WORLD JOURNAL OF FINANCE AND INVESTMENT RESEARCH (WJFIR )
E-ISSN 2550-7125
P-ISSN 2682-5902
VOL. 8 NO. 5 2024
DOI: 10.56201/wjfir.v8.no5.2024.pg56.72
Ejem, Chukwu Agwu, Nwakodo, Ogechi Blessing
This study empirically tested the Fractal Market Hypothesis (FMH) in the Nigerian Stock Market (NSM) using Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) models. The FMH suggests that financial markets exhibit fractal characteristics, with price movements influenced by heterogeneous agents and long-term memory, as opposed to the random walk assumption of the Efficient Market Hypothesis (EMH). Daily stock price data from 2015 to 2024 were analyzed to assess volatility dynamics, fractal behavior, and asymmetric effects in the NSM. The findings revealed significant volatility clustering and asymmetric effects in stock returns, consistent with fractal behaviour. The EGARCH model demonstrated that negative shocks increase volatility more than positive shocks of equal magnitude, highlighting the leverage effect in the NSM. This supports the FMH, suggesting that stock prices are influenced by historical patterns, thus challenging market efficiency in its strictest form. These results have implications for both market participants and regulators. Investors may exploit the fractal characteristics and volatility patterns to enhance risk management and trading strategies. Policymakers should consider these dynamics to improve market transparency and stability. Future research could explore the impact of macroeconomic shocks on these fractal properties and extend this analysis to other emerging markets.
Fractal Market Hypothesis, Nigerian Stock Market, EGARCH, Volatility Clustering
Ayankunle, I.A, Nwakanma, P C. & Torbira, L. L (2021) Fractal Price Hypothesis and Stock
Market Behaviour in Nigeria, Egypt, and South Africa. International Journal of
Economics, Finance and Entrepreneurship (NIRA IJEFE) ISSN: 2713-4679. 6(3), 84-112.
Bachelier, L. (1900). The Random Character of Stock Market Prices. Cambridge: MIT Press.
Black, F. (1976) Studies of stock market volatility changes. Proceedings of the American
Statistical Association, Business and Economic Statistics Section, 177-181.
Brooks, C.(2008). Introductory econometrics for finance (2nd ed). New York: Cambridge
University Press
Christie, A.A. (1982). The stochastic behavior of common stock variance-value, leverage, and
interest rate effects. Journal of Financial Economics, 10(4), 407-423.
Cowles, A. (1933). Can stock market forecasters forecast? Econometrica, 1(3), 309–324.
Ehiedu, V.C & Obi, C.K (2022) Exchange in The Midst of Global Financial Crisis. International
Journal of Academic Management Science Research (IJAMSR) ISSN: 2643-900X 6(8),
263-273.
Engle, R. F., & Patton, A. J. (2001). What good is a volatility model? Quantitative Finance, 1(1),
237-245
Engle, R.F. & Ng, V.K. (1993). Measuring and testing the impact of news on volatility. Journal
of Finance, 48, 1749-1778.
Engle, R.F., Lilien, D.M. & Robbins, R.P. (1987). Estimating time varying risk premia in the term
structure: The ARCH-M model, Econometrica, 55, 391-407.
Fama, E. F. (1965). Random Walks in Stock Market Prices. Financial Analysts Journal, 21, 55–
https://doi.org/10.2469/faj.v51.n1.1861
Fama, E. F. (1970). Efficient Capital Markets: A review of theory and empirical work. Journal of
Finance, 25(2), 383–417.
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. Journal of
Finance, 25(2), 383-417. https://doi.org/10.2307/2325486
Hartman, J. & Rodestedt, R. (2010). The Speed of Adjustment of Stock Prices to New Information-
An event study on the Swedish stock market.
Karaomer, Y (2022) Investigation of Fractal Market Hypothesis in Emerging Markets: Evidence
from the MINT Stock Markets. Organizations and Markets in Emerging Economies. ISSN
2029-4581 ISSN 2345-0037 2022, 13, no. 2(26), pg. 467–489 DOI:
https://doi.org/10.15388/omee.2022.13.89
Karp, A., & Van Vuuren, G. (2019). Investment implications of the fractal market hypothesis.
Annals of Financial Economics, 14(01), 1950001.
