TerryO

Not forgotten should be that under uncertainty in the conditional outcomes of events the right decision can produce the wrong outcome. Hockey players who are unaware of this fact skate toward where the puck was rather than where the puck will probably be. Metaphorically speaking, Watsa and company have an extraordinarily successful record of skating toward where the puck will probably be.

Recently those of us who are long the stock have been hitting the jackpot. Thank you Prem and company for your excellent work!

My hometown newspaper serves a market of about 30,000 people. Based upon the amount of advertising, it is thriving. If one wants to know what's happening in our town, one has to read this paper because the news is covered by no other source.

http://finance.yahoo.com/news/What-The-Oracle-of-the-North-fool-4232510043.html?x=0

Perhaps the preference for lumpy returns reflects recognition of the limitations of modern portfolio management theory plus the intention of making money off of the suckers who fail to recognize these limitations. As Nassim Taleb explains in his book The Black Swan, the best position can be to lose money slowly (e.g. by buying options) waiting for the rare event to occur in which one makes an amount of money that is virtually impossible if returns are normally distributed. The theory that this kind of strategy may be what Prem has in mind when he says he likes the returns to be lumpy is consistent with the strategy of buying credit default swaps and U.S. treasuries a few years ago when they were cheap, then selling them recently when their prices jumped. The performance measures of modern portfolio management theory penalize the portfolio manager whose returns are lumpy, leading companies like General Electric to boost their stock prices by smoothing the returns using accounting gimmicks. In this respect, modern portfolio management theory can be quite misleading, for the portfolio with the lumpy returns can be the better portfolio in the sense of being much less risky. That our business schools continue to churn out graduates steeped in the notion that data are normally distributed guarantees a steady supply of suckers.

Tex I believe you'd be interested in what I'm doing. There is an overview at http://www.knowledgetothemax.com. One of the applications of the technology I'm trying to peddle has been to meteorology. This application was astoundingly successful in the sense of increasing the time span over which precipitation could be forecasted by a factor of 12 to 36. The original idea was to amalgamate predictions from a mechanistic model based upon solution of the equations of motion of the atmosphere with predictions from an empirical model based upon observational data. However, non-linearities in the equations of motion made the mechanistic model unstable over time spans of a few weeks, the so-called "butterfly effect." Thus, the mechanistic model was abandoned for a purely empirical model. In this approach, an optimal decoder was built to decode "messages" from the future, where the "message" was the sequence of precipitation outcomes. Taking this approach increased the span of time over which precipitation could be forecasted from 1 month to 12 to 36 months. As it turned out, there is an oscillation in the Pacific Ocean called the El Nino oscillation with a period of a few years. By tuning a decoder to this oscillation, one can predict precipitation in California several years in advance. A similar approach has been successfully applied to prediction of financial time series. Terry

The procedure by which a prediction is made is called a "model." My job is to build models. I'd like to share a bit of what I know in the context of the Soros vs Buffett thread. Over many generations, university statistics departments, economics departments and business schools have instilled the belief called "frequentism" in their students. Frequentism is the belief that the probability a real object will be found in a given state of nature is the limiting relative frequency of this state. The "limiting relative frequency" of a state is the proportion of occurrences of this state in the limit of an infinite number of observations. According to people who have studied the matter, most practicing scientists subscribe to this belief; as frequentism was drilled into them by their professors, this is no surprise. Frequentism, however, is a trap, for it neglects the phenonenon which communications engineers call "noise." Because of this neglect, when a model is built under frequentism, it exhibits the phenomenon which I call "regression to the base-rates." This is that, when the model is tested, the observed relative frequencies lie closer to the base rates than the predicted probabilities. The prediction contains less information than the frequentist believes it to contain and may contain no information at all. Building a model that reliably predicts outcomes in financial markets is difficult and sometimes impossible. In lieu of a reliable model, one strategy is to gamble on regression to the base-rates. This, roughly speaking, is the strategy called "value investing." Because of the pervasiveness of frequentism in peoples' thinking, value investing has been a fruitful strategy. Investors exhibit belief in frequentism when they confuse signal with noise. For example, there is a 1 quarter drop in earnings and they take this to be a signal to sell. The value investor takes this "signal" to be noise and (if the price is right) buys. Judged by their actions, Buffett gambles on the reliability of regression to the base-rates. Soros gambles on the reliability of predictive models. That Soros has been so successful suggests he has somehow evaded the trap of frequentism, thus being able to distinguish signal from noise.

Upcoming taxes on carbon dioxide emissions are going to make energy more expensive. In this light, a plus for the railroads is that rail transport is much more energy efficient than truck transport. After the imposition of the taxes, a railroad that had pricing power could move much of the energy savings to the bottom line.