Decision Sciences, Economics, Finance, Business, Computing, and Big Data: Connections

Decision Sciences, Economics, Finance, Business, Computing, and Big Data: Connections

 There are many studies that link Decision Sciences, Economics, Finance, Business, Computing, and Big Data. In addition, Decision Sciences is related to many different though cognate areas, including Science, Engineering, Medical Science, Experimental Science, Psychology, Social Science, Political Science, Management, and Business.

In this paper, we will discuss different types of utility functions, stochastic dominance (SD), meanrisk (MR) models, portfolio optimization (PO), and other financial, economic, business, marketing, and management models that can be used in computational science and Big Data. Academics could develop theoretical models and thereafter develop econometric and statistical models to estimate the associated parameters to analyze a number of interesting issues in Decision Sciences, Economics, Finance, Business, Computing, and Big Data. Academics could then conduct simulations to examine whether the estimators or statistics in the theories on estimation and hypothesis develop have small size and high power. Thereafter, academics and practitioners could apply the novel and innovative theories to analyze a number of interesting issues in the six disciplines and cognate areas.

We discussed several econometric and statistical models in the areas of stochastic dominance, portfolio optimization, risk measures, testing investors’ behavorial models, analysed economic and financial indicators, technical analysis, cost of capital, unit roots, cointegration, causality tests, nonlinearity, confidence intervals, mixtures of distributions model, repeated time series, multinomial distribution model, and other econometric models and tests.

The paper also discussed research in simulation, and several applications in decision sciences, economics, finance, business, computing, and big data, as well as in cognate disciplines. Finally, we have discussed why analyzing big data is different from analyzing medium data or small data. Interested readers may wish to refer to Chang, McAleer, and Wong (2017b) for a discussion as to why analysing big data is different from analyzing medium data or small data.

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