Do narratives (stories) sometimes trump rationality in financial markets? In his January 2017 paper entitled “Narrative Economics”, Robert Shiller considers the epidemiology (spread, mutation and fading) of stories as related to economic fluctuations. He explores the 1920-21 depression, the Great Depression of the 1930s, the Great Recession of 2007-9 and the political-economic situation of today as manifestations of popular stories. Based on these examples, other examples from other fields and his experience, he concludes that:
- The human brain is highly tuned towards simplifications expressed as stories, factual or not, to justify even such basic actions as spending and investing.
- Stories motivate and connect actions to deeply felt values and needs.
- Stories “go viral” and spread far, sometimes with global economic impact.
- Mutations in stories evolve randomly, as in evolutionary biology, generating seemingly unpredictable changes in the economy.
- News media frequently mention economic impacts of stories without the constraints of academic rigor. The advent of big data and semantic search algorithms may bring credibility to the field (see the research collection below).
- Determining causality, whether stories cause behaviors or behaviors drive stories, is difficult.
- There are overlapping contagious stories at any point in time, and disaggregating their effects is hard.
- The research community should allocate substantial resources to:
- Improving methods/tools for tracking and quantifying stories and disentangling competing stories.
- Collecting data on the interactions of stories and public reactions via, for example, focus groups and social media.
In summary, while stories appear to be an important aspect of human economic and financial systems, they are hard to study scientifically.
Cautions regarding conclusions include:
- The author acknowledges: “To my knowledge, there has been no controlled experiment to prove the importance of changing narratives in causing economic fluctuations. We cannot easily prove that any association between changing narratives and economic outcomes is not all reverse causality, from the outcomes to the narratives.”
- While advocating allocation of resources to studying the interaction of stories and group behaviors, the author offers no clear payoff.
See the following for examples of formal research into narrative/meme analysis as an investment timing tool:
- “News Sentiment and Future Stock Returns”
- “Testing Sentdex Sentiment Trading Signals”
- “In Search of the Bear?”
- “High-frequency Investor Sentiment and Stock Returns”
- “Google Search Activity Predicts Stock Market Returns?”
- “Google Trends Data vs. Past Returns”
- “Processing News to Predict Stock Returns”
- “Google Trends Predict the Stock Market?”
- “Insights from Google Insights?”
- “Socially Amplified Trading?”
- “Announcement Tone and Short-term Reaction to Earnings News”
- “Monthly News Sentiment Predicts Stock Market Returns?”
- “Gross National Happiness as Stock Market Return Predictor”
- “Short-term News Premium for Individual Stocks”
