Abstract: Quantitative finance has had a long tradition of a bottom-up approach to complex systems inference via multi-agent systems (MAS) These statistical tools are based on modelling agents trading via a centralised order book, in order to emulate complex and diverse market phenomena These past financial models have all relied on so-called zero-intelligence agents, so that the crucial issues of agent information and learning, central to price formation and hence to all market activity, could not be properly assessed In order to address this, we designed a next-generation MAS stock market simulator, in which each agent learns to trade autonomously via reinforcement learning We calibrate the model to real market data from the London Stock Exchange over the years 2007 to 2018, and show that it can faithfully reproduce key market microstructure metrics, such as various price autocorrelation scalars over multiple time intervals Agent learning thus enables accurate emulation of the market microstructure as an emergent property of the MAS

Abstract: Stock trend prediction plays a crucial role in quantitative investing. Given the prediction task on a certain granularity (e.g., daily trend), a large portion of existing studies merely leverage market data of the same granularity (e.g., daily market data). In financial investment scenarios, however, there exist amounts of finer-grained information (e.g., high-frequency data) that contain more detailed investment signals beyond the original granularity data. This motivates us to investigate how to leverage multi-granularity market data to enhance the accuracy of stock trend prediction. Some straightforward methods, such as concatenating finer-grained data as features or fusing with a model based on finer-grained features, may not lead to more precise stock trend prediction due to some unique challenges. First, the inconsistency of granularity between the target trend and finer-grained data could substantially increase optimization difficulty, such as the relative sparsity of the target trend compared with higher dimensions of finer-grained features. Moreover, the continuously changing financial market state could result in varying efficacy of heterogeneous multi-granularity information, which consequently requires a dynamic approach for proper fusion among them. In this paper, we propose the Contrastive Multi-Granularity Learning Framework (CMLF) to address these challenges. Particularly, we first design two novel contrastive learning objectives at the pre-training stage to address the inconsistency issue by constructing additional self-supervised signals relying on the inherent character of stock data. We also design a gate mechanism based on market-aware technical indicators to fuse the multi-granularity features at each time step adaptively. Extensive experiments on three real-world datasets show significant improvements of our approach over the state-of-the-art baselines on stock trend prediction and profitability in real investing scenarios.

Abstract: A non-traditional type of financial asset, cryptocurrencies based on public blockchains are still little understood in their real-world behavior. Exogenous events such as cyber-attacks and their media coverage can strongly affect their supply and demand, adoption and usage, efficiency, and infrastructural development, thus influencing their price stability and market valuation. Given the great technical complexity of blockchains, we believe that a pure economic analysis of risks associated with cryptocurrencies is simply not sufficient to convey the relationships between attacks and the disruptive effects that these can bring on the operation of cryptocurrencies.On these grounds, we survey the most common types of attacks for PoW cryptocurrencies and evaluate their impact on the returns of a number of real-world cryptocurrencies for which market data are available. Due data availability, our event study analysis focuses on instances of 51% attacks, hard forks, and wallet attacks. The main goal of our analysis is to understand the relationship between technical events (cyber-attacks and coordinated user/miner behavior) and the economic impacts surrounding them. We aim to develop a deeper understanding of these systems that are object of great research interest in separate disciplines, supporting policy makers in their regulatory decisions concerning crypto-assets and associated cyber-related financial risks.

Abstract: Nowadays, financial news is an indispensable source for investors to conduct research and investment decisions. At the same time, there are many fake financial news flooded into people's daily life. This kind of information may affect public opinion and provide opportunities for some criminals to manipulate the financial market. However, due to the lack of available comparative information, the model based on linguistic features is much less effective in the real world. We believe that multi-source fact comparison and inspection should be integrated into the false news detection model to detect fake news. As the crystallization of collective wisdom, user comments can be of great benefit to this task. News sources are also crucial for detecting. Besides, existing models often ignore one point that financial fake news usually talks about the relevant market, so the market data should be token into consideration. Our proposed multi fact CNNLSTM model integrates all these dimensions mentioned above and performs well. Specially, we use attention mechanism to extract the information from the comments and make a list of authoritative websites to identify the source of news. As for the market dimension, according to the financial products mentioned in the news, we get market price and check whether the statements in the article are correct. Finally, we assign a weight to each dimension and let the model learns by itself.

