Size Effect Anomalies and Firms Financial Distress; Evidence from Nairobi Securities Exchange, Kenya
Roche, Charles
Department of Economics, Accounting and Finance, School of Business & Entrepreneur
Jomo Kenyatta University of Agriculture and Technology, Kenya.
https://doi.org/10.47191/jefms/v5-i9-21
ABSTRACT:
The universal objective of this study is to establish the relationship between size effect anomalies and financial distress of listed firms in Nairobi Securities Exchange (NSE), Kenya. Due to the size effect anomalies, firms experience financial distress. The study adopts descriptive research design and positivist research. It considered all listed firms in NSE which had been licensed by Capital Market Authority (CMA) as at 1st January 2017, totaling to 67 which constitutes the target population. The study considers secondary data which were extracted from the audited financial statements from individual firms from 2007 to 2017. This study will apply panel data model and in data analysis and presentation, the statistical software to be used is EView while the p-value will be applicable in hypothesis testing. The Z-Score, a multivariate approach was applied as the financial prediction model. The results were presented using tables. Size effect anomalies was established to have weak negative correlations with the financial distress. The size effect anomaly was statistically significant at five percent level of significance meaning that the null hypothesis failed to be accepted. The study’s recommendations will assist the management, investors, researchers, policy makers and regulators.
KEYWORDS:
financial distress, securities exchange, size effect anomalies
REFERENCES:
1) Adams, J., Hayunga, D., Mansi, S., Reeb, D., & Verardi, V. (2019). Identifying and treating outliers in finance. Financial Management, 48(2), 345-384.
2) Aguilar, E., Aziz Barry, A., Brunet, M., Ekang, L., Fernandes, A., Massoukina, M., & Zhang, X. (2009). Changes in temperature and precipitation extremes in western central Africa, Guinea Conakry, and Zimbabwe, 1955–2006. Journal of Geophysical Research: Atmospheres, 114(D2).
3) Alile, H. I. (1984). The Nigerian Stock Exchange: Historical perspective, operations and contributions to economic development. Central Bank of Nigeria Bullion, Silver Jubilee edition, 2, 65-69.
4) Almeida, H. & Philippon, T. (2006). The Risk-Adjusted Cost of Financial Distress. New York: New York University.
5) Altman, E. I., Haldeman, R. G., & Narayanan, P. (1977). ZETATM analysis a new model to identify bankruptcy risk of corporations. Journal of banking & finance, 1(1), 29-54.
6) Altman, E.I. & Hotchkiss, E. (2010). Corporate Financial Distress and Bankruptcy: Predict and Avoid Bankruptcy, Analyze and Invest in Distressed Debt, Vol. 289. New York: John Wiley & Sons.
7) Altman, Edward I. (2000). Predicting Financial Distress of Companies: Revisiting the Z-Score and Zeta models, New York University.
8) Altman, E., Hartzell, J. & Peck, M. (1995). Emerging Markets Corporate Bonds: A Scoring System, New York: Salomon Brothers Inc.
9) Arnold, G. (2016). Corporate Financial Management. (5th Edition). Harlow: Pearson Education Limited.
10) Avramov, D., Chordia, T., Jostova, G., & Philipov, A. (2013). Anomalies and financial distress. Journal of Financial Economics, 108(1), 139-159.
11) Baimwera, B. & Muriuki, A. M. (2014). Analysis of Corporate Financial Distress Determinants: A survey of Non-Financial Firms Listed in the NSE. International Journal of Current Business and Social Sciences, 1(2), 58-80.
12) Bajpai, V. & Singh, R. (2011). Orthogonal Micro-grooving of Anisotropic Pyrolytic Carbon, Materials and Manufacturing Processes, 26(10-12), 1481-1493.
13) Banz, R. (1981). The Relationship between Return and Market Value of Common Stock, Journal of Financial Economics, 9, 3-18.
14) Beaver, W. H. (1967). Financial ratios as predictors of failure. Journal of accounting research, 71-111.
15) Brav, A. & Heaton, J.B. (2002). Competing Theories of Financial Anomalies. Review of Financial Studies, 15 (2), 575.
16) Campbell, J. Y., Hilscher, J., & Szilagyi, J. (2008). In search of distress risk. The Journal of Finance, 63(6), 2899-2939.
17) Campbell, J.Y., Hilscher, J. & Szilagyi, C. (2011). Predicting Financial Distress and the Performance of Distressed Stocks. Journal of Investment Management, 9(2), 14-34.
18) Chongyu, D, Li, Z. F., & Yang, C. (2018). Measuring firm size in empirical corporate finance. Journal of banking & finance, 86, 159-176.
19) CMA (2012). Capital Markets and Securities Public Offers Listings and Amendment Regulations. Nairobi: CMA.
20) Cooper, R. D. & Schindler, P. S. (2014). Business Research Methods. (12th Edition). New York. McGraw-Hill Education
21) Deakin, E. (1972). A Discriminant Analysis of Predictors of Business Failure. Journal of Accounting Research, 10, 167-179.
22) Dickey, D.A. & Fuller, W.A. (1979). “Distribution of the Estimators for Autoregressive Time Series with a Unit Root,” Journal of the American Statistical Association, 74, 427-431.
23) Duy, N. T., & Phuoc, N. P. H. (2016). The relationship between firm sizes and stock returns of service sector in Ho Chi Minh City stock exchange. Rev. Eur. Stud., 8, 210.
