Abstract: Data markets are platforms that provide the necessary infrastructure and services to facilitate the exchange of data products between data providers and data consumers from different environments. Over the last decade, many data markets have sprung up, capitalising on the increased appreciation of the value of data and catering to different domains. In this work, we analyse the existing body of scientific literature on data markets to provide the first comprehensive overview of research into the design of data markets, regardless of scientific background or application domain. In doing so, we contribute to the field in several ways: 1) We present an overview of the state of the art in academic research on data markets and compare this with existing market trends to identify potential gaps. 2) We identify important application domains and contexts where data markets are being put into practice. 3) Finally, we provide taxonomies of both design problems for data markets and the solutions that are being investigated to address them. We conclude our work by identifying common types of data markets and corresponding best practices for designing them. The outcome of this work is intended to serve as a starting point for software architects and engineers looking to design data markets.

Abstract: Due to the increasing penetration of renewable energies, the energy imbalance market (EIM) is proposed to better facilitate the real time supply demand balance in the power system, by rewarding the market participants with better forecasts for the market conditions (i.e., the mismatch in the system). Together with many other financial instruments in the electricity sector, EIM calls for the market participants to strive for improving their forecast abilities. This increases the need for a data market in place. However, data market, compared with conventional commodity markets, has numerous unique impediments, such as distrust and data mutability issues. To tackle these challenges, we design blockchain enabled data transmission for centralized and decentralized EIM, respectively. We submit that although decentralized market is often a trade off between autonomy and market efficiency, there are conditions when decentralized market and centralized EIM achieve the same efficiency. Numerical studies further suggest, even when the conditions are violated, the efficiency loss in the decentralized EIM is still acceptable.

Abstract: Maintaining financial system stability is critical to economic development, and early identification of risks and opportunities is essential. The financial industry contains a wide variety of data, such as financial statements, customer information, stock trading data, news, etc. Massive heterogeneous data calls for intelligent algorithms for machines to process and understand. This paper mainly focuses on the stock trading data and news about China A-share companies. We present a financial data analysis application, Financial Quotient Porter, designed to combine textual and numerical data by using a multi-strategy data mining approach. Additionally, we present our efforts and plans in deep learning financial text processing application scenarios using natural language processing (NLP) and knowledge graph (KG) technologies. Based on KG technology, risks and opportunities can be identified from heterogeneous data. NLP technology can be used to extract entities, relations, and events from unstructured text, and analyze market sentiment. Experimental results show market sentiments towards a company and an industry, as well as news-level associations between companies.

Abstract: This paper presents a data market aimed at trading energy forecasts data. The system architecture is built using blockchain as a service, allowing access to data streams and establishing a distributed settlement between stakeholders. Energy Forecasts data is presented as the commodity traded in the market, whose settlement is provided through the blockchain on the basis of the extracted value provided by market stakeholders. Our proposal allows market stakeholders to acquire energy forecasts and pay according to the data accuracy, solving the confidentiality problem of freely sharing data. A data quality reward is introduced, steering the compensation sent to market participants. The data market design is presented and an evaluation campaign is performed, showing that the data market produced functionally valid results in comparison with the results achieved with a central simulated approach. Moreover, results show that the data market architecture is able to scale.

Abstract: Financial mathematics is very closely related to financial markets, and in real life, financial mathematics has a wide range of applications in financial markets. This paper discusses the specific application of financial mathematics in financial markets through case analysis, entropy weight method, multivariate statistical regression analysis, etc., and measures the development index of China's financial market and evaluates the development factors of China's financial market. Finally, based on the conclusions, some suggestions were made, hoping to help promote the more coordinated development of financial mathematics and financial markets.

Abstract: In the new financial era, the huge amount of data brings more challenges to the traditional financial business and creates unprecedented opportunities at the same time. In the financial industry, the use of data science by financial institutions has significantly deepened, from the traditional "data visualization presentation" to "data-based decision analysis". This paper analyzes the technical characteristics and development trend of data science in financial engineering against the background of rapid development of financial technology.

