Analyzing the AAII Sentiment Survey Without Hindsight
A common pitfall of examining historical data for clues of predictive value is hindsight bias.
Hindsight bias occurs when a person believes the outcome of an event was predictable in the past. In the world of investing, we commonly see this when someone claims they knew they should have bought a certain stock while it was in the early stages of a big run or when they say they knew it was time to get out of the market before a big downturn occurred.
Hindsight bias is related to the human tendency to see patterns in how events play out. Early man learned to identify various patterns as part of his survival instinct. Learning the migration habits of animals helped to hunt. Similarly, learning seasonal weather patterns allowed our ancestors to transition from being nomads to becoming farmers.
The problem with our tendency to recognize patterns is its effect on how we view both random events and events whose outcomes were not so obvious at some prior point. We naturally default to assuming a known data point, statistic or outcome can be used to analyze historical data. This assumption ignores the possibility of the data point or statistic evolving over time. It also ignores the possibility of a different outcome having occurred if the data was viewed differently at some point in the past.
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