Scheinkman, Speculation, Trading, and Bubbles

To pay tribute to one of its most famous graduates, Kenneth J. Arrow, Columbia University launched an annual lecture series dealing with topics to which Arrow made significant contributions—and there were many. Speculation, Trading, and Bubbles stems from the third lecture in the series given by José A. Scheinkman, with adapted transcripts of commentary by Patrick Bolton, Sanford J. Grossman, and Arrow himself. I’m going to confine myself here to a few excerpts that encapsulate some of the lecture’s key points, ignoring the often perceptive commentary.

Scheinkman offers a formal model of the economic foundations of stock market bubbles in an appendix to his lecture, but he lays out its basic ideas in the lecture proper. The model rests on two fundamental assumptions—“fluctuating heterogeneous beliefs among investors and the existence of an asymmetry between the cost of acquiring an asset and the cost of shorting that same asset. … Heterogeneous beliefs make possible the coexistence of optimists and pessimists in a market. The cost asymmetry between going long and going short on an asset implies that optimists’ views are expressed more fully than pessimists’ views in the market, and thus even when opinions are on average unbiased, prices are biased upwards. Finally, fluctuating beliefs give even the most optimistic the hope that, in the future, an even more optimistic buyer may appear. Thus a buyer would be willing to pay more than the discounted value she attributes to an asset’s future payoffs, because the ownership of the asset gives her the option to resell the asset to a future optimist.” (pp. 15-16)

This framework leads Scheinkman to define a bubble as “the difference between what a buyer is willing to pay and her valuation of the future payoffs of the asset—or equivalently, the value of the resale option…. An increase in the volatility of beliefs increases the value of the resale option, thus increasing the divergence between asset prices and fundamental valuation, and also increases the volume of trade. Hence, in the model, bubble episodes are associated with increases in trading volume.” (p. 16)

Scheinkman is concerned with modeling bubbles, not with policy recommendations about bubbles. But concluding his lecture with some final observations, he addresses a question he left unanswered in the lecture—“whether one could use the signals associated with bubbles, such as inordinate trading volume or high leverage, to detect and perhaps stop bubbles. One of the difficulties in using these signals is that we know next to nothing about false positives.” And, he continues, “Even if we could effectively detect bubbles, it is not obvious that we should try to stop all types of bubbles. Although credit bubbles have proven to have devastating consequences, the relationship between bubbles and technological innovation suggests that some of these episodes may play a positive role in economic growth. The increase in the price of assets during a bubble makes it easier to finance investments related to the new technologies.” The one recommendation that flows directly from his model is that “to avoid bubbles, policy makers should consider limiting leverage and facilitating, instead of impeding, short-selling.” (p. 35)