I keep promising to stop writing about lessons from the election that are applicable to markets, and then I keep finding more examples. So rather than make any promises I cannot keep, let’s just jump right into this.
Since Donald Trump’s surprise victory – though it wasn’t a surprise to those of you with the power of hindsight – there have been numerous after-the-fact explanations for why Trump beat Hillary Clinton.
Many appear to be delightful exercises in data mining, the finding of “historical patterns that are driven by random, not real, relationships.” Add to this the assumption that these explanations are durable and will repeat in the future, and you have the makings of a terrible investment process.
Consider the various claims as to what the key to the election was:
Sign Up and Save
Get six months of free digital access to The Fresno Bee
▪ Local health outcomes predict Trumpward swings (the Economist).
▪ Education, not income, predicted who would vote for Trump (FiveThirtyEight).
▪ Two economic variables perfectly predict election results (Statistical Ideas).
▪ Clinton won 64 percent of America’s economic activity versus Trump’s 36 percent (The Washington Post).
▪ Clinton won the cities, Trump won the suburbs (New York Times).
None of these elements “predicted” anything. Each was the result of an analysis of what already had occurred. Post-election, data was sifted, a midpoint in each data set was located where a majority of Trump voters resided over Clinton voters and a conclusion was reached.
This is classic data mining, and it should never be relied on to make future forecasts.
Salil Mehta, former TARP director of analytics and author of “Statistics Topics,” has been critical of pollsters’ election forecasts. He spent much of the time before the election lecturing them that their models were underestimating the possibility of a Trump victory.
In an e-mail exchange, he observed: “There is an increased craving to slice and dice the recent election data, particularly given that the major pollsters have been shamed as they all immensely errored in projecting this year’s election’s victor.
“All gave President-elect Trump (less than) 15% probability of winning. The risk of now retorting with data-mining this single election result is that they often miss an analysis of the predictive errors in this unique match-up (e.g., record high undecideds on election eve), don’t take into account budding geospatial patterns to validate evidence and, in most cases, none of this should deceptively be promoted as an election forecasting model.”
Correlations are very different from what is required to create a reliable model that correctly forecasts a future election or investing outcomes. Rather than mine data, Mehta suggests we engage in hypothesis testing.
The obvious parallel to investing is the myriad of back-tested strategies, many of which engage in similar sorts of data mining as the recent election post-mortems do. They seemed to have worked perfectly in the past, but are less robust than desired. Models that inform us of what already has happened but not what might occur in the future are of limited value.
Cliff Asness of AQR warns us not to confuse factor investing with data mining. He notes that French-Fama factors such as value, momentum and size have all been tested out of sample and proven to be robust.
Out-of-sample testing could verify whether an election model’s backtest is valid: Take the five data claims above, then apply them to Barack Obama versus John McCain, or George W. Bush versus Al Gore to see if they are at all predictive. The same is true for investing models. To avoid poorly constructed models that are form-fitted to past experience, apply them to different data sets than the test.
If a gold mine is a hole in the ground with a liar standing on top of it, a successful data miner is a quant with a data set lying to himself. You probably have never seen a sales pitch that didn’t have a back test “proving” market-beating returns. If only you had a time machine to go back to the period of time covered by the data set.
Investing after the fact is easy. Investors should be cautious when presented with results that only tell you what just happened, not what is about to occur.