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An ambitious and driven individual with knowledge and project experience in computer networks and security (BEng (Hons)), along with a masters degree at a top 10 UK university in the domain of IT, management and organizational change with a distinction, and is currently working as a Ph.D. Research fellow in Denmark.
Current Ph.D. Project - Work Improvisation, looking into more flexible and plastic management through cognition.
Recruitment & Selection
We at Mind Mingles, a Digital marketing agency utilize a flexible and dynamic approach in delivering a broad range of innovative and cost-effective business solutions in digital marketing, Web Design, and Development services. We work closely with client needs to bring results within the specified time frame. Mind Mingles takes a comprehensive way out to maximize online exposure, improving customer attainment, and helping businesses increase revenues and profits. We design comprehensive internet marketing strategies from scratch that works and are proven.
We offer an array of services-
Search Engine Optimization
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Exceeding client satisfaction has always been a motto for Mind Mingles, SEO company India and we believe in ethics and strong relationships with our customers above anything else.
We welcome any opportunity to do business with you and help you to grow your user base and revenue. Please contact us for a best-fit proposal for your online marketing needs.
In this paper, we explore the dynamic of stock prices over time by developing an agent-based market. The developed artificial market comprises of heterogeneous agents occupied with various behaviors and trading strategies. To be specific, the agents in the market may expose to overconfidence, conservatism or loss aversion biases. Additionally, they may employ fundamental, technical, adaptive (neural network) strategies or simply being arbitrary agents (zero intelligence agents). The market has property of direct interaction. The environment takes the form of network structure, namely, it takes the manifestation of scale-free network. The information will flow between the agents through the linkages that connect them. Furthermore, the tax imposed by the regulator is investigated. The model is subjected to goodness of fit to the empirical observations of the S\&P500. The fitting of the model is refined by calibrating the model parameters through heuristic approach, particularly, scatter search. Conclusively, the parameters are validated against normality, absence of correlations, volatility cluster and leverage effect using statistical tests.