Abstract: In its basic structure, the reverse mortgage (RM) is a contract where a home owner borrows a part or the totality of the future liquidation value of his home at the time of his death. The risks that are borne by the lender are linked to the volatility of the real estate market, that is the house price risk, the financial market risk, that is the interest rate risk, and the uncertainty of the borrower’s lifetime, that is the longevity risk. The quantification of the future liquidation value and its valuation at the issue time is fundamental in the construction of the RM contract either in the perspective of the lender or in the one of the borrower. In the paper, we explore the use of neural networks to project the real estate market data; this approach allows to obtain a predictive analysis of the pricing process and indeed provides a dynamic pricing algorithm.

Abstract: Latency is the time delay between an exchange streaming market data to a trader, the trader processing information and deciding to trade, and the exchange receiving the order from the trader. Liqui...

Abstract: Real estate has always been an important investment opportunity. With a diverse set of financial instruments linked to real estate assets, it is significant for both investors and intermediaries. In this paper we assess how artificial intelligence can be used to improve our understanding for the real estate market changes. We suggest and test a three-stage model in support for real estate valuation and market forecasting, that is able to account for global economic factors as well as for individual characteristics influencing property prices. Every stage provides for using different artificial intelligence and machine learning methods in order to automate processing of market data and assess how qualitative factors affect valuation. We conduct a survey on the accuracy of the model NAREIT and BGREIT index data.

Abstract: Composing a team of professional players is among the most crucial decisions in association football. Nevertheless, transfer market decisions are often based on myopic objectives and are questionable from a financial point of view. This paper introduces a chance-constrained model to provide analytic support to club managers during transfer windows. The model seeks a top-performing team while adapting to different budgets and financial-risk profiles. In addition, it provides a new rating system that is able to numerically reflect the on-field performance of football players and thus contribute to an objective assessment of football players. The model and rating system are tested on a case study based on real market data. The data from the case study are available online for the benefit of future research.

Abstract: Financial exchanges have begun a move from on-premise and custom-engineered datacenters to the public cloud, accelerated by a rush of new investors, the rise of remote work, cost savings from the cloud, and the desire for more resilient infrastructure. While the promise of the cloud is enticing, the cloud's varying network latencies can lead to market unfairness: orders can be processed out of sequence, and market data can be disseminated to market participants at incorrect times due to varying latencies between participants and the exchange. We present CloudEx, a fair-access cloud exchange, which leverages high-precision software clock synchronization to compensate for noisy network conditions in the public cloud. We also discuss refinements to the CloudEx design that were informed by lessons learned from deploying CloudEx in two academic courses and conclude by outlining future research directions.

Abstract: We propose a dynamic model for systemic risk using a bipartite network of banks and assets in which the weight of links and node attributes vary over time. Using market data and bank asset holdings, we are able to estimate a single parameter as an indicator of the stability of the financial system. We apply the model to the European sovereign debt crisis and observe that the results closely match real-world events (e.g., the high risk of Greek sovereign bonds and the distress of Greek banks). Our model could become complementary to existing stress tests, incorporating the contribution of interconnectivity of the banks to systemic risk in time-dependent networks. Additionally, we propose an institutional systemic importance ranking, BankRank, for the financial institutions analyzed in this study to assess the contribution of individual banks to the overall systemic risk.

Abstract: We illustrate how market data can be informative about the interactions between monetary and fiscal policy. Federal funds futures are private contracts that reflect investor’s expectations about monetary policy decisions.

