Equity Market Misvaluation, Financing, and Investment

Toni Whited is Professor of Finance at the University of Michigan. This post is based on an article authored by Professor Whited and Missaka Warusawitharana, Principal Economist at the Board of Governors of the Federal Reserve System.

Stock market volatility often dwarfs the volatility of real activity. Even in the 2008-2009 financial crisis, the sharp cutback in production and employment by many firms was tiny relative to the far steeper drops seen in most of their stock prices. The existence of such wide fluctuations in equity values relative to real activity raises the question of whether these swings reflect movements in intrinsic firm values. If not, then equity may be misvalued, and it is natural to wonder whether these non-fundamental movements in equity values affect managerial decisions. Put simply, does market timing occur, and how large are its effects?

Many empirical studies have examined the effect of equity misvaluation on various firm policies. However, the literature lacks model-based estimates of the forces behind market timing or the implications of market timing for firm value. In our paper, Equity Market Misvaluation, Financing, and Investment, forthcoming in the Review of Financial Studies, we fill this gap by using the estimates obtained from a dynamic model to study the effects of market timing.

According to our model estimates, we find that such activity increases value to long-term shareholders in small and large firms by 4% and 1.9%, respectively. While these estimates are best viewed as an upper bound on how much long-term shareholders can benefit from market timing by managers, they do suggest that market timing by managers has the potential to generate substantial gains.

Our parameter estimates imply that for large firms, misvaluation has almost no effect on capital expenditures, which are instead driven by operating considerations. Large firms save the proceeds from issuing overvalued equity to build financial flexibility, enabling them to finance repurchases or capital expenditures in the future. While small firms also save the bulk of their proceeds from such issuances, they use some of the proceeds to boost capital expenditures. This reaction reflects the fact that selling overvalued equity helps attenuate the effect of the higher financial frictions faced by small firms, boosting investment.

We obtain the above findings by augmenting a workhorse model of firm investment and saving to incorporate financial frictions and equity misvaluation shocks. In our framework, the market value of the firm can deviate from the value implied by fundamentals, so equity can be over- or undervalued. Naturally, managers sell overvalued equity and repurchased undervalued equity to benefit long-term buy and hold shareholders—who may be viewed as members of a controlling block—at the expense of short-term traders. Put differently, managers time their equity issuance and repurchase to take advantage of these fluctuations in equity values.

Our estimates of the model parameters dictate the extent of such activity. In particular, we obtain economically and statistically significant estimates of misvaluation shocks, providing evidence that equity values of nonfinancial firms can and do deviate meaningfully from those implied by fundamentals. We also obtain significant estimates of financial frictions and real adjustment frictions.

The data-driven configuration of these parameter estimates translates into our estimates of value gains via the structure of the model. These parameter estimates also produce our results concerning saving, investment, and managerial equity trades by implying whether the funds received from issuances or required for repurchases optimally flow into or out of capital expenditures or savings.

Our results are credible inasmuch as when we take our model to the data, we find that it does a good job capturing patterns observed in the data. Moreover, we find that including misvaluation shocks in our model is necessary for achieving this good fit. Finally, the model passes stringent out-of-sample tests by matching features of the data that were not used in its estimation.

The full paper is available for download here.

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