Better Benchmarking Achieved through the Collective Wisdom of Investors
“We propose a new approach to this age old problem of identifying peer firms for benchmarking: We query investors’ perceptions about who the relevant benchmarks ought to be.”
While investors agree that Target and Walmart operate in the same industry, an international system that classifies similar firms assigns the two retailers to entirely different economic sectors.
The Global Industry Classification Standard (GICS) is not ideal for benchmarking—identifying economically similar firms to compare to one another.
“Existing industry classification schemes generally classify firms based on their inputs and outputs. But in today’s service and knowledge-based economy, inputs and outputs are becoming harder to define. For example, is Tiffany’s selling jewelry, or is it selling luxury?” explains Assistant Professor Paul Ma.
Proper performance benchmarking is a fundamental issue for investors, boards of directors, and executives.
For example, a firm’s board of directors would like to compensate the CEO based on the firm’s performance through the component driven by managerial skill, and not luck. To determine this, the board would benchmark similar firms to study whether a trend is evident across the industry (luck), or specific to the firm (managerial skill).
To help decision-makers perform better benchmarking, Ma proposes a new method for identifying economically related firms that leverages the collective wisdom of expert investors. By analyzing 3.4 billion searches performed on the SEC’s Electronic Data Gathering Analysis and Retrieval website, the central repository for financial information, he and his colleagues discovered they could group similar firms based on how frequently they were searched concurrently by the same investor.
“Assuming investors are searching for financial information of related firms for benchmarking purposes, firms which are commonly searched together, like Microsoft and Google, are presumed to be more similar along various fundamental firm characteristics,” says Ma.
The data reveal, with stunning accuracy, that firms which are searched together (search-based peers) are more similar to the target firm along several fundamental measures relative to peers from traditional industry classification schemes.
Ma believes that by harnessing the collective intelligence of investors’ search patterns, researchers could develop a fresh framework for identifying firms for performance benchmarking purposes.
“Search Based Peer Firms: Aggregating Investor Perceptions through Internet Co-Searches” Lee, Charles M.C., Ma, Paul, Wang, Charles, C.Y., Journal of Financial Economics (forthcoming)