Figure 8 - uploaded by Gary van Vuuren
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Rolling H(t) for the FTSE 100 from January 1998-December 2017 (note the same vertical scale as used for the S&P 500 for comparison).
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The Efficient Market Hypothesis (EMH) has been repeatedly demonstrated to be an inferior — or at best incomplete — model of financial market behavior. The Fractal Market Hypothesis (FMH) has been installed as a viable alternative to the EMH. The FMH asserts that markets are stabilized by matching demand and supply of investors’ investment horizons...
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... rolling H for the FTSE 100 is shown in Fig. 8 on the same vertical and timescale as Fig. 7(a). Again, in line with the findings of Cajueiro and Tabak (2004a, b), Investment Implications of the Fractal Market Hypothesis 12:09:13pm WSPC/276-AFE 1950001 ISSN: 2010 H % 0:5. Unlike the results obtained by Grech and Mazur (2004), no sharp decrease of H was observed for the crisis which ...
Context 2
... and of considerable severity, yet the UK market appears to have been unaffected. The FTSE 100 exhibits slight persistence (H > 0:5) between the time of the onset of the 2008 crisis and early 2012 when the sovereign crisis (which affected several European countries, including the UK albeit not as dramatically) began (Gärtner et al., 2011) -see Fig. 8. At this point, the market changes gradually to become slightly mean reverting and has since followed a random walk since 2014. From 2012, the behavior of H for the FTSE 100 closely resembles that of the S&P 500 over the same period. These developed market results reinforce results obtained previously (e.g. Alvarez-Ramirez et al. ...
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... The results showed that stock markets around the world exhibit increased frequency dominance during times of crisis, supporting the claims of the FMH. Karp and Van Vuuren (2019) argue that the FMH is a strong replacement for the EMH. ...
In this study, an attempt is made to identify commonly considered safe-haven assets through the Fractal Market Hypothesis (FMH) which assumes that the market is made up of heterogeneous agents and that there will be a dominance of the short-term investment horizon over the long-term investment horizon during a crisis period. The study considers the COVID-19 crisis as a critical period and examines the fractal behavior of the most commonly considered safe-haven assets (Gold, Bitcoin, Swiss franc and US dollar) for the sample period of 2015–2022 by using Continuous Wavelet Transform (CWT) methodology. The findings indicate that potential safe-haven assets can be identified through FMH. It also examines the potential safe-haven asset’s properties in more detail by employing a new methodology; the Wavelet Quantile Correlation (WQC) method. The study found that Gold is a safe haven only for the short run, whereas Bitcoin is a good diversifier. From the currencies, the Swiss franc is also not a good safe haven both in the short run and long run, and the US dollar is a safe haven for the long run.
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... Strong form efficiency is considered impossible in the real world [25]. Thus, the weak and semi-strong forms of the EMH are assumed to be applicable in practice [26]. ...
... The MPT, CAPM, and EMH have their own successes in financial markets, however, a large and growing literature criticizes the models [26]- [41]. Mandelbrot [42] first challenged the EMH informing those returns are non-normal. ...
... Based on the debate above, there are several studies which investigate the distributions of returns that fit the real world [26], [53]- [60]. Those studies mainly focus on chaos and fractal behaviors; however, they were confined with individual asset returns. ...
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Keywords:
econophysics; fractal markets hypothesis; efficient market hypothesis; emerging and developed markets
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This is to certify that the doctoral study by Thandi Lasana has been found to be complete and satisfactory in all respects, and that any and all revisions required by the review committee have been made. Abstract Poor hedge fund performance can impede the financial performance of an organization.
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As one of the main areas of value investing, the stock market attracts the attention of many investors. Among investors, market index movements are a focus of attention. In this paper, combining the efficient market hypothesis and the fractal market hypothesis, a stock prediction model based on mixed fractional Brownian motion (MFBM) and an improved fractional-order particle swarm optimization algorithm is proposed. First, the MFBM model is constructed by adjusting the parameters to mix geometric Brownian motion (GBM) and geometric fractional Brownian motion (GFBM). After that, an improved fractional-order particle swarm optimization algorithm is proposed. The position and velocity formulas of the fractional-order particle swarm optimization algorithm are improved using new fractional-order update formulas. The inertia weight in the update formula is set to be linearly decreasing. The improved fractional-order particle swarm optimization algorithm is used to optimize the coefficients of the MFBM model. Through experiments, the accuracy and validity of the prediction model are proven by combining the error analysis. The model with the improved fractional-order particle swarm optimization algorithm and MFBM is superior to GBM, GFBM, and MFBM models in stock price prediction.
... Karp ve Van Vuuren (2019)'un da belirttiği gibi etkin piyasa hipotezi ve hâkim finans anlayışında varlık getirilerinin normal dağılıma uyduğu ve rassal seyir izlediği varsayılmaktadır. FPH'ta ise varlık getirileri Hurst Üstelini takip etmektedir (Karp ve Van Vuuren, 2019). ...
