US stocks near the top of an alphabetical listing experience trading activity and liquidity that is 5%–15% higher than stocks near the bottom of a list. The magnitude of the results correlates inversely to firms’ visibility and investor sophistication. The authors observe the influence of ordering effects on trading and liquidity in international stock markets and the mutual fund universe.

Alphabetic bias occurs in academia and in other aspects of everyday life. The authors appear to be the first to explore the influence of ordering effects—alphabetization—in the stock market. Conventionally, stocks are listed alphabetically in both print and digital media as well as in information sources and watch lists. The authors hypothesize that alphabetic bias affects trading and liquidity, which are interrelated and economically relevant. Greater liquidity engenders more informed trading, which, in turn, leads to greater price discovery, which should reflect a firm’s value more accurately.

The authors examine dates from January 1983 to December 2011. Their analyses and robustness checks confirm that US firms closer to the beginning of an alphabetically ordered list do experience greater trading activity and liquidity. NYSE, AMEX, and NASDAQ firms in the top 5% of the alphabetically arranged stock universe experience approximately 12% higher monthly turnover (trading activity) and 13% lower illiquidity than firms lower in the list. The authors extend their hypothesis to the mutual fund universe, whose fund flows exert significant economic influence. Empirical research confirms that consumers find more-visible funds to be easier and less costly to identify. A plethora of mutual fund choices tends to lead investors to choose those near the top of an alphabetical list.

The authors make meaningful contributions to the literature. First, they add to the growing body of research that explores how factors separate from company fundamentals can influence investor behavior and market activity. Second, the authors’ findings shed light on how ordering can affect trading decisions. Third, they demonstrate how limited investor attention may induce individuals to reduce complexity in the face of complex markets and cognitive limitations. Finally, alphabetical ordering could serve as a useful exogenous source of trading input in which trading activity and liquidity are key explanatory variables in the presence of possible endogeneity.

Traders, analysts, and portfolio managers—as well as behavioral economists—could glean useful information from the authors’ findings for the purposes of portfolio construction and implementation. Marketing departments for index providers, brokerages, banks, and mutual fund companies could find this research useful as well.

The authors perform regression analyses using equity measures of trading activity and liquidity. The number of shares traded divided by the number of shares outstanding proxies for stock turnover. They proxy for liquidity using the illiquidity ratio, which captures the monthly average of the ratio of the absolute daily stock return and daily trading volume in millions of dollars. Using log transformations of both variables helps to minimize the influence of outliers. The authors run separate analyses for NYSE, AMEX, and NASDAQ stocks to avoid double-counting effects. They also use numerous control variables that could influence trading activity and liquidity, including such items as prior month stock returns, book-to-market ratio, firm market capitalization, age of firm, share price, advertising expenses scaled to total sales, media coverage, and controls for stock index effects.

The authors’ sample period runs from January 1983 to December 2011. They quantify firm name distribution through the construction of two time-series measures of historical and current company names from CRSP. The first, continuous positioning, is the relative position of the company’s name in alphabetically ascending order. Positioning dummies, the second, account for the potentially disproportionate bias that alphabetization exerts on firms closer to the beginning of a list. Regressing the firm metric on the aforementioned control variables is statistically significant for media coverage only.

For the main findings, the authors confirm that continuous positioning matters for both trading activity and liquidity. Firms whose alphabetical listing is near the top of a list experience more of both when measured by the continuous positioning and positioning dummies metrics.

The authors test their hypothesis in six international markets: Canada, the United Kingdom, France, Germany, Australia, and Japan. There is no evidence of alphabetical ordering bias in Japan because stocks there are presented by numerical symbols rather than by individual names. Alphabetic bias does exist in the markets of all the other countries tested. Control variables used for international stocks include firm size, age, stock price, book-to-market ratio, leverage, beta, and analyst coverage. Using the same methodology, the authors also look at the cross-sectional determinant of the effects of alphabetic bias that include firm visibility and investor sophistication. Alphabetic bias correlates inversely to investor sophistication and visibility. Ordering bias is confirmed by numerous robustness checks that add controls and change the econometric approach and data transformation as well as measures of trading activity and liquidity. Finally, the authors test their hypothesis in the US mutual fund universe. They use the CRSP Mutual Fund Database and scrub the data to remove funds for which there are none of the requisite control variables and to mitigate outliers. They find that alphabetic bias affects fund flows meaningfully in this space as well.

In the face of increased financial market complexity and the cognitive limitations of individual investors, there appears to be a proclivity to simplify the investment decision-making process. Indeed, the phenomenon of alphabetic bias, observed in everyday existence, occurs in the US stock market. The authors’ research holds up to numerous robustness checks and out-of-sample tests. Examining how this occurrence may be prevalent in markets beyond these authors’ exploration could be a behavioral finance area worthy of additional research on a global scale.