Financial time series modelling: return on assets

Mykola Kushnir, Kateryna Tokarieva


The paper discovers certain aspects of financial time series, in particular, modeling of return on assets. The object of research is a system of indicators for analyzing the returns of financial time series. There is a key feature that distinguishes the analysis of financial time series from the analysis of other time series, as financial theory and its empirical time series contain an element of uncertainty. As a result of this additional uncertainty, statistical theory and its methods and models play an important role in the analysis of financial time series.

One of the most problematic places is the use of asset prices and their volatility in the analysis and forecasting of financial time series, which is false because such series contain an element of uncertainty. Therefore, the so-called return on financial assets and instruments should be used in tasks of this type.

The paper deals with the types of return on financial assets that can be used in mathematical modeling and forecasting of stock indices. Static methods are used to eliminate the disadvantages of using financial asset prices in the analysis and forecasting of financial time series. The empirical properties of financial time series are examined using the PFTS (First Stock Trading System) and S&P 500 indices.

A comprehensive system of indicators of time series analysis of financial assets is obtained. The proposed system involves the use of numerous methods of calculating the profitability (return) of assets in order to determine significant statistical characteristics of the data. Compared to similarly known methods of using prices (rather than profitability) of assets, this provides a key advantage that allows elements of uncertainty in financial and economic data.


financial time series; profitability of assets; mathematical modeling of stock indices


Lin, C.-S., Chiu, S.-H., Lin, T.-Y. (2012). Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting. Economic Modelling, 29 (6), 2583–2590. doi:

Yoo, P. D., Kim, M. H., Jan, T. (2005). Machine learning techniques and use of event information for stock market prediction: A survey and evaluation. Computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce, 835–841. doi:

Tay, F. E., Cao, L. (2001). Application of support vector machines in financial time series forecasting. Omega, 29 (4), 309–317. doi:

Giacomini, R., Gottschling, A., Haefke, C., White, H. (2008). Mixtures of t-distributions for finance and forecasting. Journal of Econometrics, 144 (1), 175–192. doi:

Campbell, J. Y., Lo, A. W., MacKinlay, A. C. (1997). The Econometrics of Financial Markets. Princeton: Princeton University Press. doi:

Kamaruzzaman, Z. A., Zaidi, I., Mohd Tahir, I. (2012). Mixtures of Normal Distributions: Application to Bursa Malaysia Stock Market Indices. World Applied Sciences Journal, 16, 781–790.

Tsay, R. S. (2010). Analysis of Financial Time Series. Wiley. doi:

Murphy, J. J. (1999). Technical analysis of the financial markets. New York: New York Institute of Finance, 542.

Lam, M. (2004). Neural network techniques for financial performance prediction: integrating fundamental and technical analysis. Decision Support Systems, 37 (4), 567–581. doi:

Vanstone, B., Finnie, G. (2010). Enhancing stockmarket trading performance with ANNs. Expert Systems with Applications, 37 (9), 6602–6610. doi:

GOST Style Citations

Copyright (c) 2019 Mykola Kushnir, Kateryna Tokarieva

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN (print) 2664-9969, ISSN (on-line) 2706-5448