There was a time not too long ago when, if you posed the question “Where does alpha come from?” to a roomful of academic financial economists, most of them would complain: “It’s a trick question! There is no alpha! Markets are strong-form efficient and you are a heathen!” Those complaints are rarer now, even among economists. Two of their own, Sanford Grossman and Joseph Stiglitz, crystallized the contradiction of strongly efficient markets in their eponymous paradox. It is summarized in Stiglitz’s 2001 Nobel Prize citation: If a market were informational efficient, i.e., all relevant information is reflected in market prices, then no single agent would have sufficient incentive to acquire the information on which prices are based.
Trading Alpha and Capital Market Efficiency
If there is no profit to be had from trading on information, traders with information will not trade, so prices will not reflect information and will not be efficient. The joke based on this paradox has an economist and his friend walking down the street, and the economist walks right over a $ 100 bill on the sidewalk. The friend asks why, and the economist replies, “If it was real, someone would have already picked it up.”
How Alfred E. Neuman might illustrate the Grossman-Stiglitz paradox. If markets are efficient, they reflect all information, and there is no profit to be had from trading on information. If there is no profit to be had, traders with information won’t trade, so markets won’t reflect it, and will not be efficient.
Warren Buffett expressed his appreciation to proponents of the efficient markets hypothesis (EMH) in the 1985 Berkshire Hathaway annual report: In the 1970s . . . institutions were . . . under the spell of academics at prestigious business schools who were preaching a newly fashioned investment theory: the stock market was totally efficient, and therefore calculations of business value — and even thought itself — were of no importance in investment activities.
We are enormously indebted to those academics. What could be more advantageous in an intellectual contest — whether it be bridge, chess, or stock selection — than to have opponents who have been taught that thinking is a waste of energy?
Investment Alpha, Investment Information, and Inefficient Market
Some academics crossed the road as well. Fischer Black, after leaving MIT for Goldman Sachs, said, “Markets look a lot more efficient from the banks of the Charles than from the banks of the Hudson Someone gets to pick up that $ 100 bill.
Back on the banks of the Charles in Boston 25 years later, Andy Lo wrote, “Profits may be viewed as the economic rents which accrue to [the] competitive advantage of . . . superior information, superior technology, financial innovation. . . .”(4) If this conjures up images of ever faster, better, larger computing engines at giant quantitative hedge funds, you are getting the message. But this idea is not suddenly true today; it has been true forever. Innovations used to use less electricity, though. In 1790, the technology that produced vast alpha for innovative traders was boats. After the American Revolution, war bonds were trading for less than a nickel on the dollar. There was a general expectation that the new country and the states would default on the substantial debt. George Washington thought this would be a bad rap for a new country, so the Funding Act of 1790 guaranteed, dollar for dollar, all debts of the new Union and the states. Word spread from the first Congress, in New York, by land messengers.
Technologically innovative traders chartered every fast-moving boat in the city, front-running the messengers and buying up bonds for pennies on the dollar.
Trading Alpha from Finance Technology and Innovation
In the early days of electronic market data feeds, the 1970s and 1980s, traders who noticed that the crusty slow centralized systems lagged the fast broadcast streams by up to 20 minutes played the same game — without boats.
In 1815, technological information advantage came from birds. In June of that year, there was a general panic in London that the empire would be routed by Napoleon. Financial markets crashed, and dealers frantically unloaded government bonds. Nathan Meyer Rothschild knew the outcome before the British press, by virtue of his use of fast carrier pigeons to bring him the news of Napoleon’s surprising defeat before the rest of the market knew. He quietly bought everything British he could get his hands on, and a few days later, when news of Napoleon’s catastrophic defeat at Waterloo arrived for the non-bird owning traders, prices soared, and Rothschild became one of Europe’s wealthiest men.
We see an important part of the beginnings of financial information technology innovation in the form of the blinking, humming refrigerator – sized computers of the 1970s. Bill Fouse, at Wells Fargo, bought a Prime computer and used it to run the first index fund, 5 the granddaddy of quantitative equity investing and the vast systematic investment industry.(6) John C. Bogle founded Vanguard in 1974, doing the same thing for retail mutual fund investors. Alas, I can’t find a picture of Bogle and his first computer, so Figure 4.2 shows Fouse with his.
Figure 4.2 Indexing pioneer Bill Fouse with the Prime minicomputer used to run the first index fund. This machine has less computational power than a mid-range high-end digital watch of 2008. Source: Anise Wallace, “How Did Wells Fargo Get to the Top?” Institutional Investor, June 1976.
Further innovation came in the form of factor models, notably “Barr’s better betas,” a fundamental multifactor model developed by Barr Rosenberg at Berkeley. The beta that Barr had better versions of was the one in the capital asset pricing model (CAPM). The conventional wisdom in writing a book popularizing a technical topic is that each equation included cuts book sales in half.
The CAPM Capital Asset Pricing Model Does Not Address Stock-Specific Components of Investment Return
Bill Sharpe shared the Nobel Prize in economics for the capital asset pricing model. This is a simple representation of the key idea that the return to a stock is explained by the return to the broad market (e.g., the S & P 500) times the stock’s sensitivity to the market (beta) plus stock-specific returns (e.g., from news). This is a simple idea. Think of it as “a rising tide lifts all boats” and you’re pretty close. Some stocks, like utilities, are less sensitive to market returns than others (like tech or finance); they have lower betas. The average beta over all stocks is 1.0. Of course, the rising tide doesn’t explain everything; there are stock-specific components of return — things like news and earnings events — that are added on …
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