Working Paper 145 - Assessing the Returns to Education in the Gambia

Using three nationally representative surveys from the country, we estimate the private rates of returns to education in The Gambia. To obtain consistent estimates, we exploit exogenous variation in school availability in the country at the district level at the time current wage earners where born. Our results show that the private rates of returns to education are quite high, although heterogeneous across regions of the country. The high rates of returns are robust to alternate formulations.

The Variability of IPO Initial Returns

The monthly volatility of IPO initial returns is substantial, fluctuates dramatically over time, and is considerably larger during "hot" IPO markets. Consistent with IPO theory, the volatility of initial returns is higher among firms whose value is more difficult to estimate, i.e., among firms with higher information asymmetry. Our findings highlight underwriters' difficulty in valuing companies characterized by high uncertainty, and, as a result, raise serious questions about the efficacy of the traditional firm commitment underwritten IPO process. One implication of our results is that alternate mechanisms, such as auctions, may be beneficial, particularly for firms that value price discovery over the auxiliary services provided by underwriters.

Alternate Models for Forecasting Hedge Fund Returns

Alternate Models for Forecasting Hedge Fund Returns Michael Holden Faculty Sponsor: Gordon Dash, Finance and Decision Sciences Investors have always wanted to improve the efficiency of modeling realized volatility to maximize directional trading returns and substantially improve profitability. As proposed, this honors project will provide evidence from hedge fund returns that a Radial-Basis Function (RBF) artificial neural network (ANN), specifically the Kajiji-4 RBF-ANN dominates other forecast methods in producing one-period ahead change-of-direction when forecasting the expected returns of various hedge fund indexes. I began this project by collecting historical economic data in monthly increments to serve as the dependent variables. The primary independent variable used in this study are two types of Treasury securities (short-term and long-term) to represent interest rates as well as the Volatility Index (VIX). The VIX index serves as a proxy for options implied volatility in the equity markets. These independent variables are used to predict the returns of multiple hedge fund indexes which serve as the dependent variables. The data was plugged into the RBF-ANN in order to solve the economic models. The ANN first took time to train using 33% of the data, and then it validated the remaining 67% of the data to measure the fitness. The study proceeded to calculate the residual by taking the difference of the actual data and the RBF-ANN predicted data. The RBF-ANN showed that the data was very fit as the mean square error (MSE) was relatively small. Overall, I have found that the RBF-ANN has done quite well in predicting the returns of various hedge fund indexes. The scope of the project will examine three well-known hedge fund styles

Asset Pricing with Incomplete Information under Stable Shocks

We study a consumption based asset pricing model with incomplete information and alpha-stable shocks. Incomplete information leads to a non-Gaussian filtering problem. Bayesian updating generates fluctuating confidence in the agents' estimate of the persistent component of the dividends’ growth rate. Similar results are obtained with alternate distributions exhibiting fat tails (Extreme Value distribution, Pearson Type IV distribution) while they are not with a thin-tail distribution (Binomial distribution). This has the potential to generate time variation in the volatility of model-implied returns, without relying on discrete shifts in the drift rate of dividend growth rates. A test of the model using US consumption data indicates strong support in the sense that the implied returns display significant volatility persistence of a magnitude comparable to that in the data.asset pricing, incomplete information, time-varying volatility, fat tails, stable distributions

REITs and Inflation: A Long-Run Perspective

We examine whether REITs provide an inflation hedge in the long run. We also investigate whether the apparent lack of a positive relationship between general prices and REIT returns in prior studies arises from the impact that stock market movements have on REITs. As in most prior research, regression analysis provides no evidence that REIT returns are positively related to temporary or permanent components of inflation measures. We rule out the possibility that a stock market-induced proxy effect is the cause for the apparent lack of relationship between REITs and inflation. On the other hand, we find some evidence that REITs provide a long-run inflation hedge. Johansen (1988) tests for cointegration isolate cointegrating vectors between alternate REIT indices and the CPI over the 1972-95 interval. However, the more standard residual-based cointegration techniques failed to provide similar evidence.

Analogy Mining for Specific Design Needs

Finding analogical inspirations in distant domains is a powerful way of solving problems. However, as the number of inspirations that could be matched and the dimensions on which that matching could occur grow, it becomes challenging for designers to find inspirations relevant to their needs. Furthermore, designers are often interested in exploring specific aspects of a product-- for example, one designer might be interested in improving the brewing capability of an outdoor coffee maker, while another might wish to optimize for portability. In this paper we introduce a novel system for targeting analogical search for specific needs. Specifically, we contribute a novel analogical search engine for expressing and abstracting specific design needs that returns more distant yet relevant inspirations than alternate approaches

Aggregational Gaussianity In The South African Equity Market

While stylized facts of South African asset returns have been studied extensively, Aggregational Gaussianity has largely been overlooked. The aggregational aspect arises from the n-day log-return being the sum of n one-day log-returns and empirical asset returns tending to normality as the term increases. This fact is commonly corroborated graphically using overlapping return series depicted in Q-Q plots. Using a resampling-based statistical methodology to test for Aggregational Gaussianity while catering for overlapping data, an alternate picture emerges. Here the authors describe evidence from the South African market for a discernible absence of Aggregational Gaussianity and briefly discuss the implications thereof

Regime specific predictability in predictive regressions

Predictive regressions are linear specications linking a noisy variable such as stock returns to past values of a more persistent regressor such as valuation ratios, interest rates etc with the aim of assessing the presence or absence of predictability. Key complications that arise when conducting such inferences are the potential presence of endogeneity, the poor adequacy of the asymptotic approximations amongst numerous others. In this paper we develop an inference theory for uncovering the presence of predictability in such models when the strength or direction of predictability, if present, may alternate across dierent economically meaningful episodes. This allows us to uncover economically interesting scenarios whereby the predictive power of some variable may kick in solely during particular regimes or alternate in strength and direction (e.g. recessions versus expansions, periods of high versus low stock market valuation, periods of high versus low term spreads etc). The limiting distributions of our test statistics are free of nuisance parameters and some are readily tabulated in the literature. Finally our empirical application reconsiders the literature on Dividend Yield based stock return predictability and contrary to the existing literature documents a strong presence of predictability that is countercyclical, occurring solely during bad economic times

Regime specific predictability in predictive regressions

Predictive regressions are linear specifications linking a noisy variable such as stock returns to past values of a more persistent regressor such as valuation ratios, interest rates etc with the aim of assessing the presence or absence of predictability. Key complications that arise when conducting such inferences are the potential presence of endogeneity, the poor adequacy of the asymptotic approximations amongst numerous others. In this paper we develop an inference theory for uncovering the presence of predictability in such models when the strength or direction of predictability, if present, may alternate across different economically meaningful episodes. This allows us to uncover economically interesting scenarios whereby the predictive power of some variable may kick in solely during particular regimes or alternate in strength and direction (e.g. recessions versus expansions, periods of high versus low stock market valuation, periods of high versus low term spreads etc). The limiting distributions of our test statistics are free of nuisance parameters and some are readily tabulated in the literature. Finally our empirical application reconsiders the literature on Dividend Yield based stock return predictability and contrary to the existing literature documents a strong presence of predictability that is countercyclical, occurring solely during bad economic times.Endogeneity, Persistence, Return predictability, Threshold models