. (2009). Die Volatilität von Finanzmarktdaten. Theoretische Grundlagen und empirische Analysen von stündlichen Renditezeiten und Risikomaßen (Dissertation an der Wirtschafts- und Sozialwissenschaftlichen Fakultät der Universität zu Köln).
- Shalini, T., Pranav, S., & Utkarsh, S. (2019). Picking buy-sell signals: A practitioner’s perspective on key technical indicators for selected Indian firms. Studies in Business and Economics, 14(3), 205-219.
- Sun, F., Roderick, M. L., & Farquhar, G. D. (2018). Rainfall statistics, stationarity and climate change. PNAS.
- Tafti, F. S., Jalili, E., & Yahyaeian, L. (2013). Assessment and Analysis Strategies according to Space Matrix-case Study: Petrochemical and Banking Industries in Tehran Stock Exchange (TSE). Procedia – Social and Behavioral Sciences, 99, 893-901.
- Taylor, S. J. (2007). Modelling Financial Time Series (2nd ed.).
- Tsay, R. S. (2005). Analysis of Financial Time Series (2nd ed.). New Jersey: Hoboken.
- Wagner, W. (2019). Nichtlineare Zeitreihenanalyse als neue Methode für Eventstudien. Eine empirische Studie am Beispiel der Ergebnismeldungen von NASDAQ-Unternehmen. Wiesbaden: Gabler Verlag.
- Warner, R. (1998). Spectral Analysis of Time-Series Data (Methodology in the Social Sciences). Guilford Publications.
- Xidonas, P., Mavrotas, G., & Psarras, J. (2009). A multicriteria methodology for equity selection using financial analysis. Computers & Operations Research, 36(12), 3187-3203.
- Yi, L. (2010). The Pricing of Options With Jump Diffusion and Stochastic Volatility (Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, School of Engineering and Natural Sciences, University of Iceland).
- Yossi, A., Fiat, A., Karlin, A., McSherry, F., & Saia, J. (2001). Spectral Analysis of Data.
- Yu, J. (2011). Disagreement and return predictability of stock portfolios. Journal of Financial Economics, 99(1), 162-183.
- Zhang, L., Aggarwal, C., & Qi, G. J. (2017). Stock Price Prediction via Discovering Multi-Frequency Trading Patterns (KDD 2017 Applied Data Science Paper).