With the return of volatility in stocks, those investors and trades that profit when markets are calm are suffering heavy losses.
In my opinion, if you were to read only one book about finance, it would have to be “A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing” by Burton G. Malkiel. Malkiel’s book (now in its 11th edition) provides a compelling argument in favor of efficient markets theory and investing in (passively managed) index funds.
Efficient market theory implies that stock prices follow a random walk. These ideas were originally conceived of by Professors Paul Samuelson and Eugene Fama in the 1960’s, and subsequently popularized by folks like Professor Malkiel. In Finance 4366, we rely extensively upon the notion that prices of speculative assets (e.g., stocks, bonds, commodities, foreign exchange, etc.) follow random walks as we consider the technical details associated with pricing and hedging risk using financial derivatives.
Tim Harford also features the index fund in his “Fifty Things That Made the Modern Economy” radio and podcast series. This 9 minute long podcast lays out the history of the development of the index fund in particular and the evolution of so-called of passive portfolio strategies in general. Much of the content of this podcast is sourced from Vanguard founder Jack Bogle’s September 2011 WSJ article entitled “How the Index Fund Was Born” (available at https://www.wsj.com/articles/SB10001424053111904583204576544681577401622). Here’s the description of this podcast:
“Warren Buffett is the world’s most successful investor. In a letter he wrote to his wife, advising her how to invest after he dies, he offers some clear advice: put almost everything into “a very low-cost S&P 500 index fund”. Index funds passively track the market as a whole by buying a little of everything, rather than trying to beat the market with clever stock picks – the kind of clever stock picks that Warren Buffett himself has been making for more than half a century. Index funds now seem completely natural. But as recently as 1976 they didn’t exist. And, as Tim Harford explains, they have become very important indeed – and not only to Mrs Buffett.”
From November 2016 through October 2017, Financial Times writer Tim Harford presented an economic history documentary radio and podcast series called 50 Things That Made the Modern Economy. This same information is available in book under the title “Fifty Inventions That Shaped the Modern Economy“. While I recommend listening to the entire series of podcasts (as well as reading the book), I would like to call your attention to Mr. Harford’s episode on the topic of insurance, which I link below. This 9-minute long podcast lays out the history of the development of the various institutions which exist today for the sharing and trading of risk, including markets for financial derivatives as well as for insurance.
“Legally and culturally, there’s a clear distinction between gambling and insurance. Economically, the difference is not so easy to see. Both the gambler and the insurer agree that money will change hands depending on what transpires in some unknowable future. Today the biggest insurance market of all – financial derivatives – blurs the line between insuring and gambling more than ever. Tim Harford tells the story of insurance; an idea as old as gambling but one which is fundamental to the way the modern economy works.”
For a non-technical introduction to forward and futures contracts, it’s hard to beat the following video tutorial on this topic:
For two decades, government bonds have provided what amounts to free insurance against stock-market struggles. But that’s a historical anomaly.
During last Thursday’s Finance 4366 class meeting, I introduced the concept of statistical independence. This coming Tuesday, much of our class discussion will focus on the implications of statistical independence for probability distributions such as the binomial and normal distributions which we will rely upon throughout the semester.
Whenever risks are statistically independent of each other, this implies that they are uncorrelated; i.e., random variations in one variable are not meaningfully related to random variations in another. For example, auto accident risks are largely uncorrelated random variables; just because I happen to get into a car accident, this does not make it any more likely that you will suffer a similar fate (that is, unless we happen to run into each other!). Another example of statistical independence is a sequence of coin tosses. Just because a coin toss comes up “heads,” this does not make it any more likely that subsequent coin tosses will also come up “heads.”
Computationally, the joint probability that we both get into car accidents or heads comes up on two consecutive tosses of a coin is equal to the product of the two event probabilities. Suppose your probability of getting into an auto accident during 2017 is 1%, whereas my probability is 2%. Then the likelihood that we both get into auto accidents during 2017 is .01 x .02 = .0002, or .02% (1/50th of 1 percent). Similarly, when tossing a “fair” coin, the probability of observing two “heads” in a row is .5 x .5 = 25%. The probability rule which emerges from these examples can be generalized as follows:
Suppose Xi and Xj are uncorrelated random variables with probabilities pi and pj respectively. Then the joint probability that both Xi and Xj occur is equal to pipj.
Graph of the day – daily volatility of Bitcoin (BTC) vis-à-vis other asset classes (WTI (oil), silver, gold, US stocks (SP500), Euro/Dollar exchange rate, 10 year T-bond, 1 year T-bill, and 1 month T-bill). Source: WSJ Daily Shot, 11-January-2018.
The Wall Street Journal recently published an important article (linked below) which documents the (unprecedented) synchronized compression of implied volatility across multiple asset classes; specifically, US equities, oil, gold, and US interest rates.