Comparative Approach to the Backward Elimination and for-ward Selection Methods in Modeling the Systematic Risk Based on the ARFIMA-FIGARCH Model

Document Type: Research Paper

Authors

Department of accounting, Kashan Branch, Islamic Azad University, Kashan, Iran

Abstract

The present study aims to model systematic risk using financial and accounting variables. Accordingly, the data for 174 companies in Tehran Stock Exchange are extracted for the period of 2006 to 2016. First, the systematic risk index is estimated using the ARFIMA-FIGARCH model. Then, based on the research background, 35 affective financial and accounting variables are simultaneously used with the help of the backward elimination and forward selection method for modeling. After analyzing and evaluating the variables in Eviews software, the four variables of debt ratio (CL. E), size (SIZE), net profit to sales ratio (NETP. S), and interest rate coverage ratio (ICR) are selected in the backward elimination method. In the forward selection method, in addition to the above variables, operating profit margin (OPM) is also chosen. The estimated model of these variables in both methods shows a low ratio of R2 coefficient that is approximately 7%. In the test case, the model of forward selection method has less error in all four criteria of root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and Tile coefficient (TIC) compared to the backward elimination method.

Keywords


[1] Ahmadpour, A., Gholami Jamkarani, R., Relationship between accounting information and market risk (Companies Listed in Tehran Stock Exchange), Social Sciences and Human Sciences of University of Shiraz, 2005, 43, P.18-33.

[2] Eslami Bigdeli Gh. and Tayebi Sani, A. Optimization of investment portfolio based on value at risk using ant colony algorithm, Journal of Financial Engineering and Management of Securities, 2013, 18, P.163-184.

[3] Haji Bozorgi, J. and Akhundian, M.H., Static survey on the systematic risk of investment portfolio in Tehran Stock Exchange, Journal of Financial Engineering and Management of Securities, 2011, 6, P.215-244.

[4] Hosseinpour, A., Saeedi, P., The Relationship between financial ratios and systematic risk in the cement industry in Tehran Stock Exchange, Management Studies,2013, 2, P.80-84.

[5] Rahmani, A., Peikarjo, K., Azizi, M., The relationship between stock market beta with macroeconomic variables and accounting information, Investment Knowledge, 2014, 10, P.47-66.

[6] Saeedi, A., Rameshe, M.,The determinants of the systematic risk of shares in Tehran Stock Exchange, Financial Accounting Research, 2011, 7, P.125-142.

[7] Kiani Harchegani, M., Nabavi Chashami, S.A., Memarian, A., Stock portfolio optimization based on the minimum risk acceptance level and its components using the Genetic Algorithm. Investment Knowledge, 2014, 11, P.125-165.

[8] Gujarati, D., The Foundations of Econometrics, University of Tehran Pub., 1995.

[9] Namazi, M., Khajavi, Sh., The usefulness of accounting variables in predicting the systematic risk of companies accepted in Tehran Stock Exchange, Accounting and Auditing Reviews, 2004, 38, P.93-119.

[10] Abonongo. J., Ackora-Prah J., Boateng. K., Measuring the Systematic Risk of Stocks Using the Capital Asset Pricing Model, Journal of Investment and Management, 2017, 6, P.13-21

[11] Alqaisi Kh., The economic determinants of systematic risk in the Jordanian capital market, International Journal of Business and Social Science. 2011, 20, P.85-95.

[12] Bai, X., Ding, Y., Particle Swarm Optimization Based on an Improved Learning Strateg. Wuhan, Hubei: Second International Workshop in Education Technology and Computer Science, IEEE., 2010, 2, P.395-398

[13] Bollerslev, T., Mikkelsen, H., Modeling and pricing long memory in stock market volatility, Journal of Econometrics, 1996, 1, P.151-184.

[14] Bowman R.G., The theoretical relationship between systematic risk and financial (accounting) numbers, The Journal of Finance, 1979, 3, P.617-630.

[15] Brimble M., Hodgson A., Assessing the risk relevance of accounting variables in diverse economic conditions. Managerial finance, 2007, 33, P.553–573.

