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Machine Learning Methods in Algorithmic Trading Strategy Optimization – Design and Time Efficiency

from the statistics of the equity line for the specified strategy parameters. The calculations were not very complicated, but they required relatively long time to be executed. The high time complexity was caused by the calculation of profits and losses for every trading day. Therefore, the main difficulty was caused rather by the time expensive criterion evaluations, than by the big number of possible parameter combinations ( 194 481 ). Additionally, the performance of automatic strategies is usually sensitive to the parameters’ change; therefore, even subtle

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Survey vs Scraped Data: Comparing Time Series Properties of Web and Survey Vacancy Data

specific case study using data from an NSO to benchmark a very large data set collected from the Internet, with the aim of shedding light on the relationship between the population collected online and the population at large as inferred by traditional scientific methods. More specifically, we focus on the number of vacancies in the economy inferred by survey methods by a statistical office compared to the number of vacancies obtained from web crawling. In economics research, labor markets are among the areas in which Big Data is increasingly being used ( Choi and

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Migration and Welfare Systems – State of the Art and Research Challenges

strain on social security systems is more likely. Moreover, some estimates show that the number of working-age immigrants, necessary to effectively solve European demographic problem, is extremely high (UN 2000). Third, classical economics predict a positive demand effect of immigration, which should lead to an increase in production. However, such outcome is conditional on the means of financing immigrant consumption and can be reversed in the presence of welfare benefits. Two of the arguments listed above are closely tied to the presence of redistributive policies in

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