Abstract: In an oligopoly market, producers compete together to seize the electricity market share. Since they cannot obtain their desired profits through fair competition, they may collude to set their bid prices illegally higher than the oligopoly level. Manipulation and increasing market price decrease social welfare and then market efficiency. This article intends to provide independent system operators (ISOs) with a tool to analyze day-ahead market data so as to identify generator units who intend to exercise collusion and raise the market prices. Toward this goal, all possible collusion and competition scenarios are simulated and then the generated data are used to train a supervised learning algorithm. By applying the proposed approach to the IEEE 57 and 30 bus test systems, the efficiency of the proposed approach was assessed. Furthermore, it is demonstrated how colluding generators choose between maximizing their colluded profit and reducing the risk of being detected by ISO. The results show machine learning is capable of identifying colluding companies with accuracy of 95%. Also, it was rightly obvious that the closer the bidding price of companies is to competitive level, the more downward the efficiency of the machine is.

Abstract: This paper proposes a regression market for wind agents to monetize data traded among themselves for wind power forecasting. Existing literature on data markets often treats data disclosure as a binary choice or modulates the data quality based on the mismatch between the offer and bid prices. As a result, the market disadvantages either the data sellers due to the overestimation of their willingness to disclose data, or the data buyers due to the lack of useful data being provided. Our proposed regression market determines the data payment based on the least absolute shrinkage and selection operator (lasso), which not only provides the data buyer with a means for selecting useful features, but also enables each data seller to individualize the threshold for data payment. Using both synthetic data and real-world wind data, the case studies demonstrate a reduction in the overall losses for wind agents who buy data, as well as additional financial benefits to those who sell data.

Abstract: The paper models evolution in pecunia—in the realm of finance Financial markets are explored as evolving biological systems Diverse investment strategies compete for the market capital invested in long-lived dividend-paying assets Some strategies survive and some become extinct The basis of our paper is that dividends are not exogenous but increase with the wealth invested in an asset, as is the case in a production economy This might create a positive feedback loop in which more investment in some asset leads to higher dividends which in turn lead to higher investments Nevertheless, we are able to identify a unique evolutionary stable investment strategy The problem is studied in a framework combining stochastic dynamics and evolutionary game theory The model proposed employs only objectively observable market data, in contrast with traditional settings relying upon unobservable investors’ characteristics (utilities and beliefs) Our method is analytical and based on mathematical reasoning A numerical illustration of the main result is provided

Abstract: We present an integrated market model which considers the dependencies between the wholesale market and the highly regulated balancing power markets. This fosters the understanding of the mechanisms of these markets and, thus, allows the evaluation of the designs of these markets and their interplay. In contrast to existing literature, in our model the prices on the different markets are interdependent and endogenously determined, which also applies to the switch from inframarginal suppliers to extramarginal suppliers. Linked to this, the implementation of a specific assignment of the suppliers to the different markets is according to their production costs and their ability to provide balancing power. We prove the existence of a market equilibrium, analyze its outcome and contrast this with German market data. Based on this model, we assess design changes, partly stipulated by recent European regulation. This includes uniform pricing as a common settlement rule (effect: no truthful bidding in general), standardized prequalification criteria (promising measure for cost reduction), market flexibilization via “free energy bids” (no increased competition) and the alternative score “mixed-price rule” (no effect on the equilibrium).

Abstract: We develop a model of competition between banks and a marketplace lender to motivate empirical tests using local market data on U.S. banks and the largest marketplace lending platform. Employing me...

Abstract: Stock market forecasting is an important research area, especially for better business decision making Efficient stock predictions continue to be significant for business intelligence Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices, moving averages, or daily returns However, major events’ news also contains significant information regarding market drivers An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market This research proposes an efficient model for stock market prediction The current proposed study explores the positive and negative effects of coronavirus events on major stock sectors like the airline, pharmaceutical, e-commerce, technology, and hospitality We use the Twitter dataset for calculating the coronavirus sentiment with a Long Short-Term Memory (LSTM) model to improve stock prediction The LSTM has the advantage of analyzing relationship between time-series data through memory functions The performance of the system is evaluated by Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) The results show that performance improves by using coronavirus event sentiments along with the LSTM prediction model © 2021 Tech Science Press All rights reserved

Abstract: We investigate how the introduction of market data fees impacts trading and market quality. We find that data fees decrease the fee-introducing exchange’s market volume, its time with competitive quotes, its visible liquidity, and its role in price discovery. We observe brokers routing market and limit orders away from the impacted exchange. Market-wide the fee introduction decreases visible depth and price discovery. The results indicate that some traders have an elastic demand for data, and that their response to the introduction of data fees on one exchange can reverberate throughout the stock market.

