Research Project: Fractal Forecasts Models

To build new breed of Fractal Forecasts Models, LR-Physics partners with Cedric Krier and Nicolas Evrard from B2CK (Trython development).

Purpose of Fractal Forecasts Research

LR-Physics will improve Fractal Forecasts Models to be able to give decisional advices to:

  • Agricultural industry
  • Energy brokerage
  • Finance observatories

To achieve this deliverable, a team of seasoned Physicists, Mathematicians, Computer Scientists and Marketers have built a model based on the research publish by several key Professors in Zurich (Dr Didier Sornette) and in Liege (Dr Ausloos)

A lot of solution are already available on the market but they mainly focus on Stock Trading and they are not backed by Research.

Brief History

In 2014, Dr Leila Rebbouh, Physicists, Dr Elia Liitiainen, Computer Scientist, Dr Céline Brandt, Marketer et Stephan Pire, Business Developer started working on a Web Service calculating a Hurst coefficient based on set of data downloaded from Google Finance, Yahoo Finance and Bloomberg to create Fractal Forecasts Models.

We managed to put together a Web Service serving data mined and processed thru the Hurst coefficient and displayed with 3 lines (Price, Beta, Hurst) as per this Apple example:

Fractal Forecasts Models about Apple's Beta ratioFractal Forecasts Models about Apple's Hurst ratio

Fractal Forecasts Models inspired by Benoit Mandelbrot and Didier Sornette

(introduction source: Rossitsa Yalamova at University of Lethbridge)
Although Gene Fama championed the publication of Benoit Mandelbrot’s article in the Journal of Business in 1963, he also wrote a critique and thence stayed on the path of the three dominant Finance paradigms, the efficient market hypothesis (EMH; Fama, 1970), the capital-asset pricing model (CAPM; Sharpe, 1964; Lintner, 1965; & Black, 1972), and the Black-Scholes (1973) options-pricing model. In parallel, Mandelbrot (1970, 1982, 1997, 1999, Mandelbrot & Hudson, 2004) kept applying his fractal geometry ideas to stock markets. His views have more recently been picked up by others (Peters, 1991, 1994; Rosser, 2000; Sornette, 2003; Malevergne & Sornette, 2005; Jondeau et al., 2007; Calvert and Fisher, 2008).

Two competing Schools arguing about the same turf

Over the years these two competing Schools have remained contentious, seemingly arguing about the same turf, with little basis of reconciliation apparent. For example, in his 2004 book with Hudson, Mandelbrot says: “‘Modern’ financial theory is founded on a few, shaky myths that lead us to underestimate the real risk of financial markets” and: “Orthodox financial theory is riddled with false assumptions and wrong results” (pp. ix, x). According to Fama (1998), however, until new and better paradigm is put forth, one cannot criticize EMH/CAPM. Fama reduces behavioral finance–and trading dynamics—to anomalies and over-/under reaction episodes that are normally distributed.

The mathematical descriptions of financial market behavior by each School are now equally robust. Still, EMH and other paradigms have successfully remained at the center of market analysis for most researchers in the Finance community, even though the Fractal School has continued to grow in numbers of participants and depth of mathematical analysis (Adler, Feldman & Taqqu, 1998; Rachev & Mittnik, 2000; Malevergne & Sornette, 2005; Jondeau, Poon, & Rockinger, 2006). Since we have just passed through another of what Martin Greenspan recently termed a once-in-a-century market crash (the first being the 1929 crash) our concern about what sets off unusual volatility sequences and occasional extreme crashes is surely timely and calls for further analysis of when and why the EMH view of market trading shifts into behaviors better fitting fractal mathematics.

Want to read more about Fractal Forecasts Models and Financial bubbles?

Why Stock Markets Crash by Didier Sornette

The Misbehavior of Markets: A Fractal View of Financial Turbulence by Benoit Mandelbrot

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