Knowledge that Transforms

Abstract Studies analyzing return expectations of financial market participants like fund managers, CFOs or individual investors are highly influential in academia and practice. We argue and show that the results in these surveys above are easily influenced by the elicitation mode of return expectations. Surveys that ask for future stock price levels are more likely to produce mean reverting expectations than surveys that directly ask for future returns. Furthermore, we conduct a questionnaire study that explicitly analyzes whether the specific elicitation mode affects return expectations in the above direction. In our study, subjects were asked to state mean forecasts for seven time series. Using a between subject design, one half of the subjects was asked to state future price levels, the other group was directly asked for returns. We observe a highly significant framing effect. For upward sloping time series, the return forecasts stated by investors in the return forecast mode are significantly higher than those derived for investors in the price forecast mode. For downward sloping time series, the return forecasts given by investors in the return forecast mode are significantly lower than those derived for investors in the price forecast mode. We argue that this finding is consistent with behavioral theories of investor expectation formation based on the representativeness heuristic.

Abstract This paper examines the determinants of the time it takes for an index options market to return to no arbitrage values after put-call parity deviations, using intraday transactions data from the French index options market. We employ survival analysis to characterize how limits to arbitrage influence the expected duration of arbitrage deviations. After controlling for conventional limits to arbitrage, we show that liquidity-linked variables are associated with a faster reversion of arbitrage profits. The introduction of an Exchange Traded Fund also affects the survival rates of deviations, but this impact essentially stems from the reduction in the level of potential arbitrage profits.

Abstract This paper shows that some of the most prominent risk-based theories offered as explanation for the value premium are at odds with data. The models proposed by Fama and French (1993), Lettau and Ludvingson (2001), Campbell and Vuolteenaho (2004), and Yogo (2006) can capture the cross-section of returns of portfolios sorted on book-to-market ratio and size, but not of portfolios sorted on book-to-market ratio and institutional ownership. These models generate economically large pricing errors in all the institutional ownership quintiles and each statistical test indicates that these pricing errors are significant. More generally, these results show that a minor alteration of the test assets can lead to a dramatically different answer regarding the validity of a given asset pricing model.

Abstract This paper examines whether firms manage analyst forecasts and the associated value consequences. We find that earnings forecasts tend to grow pessimistic over the forecast horizon and these forecast changes and their timing are key determinants of whether firms generate positive earnings surprises: Late forecasts that raise (lower) the consensus sharply reduce (raise) the probability of positive surprises. This findng is the opposite of that predicted if consensus revisions reflected new information arrival. Investors seem to be “misled”: downward consensus revisions lead to large abnormal returns following the earnings announcement. Paradoxically, downward forecast management reduces post-announcement share price, as the impact of reduced forecasts dominates the gain from generating positive surprises.

Abstract Despite the wide acceptance of return-based style analysis, the method has several limitations. One important drawback is the assumption that style exposures are time-invariant. We apply results on break tests developed in Bai and Perron (1998, 2003) to test for style breaks. We find strong evidence against the hypothesis of constant time exposures in daily return data for European equity funds. All funds exhibit at least one break, and 60% exhibit more than one break. We show that the main reason for style breaks is the mutual funds' reliance on conditional investment strategies based on public information and volatility estimates.

Abstract 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. This problem can be attenuated by back testing the statistical model fund by fund. The back test used here requires a statistical model to exhibit some past predictive success for a particular fund before it is allowed to make predictions about that fund in the current period. Another estimation problem concerns the use of a single statistical model for all available mutual funds. Since no one statistical model is likely to fit every fund, the result is a great deal of misspecification error. This paper shows that the combined use of an OLS and Kalman filter model increases the number of funds with predictable out of sample alphas by about 60%. Overall, a strategy that uses very modest ex-ante filters to eliminate funds whose parameters likely derive primarily from estimation error produces an out of sample risk-adjusted return of over 4% per annum.

Abstract This paper investigates why traders hide their orders and how other traders respond to hidden depth. Using a logit model, we provide empirical findings suggesting that traders use hidden orders to manage both exposure risk and picking off risk. Using probit models, we show that hidden depth increases order aggressiveness. Our interpretation of this empirical evidence is threefold. First, hidden depth detection is possible and frequent. Second, when traders detecthidden volume at the best opposite uote, they strategically adjust their order submission to seize the opportunity for depth improvement. Third, traders' response when hidden depth is detected suggests either that they do not associate hidden orders with informed trading or that the risk of trading with an informed trader is widely offset by the opportunity for depth improvement.

Abstract This paper complements theoretical studies on the Kelly rule in evolutionary finance by studying a Darwinian model of selection and reproduction in which the diversity of investment strategies is maintained through genetic programming. We find that investment strategies which optimize long-term performance can emerge in markets populated by unsophisticated investors. Regardless whether the market is complete or incomplete and whether states are i.i.d. or Markov, the Kelly rule is obtained as the asymptotic outcome. With price-dependent rather than just state-dependent investment strategies, the market portfolio plays an important role as a protection against severe losses in volatile markets.