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The aim of this research is twofold. Firstly, we aim to improve the efficiency of closed funds (a closed fund represents a fund that is developed as a financial product and is multiplied after its conception to a ratio that could reach 250:1. This depends on investor’s requirements (i.e. an initial fund that uses 20 million USD for buying its stakes, can be multiplied 250 times and reach a value, when it is sold as a financial derivative, of 5 billion USD [Bodislav, The optimal corporative model for sustainable economic growth for an emergent country 1. Bucharest: ASE Publishing, 2013). These types of financial vehicles are developed and sold by investment banks such as Goldman Sachs and JP Morgan. The preferred closed funds vehicle for selling to clients is through financial derivatives. According to the International Monetary Fund (IMF) financial derivatives are: ‘financial instruments that are linked to a specific financial instrument or indicator or commodity, and through which specific financial risks can be traded in financial markets in their own right. Transactions in financial derivatives should be treated as separate transactions rather than as integral parts of the value of underlying transactions to which they may be linked. The value of a financial derivative derives from the price of an underlying item, such as an asset or index [IMF. International Monetary Fund. Financial Derivatives. http://www.imf.org/external/np/sta/fd/, 2015]) by developing an algorithm which uses data from the US stock market. The secondary output is to use the same algorithm as a model, which is scaled to fit and solve issues regarding automated decision making at the government level. This is similar to a basic Business Intelligence (BI) solution (it follows similar procedures to the workflow of IBM Cognos), which offers a solution in identifying the most suitable path from which governments are able to make decisions. It is particularly important for a country to identify its needs and requirements related to new investments in infrastructure, healthcare and/or education. All the principles developed in this model can be scaled through their results to determine the best solution or best fit when considering global economic output as a forecasted variable.
The model developed in this research is based on companies traded on NASDAQ because they offer transparency, the companies in the sample are deemed reliable as far as reporting. Furthermore, and most importantly, on the whole the companies listed on these exchanges are found to be a true reflection of the economic sectors that form a nationwide economy.
The synergy between Big Data analysis, BI practices and processing power could lead to new businesses designed by investment banks and complex software developers. In the long run the result of their work would be the business of automated decision making used to reduce the paths that could be followed in developing a country or even to make decisions concerning private investment.
The novelty of our sophisticated Business-Automated Data Economy Model (BDM) is mainly due to how our model is applied. This research is the first time Big Data has been analyzed by an integrated model with a focus on automated decision making as a proxy for developing better and smarter investment procedures. The analysis contributes to decision making made by individuals, corporations as well as offering viable solutions for governments. This interdisciplinary research is created as a path to adjust policy making through the use of intelligent systems based on Big Data and BI for creating policy and to select the most suitable path to follow.
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- Business Intelligence for Decision Making in Economics
PhD Bodislav Dumitru-Alexandru
- Palgrave Macmillan UK
Neuer Inhalt/© Stellmach, Neuer Inhalt/© Maturus, Pluta Logo/© Pluta, Frankfurt School