Abstract: In this paper, the author explores current trends in regulating the process of digitalization of the financial market. In the first part of the article, international trends in the development of financial markets were considered, modifying classical approaches to the provision of financial and banking services, including: transformation of the needs and behavior of customers; creation of ecosystems of financial and non-financial services; use of «Open API» in the financial and non-financial sectors of the economy; application of financial technologies on a global scale; strict regulation of cryptocurrencies and the development of digital currencies of central banks; active growth of operational risks and information security risks. Based on the results of the material of the first part, a conclusion was drawn about the legislative provision for regulating the process of digitalization of the financial market, which is the most important priority for national development. This study consists of three parts.

Abstract: The article provides the necessary background information about the microstructure of the market, presents a market model, and derives pricing equations based on the market-making algorithm. This article introduces the Market Making Algorithm, an online probability density estimation method used by a market maker to track the true underlying value of a stock. Taking into account the desire of the market maker to make a profit and the desire to control the risk of the portfolio, the practical implementation of the market making algorithm is described. Under various market conditions, an empirical analysis of the market making algorithm is presented, including the presence of several competing market makers. Modeling markets based on the market making algorithm provides information about the behavior of price processes. A comparison of the properties of time series price data obtained as a result of modeling with the known properties of real markets is presented. This makes it possible to simulate real financial time series, such as the leptokurtic return distribution, without postulating complex models of agent interactions and herd behavior of agents. The influence of competition, volatility and jumps in the value of the underlying asset on the profit of market makers, the spread between the purchase and sale price and the execution of transactions is analyzed.

Abstract: — Financial fragility is the own property of the financial system. As an important participant in the financial market, companies are closely related to the financial system. Based on this, from the perspective of corporate financial distress, SVAR model is adopted to study its impact on financial fragility. The results show that financial distress has a large positive impact on financial fragility in the short term and has a time delay effect. Further discussion shows that corporate financial distress can affect financial fragility through the macroeconomic environment and the banking sector, and the banking sector plays a more significant role. ROA, Ratio of profits to cost, Main business vivid rate, TATO, Stock turnover, AR turnover, Ratio of operating assets to total assets, Ratio of working capital to sales revenue, FATO, MBRG, Capital accumulation rate, TAGR, Operating profit growth rate, LOG (total assets).

Abstract: the investment prediction and analysis method of China’s financial market has always been a research and innovation hotspot in the field of financial investment. With the continuous and rapid development of China’s securities market, investment prediction and analysis methods have also begun to get continuous research innovation and progress. On the one hand, the main application scope of the traditional investment prediction and analysis system mainly depends on some hypothetical conditions, so its application scope is greatly limited; On the other hand, because the data structure of the time cycle series of real economy and financial business activities often changes gradually, it is not very appropriate to use some economic global analysis model methods with fixed structure to describe it. With the continuous popularization of economic information collection technology in China’s financial industry and the substantial improvement of people’s ability to collect economic data in the future, a huge amount of financial data containing rich economic information has been accumulated in the process of the continuous and rapid development and change of China’s financial market. Therefore, an efficient, accurate and simple securities market prediction system is of great significance. Firstly, this paper briefly introduces the existing financial market, and then briefly introduces the data mining, XML technology, stock market timing analysis and calendar effect used in this paper. Then it explains the key technical problems of the financial securities prediction system, then makes a systematic analysis of the existing securities market prediction, expounds the common points of the existing system, and uses empirical analysis to demonstrate the feasibility of the system, which has reference significance for the design of a securities market prediction system.

Abstract: Disney, as the world's biggest diverse entertainment firm, faces both possibilities and difficulties. The article will assess Disney's company state and the influence of the market environment using the 4Ps market analysis approach and the SWTO method. Apply the findings of the research to Disney's projected market development. In the last section of the essay, this part will look at Disney's financial status during the last two years, as well as other data, to determine the company's present operational situation and potential future development trends. In summary, by thoroughly evaluating Disney's data and conditions, this article aids relevant persons in understanding Disney from the standpoint of financial development.

