Buy-and-Forget Portfolio: 10 Stocks To Last The Decade. This time of year, newspapers and magazines are filled with predictions and stock recommendations and trading ideas. I have repeatedly explained why these are terrible ideas and you should ignore them. Sometimes, you just have to let the performance speak for itself. And for that, I present Fortune: 10 Stocks To Last The Decade A few major trends will likely shape the next ten years. Here’s a buy-and-forget portfolio to capitalize on them. August 14, 2000 1. Closing prices December 19, 2012: 1. The portfolio managed to lose 74.31%, with 3 bankruptcies, one bailout, and not a single winner in the bunch. (Update: Forgot about Univision takeover — I’ll pull the TO price and recalculate when I get into the office) (Update 2: Yeah, I forgot about Oracle 2 for 1 split — I’ll adjust that as well Broadcom 3 to 2 split) Had you merely bought the S&P500 index via the Spyders, you would have seen a gain of 23.43%.
Have fun forecasting! 2008 Investment Guides Are HILARIOUS (December 31st, 2008) Cathy O’Neil: Why Nate Silver is Not Just Wrong, but Maliciously Wrong. By Cathy O’Neil, a data scientist. Cross posted from mathbabe I just finished reading Nate Silver’s newish book, The Signal and the Noise: Why so many predictions fail – but some don’t. The Good News First off, let me say this: I’m very happy that people are reading a book on modeling in such huge numbers – it’s currently eighth on the New York Times best seller list and it’s been on the list for six weeks. This means people are starting to really care about modeling, both how it can help us remove biases to clarify reality and how it can institutionalize those same biases and go bad.
As a modeler myself, I am extremely concerned about how models affect the public, so the book’s success is wonderful news. Moreover, the book serves as a soft introduction to some of the issues surrounding modeling. In particular, Silver does a nice job of explaining Bayes’ Theorem. The Bad News Having said all that, I have major problems with this book and what it claims to explain. Medical Research Why? How To Win At Forecasting. One thing that became very clear, especially after Gorbachev came to power and confounded the predictions of both liberals and conservatives, was that even though nobody predicted the direction that Gorbachev was taking the Soviet Union, virtually everybody after the fact had a compelling explanation for it. We seemed to be working in what one psychologist called an "outcome irrelevant learning situation. " People drew whatever lessons they wanted from history. There is quite a bit of skepticism about political punditry, but there's also a huge appetite for it.
I was struck 30 years ago and I'm struck now by how little interest there is in holding political pundits who wield great influence accountable for predictions they make on important matters of public policy. The presidential election of 2012, of course, brought about the Nate Silver controversy and a lot of people, mostly Democrats, took great satisfaction out of Silver being more accurate than leading Republican pundits. Freakonomics: The Folly of Prediction. Predicting the improbable: Evidence from playing the lottery.
Japan’s trio of tsunami, earthquake, and nuclear disaster has left the world stunned. As this column points out, even the experts were shocked. But while these events were highly unlikely, they were still possible. This column uses evidence from the Danish lottery to show that people tend to adjust their expectations of future events based on only small pockets of recent experience, often at their cost. Important events are hard to predict – a fact that is particularly hard-felt when it comes to low probability events with dramatic consequences. Nuclear catastrophe, financial crisis and the like are things that even experts struggle to predict. Experts are thus to some extent forced to base their predictions on inference from observing the past. While experts struggle to predict such events accurately, the average person is often simply baffled.
One common tendency is to see patterns in random data when there are none. Biased players tend to lose more money. Camerer, C. (1989). The Importance of Goodhart's Law. This article introduces Goodhart's law, provides a few examples, tries to explain an origin for the law and lists out a few general mitigations. Goodhart's law states that once a social or economic measure is turned into a target for policy, it will lose any information content that had qualified it to play such a role in the first place. wikipedia The law was named for its developer, Charles Goodhart, a chief economic advisor to the Bank of England.
The much more famous Lucas critique is a relatively specific formulation of the same. The most famous examples of Goodhart's law should be the soviet factories which when given targets on the basis of numbers of nails produced many tiny useless nails and when given targets on basis of weight produced a few giant nails. Numbers and weight both correlated well in a pre-central plan scenario. We laugh at such ridiculous stories, because our societies are generally much better run than Soviet Russia. A speculative origin of Goodhart's law. Fed Forecasts? PUH-Leeze! Good Thursday morning. We are seeing Markets lower in Europe and Asian after Uncle Ben and the Fedettes lowered their perennially over-optimistic forecasts for growth in the US. Global Markets reversed their modest gains and sold off yesterday, and there seems to be follow through in the futures this morning.
From Sub-prime being contained (James Grant quipped “Yes, contained to planet earth”) to the panicked end of the world forecast during the crisis (thus preventing a Swedish pre-packaged bankruptcy for insolvent banks) to the over-optimistic recovery, the forecasts of the Fed in general and BB specifically have been little short of awful. Then again, the forecasts of 90% of the economic community ain’t worth a plug nickel. Beyond the institutional habit of being excessively optimistic, the Fed’s economic forecasts have been working off the wrong data set, stubbornly refusing to recognize that this is a credit driven crisis, and not your run of the mill business cycle contraction. Economists are terrible forecasters - why trust them anyway? The herd that is 'consensus' clings to this hope that GDP will bounce back smartly in Q3 and Q4, where all the while some pretty miserable data and financial conditions are staring us in the face.
On May 26 I worried about the 'soft patch'. On July 24, the data doesn't look too much better. This week we get the Q2 GDP report from the Bureau of Economic Analysis, of which the mean growth forecast from 69 polled economists by Bloomberg is 1.8% Q/Q SAAR (Bloomberg terminal, no link). That may change as we near Friday (unlikely by much), but those same economists (on Bloomberg) are forecasting a 3.2% rebound in Q3. Alas, after the huge forecast miss in the first half of the year, I no longer put much stock in what economists expect by way of GDP growth just one quarter ahead (some yes, but broadly no).
See, economists are generally terrible forecasters. They do okay looking broadly at the business cycle; but the SFP point-to-point forecast error is huge. I don't trust economic forecasts.