Using real stock market records, Adam Alter and Daniel Oppenheimer at Princeton University have shown that, over a year, new shares in companies with fluent, easy-to-pronounce names like ‘Barnings Inc.’ tend to outperform shares in companies with awkward names like ‘Aegeadux Inc’.
Alter and Oppenheimer asked 29 students to rate the fluency of a random sample of real company names. They then calculated that over their first year’s performance, $1000 invested in the 10 companies rated to have the most fluent names would have netted $333 more profit than $1000 invested in the 10 companies with the least fluent names.
Further analysis showed the association between a company’s name and its share performance was not due to larger companies, or companies in a certain industry sector, tending to have simpler names.
Nor was it due to simpler company names conveying some kind of appealing meaning – the researchers found companies with pronounceable ticker codes (used for abbreviation on TV and on websites) like KAR tended to outperform companies with an unpronounceable ticker code like RDO.
In fact, across the entire NYSE and AMEX markets, Alter and Oppenheimer calculated $1000 invested in shares with pronounceable ticker codes would have netted $85.35 more profit after one day compared with an equal amount invested in companies with an unpronounceable ticker code.
“Given that investors traded shares valued at roughly $2 billion on the average day in 2006, these differences have dramatic practical consequences”, they said.
“Researchers’ intuitive attempts to understand complex real-world phenomena with equally complex models may not always be the best approach” Alter and Oppenheimer concluded, adding: “Keeping in mind that humans are forced to seek a simple thread of understanding when bombarded with excessive information, sometimes a surprisingly simple theory is a successful predictor of human behaviour”.
Alter, A. & Oppenheimer, D.M. (2006). Predicting short-term stock fluctuations by using processing fluency. Proceedings of the National Academy of Sciences, USA, 103, 9369-9372.
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