[16] Brooks, R.D., Foff R.W., Mckenzie M.D., Timevarying beta risk of Australian industry portfolios: a comparison of modelling techniques. Australian Journal of Management, 1998, 23 (1), P.1-22.

[17] Choudhry, T. Wu. H., Forecasting the weekly time- varying beta of UK firms: Garch models vs Kalman filter method, European Journal of Finance, 2009, 15(4), P.437-444.

[18] Chun L.S., Ramasamy R., Accounting variables as determinants of systematic risk in Malaysian common stocks. Asia pacific journal of management, 1989,  6, P.339–350.

[19] Cai, Z., Ren. R.W., A new estimation on time-varying betas in conditional camp, Miscellaneous papers, 2011, 7, P.211-217.

[20] Dibiase P., Apolito. E.D., The determinants of systematic risk in the Italian banking system a cross sectional time series analysis, international journal of economic and finance, 2012, 11, P.152-164.

[21] Fabozzi, F., Francis J., Beta as a random coefficient. Journal of Financial and Quantitative Analysis, 1978, 13, P.101-116.

[22] Fama, E., French. K.R., Size and book to market factors in earning and returns. Journal of Finance, 1995, 50, P.131-155.

[23] Fong C.Y., Lee. C. H., Using least square support vector regression with genetic algorithm to forecast beta systematic risk. Journal of Computational Science, 2011, 15, P.1-14.

[24] Foster G., Financial statement analysis, 2nd edition, prentice hall international, 1986.

[25] Ghasemi A., Zahediasl S., Normality Tests for Statistical Analysis: A Guide for Non-Statisticians, Endocrinal Metab, 2012, 10(2), P.486-489.

[26] Ibrahim K., Haron R., Examining systematic risk on Malaysian firms: panel data evidence.  Journal of global business and social entrepreneurship (gbse), 2016, 2, P.26-30.

[27] Koussis N., Makrominas M., Growth options, option exercise and firms’ systematic risk. Rev quant finance, 2015, 44, P.243-267.

[28] Li. J., Systematic risk, financial indicators and the financial crisis: a risk study on international airlines. University of Groningen, Netherlands, 2016

[29] Li J., FGP: A genetic programming based financial forecasting tool, Ph.D. dissertation, University of Essex, 2001.

[30] Maginn J.L.,  Tuttle D. L., Mcleavey D.W.,  Pinto J.E., Managing investment portfolios: a dynamic process. CFA Institute Investment, 2007, 3, P.164-180.

[31] Mihaylova B., Briggs A., Ohagan A., Review of statistical methods for analyzing healthcare resources and costs, Health Economics, 2011, 20, P.897–916.

[32] Mulli J.S., The effect of financial performance on systematic risk of stocks listed at the Nairobi securities exchange, School of Business, MSc Thesis, University of Nairobi, Nairobi, 2014.

[33] Mungai M.P., The relationship between working capital management and systematic risk of companies quoted at the Nairobi stock exchange, MSc thesis, university of Nairobi, Nairobi, 2010.

[34] Nishat M., Systematic risk and leverage effect in the corporate sector of Pakistan, The Pakistan development review, 2000, 39, P.951-962.

[35] Park S.Y., Kim S.H., Determinants of systematic risk in the US restaurant industry: a technical perspective, tourism economic, 2016, 22, P. 621-628.

[36] Poon S.H., Grenger C., Forecasting Volatility in Financial Markets: A Review, Journal of Economic Literature, 2003,41, P.478-539

[37] Salari L., Analysis of systematic risk impact of common stock on financial ratios of accepted plants in Tehran stock exchange. Indian Journal of Fundamental and Applied Life Sciences, 2015, 5, P.288-294.

[38] Stolbov M., Assessing systematic risk and determinants for advanced and major emerging economics: The case of Δ cover, Int. Econ. Policy, 2015, 2, P.1-34.

[39] Valipour M., Amin V., Kargosha M., Akbarpour K.,Forecasting stock systematic risk using Heuristic Algorithms.Journal of Productivity and development. 2015, 1(1), P.36-41.