Quantitative Hedge Funds: Discretionary, Systematic, AI, ESG and Quantamental by Richard D Bateson
Chapter 1
Efficient Markets
1.1 Introduction
The abstract world of efficient markets was where my rollercoaster financial
career started, almost three decades ago. The efficient market hypothesis
(EMH) and its offspring, the Black–Scholes (BS) equation, generated the
theoretical rocket fuel for the fast-growing derivatives markets. This
introductory chapter explores my front row experience of this remarkable
revolution, where increasingly exotic derivatives were invented using
human ingenuity, mathematical assumptions and evolving technology. With
the pace of innovation, financial products became progressively more
absurd and incomprehensible. Warren Buffett’s warning “beware of geeks
bearing formulas”, proved prescient of the global 2008 financial crisis.
However, the cracks in the tenets of EMH and the financial firmament had
been already observed. Numerous market “anomalies” that violated EMH
were being discovered and could be mined to derive investment alpha. The
rest of this book describes the exploitation of these anomalies and the rise
of quantitative hedge funds.
1.2 Brownian Motion
It is amusing that the financial theories which dominated financial markets
in the 20th century originated from the study of plants and pesky
mosquitoes. In the 19th century the British became mildly obsessed with
botany. Many exotic plant specimens were shipped from all corners of the
British Empire. One of the leading botanists who came back laden with
samples from New Holland (now Australia) was Robert Brown, who was a
friend of Charles Darwin. In 1827, he surprisingly observed the chaotic
movement of pollen grains using a microscope. The endless, random
movement, now called Brownian motion, appeared inexplicably devoid of
any external stimulus such as light or temperature.
However, the observations lay ignored and without explanation for
decades. As is often the case in science, there was simultaneous renewed
interest by several parties. In 1905, Karl Pearson wrote a letter to Nature
about mosquito infestations and proposed a simple model. That at each time
interval, a mosquito would move a defined distance at a random angle. He
called this a “random walk” and apparently this was the first use of the
term. Lord Rayleigh neatly provided a calculation for the probability
distribution of the number of mosquitoes after a certain number of steps. It
seems that he had tackled a similar problem with sound waves in media in
the 1880s.
Simultaneously in 1905, Einstein published his famous paper on
Brownian motion, deriving a diffusion equation and using it to estimate the
size of atoms. It was a great year for Einstein since he also published his
seminal works on special relativity and the photo-electric effect but the
most cited of these papers was the one on Brownian motion. Although
Einstein dismissed Brownian motion as trivial compared with his other
exploits, the concept of Brownian fluctuations has subsequently proved
important to a huge range of applications from thermodynamics and cellular
biology to traffic flow and finance.
However, the great Einstein was arguably beaten in his mathematical
formulation of Brownian motion by the remarkable 1900 doctoral thesis of
Louis Bachelier. Einstein was seemingly unaware of Bachelier’s work.
Bachelier was a mathematics student at the Sorbonne in Paris. Whilst
studying the movements of bond prices on the Paris Bourse, he was inspired
by the physics analogy and later revealing remarked that he “copied it from
the physics of a gas in equilibrium”. He proposed that prices followed a
random walk and were impacted by the unpredictable flow of news and
information. This important analysis was to form the core tenet of the
efficient market hypothesis (EMH) many decades later.
1.3 The Efficient Markets Hypothesis
Quantitative Hedge Funds: Discretionary, Systematic, AI, ESG and Quantamental by Richard D Bateson