A Condensed History of Quantitative Finance
Quantitative finance is the application of math, especially probability theory, to financial markets. As traders and investors, we’re always looking to employ high probability strategies to increase profits and returns. Regardless of what investing strategy you use, chances are you are applying some type of quantitative finance in your approach to investing or trading. The historical timeline below shows some of the most notable contributions to the field over the past 200 years.
1827
Botanist Robert Brown, while looking through a microscope at particles trapped in cavities inside pollen grains in water, he noted that the particles moved through the water; but he was not able to determine the mechanisms that caused this motion. This random movement of the particles was aptly named Brownian motion.
Atoms and molecules had long been theorized as the constituents of matter, and Albert Einstein published a paper in 1905 that explained in precise detail how the motion that Brown had observed was a result of the pollen being moved by individual water molecules. This explanation of Brownian motion served as convincing evidence that atoms and molecules exist.
1863
Jules Augustin Frédéric Regnault was a French stock broker’s assistant who first suggested a modern theory of stock price changes in Calcul des Chances et Philosophie de la Bourse (1863) and used a random walk model.
He is also one of the first authors who tried to create a “stock exchange science” based on statistical and probabilistic analysis. His hypotheses were used by Louis Bachelier.
Interesting note on him is that he held about 70% of his 3.8 million fortune in bonds when he died suggesting he had applied financial theory to establish a fortune.
1900
Louis Bachelier was a French mathematician at the turn of the 20th century. He is credited with being the first person to model the stochastic process now called Brownian motion, which was part of his PhD thesis The Theory of Speculation, (published 1900). His thesis, which discussed the use of Brownian motion to evaluate stock options, is historically the first paper to use advanced mathematics in the study of finance. Thus, Bachelier is considered a pioneer in the study of financial mathematics and stochastic processes.
His work was largely ignored until the 1950s when financial economists began making heavy use of probability theory and statistics to model asset prices (in particular, options prices)
1905
Karl Pearson introduces the term Random Walk. His book The Grammar of Science was later to be a part of Einstein’s theories as well as other scientists.
Modern description of a random walk is a mathematical object which describes a path that consists of a succession of random steps. For example, the path traced by a molecule as it travels in a liquid or a gas, the search path of a foraging animal, superstring behavior, the price of a fluctuating stock and the financial status of a gambler can all be approximated by random walk models, even though they may not be truly random in reality.
1945
Economist Friedrich Hayek, published in the September 1945 issue of The American Economic Review, makes the argument that markets are the most effective way of aggregating the pieces of information dispersed among individuals within a society.
Given the ability to profit from private information, self-interested traders are motivated to acquire and act on their private information. In doing so, traders contribute to more and more efficient market prices.
In the competitive limit, market prices reflect all available information and prices can only move in response to news. This notion preceded Efficient Market Hypothesis but there is a very close link to both EMH and Random Walk.
1970
Eugene Fama publishes an article in the May 1970 issue of the Journal of Finance, entitled “Efficient Capital Markets: A Review of Theory and Empirical Work.” In this article, Fama proposed two concepts that have been used on efficient markets ever since.
Fama is an American economist, often referred to as “The Father of Finance”, best known for his empirical work on portfolio theory, asset pricing and stock market behavior.
The efficient-market hypothesis (EMH) was developed by Professor Fama who argued that stocks always trade at their fair value, making it impossible for investors to either purchase undervalued stocks or sell stocks for inflated prices. As such, it should be impossible to outperform the overall market through expert stock selection or market timing, and that the only way an investor can possibly obtain higher returns is by chance or by purchasing riskier investments.
In financial economics, the EMH states that asset prices fully reflect all available information. A direct implication is that it is impossible to “beat the market” consistently on a risk-adjusted basis since market prices should only react to new information or changes in discount rates.
Is Efficient-Market-Hypothesis Right?
Is it really impossible to “beat the market” consistently as EMH suggests? Well if you’re simply going to buy stocks and hope they go up then yes, it may be difficult to “beat the market”.
Applying probability theory to the options market though is another story and you can definitely “beat the market” if it’s done correctly. Consistent gains in the stock market using options is what I focus on over on my YouTube Channel. Here’s an example of what I’m talking about.
That’s All For Now
Thanks for reading and I hope you’ll join me over on YouTube.