Abstract: China’s financial market also faces some outstanding problems, namely, obvious structural imbalance in the size of the financial market, on the one hand, the absolute dominance of the size of the indirect financing market, mainly the bank lending market and on the other hand, the imbalance in the size of the direct financing market and the indirect financing market. At the same time, there is also a structural imbalance of the financial market among regions in China. How to verify the objective existence of such structural imbalance in different regions and measure the difference in the level of financial market development among regions is the focus of this paper. This paper constructs a financial market development level indicator system from three dimensions, equity market, bond market, and lending market, and measures the financial market development level by using inter-provincial panel data from 2001 to 2020 through neural network algorithm, time series model, and support vector machine algorithm, and analyzes the regional heterogeneity of financial market development on this basis. The results show that the overall market level of the financial industry in the eastern coastal region of China is the highest, but the intra-regional differences are also the most obvious; the overall market level of the financial industry in the northeastern region of China is the lowest, and the differences with the eastern region rise significantly; the market level of the financial industry at the provincial level in the central and eastern regions shows an all-time decreasing trend, while the western and northeastern regions of China show a convergence, then divergence, and then convergence trend. Therefore, differentiated financial policies should be implemented according to the stage of regional development in order to enhance the financial development level of each economic region and gradually narrow the regional financial development gap.

Abstract: Aiming at the problem that the selection of financial and economic risk indicators is not comprehensive, resulting in excessive financial and economic operational risks, a financial and economic monitoring system is designed based on big data clustering. Financial and economic risks are risks that are formed and accumulated in the financial system in the process of economic cyclical and financial unbalanced development. According to its formation mechanism, complex network models are used to analyze the dynamic correlation of financial and economic risks. Select financial and economic risk indicators based on big data clustering, and strengthen the supervision of high-risk financial sub-markets such as stocks and foreign exchange markets. Establish a financial and economic monitoring system from three aspects of financial and economic revenue and expenditure, debt and external risks, and jointly determine the development trend of financial and economic risks. The test results show that the financial and economic monitoring system based on big data clustering can reduce the value at risk of operational risk, namely VaR value, and promote the balanced development of the financial market.

Abstract: The mass valuation of real estate in Slovenia is based on valuation models, which are made based on realised real estate market data and illustrate the functioning of the real estate market. The system is based on valuation models that simulate the behaviour of the real estate market and allow a statistically reliable assessment of market value. Models are formed using statistical methods of processing real estate market data. This paper presents the quality verification process and preparation of real estate market data. These data are used to design evaluation models or modelling, which consists of time adjustment of prices or rents, zoning, levelling, calibration, and analysis of quality parameters. Finally, the critical information of the modelling process made in the context of real estate mass valuation in Slovenia is summarised.

Abstract: Aiming at the problems of poor effect and low accuracy of traditional financial market risk prevention and control methods, a financial risk prevention and control information monitoring model based on double support vector machine is proposed. The improved support vector machine and asymmetric COvAR data were combined with covariance operation to reduce the actual tail risk overflow. Through financial aggregation and covariance tail data of current characteristic financial system, the spillover effect of financial risk is obtained. According to the extreme value statistical analysis theorem, it is determined that the current financial risk gradually obeys the extreme value. In order to verify the effectiveness of the method, the financial data from 2012 to 2018 provided by a bank were used as experimental samples to conduct simulation experiments. Experimental data show that the proposed method has lower boundary cost and higher market arbitrage, and has a strong market applicability.

Abstract: News dissemination in social media causes fluctuations in financial markets. (Scope) Recent advanced methods in deep learning-based natural language processing have shown promising results in financial market analysis. However, understanding how to leverage large amounts of textual data alongside financial market information is important for the investors’ behavior analysis. In this study, we review over 150 publications in the field of behavioral finance that jointly investigated natural language processing (NLP) approaches and a market data analysis for financial decision support. This work differs from other reviews by focusing on applied publications in computer science and artificial intelligence that contributed to a heterogeneous information fusion for the investors’ behavior analysis. (Goal) We study various text representation methods, sentiment analysis, and information retrieval methods from heterogeneous data sources. (Findings) We present current and future research directions in text mining and deep learning for correlation analysis, forecasting, and recommendation systems in financial markets, such as stocks, cryptocurrencies, and Forex (Foreign Exchange Market).