Works of James R. Thompson

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Browsing Works of James R. Thompson by Author "Thompson, James R."

Now showing 1 - 7 of 7
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    Market truths: theory versus empirical simulations
    (2006) Wojciechowski, William C.; Thompson, James R.
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    Marketplace Competition in the Personal Computer Industry*
    (Wiley, 1992) Bridges, Eileen; Ensor, Katherine B.; Thompson, James R.
    A decision regarding development and introduction of a potential new product depends, in part, on the intensity of compeitition anticipated in the marketplace. In the case of a technology-based product such as a personal computer (PC), the number of competing products may be very dynamic and consequently uncertain. We address this problem by modeling growth in the number of new PCs as a stochastic counting process, incorporating product entries and exits. We demonstrate how to use the resulting model to forecast competition five years in advance.
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    Nobels for nonsense
    (2005) Thompson, James R.; Baggett, L. Scott; Wojciechowski, William C.; Williams, Edward E.
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    Simulation-based estimation: a case study in oncology (SIMEST) and a case study in portfolio selection (SIMUGRAM)
    (Wiley, 2014) Thompson, James R.
    Since the time of Poisson, stochastic processes have been axiomatized in the temporally forward direction. Yet for nearly a century, estimation of parameters and even forecasts have been based on likelihood approaches that start with temporally indexed data and then look backward in time. I shall be using the philosophy of Karl Pearson [Scott DW, Tapia RA, Thompson JR. Karl Pearson was right. Computer Science and Statistics: Tenth Annual Symposium on the Interface; 1978, 179?183] in this article where I create, using a forward model, a large virtual universe of happenings based on the assumption of four parameters characterizing an oncological process based on four Poissonian processes. Bins will be formed in the real time space based on the actual data of real world system of times of discovery of primary and secondary tumors and use bin boundaries that enclose roughly 5% of the actual tumor discover data and compare the bin proportions of virtual data with the proportions of actual data in each of the bins. This will enable us to use Karl Pearson's goodness-of-fit criterion as the objective function for a Nelder-Mead optimization. We present here an oncological example where the objective is to estimate four parameters relevant to the progression of breast cancer. This procedure is termed the SIMEST paradigm.Then we briefly describe the patented SIMUGRAM for estimating the distribution of portfolio values using daily resampling strategies. This procedure makes minimal model assumptions and is completely based on data.
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    The Age of Tukey
    (2001) Thompson, James R.
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    Time Series Analysis of Mosquito Population Data
    (Entomological Society of America, 1973) Hacker, Carl S.; Scott, David W.; Thompson, James R.
    A statistical technique, time series analysis, has been introduced, allowing the development of objective and quantitative insights into the behavior of relative mosquito densities as a function of time. This method is particularly useful for detecting periodicities in mosquito densities as well as the relationship between meteorological phenomena and mosquito densities.
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    Tukey's transformational ladder for portfolio management
    (Springer, 2017) Ernst, Philip A.; Thompson, James R.; Miao, Yinsen
    Over the past half-century, the empirical finance community has produced vast literature on the advantages of the equally weighted Standard and Poor (S&P 500) portfolio as well as the often overlooked disadvantages of the market capitalization weighted S&P 500’s portfolio (see Bloomfield et al. in J Financ Econ 5:201–218, 1977; DeMiguel et al. in Rev Financ Stud 22(5):1915–1953, 2009; Jacobs et al. in J Financ Mark 19:62–85, 2014; Treynor in Financ Anal J 61(5):65–69, 2005). However, portfolio allocation based on Tukey’s transformational ladder has, rather surprisingly, remained absent from the literature. In this work, we consider the S&P 500 portfolio over the 1958–2015 time horizon weighted by Tukey’s transformational ladder (Tukey in Exploratory data analysis, Addison-Wesley, Boston, 1977): 1/x2,1/x,1/x−−√,log(x),x−−√,x,andx2, where x is defined as the market capitalization weighted S&P 500 portfolio. Accounting for dividends and transaction fees, we find that the 1/x2 weighting strategy produces cumulative returns that significantly dominate all other portfolio returns, achieving a compound annual growth rate of 18% over the 1958–2015 horizon. Our story is furthered by a startling phenomenon: both the cumulative and annual returns of the 1/x2 weighting strategy are superior to those of the 1 / x weighting strategy, which are in turn superior to those of the 1/x−−√ weighted portfolio, and so forth, ending with the x2 transformation, whose cumulative returns are the lowest of the seven transformations of Tukey’s transformational ladder. The order of cumulative returns precisely follows that of Tukey’s transformational ladder. To the best of our knowledge, we are the first to discover this phenomenon.