Predicting the Unknowable

Posted on February 9, 2012 by randy

If you want a headache, read the comments in an article like this. Note that I am not knocking the article, but the bantering of the commenters.

Apple the Next Trillion Dollar Company

More fanboy arguing than a Gizmodo blog post.

What I believe at this point is that you cannot know the future of the world well enough to predict future earnings of a company past a few months. The researcher Phillip Tetlock performed a seminal twenty-year study in which 284 experts in many fields, from professors to journalists, and with many opinions, from Marxists to free-marketeers, were asked to make 28,000 predictions about the future, finding that they were only slightly more accurate than chance, and worse than basic computer algorithms.

This is classic Behavioral Economics to have various “logical” commenters vigorously fight over predictions of an unknowable nature. Behavioral Economists would say, they made an instinctive conclusion about the stock/company, then used their logical mind to back-in and justify their instinctive feelings. Humans often confuse intuition for rationality. Humans often feel like they have thought long and hard about a decision when they are really just reinforcing the conclusion they intuitively came to. You will see the same behavior whenever you see two sports fanatics with differing opinions on the same upcoming game. They may as well argue over what the weather will be in 365 days.

Predicting earnings of companies far into the future is a crazy waste of human energy. For further reading, check out what Daniel Kahneman has to say on the subject of flawed intuition: What was I thinking?

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Superbowl Predictions

Posted on February 3, 2012 by randy

I love the Freakonomics book, and I think it is a great introduction to economics. The book really shows how hard numbers can be used to debunk some commonly held biases and heuristics. The author did a nice spot on Yahoo! today to talk about commonly held thoughts about the Superbowl and stocks. If the Giants win it will not be a good year for stocks because they were one of the original NFL teams. The danger when using hard numbers to back-test predictions is that they often lead to spurious relationships. Any time there is a relatively small data set like a yearly event, there will be correlations that are not actually causal. The Freakonomics author does a pretty good explanation in the video, so I will not go into it here. What I will say is that all hope for using statistics is not lost because some relationships turn up spurious. It all comes down to the law of large numbers.

If you flip a coin three times, there is a pretty decent chance that you will get heads all three times. If you flipped the same coin 3,000 times, you will get almost exactly 50% heads and 50% tails. Whenever you hear people or economists (they are different) make predictions, consider if they have enough data. There is nothing wrong with a slight amount of predictive guidance if there isn’t enough data (it is better than nothing), but don’t bet the house on it. Any prediction based on an annual event like the Superbowl will not likely have enough data. Presidential elections are even worse because they only happen every four years.

So, how much is enough data? At AlphaGenius we believe the more the better, but sometimes that is not realistic. As a rule of thumb, and that’s all this is, we do not look at a relationship unless we have over 500 data points. So, that unfortunately rules out predicting Superbowls because there has not been enough of them! Us humans love making predictions though, so I am going to go ahead and say the Patriots will win even though I can’t be statistically certain.

Tagged with: election, predictions, stock, stocks, Superbowl
Posted in AlphaGenius

Daniel Kahneman

Posted on January 16, 2012 by randy

Daniel Kahneman’s new book Thinking, Fast and Slow is a must read for anyone who is interested in sentiment investing. It is not a pure investing book and mostly focuses on psychology. Most of the book focuses on the seminal ideas he won his Nobel Prize for: biases and heuristics. Humans make irrational decisions, especially in matters of money. What I was most impressed with is chapter 21 where he states that “rule based” decision-making is often superior to human expert. Obviously, there are many many investing professionals who take issue with this.

Kahneman gives very detailed and methodical reasoning and evidence to prove the case for “rule based” decision-making and why experts are often over rated. Philip Tetlock’s twenty-year study in which 284 experts in many fields, from professors to journalists were asked to make 28,000 predictions about the future, finding that they were only slightly more accurate than chance, and worse than basic computer algorithms.

Kahneman also gives a fun story about Orley Ashenfelter, a Princeton professor who came up with a simple rule based model for Predicting the Quality and Prices of Bordeaux Wines. The wine community is still in denial that their fine training is inferior to an algorithm.

Investing rules are important. You don’t want to mess up your interpretation of sentiment from news, blogs, Twitter, etc. for investing with your own biases and heuristics.

Tagged with: behavioral economics, investing, Kahneman, sentiment
Posted in AlphaGenius

Invest Different

Posted on October 27, 2011 by randy

Investors should use Twitter and the social Internet as a tool to help make investment decisions. The social Internet has the major advantage of providing the collective consciousness of the world. For the first time in human history the well proven concepts of behavioral economics can be applied to mass psychology to help predict securities prices. We believe that behavioral investing will some day be as ubiquitous as fundamental and technical analysis.

Websites like StockTwits.com are free and can be used by any investor to supplement their investment thesis. AlphaGenius is a quantitative technology company that applies econometrics and machine learning algorithms in order to build investing models. The social Internet is a rich data source for fundamental, technical, and quantitative investors.

Posted in AlphaGenius