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Complex Systems in Finance and Econometrics by Moshe Levy (auth.), Robert A. Meyers Ph. D. (eds.)

By Moshe Levy (auth.), Robert A. Meyers Ph. D. (eds.)

Complex platforms in Finance and Econometrics is an authoritative connection with the elemental instruments and ideas of complexity and platforms thought as utilized to an realizing of advanced, financial-based company and social platforms. Fractals, nonlinear time sequence modeling, mobile automata, video game idea, community conception and statistical physics are one of the crucial instruments and methods for predicting, tracking, comparing, coping with, and decision-making in a variety of fields from future health care, poverty relief, and effort and the surroundings, to production and caliber coverage, version construction, organizational studying. and macro and microeconomics. Sixty of the world’s major specialists current forty seven articles for an viewers of complex undergraduate and graduate scholars, professors, and execs in all of those fields.

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Initial Conditions: Each investor is endowed at time t D 0 with a total wealth of $1000, which is composed of 10 shares worth an initial price of $50 per share, and $500 in cash. 5 (for an annual dividend yield of about 4%). As will soon become evident, the dynamics are not sensitive to the particular choice of initial conditions. Figure 1 shows the price dynamics in a typical simulation with these parameters (simulations with the same parameters differ one from the other because of the different random dividend realizations).

We show that this fuller heterogeneity of investors leads to very realistic price and volume patterns. Full Spectrum of EMB Investors Up to this point we have analyzed markets with at most three different subpopulations (one RII population and two EMB populations). The market dynamics we found displayed the empirically observed market anomalies, but they were unrealistic in the magnitude, frequency, and semi-predictability of booms and crashes. In reality, we would expect not only two or three investor types, but rather an entire spectrum of investors.

In a recent (at the time of writing) issue of The Journal of Economic Dynamics and Control, Page and Tassier [34] modeled the development of chain stores across markets. A firm was defined by its product. Each product was assigned an “intrinsic quality” represented by an integer drawn at random from a distribution  q , and a set of I “hedonic attributes” represented by I positive integers in a range from 0 to some arbitrary, user selected number A. Consumers are represented by utility functions that are positively related to “quality” and negatively related to the difference between some desired set of hedonic attributes and the hedonic attributes of the product.

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