Browsing by Author "Williams, Edward E."

Now showing 1 - 6 of 6
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    Design and Validation of Ranking Statistical Families for Momentum-Based Portfolio Selection
    (2013-07-24) Tooth, Sarah; Thompson, James R.; Dobelman, John A.; Williams, Edward E.
    In this thesis we will evaluate the effectiveness of using daily return percentiles and power means as momentum indicators for quantitative portfolio selection. The statistical significance of momentum strategies has been well-established, but in this thesis we will select the portfolio size and holding period based on current (2012) trading costs and capital gains tax laws for an individual in the United States to ensure the viability of using these strategies. We conclude that the harmonic mean of daily returns is a superior momentum indicator for portfolio construction over the 1970-2011 backtest period.
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    Identifying and Dealing with the Approach of Bears and their Departure
    (2013-05-29) Affinito, Ricardo; Thompson, James R.; Ensor, Katherine B.; Williams, Edward E.
    Based on the identification of market dynamics, capital allocation in long positions can be dynamically controlled by means of interrupting an otherwise strictly-long investment strategy allowing for an overall improved risk profile and faster response times during periods of persistent negative market returns. Herein, a portfolio selection methodology updating a reasonably diversified selection of competing S&P 500 constituents within and across various predefined industry groups and which produced above average long-term returns with minimized downside-risk, is proposed. Within the various predefined groups of stocks, Simugram methods are used to model and optimize on the distribution of returns up to and including a horizon of interest. Improvements to previous methods are focused toward calibrating the sampling distribution based on an empirical dataset within the various groups comprising the investor's portfolio, optionally allowing for a varying sampling frequency as dictated by the various group dynamics. By combining within-group optimization alongside with the capability of exiting aggressive long-strategies at seemingly riskier times, focus is on providing more frequent updates on a list of constituents with improved performance in both terms of risk and return.
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    Market outperformance by nonparametric, simugram-based portfolio selection
    (2004) Dobelman, John August; Thompson, James R.; Williams, Edward E.
    A new portfolio selection system is presented which weights components in a target major market index such that the resulting portfolio consistently outperforms the underlying market index by most any multi-period return measure. This is accomplished by use of the simugram, which gives a simulation-based distribution of outcomes of a stochastic experiment. This distribution is time- or space indexed and presents the whole distribution instead of a few moments. When applied to financial engineering problems, it provides a time-indexed risk profile of positions, which is applied as the objective function in the non-linear optimization of portfolio weights. This technique is in contrast to the mean-variance selection model, which seeks to minimize portfolio variance subject to a target return. The simugram-based selection system maximizes portfolio return subject to a non-linear risk tolerance parameter based on the simugram risk profile of all possible portfolio outcomes. For the SP-100 stock index portfolio in the 33-year study period, using multi-period return measures of annualized return and terminal value, the simugram annualized return is on the order of 3 times that of the market benchmark. And for every $l million the market returned in terminal value over this time, the simugram portfolio returned $45 million.
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    Nobels for nonsense
    (2005) Thompson, James R.; Baggett, L. Scott; Wojciechowski, William C.; Williams, Edward E.
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    The Sarmatian Review, Vol. 10, No. 1
    (The Polish Institute of Houston, Inc., 1990-01) Xingri, Wang; Boss, Sally; Williams, Edward E.; Thompson, James R.; Podniesinski, B. Peter; Xingri, Wang; Boss, Sally; Williams, Edward E.; Thompson, James R.; Podniesinski, B. Peter; Thompson, Ewa
    Wang Xingri, "The Paddles"; Sally Boss, "Zakopane Sweaters in America?"; Edward E. Williams, "Economic Transformations in Eastern Europe"; BOOKS; Letters; James R. Thompson, "Prospects for Rapid Economic Improvement in Poland "; PIASA Meeting; From the Editor; B.Peter Podniesinski, "Grams, Loans, Training and the IMF"; Facts and figures about the Polish Parliament
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    Venture capital, entrepreneurship, and long-run performance prediction: An application of data mining
    (2003) Miller, John Michael; Thompson, James R.; Williams, Edward E.
    The critical nature of the venture capital-entrepreneur relationship is emphasized by the 46.4% exponential growth rate of venture capital investments throughout the 1990s. It is that time in the venture capital cycle between the time the first stage funding is made and the venture capitalist exits that the greatest opportunity exists for the venture capitalist to influence the outcome of his limited partners' investment. Theories have been offered to explain the effectiveness of the venture capitalist through agency, procedural justice, information, environment, and power theories. The first stage of this study investigates the predictive ability of the entrepreneur's attitudes toward his venture capital partner for long-term performance using entrepreneur attitudes in the light of these theories. The focus of the second and third stages of this analysis is on the ability of internal auditing and environmental factors characterizing the firm at the time of its IPO as predictors of long-term investor wealth appreciation. Data mining involves conducting all three steps in the development of a mathematical model of any phenomenon: structure generation, parameter estimation, and model confirmation, on the same set of data. In this development of a prediction scheme of firm performance we focus on model generation and preliminary model parameter estimation. The data for these analyses were obtained from a 1990 survey of top management of 145 venture capital funded enterprises, plus SEC filings on 563 Initial Public Offerings (IPOs) issued in 1997, stock market prices, and public accounting data. Both sets of data are treated according to an operational measurement theory rather than the traditional representational mode. As a result: (1) entrepreneur appreciation for strategic information, and new idea support, from his venture capitalist, are found to be predictive of subsequent business performance as successful IPO or merger/acquisition harvests; (2) routine application of non-parametric methods to wealth appreciation data for the time 1997--2001 casts doubt on the characterization of that time as a "boom," while confirming the anomaly of IPO underperformance; and (3) accounting data available at the time of IPO may be able to predict subsequent stock market performance three years out.