The Economic Consequences of Noise Traders

The claim that financial markets are efficient is backed by an implicit argument that misinformed "noise traders" can have little influence on asset prices in equilibrium. If noise traders' beliefs are sufficiently different from those of rational agents to significantly affect prices, then noise traders will buy high and sell low. They will then lose money relative to rational investors and eventually be eliminated from the market. We present a simple overlapping-generations model of the stock market in which noise traders with erroneous and stochastic beliefs (a) significantly affect prices and (b) earn higher returns than do rational investors. Noise traders earn high returns because they bear a large amount of the market risk which the presence of noise traders creates in the assets that they hold: their presence raises expected returns because sophisticated investors dislike bearing the risk that noise traders may be irrationally pessimistic and push asset prices down in the future. The model we present has many properties that correspond to the "Keynesian" view of financial markets. (i) Stock prices are more volatile than can be justified on the basis of news about underlying fundamentals. (ii) A rational investor concerned about the short run may be better off guessing the guesses of others than choosing an appropriate P portfolio. (iii) Asset prices diverge frequently but not permanently from average values, giving rise to patterns of mean reversion in stock and bond prices similar to those found directly by Fama and French (1987) for the stock market and to the failures of the expectations hypothesis of the term structure. (iv) Since investors in assets bear not only fundamental but also noise trader risk, the average prices of assets will be below fundamental values; one striking example of substantial divergence between market and fundamental values is the persistent discount on closed-end mutual funds, and a second example is Mehra and Prescott's (1986) finding that American equities sell for much less than the consumption capital asset pricing model would predict. (v) The more the market is dominated by short-term traders as opposed to long-term investors, the poorer is its performance as a social capital allocation mechanism. (vi) Dividend policy and capital structure can matter for the value of the firm even abstracting from tax considerations. And (vii) making assets illiquid and thus no longer subject to the whims of the market -- as is done when a firm goes private -- may enhance their value.

Measurement Uncertainty for Finite Quantum Observables

Measurement uncertainty relations are lower bounds on the errors of any approximate joint measurement of two or more quantum observables. The aim of this paper is to provide methods to compute optimal bounds of this type. The basic method is semidefinite programming, which we apply to arbitrary finite collections of projective observables on a finite dimensional Hilbert space. The quantification of errors is based on an arbitrary cost function, which assigns a penalty to getting result x rather than y, for any pair (x,y) . This induces a notion of optimal transport cost for a pair of probability distributions, and we include an Appendix with a short summary of optimal transport theory as needed in our context. There are then different ways to form an overall figure of merit from the comparison of distributions. We consider three, which are related to different physical testing scenarios. The most thorough test compares the transport distances between the marginals of a joint measurement and the reference observables for every input state. Less demanding is a test just on the states for which a “true value” is known in the sense that the reference observable yields a definite outcome. Finally, we can measure a deviation as a single expectation value by comparing the two observables on the two parts of a maximally-entangled state. All three error quantities have the property that they vanish if and only if the tested observable is equal to the reference. The theory is illustrated with some characteristic examples.BMBF/Q.com-QDFG/WE1240/20DQSIMSIQ

Fixing Innovation Policy: A Structural Perspective

Innovation is central to economic growth and human welfare. Government officials and commentators have recognized this reality and have called for a variety of different substantive incentives for stimulating innovation. But the question of how an innovation regulator should be structured has received little attention. Such consideration is important not only because of the significance of innovation but also because current government innovation policy is so haphazard. There is no government entity that looks at innovation broadly, and the narrower agencies that regulate aspects of innovation policy not only fail to pay systematic attention to innovation goals but often act at cross-purposes with each other. In this article, Professors Benjamin and Rai analyze how government policy on innovation should be structured. Drawing on existing theoretical and empirical work, as well as their own original empirical research, they propose the creation of an entity in the executive branch that would both analyze pending agency action and offer regulatory suggestions of its own. This entity would introduce a new, trans-agency focus on innovation while drawing upon, and feeding into, existing executive branch processes that aim to rationalize the work of disparate federal agencies. This approach, Professors Benjamin and Rai contend, offers a great improvement over existing government institutions while avoiding a costly (and politically infeasible) remaking of the administrative state

Crew Planning at Netherlands Railways: Improving Fairness, Attractiveness, and Efficiency

The development and improvement of decision support voor crew planning at Netherlands Railways (NS