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Dedicated to better understanding bubbles in and out of finance



Berman Sarrazin is an independent research and education center that dedicates its work to better understanding the birth and burst of bubbles in and out of financial markets.
Bubbles arise from complexity, randomness, and irrationality. Berman Sarrazin creates and supports scientific research on those three topics. 
From this research, Berman Sarrazin derives tools and solutions for regulators, financial firms, and medical institutions. 



Berman Sarrazin pursues four goals:


To provide market regulators and governemental entities with tools to better anticipate financial bubbles.

Market efficiency

To cater to the finance industry with investment solutions designed to take advantage of bubbles and make markets more efficients.

knowledge beyond Finance

To improve human knowledge on bubbles from a social and medical perspective.


To educate business professionals on the creation and burst of bubbles and the limits of rational reasoning.


“Bubbles are natural states of equilibrium.”

Nicolas Martelin | Founder



In the economic world, the efficiency of markets has long been considered a truism. Efficient markets cannot be predicted. Their randomness should turn any prediction about future prices into a matter of luck. Yet bubbles come and go every now and then. In a bubble, it is easier to forecast prices since they have a tendency to follow a trend: the random walk hypothesis of prices does not seem to always hold.
Financial markets are often compared to casino games. Their partial randomness gives them the look of a roulette. But unlike a fair game, a market's decision power - the ball - is influenced by the bets of its players. The bets being the players' beliefs in the game's outcome, the randomness of markets is just the materialization of people believing in the randomness of markets.
When this belief is no longer agreed on, a bubble appears.

In 2013, we started to develop an algorithm aimed at detecting non-random patterns in financial time series. We were able to detect bubbles in the making, which was a promising finding as the mainstream idea at the time was that the detection of bubbles was only possible after their burst.
For three years, we fine-tuned the algorithm and applied it to a variety of assets (stocks, indices, bonds, artworks). Its results for artworks led to a scientific publication in the Journal of Empirical Finance.
In 2015, we realized that we could derive more information from the algorithm than just the binary outcome: "bubble" or "not bubble". We managed to quantify the increase or decrease in randomness in a time series so that even in a non-bubble period, we could identify low and high price levels.
In 2016, we built on this work to create BASTT - Bubble-based Automated Strategy of Trade Timing - an investment decision helper that times the market by taking advantage of mis-pricing opportunities and whose purpose is to bring more efficiency to the market.
In 2017, we continue to further enhance research on bubbles and seek concrete applications. We are now engaged in the development of a new algorithm in the field of epidemiology.


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