KEY TRENDS IN THE DEVELOPMENT OF MARKETPLACES AS A TRIGGER FOR THE TRANSFORMATION OF GLOBAL BUSINESS

This research analyses marketplaces in Europe and some other developed countries in order to determine their impact on global trade and business. The paper aimed to answer an important question - how e-commerce could transform modern business taking into account digital changes, a boom of cryptocurrency erasing trade borders and globalization. Sufficient evidence of this transformation is illustrated. A subsidiary objective of this research involves the building of a logical model describing correlation between marketplaces as integral part of e-commerce and global business development. Statistical data were based on variable indicators describing four categories of trading platforms, particularly online stores, price aggregators, marketplaces and classifiers. In addition, global indexes and macro-economic criteria were used in analysis. The methodology of statistical and regression methods was employed for economic-mathematical modelling. This allowed revealing the most important indicators affecting e-commerce and to create reasonable predictions for global business and trade. The research highlights important trends in the development of e-business under the digital economy

An assessment of strategies for choosing between competitive marketplaces

Traders that operate in markets with multiple competing marketplaces must often choose with which marketplace they will trade. These choices encourage marketplaces to seek competitive advantages against each other by adjusting various parameters, such as the price they charge, or how they match buyers and sellers. Traders can take advantage of this competition to improve utility. However, appropriate strategies must be used to decide with which marketplace a trader should shout. In this paper, we assess several different solutions to the problem of marketplace selection by running simulations of double auctions using the JCAT platform. The parameter spaces of these strategies are explored to find the best performing strategies. Results indicate that the softmax strategy is the most successful at maximising trader profit and global allocative efficiency in both adaptive and non-adaptive markets. The -decreasing strategy performs well in adaptive markets, while also showing greater stability in its parameter space than softmax. All marketplace selection strategies outperform the random marketplace selection strategy

Using Transfer Learning in Network Markets

Mechanism design is the sub-field of microeconomics and game theory, which considers agents have their own private information and are self-interested and tries to design systems that can produce desirable outcomes. In recent years, with the development of internet and electronic markets, mechanism design has become an important research field in computer science. This work has largely focused on single markets. In the real world, individual markets tend to connect to other markets and form a big “network market”, where each market occupies a node in the network and connections between markets reflect constraints on traders in the markets. So, it is interesting to find out how the structure of connected network markets impacts the performance of the resulting network markets and how we can optimize performance by varying the things that one could control in a network market. In this dissertation, I aim to find out whether we can apply transfer learning to other machine learning techniques like reinforcement learning in the design of network markets to help optimize the performance of the network markets. I applied transfer learning on both machine learning trading strategies and machine learning strategies for selecting which market to trade in. I found that, in most cases, by applying transfer learning to machine learning trading strategies or machine learning market selection strategies, we can improve the performance of the network market significantly