This article should not surprise you, but I think it important to emphasize points I have made earlier about the predictable irrationality of investors. I recently came across a paper written by Dale Griffin and Amos Tversky, entitled The Weighing Of Evidence and the Determinants of Confidence (no full-text version available online). This article gives evidence as to why investors ignore regression to the mean and why they do not pay attention to the reliability of certain kinds of financial information.
The research behind the article is not brilliant nor even very interesting. What is exciting is the theory that Griffin and Tversky put forth. They also review relevant prior research. It is best to start with their theory.
Their theory is that people pay too much attention to the strength or extremity of evidence and not enough attention to its weight or reliability. They do not put forth a theory as to why people do this, but such a theory does not matter to us. What is important is what this theory predicts and how we can use it to predict how other investors will behave so that we may profit from it. One of the most important predictions of this theory is that people will be overconfident when information strength is high and information weight is low but they will be underconfident when weight is high and strength is low.
What do I mean by information weight and strength? The strength of information would be its extremity. For example, when hiring from among a pool of job applicants, a job interview that goes very poorly is strongly negative information. Information weight is the reliability of the information. A 20 minute job interview is not a reliable indicator of a potential employee’s demeanor and character and thus can be characterized as low-weight information.
The simplest test of this theory involves having people guess about a spinning coin. Unbeknownst to most, coins that are spun will tend to land on either one side or the other, due to imperfections in the manufacture of said coins. Griffin and Tversky told their subjects about a series of coins. They gave their subjects results of coin spinning experiments that varied in both the strength of evidence (the percentage of spins that landed on either side) and the weight of that evidence (the number of times the coin had been spun). Subjects had to guess whether the coin was weighted towards heads or tails. Subjects also had to give their confidence for each decision they made. Keep in mind that even if a coin lands on head 60% of the time, it would not be implausible for it to wind up on heads four out of 10 times or 10 out of 20 times, due to random error.
If people were perfectly logical, their confidence would increase as a function of both the strength of the evidence (the percentage of heads) and the weight of the evidence (the number of spins). There is a way to calculate the statistically correct inference and the confidence one can have in that inference given a certain weight and strength. This can be done by using Bayes’ theorem, which I will mercifully avoid describing here. While it would not be reasonable to expect people to always draw the statistically correct conclusion, it would be reasonable for them to be consistent. That was not the case.
In fact, the researchers’ theory was confirmed: given strong evidence with little weight (e.g., a coin that lands on heads 80% of the time, after five spins), people tend to be overconfident. Given weak evidence with a high weight (e.g., a coin that lands on heads 60% of the time, after 17 spins), people tend to be less confident than they should be. Overall, the researchers found that people relied over twice as much on the strength of the information as they did on its weight.
What does this mean for us as investors? First, if we pick our stocks using our intuitive judgment, we are at risk of becoming overconfident in our stock picking when our decision is heavily influenced by strong information of low weight (low reliability). A good example of this would be the novice investor’s choice to invest in a company primarily because he likes their product. Unless that investor is an expert in that type of product, such information is rarely useful. Conversely, we are at risk of being underconfident in our stock picking when the evidence is rather weak but highly reliable. I think Wal-Mart [[wmt]] is a great example of an investment with weak but reliable information in its favor: its PE ratio is average, its recent growth has been steady but unremarkable, and its brand name is well-known. These pieces of information are all highly reliable, but they indicate that Wal-Mart is a pretty good investment, not a great investment.
Oftentimes, conflicting information about a company will be of different strengths and weights. Whether by using a quantitative investment method or by just being aware of how much weight you should give to each piece of information, you must always consider the reliability or weight you should put on a piece of information when you are making investment decisions.
Besides the implications for costs and our personal investment decisions, this research has important implications for us because other investors will make these mistakes. They will not pay enough attention to the reliability of information (its weight). This will lead to consistent mispricing of stocks. This explains why value stocks outperform growth stocks: investors put too much weight in unreliable estimates of future growth, which leads them to bid up the price of growth stocks too high.
Other consistent inefficiencies in the market are also likely caused by investors not paying enough attention to the reliability of information. Companies in exciting sectors or industries are valued more highly (have higher PE ratios) than companies in less exciting industries, despite evidence that hot sectors do not outperform the market and may under-perform the market as a whole. While some might say that investors buying into the latest hot sector are just being stupid, I would argue that they are just over-weighting the importance of the industry’s future and under-weighting the importance of value (as measured by the PE ratio).
There are probably other stock market inefficiencies that this can explain. I will address these in future articles.
Disclosure: I have no position in WMT. I have a disclosure policy. This article was originally written two years ago and published elsewhere. I have a disclosure policy.