An IPO’s Impact on Rival Firms
Yale University – Yale School of Management, International Center for Finance
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Yale University – Yale School of Management; Yale University – International Center for Finance
There is a long literature documenting the process by which firms conduct initial public offerings (IPOs). However, there has been a relative paucity of research into how one firm’s decision to switch from being private to public impacts its rivals. This paper uses a structural approach to address that question. We develop a continuous time model in which heterogeneous firms producing heterogeneous goods compete for consumers. Because the model takes place in real time it produces a structure with parameters that can be estimated empirically. Importantly, this allows the empirical work to make meaningful statements about parameter magnitudes as well as signs. In general, when firms conduct IPOs, they are relatively small and their impact on rivals’ values amounts to a few basis points one way or the other. However, we do find that new issues presage a more homogenous product environment which ultimately cuts down on industry profits. The paper’s structural model also offers a new way to test for whether an IPO causes these industry changes or simply presages them. Roughly, if the IPO makes the newly public firm stronger, the IPO should benefit the IPO firm and negatively impact its rivals. By comparing forecasts of changes in fundamentals and out-of-sample forecast errors over time, one can see if the IPO firm’s future looks fundamentally different from its rivals. Our tests indicate they look the same, implying that IPOs forecast future industry changes but do not cause them.
An IPO’s Impact on Rival Firms
An initial public offering (IPO) is a major event in the life of any firm. But what does an IPO imply for the industry’s future? Does going public signal that competitive pressures will increase or decrease? Pressures may increase if the newly public firm is now a more formidable rival. Alternatively, the IPO firm’s now mandatory disclosures may prove useful to rivals that can now better copy its strategy. It is also possible that the firm may decide to go public to take advantage of new opportunities facing the industry, implying greater profitability in the future for it and its rivals. Typically, papers in corporate finance focus on one dimension of problems like this, or they attempt to characterize a dominant effect, to be applied across all industries. This paper takes a structural approach that allows different industries to progress in different ways post IPO. We uncover a great deal of heterogeneity in the data, which improves our understanding of the range of economic forces that are associated with IPO activity. If one is forced to make a sweeping generalization, then this paper finds an IPO augurs in an era of reduced profits and greater consumer mobility within an industry. However, in the upper tail of the interquartile range, industries are forecasted to see higher profits and lower consumer mobility.
If the goal is to see what an IPO may portend for an industry then it seems natural to define an industry as a set of firms that compete with each other for the same customers – as a practical matter, those based on 4-digit Standard Industrial Codes (SIC).1 However, the fact that there are hundreds of 4-digit industries makes it difficult to know what variables should or should not be included. A structural model offers a potential solution. Using one that describes industry dynamics with a relatively small set of variables makes estimation on an industry-by-industry basis feasible. Here, we begin with the Spiegel and Tookes (2013) continuous time model of competition in a heterogeneous product oligopoly. Their model is then modified to allow for a gradual change in the competitive environment over time post IPO.
The setting is general enough that the change can be due to a number of factors that play out in a variety of ways. For example, information disseminated by the IPO firm may increase or decrease competitive pressures within the industry, leading to changes in spending on customer acquisition. Unlike a purely empirical model, a structural one can be tested on the basis of its dynamic forecasts. If those prove accurate then it at least sets a benchmark against which other explanations (and ultimately forecasts) can be measured. Several tests show that this paper’s dynamic model does quite well relative to current alternatives in this regard.
See full study here