In principle, most people understand that financial decisions hinge on balancing risk and returns over time. In practice, however, how do consumers weigh the pros and cons of a particular stock? How does one predict future price fluctuations? And accordingly, how does one decide when to buy, hold, or sell equities? Given their inexperience (41% of Americans give themselves a C, D, or F on their knowledge of finance), nonprofessional investors largely rely on outside recommendations to make such decisions. Typically, these recommendations originate from banks, brokers, and financial-data providers (e.g., Bloomberg, Reuters). But given the indigestible magnitude of information afforded by modern technologies, financial-services providers usually summarize their so-called “market intelligence” to ease its interpretation. And the method used most commonly to convey performance over time is graphs (i.e., graphic representations of quantitative information). In fact, most industry players enable consumers to customize the visual representation of data relevant to them. Stocks, debt, commodities, and foreign-exchange markets can thus be reviewed at a glance, thanks to sophisticated yet user-friendly graphic interfaces.

Given the implications of financial decision-making for individual as well as societal welfare, the present enquiry examined how graphic representation of quantitative information may bias information processing and, ultimately, investment decisions.

What did we find?

Allying experimental manipulations to eye-tracking technology, five studies found that the last trading day(s) of a stock bear a disproportionately (and unduly!) high importance on investment behavior. Specifically, graphs that depict a stock-price closing upward (downward) foster upward (downward) forecasts for tomorrow and, accordingly, more (less) investing in the present. Substantial investment asymmetries (up to 75%) emerged even when stock-price distributions were generated randomly to simulate times when the market conjuncture is hesitant (i.e., trendless). In such times, recent price-movements are no more diagnostic than earlier ones. Yet, we find they do bias decision-making and, ultimately, can be quite harmful for investors. We coin this phenomenon end-anchoring.

Implications of the research for consumers, firms, and policymakers

The proliferation of online trading-platforms in the last 15 years (e.g., e-Trade, Fidelity) has allowed private, inexperienced investors to engage in activities reserved until now to finance professionals. As a result, the number of lay investors skyrocketed in not only the US but also Europe and Asia. Worldwide, we estimate in the millions the number of people gambling away their money on financial markets in hope of a better future.

Like their professional counterparts, private investors rely on readily accessible graphs to interpret past market-performance and forecast future trends. The present findings should thus sound a tune of caution for consumers as much as for industry players and regulators. Indeed, biases such as end-anchoring can easily lead to precipitated sale (or purchase) of assets, lopsided portfolio allocations, and other irrational behaviors. It is thus important, both managerially and societally, to understand how displays of data impact investment behavior. To this effect, one of our experiments (i.e., study 2) finds that graphic displays encourage end-anchoring whereas numeric ones (e.g., tables of numbers) mitigate it. As such, it may sometimes be beneficial to convey quantitative information in less “perceptual” ways. Because of their visual nature, graphs may indeed be more likely to foster perceptual processing and heuristic decision-making. In contrast, numeric displays may be less prone to such a pitfall.

Many times in the past have financial markets been erratic (e.g., 1980s’ Japanese bubble, 1987’s market crash, 1997’s Asian crisis, 2008’s subprime burst). Every time, individual-level behaviors aggregated to form market-level meltdowns. Given our ever-increasing reliance on graphs to convey financial information, understanding biases in data interpretation (like end-anchoring) will help (i) financial-services providers refine their presentation of performance data; (ii) lay and professional investors free themselves from detrimental heuristics; and (iii) policymakers organize the dissemination of financial information so as to avoid panics in the market. Mirroring the rules governing product packaging, our findings may help inform (and perhaps regulate) communications around financial products.