Present value based asset pricing models are explored empirically in this thesis. Three contributions are made. First, it is shown that a market timing strategy may be implemented in an excessively volatile market such as the S&P500. The main premise of the strategy is that asset prices may revert to the present value over time. The present value is computed in real-time where the present value variables (future dividends, dividend growth and the discount factor) are forecast from simple models. The strategy works well for monthly data and when dividends are forecast from autoregressive models. The performance of the strategy relies on how discount rates are empirically defined. When discount rates are defined by the rolling and recursive historic average of realized returns, the strategy performs well. The discount rate and dividend growth can also be derived using a structural approach. Using the Campbell and Shiller log-linearized present value equation, and assuming that expected and realized dividend growth are unit related, a state space model is constructed linking the price-dividend ratio to expected returns and expected dividend growth. The model parameters are estimated from the data and, are used to derive the filtered expected returns and expected dividend growth series. The present value is computed using the filtered series. The trading rule tends to perform worse in this case. Discount rates are again found to be the major determinant of its success. Although the structural approach offers a time series of discount rates which is less volatile, it is on average higher than that of the historical mean model. The filtered expected returns is a potential predictor of realized returns. The predictive performance of expected returns is compared to that of the price-dividend ratio. It is found that expected returns is not superior to the price-dividend ratio in forecasting returns both in-sample and out-of-sample. The predictive regression included both simple Ordinary Least Squares and Vector Autoregressions. The second contribution of this thesis is the modeling of expected returns using autoregressive fractionally integrated processes. According to the work of Granger and Joyeux(1980), aggregated series which are derived from utility maximization problems follow a Beta distribution. In the time series literature, it implies that the series may have a fractional order (I(d)). Autoregressive fractionally models may have better appeal than models which explicitly posit unit roots or no unit roots. Two models are presented. The first model, which incorporates an ARFIMA(p,d,q) within the present value through the state equations, is found to be highly unstable. Small sample size may be a reason for this finding. The second model involves predicting dividend growth from simple OLS models, and sequentially netting expected returns from the present value model. Based on the previous finding that expected returns may be a long memory process, the third contribution of this thesis derives a test of long memory based on the asymptotic properties of the variance of aggregated series in the context of the Geweke Porter-Hudak (1982) semiparametric estimator. The test makes use of the fact that pure long memory process will have the same autocorrelation across observations if the observations are drawn at repeated intervals to make a new series. The test is implemented using the Sieve-AR bootstrap which accommodates long range dependence in stochastic processes. The test is relatively powerful against both linear and nonlinear specifications in large samples.

This book directly focuses on finding optimal trading strategies in the real world and supports that with a well-defined theoretical foundation that allows trading strategy problems to be solved. Critically, it also delivers a menu of actual solutions that can be applied by traders with various risk profiles and objectives in markets that exhibit substantial tail risk. It shows how the Markowitz approach leads to excessive risk taking, and trader underperformance, in the real world. It summarizes the key features of Utility Theory, the deficiencies of the Sharpe Ratio as a statistic, and develops an optimal decision theory with fully developed examples for both 'Normal' and leptokurtotic distributions.

Hugely popular market guru updates his popular trading strategy for a post-crisis world From Larry Williams—one of the most popular and respected technical analysts of the past four decades—Long-Term Secrets to Short-Term Trading, Second Edition provides the blueprint necessary for sound and profitable short-term trading in a post-market meltdown economy. In this updated edition of the evergreen trading book, Williams shares his years of experience as a highly successful short-term trader, while highlighting the advantages and disadvantages of what can be a very fruitful yet potentially dangerous endeavor. Offers market wisdom on a wide range of topics, including chaos, speculation, volatility breakouts, and profit patterns Explains fundamentals such as how the market moves, the three most dominant cycles, when to exit a trade, and how to hold on to winners Includes in-depth analysis of the most effective short-term trading strategies, as well as the author's winning technical indicators Short-term trading offers tremendous upside. At the same time, the practice is also extremely risky. Minimize your risk and maximize your opportunities for success with Larry Williams's Long-Term Secrets to Short-Term Trading, Second Edition.

This book is a collection of original, thought-provoking essays on critical issues in contract, commercial and corporate law. It is dedicated to the memory of the late Professor Jill Poole, who inspired so many and made such important contributions to these fields of law. The essays are written by leading practitioners and academics in the field, building on Jill’s work. As such this collection will be of interest and importance to professionals, academics and students in these fields of law. The Professor Jill Poole Educational Fund has been established in memory of Jill. It will be used to support undergraduate students in obtaining 'excellence scholarships' at Aston Law School and to reward 'excellence' at the annual law graduation ceremony. All contributions are welcome, and the royalties from this collection of essays have been donated to it.

Volumes 45a and 45b of Advances in Econometrics honor Professor Joon Y. Park, who has made numerous and substantive contributions to the field of econometrics over a career spanning four decades since the 1980s and counting.

This dissertation tackles the problem of non-normality in the distribution of returns and attempts to formulate a proprietary trading strategy to arbitrage the markets using appropriate statistical and mathematical tools. The first essay provides fundamental understanding to fractional Brownian motion (fBm) process, its characteristic Hurst exponent, and the concept of unit root in time series data. The study shows that a simple autoregressive (AR) process with suitable lag coefficients is able to effectively replicate the fractal time series and preserves its characteristic Hurst exponent. More interestingly, an equation that defines the relationship between the AR lag coefficients and the Hurst exponent that described a particular fBm process is also derived. The second essay introduces the concept of excursion measures and illustrates how the Itô's excursion theory can be used as a tool to understanding fractals. The excursionsvalued process is shown to follow a binomial distribution which is a robust substitute for Poisson distribution as suggested from the theory. The results also show that a process with low Hurst exponent or short-memory process has higher mean excursion measure at low excursion length as compared to a process with high Hurst exponent or a longmemory process. On the other hand, we see systematic wandering with longer excursion in a long-memory process with Hurst exponent higher than 0.5. Based on the discovery from the first two essays, the third essay combines these findings together to form a trading strategy called "Hurst Trading" with trading signals generated from the fluctuation in the dynamics of the Hurst exponent across time, among other indicators. We find that for the period between 2002 to 2011 the Hurst Trading strategy is able to outperform the traditional momentum strategy and the "Buy and Hold" strategy by a wide margin on stock trading in the DJIA Index, SPX Index, and R2500 Index. Furthermore, the more fractal the process is, the higher the chance that the Hurst Trading algorithm would be able to correctly time the entry/exit points in the market.

It is the editor’s distinct privilege to gather this collection of papers that honors Subhal Kumbhakar’s many accomplishments, drawing further attention to the various areas of scholarship that he has touched.