24) Fama, E. & French, K. (1993). Common Risk Factors in the Returns of Stocks and Bonds, Journal of Financial Economics, 33, 3-56.
25) Gompers, P.A. & Metrick, A. (2001). Institutional Investors and Equity Prices. Quarterly Journal of Economics, 116, 229–259.
26) Gujarati, C.M. & Porter, D.L (2010). Essential of Econometrics (6th Ed). New York: McGraw. Hill International Edition.
27) Gupta, S.P. (2002). Statistical Methods. New Delhi: Sultan Chand & Sons.
28) Hair, J., Blacks, W., Babin, B., Anderson, R. & Tatham, R. (2006). Multivariate Data Analysis (6th Ed.). Upper sadle River, N.J: Pearson Prentice Hall.
29) Hausman, J.A. (1978). Specification Tests in Econometrics. Econometrica, 46, 1251-1271.
30) Hawawini, G. & Keim, D. (2000). The Cross Section of Common Stock Returns: A Review of the Evidence and Some New Findings, in Keim, D.B. and W.T. Ziemba, Security Market Imperfections in Worldwide Equity Markets, Cambridge: Cambridge University Press.
31) Hubbard, R. G. (2008). Money, the financial system, and the economy, (6th Ed.). USA: Pearson Education, Inc.
32) Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for Unit Roots in heterogeneous Panels. Journal of Economics. 115, 53-74.
33) Karugu, R.M., Memba, F.S. & Muturi, W.M. (2018). Effects of Securities Behaviour on Performance of Nairobi Securities Exchange Indices. Research Journal of Finance and Accounting, 9(14), 1-16.
34) Keim, D. B. (1983). Size-related Anomalies and Stock Return Seasonality: Further Empirical Evidence. Journal of Financial Economics, 12(1), 13-32.
35) Kothari, C. R. & Garg G. (2014). Research Methodology. Methods and Techniques, (3rd Ed.) New Delhi: New Age International Publishers.
36) Levin, A., Lin, C., & Chu, C. (2002). Unit Root Test in Panel Data: Asymptotic and Finite Sample Properties. Journal of Econometrics, 108(1), 1-24.
37) Maina F. G. & Sakwa M. M. (2012). Understanding Financial Distress among Listed Firms in Nairobi Stock Exchange: A Quantitative Approach Using the Z-Score Multi-Discriminant Financial Analysis Model, Juja: JKUAT, Nairobi, Kenya.
38) Marcus, M., Leporcher, Y. M., & Eu, C. H. (2015). Applied asset and risk management. Retrieved March, 20, 2018.
39) Miles, M. B., & Huberman, A. M. (2009). Qualitative Data Analysis: An Expanded Source Book (2nd Ed.). Newbury Park, CA: Sage.
40) Muchina, S.W. (2015). Determinants of Share Price Volatility of Companies Listed in Nairobi Securities Exchange in Kenya, Juja: JKUAT.
41) Mugenda A. & Mugenda M. (2013). Research Methods, Quantitative and Qualitative Approaches, Nairobi. African Centre for Technology Studies (ACTS).
42) Nelson, C.R. & Plosser, C.I. (1982). Trends and Random Walk in Macroeconomic Time Series; Some Evidence and Implications,” Journal of Monetary Economics, 10, 139-162.
43) Nunzio, C. & Diego, L. (2016). Unit Root Tests: The Role of Univariate Models Implied by Multivariate Time Series. Econometrics, 4(2), 1-11.
44) Olweny, T.O. & Kimani, D. (2011). Stock Market Performance and Economic Growth: Empirical Evidence from Kenya Using Causality Test Approach. Advances in Management & Applied Economics, 1(3), 153-196.
45) Polit, D. & Beck, C. (2017). Nursing Research: Generating and Assessing Evidence for Nursing Practice, (10th Edition), Wolters Kluwer: Lippincott, Williams & Wilkins.
46) Reed, W.R. (2014). Unit Root Tests, Size Distortion, and Cointegrated Data; Working Paper; Christchurch, New Zealand: University of Canterbury.
47) Saunders, M., Lewis, P. & Thornhill, A. (2012). Research Methods for Business Students, Harlow: Pearson Education Ltd.
48) Sekaran, U. & Bougie, R. (2011). Research Methods for Business: A Skill Building Approach. (5th Edition). Delhi: Aggarwal Printing Press.
49) Shaughnessy, J. J., Zechmeister, E. B. & Zechmeister, J. S. (2002). Research Methodology in Psychology, 5/e. New York: mcgraw-Hill.
50) Szyszka, A. (2007). From the efficient market hypothesis to behavioral finance: How investors' psychology changes the vision of financial markets. Retrieved from: SSRN 1266862.
51) Ulf von Kalckreuth, (2005). A "Wreckers Theory" of Financial Distress, No 2005, Vol. 40, Discussion Paper Series 1: Economic Studies, Deutsche Bundesbank.
52) Wang, Y., Wang, W. C., & Wang, J. J. (2017). Credit Risk Management Framework for Rural Commercial Banks in China. Journal of Financial Risk Management, 6, 48-65.
53) Warner, J.B. (1977). Bankruptcy Costs: Some Evidence. The Journal of Finance, 32(2), pp 337-347.
54) Westfall, P. (2014). Kurtosis as Peakedness, 1905 – 2014, RIP. The American Statistician, 68.
55) Zikmund, G.W., Babin, B.J., Carr, C.J. & Griffin, M. (2013). Business Research Methods, (9th Edition), SouthWestern: Cengage Learning.