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


Evaluating Market Dynamics: Applying The Fractal Market Hypothesis to Stock Behaviour in Nigeria

Ejem, Chukwu Agwu, Nwakodo, Ogechi Blessing


Abstract


This study empirically tested the Fractal Market Hypothesis in the Nigerian Stock Market (NSM) using Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) models. The Fractal Market Hypothesis 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. Daily stock price data from 2015 to 2024 were analyzed to assess volatility dynamics, fractal behaviour, and asymmetric effects in the Nigerian Stock Market. 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 Nigerian Stock Market. This supports the Fractal Market Hypothesis, 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.


keywords:

Fractal Market Hypothesis, Nigerian Stock Market, EGARCH, Volatility Clustering


References:


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


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