Kendal, M. (1953). The analysis of economic time series, part 1: prices. Journal of the Royal
Statistical Society, Series A, 96, 11-25.
Kristoufek, L. (2013). Fractal markets hypothesis and the global financial crisis: Wavelet power
evidence. Scientific reports, 3.
Kumar, A. S., Jayakumar, C., & Kamaiah, B. (2017). Fractal market hypothesis: evidence for nine
Asian forex markets. Indian Economic Review, 52(1), 181-192.
Kurov, A., Sancetta, A., Strasser, G. and Wolfe, M. H. (2015). Price drift before US
macroeconomic news: Private information about public announcements. West Virginia
University, University of London, Boston College, and Washington State University.
Lo, A. W., & MacKinlay, A. C. (1988). Stock Market Prices Do Not Follow Random Walks:
Evidence from a Simple Specification Test. The Review of Financial Studies, 1(1), 41-66.
Mandelbrot, B. (1972). Statistical Methodology for Nonperiodic Cycles: from the Covariance
R/S Analysis. In Annals of Economic and Social Measurement,1(3), 259-290.
Mandelbrot, B. B. (1982). The Fractal Geometry of Nature. SanFrancisco: Freeman.
Mandelbrot, B., (1971), The Pareto-Levy Law and the Distribution of Income. International
Economic Review 1; https://doi.org/10.2307/2525289
Metuscu, A (2022) Fractal Market Hypothesis VS. Efficient Market Hypothesis: Applying the R/S
Analysis on the Romanian Capital Market. Journal of Public Administration, Finance and
Law. Pg 199-209. https://doi.org/10.47743/jopafl
Mutinda J.M, Njeru D.M & Mwangi C.I (2022) Test of existence of long-term Memory in Stock
Market Returns at the Nairobi Securities Exchange. African Development Finance Journal
3(1) ISSN 2522—3138. 171-184
Mutinda, J.M (2018) Test of Existence of Long-term Memory in Stock Market Returns at Nairobi
Securities Exchange. A Research project submitted in partial fulfillment for the award for
the Degree of Masters of Finance, School of Business, University of Nairobi.
Nelson, D. B. (1991) Conditional Heteroskedasticity in Asset Returns: A New Approach.
Econometrica, 59, 347-370.
Ngozi, B. I (2017) The Effect of Global Financial Crisis on the Performance of Nigerian Stock
Exchange. International Journal of Accounting and Economics Studies, 5 (1) (2017) 46-
50 Website: www.sciencepubco.com/index.php/IJAES doi: 10.14419/ijaes.v5i1.7344
Nicola A and Joseph. N (2013)The Fractal Market Hypothesis and its implications for the stability
of financial markets. Financial Stability Paper No. 23 – August. Bank of England ISSN
1754–4262.
Nwosa, P.I and Oseni, I.O (2011) Efficient Market Hypothesis and Nigerian Stock Market.
Research Journal of Finance and Accounting Vol 2, No 12, Pg 38-48 www.iiste.org ISSN
2222-1697
Nyamute, W., Oloko, M., & Lishenga, J. (2017). The Relationship between Investor Behavior and
Portfolio Performance at the Nairobi Securities Exchange
Okpara, G.C (2010) Stock market prices and the random walk hypothesis: Further evidence from
Nigeria.
Journal of
Economics
and International Finance,
2(3), 049-057,
http://www.academicjournals.org/JEIF
Peters, E. E. (1994). Fractal market analysis: Applying chaos theory to investment and economics.
Wiley.
Peters, E. E., (1991) Chaos and Order in the Capital Markets – A New View of Cycles, Prices, and
Market Volatility. Financial Analysts Journal, 47(2), 55-62.
Peters, E. E., (1994), Fractal Market Analysis. Applying Chaos Theory to Investment and
Economics, London: John Wiley & Sons, Inc.
Samuelson, P. A. (1965). Proof that Properly Anticipated Prices Fluctuate Randomly. Industrial
Management Review, 6, 41–49. https://doi.org/10.1142/9789814566926_0002
Sapong, P.K (2017) Trading in Chaos: Analysis of Active Management in a Fractal Market. A
thesis submitted in the fulfilment of the requirements for the degree Of Doctor of
Philosophy in Finance School of Accounting, Economics and Finance University of
KwaZulu-Natal