The Physics of Wall Street

Scientists applied mastery in theories of their respect fields to real world problems of stock market. It feels like a sequel of “The Man Who Solved Market” book. This book is more scientifically oriented than “The Man..” though as it was written by physicist, while the other was by journalist. It was about multiple intellectuals who influenced market in one way or another with various degree of financial success, ranging from the obscure to the absolute rock star, looking back hundreds of years.

As the book tells story about many people over a long stretch of time more or less chronologically, I feel like it forces the pieces into an overarching narrative. The story overall does work and is convincing, but in the back of my mind I still vaguely doubt the authenticity of it as stories fit too well together (it is entirely possible though that the author is just very good at what he does). The annoying feeling in the back of my head was the same as “The Man..”. Perhaps just taking the ‘analytical’ hat off and putting the ‘enjoy the ride’ hat on would have been more fun reading the book.

Quantitative investing in perspective

Jim Simon’s fund - the basis of “The Man Who Solved Market” book - earned an unparalleled 2,478.6% return, blowing every other hedge fund in the world out of the water. To give a sense of how extraordinary this is, George Soros’s Quantum Fund, the next most successful fund during this time, earned a mere 1,710.1% over the same period.

Derivatives

They are contracts based on some other kind of security, such as stocks, bonds, or commodities. For instance, one kind of derivative is called a futures contract. If you buy a futures contract on, say, grain, you are agreeing to buy the grain at some fixed future time, for a price that you settle on now. The purpose of such contracts is simple: they reduce uncertainty.

Arithmetic vs Logarithmic distribution and it’s not just cold hard mathematics, there’s human psychology behind the model too.

Logarithms of prices better reflect how investors feel about their gains and losses. In other words, it’s not the objective value of the change in a stock price that matters, it’s how an investor reacts to the price change. In fact, Osborne’s motivation for choosing logarithms of price as his primary variable was a psychological principle known as the Weber-Fechner law. The psychological effect of a change in stimulus isn’t determined by absolute magnitude of the change, but rather by its change relative to the starting point. But back then computer processing power was too low to fully utilise this concept. It would be like telling a carpenter that screws are much stronger than nails, when the carpenter has a hammer and no one has yet invented the screwdriver. Even if the house would be stronger if built with screws, you’d still get much farther working with a hammer and nails, at least for a while.

Capital Asset Pricing Model 

The basic idea underlying CAPM was that it should be possible to assign a price to risk. CAPM was a model that allowed you to link risk and return, via a cost-benefit analysis of risk premiums.

Black-Scholes equation

The essential insight was that at any given instant, it is always possible to create a portfolio consisting of a stock and an option on that stock that would be perfectly risk-free. If this sounds familiar, it’s because the idea is very similar to the one at the heart of Thorp’s delta hedging. This portfolio should be expected to earn the risk-free rate of return. Black’s strategy of building a risk-free asset from stocks and options is now called dynamic hedging.

On algorithm

An algorithm is just a set of instructions that can be used to solve a particular problem.

On models

Models are at bottom tools for approximate thinking. They are never the final word — they rely on assumptions that never hold perfectly, and that sometimes fail entirely. Appropriate use of models requires a good dose of common sense and an awareness of the limitations of whatever model you happen to be using. In this way, they are like any tool. A sledgehammer may be great for laying train rails, but you need to recognize that it won’t be very good for hammering in finishing nails on a picture frame. 

The goal isn’t to find the final theory that will give the right answer in every market setting. It’s much more modest. You’re trying to find some equations that give you the right answer some of the time, and to understand when they can be relied on. We should never mistake a good model for the “truth” about financial market.

Physicist contributions to finance

If physicists have been successful at improving our understanding of finance, it is because they have approached problems in a novel way, using methodological insights that are commonplace in physics (and engineering) and that are useful in studying virtually anything.

Good ideas come from many ideas

In a 1965 Supreme Court decision on freedom of speech, Justice William Brennan coined the expression “marketplace of ideas” to describe how the most important insights might be expected to arise out of a free and transparent public discourse. If this is right, then you would expect that the best new ideas about economics would get taken up — even if powerful economists rejected them.

Quantitative vs Fundamental investing

Mathematical model vs Information, Experience, Gut feeling in stock market

Some people dismiss quantitative investing citing incidents of market crashes in ’87, ’97, ’08. The author believed that random and drastic events that invalidate models doesn’t mean we should dismiss model building efforts.

The process of building and revising models is the basic methodology underlying all of science and engineering (i.e. scientific method). Science is a way of learning about the world — an ongoing process of discovery, testing, and revision. It’s the best basic tool we have for understanding the world. We use mathematical models (in quantitative investing) cut from the same cloth to build bridges and to design airplane engines, to plan the electric grid and to launch spacecraft. What does it mean to say that the methodology behind these models is flawed — that since it cannot be used to predict everything that could ever happen, it should be abandoned altogether? Should we stop hopping on airplanes just because they are known to be crashable? No, we learn from it and improve upon it to minimise the crash rates and their impact.