Corporate Finance

This paper proposes a simple back testing procedure that is shown to dramatically improve a panel data model's ability to produce out of sample forecasts. Here the procedure is used to forecast mutual fund alphas. Using monthly data with an OLS model it has been difficult to consistently predict which portfolio managers will produce above market returns for their investors. This paper provides empirical evidence that sorting on the estimated alphas populates the top and bottom deciles not with the best and worst funds, but with those having the greatest estimation error.

In this paper, we provide an exact particle filtering and parameter learning algorithm. Our approach exactly samples from a particle approximation to the joint posterior distribution of both parameters and latent states, thus avoiding the use of and the degeneracies inherent to sequential importance sampling. Exact particle filtering algorithms for pure state filtering are also provided. We illustrate the efficiency of our approach by sequentially learning parameters and filtering states in two models.

The conference paper by Johnson (2006, Review of Accounting Studies, forthcoming) develops an incomplete-contracting transfer pricing model with a number of novel features: taxation, sequential investments, and intangible assets being transferred. This discussion aims to disentangle these features so as to highlight those that are the key drivers of the results. Moreover, I show that some of the results can be generalized to settings involving a greater level of technological interdependency between the divisions.

Can one identify a "philosophy of regulation" that underlies the regulatory advocacy of the Fed under Chairman Greenspan? Although the Fed's advocacy on various matters may appear somewhat contradictory or, at least, philosophically heterodox, the Fed has behaved in a manner that is remarkably predictable, once one takes account of the political arena in which both regulatory and monetary policy are made. There is fairly straightforward logic to the Fed's regulatory advocacy.

When a boom ends, the downturn is generally sharp and short. When growth resumes, the boom is more gradual. Our explanation rests on learning about productivity. When agents believe productivity is high, they work, invest, and produce more. More production generates higher precision information. When the boom ends, precise estimates of the slowdown prompt decisive reactions: Investment and labor fall sharply. When growth resumes, low production yields noisy estimates of recovery. Noise impedes learning, slows recovery, and makes booms more gradual than downturns.

Records of over half a million participants in more than 600 401(k) plans indicate that participants tend to allocate their contributions evenly across the funds they use, with the tendency weakening with the number of funds used. The number of funds used, typically between three and four, is not sensitive to the number of funds offered by the plans, which ranges from 4 to 59. A participant?s propensity to allocate contributions to equity funds is not very sensitive to the fraction of equity funds among offered funds.

Recent empirical research documents that the strong short-term relationship between U.S. monetary aggregates on one side and inflation and real output on the other has mostly disappeared since the early 1980s. Using the direct estimate of flows of U.S. dollars abroad we find that domestic money (currency corrected for the foreign holdings of dollars) contains valuable information about future movements of U.S. inflation and real output.

We examine the pricing of aggregate volatility risk in the cross-section of stock returns. Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns. Stocks with high idiosyncratic volatility relative to the Fama and French (1993, Journal of Financial Economics 25, 2349) model have abysmally low average returns. This phenomenon cannot be explained by exposure to aggregate volatility risk.

This paper yields a summary score that informs about the sustainability (or persistence) of earnings and about the trailing P/E ratio. The score is delivered from a model that identifies unsustainable earnings from the financial statements by exploiting accounting relations that require that unsustainable earnings leave a trail in the accounts. The paper also builds a P/E model that recognizes that investors buy future earnings, so should pay less for current earnings if those earnings cannot be sustained in the future.