Academics Research on Ad Infinitum

• Estimating the level of cash invested in financial markets, Andersen, J. V. (2004). Estimating the level of cash invested in financial markets. Physica A: Statistical Mechanics and its Applications, 344(1-2), 168-173. Long-term trends of financial markets are considered as a growth phenomenon, with a steady influx of money needed to ensure a sustainable growth. A study is made of the impact of money supply, dividends and interest rates, on the growth of a market. A method to estimate the level of investment of cash into stocks is introduced, thereby eventually also identifying periods when stock markets are likely to have topped. 
• Financing the lodging industry in the next millennium, Singh, A. J., & Kwansa, F. A. (1999). Financing the lodging industry in the next millennium. International Journal of Hospitality Management, 18(4), 415-425. Lending to the industry has seemed to occur in 5-year cycles where lenders alternate between a period of marked flexibility with generous lending terms and then followed by a period of austere lending terms. During the last decade of the 20th century indications are that there is prudence both in the demand and supply markets and some expect that this environment will persist long enough into the new millennium to prevent the recurrence of the boom and bust cycle of hotel financing. With many new financial instruments and new sources of financing introduced in the last decade of the century this paper presents the results of a Delphi study about lodging financing in the next millennium. It highlights the predictions of selected experts from the lodging and financial services industries. 
• Visualization of chaos for finance majors, Los, C. A. (2000). Visualization of chaos for finance majors. Efforts to simulate turbulence in the financial markets include experiments with the logistic equation: x(t)=kappa x(t-1)[1-x(t-1)], with 0 < x(t)<1 and 0 = < kappa < 4. Visual investigation of the logistic equation show the various stability and instability regimes for the various value of the Feigenbaum number kappa. Visualizations for t=20 observations provide clear demonstrations of the stability regimes. We visually investigate these regimes in more detail in the t=101-110 range. For 0 < kappa < 3, the process settles to a unique stable equilibrium. For 3= < kappa < 3.6 the process bifurcates, or, as colored visualization shows but not black-and-white, its pitchfork bifurcation branches "bang-bang" switch between two regimes. For 3.6= < kappa = < 4.0 the process becomes chaotic, i.e., deterministically random. In this regime are windows of stability, e.g., at kappa=3+2sqrt=3.8284. At kappa=4, pure chaos, the process is extremely sensitive to initial values, as visually is clearly demonstrated. We increase the number of observations to t=1000 and compute the homogeneous Hurst exponent of the process at kappa=4: H=0.004, indicating that x(t) is blue noise, i.e., extreme anti-persistent. A histogram shows a highly platykurtic distribution of x(t), with an imploded "mode," with extremely fat tails higher than the "mode," against the reflecting values at x=0 and x=1. Several plots of the state directory of the system in the (x(t),x(t-1))-space trace out the parabolic strange attractor. Although the strange attractor is a well-defined parabole, the points on the attarctor set are deterministically random and unpredictable. 
• Beyond behavioral finance, McGoun, E. G., & Skubic, T. (2000). Beyond behavioral finance. The Journal of Psychology and Financial Markets, 1(2), 135-144. Throughout its history, finance theory has made certain simplifying assumptions regarding human behavior and concerned itself with whether the implications of these assumptions were true and not with whether the assumptions themselves were. Recently, however, more interest has been shown in experimental investigation of these assumptions, and the resultant behavioral finance has been presented as a significant departure from the current research paradigm. Recent research in cognitive science, however, is finding that the mind can and does work differently than traditional finance assumes, and the differences between the behavioral assumptions of traditional finance and the supposedly more realistic ones of today's behavioral finance are frequently superficial. Knowledge and knowing are likely to be profoundly different from the forms in which we have incorporated them in our extant models, both traditional and behavioral, and they differ in ways similar to those which, for example, have differentiated corporations from corporate images in marketing. To truly understand what is going on we must go beyond behavioral finance to address these differences.