Abstract: A financial agent-based price model is developed by a combination of the contact system and the social network theory. Agents are supposed to live on a small-world network described by the Watts–Strogatz model and their attitude interinfluences along the network are characterized by particle interactions in the Markov contact process. The financial time series of the model are generated by Monte Carlo simulations and analysed with respect to a number of “stylized facts”, focusing on the role played by network topology. Return series of the model can reproduce nonnormality, volatility clustering and multifractality, consistent with an empirical observation of real market data. And these “stylized facts” are most salient when the model is of the small-world network topology rather than completely ordered or random one in our experiments, highlighting a potential influence of the structure of agent attitude exchange channel on financial price fluctuation behaviours.

Abstract: Policymakers often seek to integrate markets as a way to maximize social welfare. In this article, the authors consider the spectrum of all possible integration policies, from full isolation to com...

Abstract: Introduction: The field of research treating debt capacity can be comprehended as a unique piece of a lot more extensive capital structure hypothesis. This started with the paper of Modigliani/Miller in 1958. There has been a continuous and serious hypothetical dialog about the ideal capital structure of an organization. One generally new piece of the related discussion is debt capacity and potential connection to the capital structure of an organization. Purpose: The purpose of the study was to examine the effect of debt capacity and financial performance of quoted firms in Nigeria. This study expected that debt capacity can be a way to characterize and deal with the capital structure of an organization. Methodology: The study formulated 3 hypotheses and the least square multiple regression was used for hypothesis testing empirical results based on 2014 to 2018 accounting and marketing data for 20 quoted firms in Nigeria lend some support to the pecking order and static tradeoff theories of optimal capital structure. Data were sourced from the Nigeria Stock Exchange, Security and Exchange Commission, and other relevant data sources. This study investigated, experimentally, if there might be a significant relationship between the debt capacity of organizations and their financial and market performance. Findings: A firm’s debt capacity was found to have a significant impact on the firm’s accounting performance measure. Debt capacity measures have a positive and significant relationship with the market performance measure (Tobin’s Q). A fascinating finding is that all the influence estimates have a positive and exceptionally critical association with the market execution measure (Tobin’s Q), which could somewhat bolster Myers, (1977)’s contention that organizations with high transient obligation to add up to resources have a high development rate and superior. Unique contribution to theory, practice and policy: The consequences of this result further affirm some earlier discoveries by different researchers and prior analysts and the exploration work has had the option to discover answers to the examination addresses prior brought up in the basic part in the accompanying ways. It was therefore recommended that Companies can finance themselves with debt and equity capital. By increasing the amount of debt capital relative to its equity capital, a company can increase its return on equity. Also, in transition, the economic environment is more volatile and riskier than in developed markets. Therefore, a management scheme of capital structure that provides for flexibility in financing is preferable.

Abstract: Features of the electric power market functioning, in comparison with common properties of modern markets, are considered. It is shown that continuous reforms, which take place in many countries, are far from reaching effective solution. Sectoral peculiarities, connected with both technological complexity of the field and economic specificity of the market participants, form very special model of the market. The main way to increase effectivity of the market, to make it more competitive and, in the same time, socially responsible, is to use widely and deeply the modern information technologies, including technologies of artificial intelligence. A technological scheme (especially targeted on features of the electric power market) has been proposed in the article. First, the technologies merge methods of collection, storage, processing, and presentation of information. The collection of market data should be automated, particularly, by the so-called intellectual avatars, which operate in the market as virtual agents. A reliable distributed storage of data, based on blockchain technology, is chosen. The big data technologies of data structuring, storage, and processing are involved. Datamining methods include effective presentation of results in forms of graphs and diagrams. The second set of technologies includes core mathematical models and “digital twins.” They are aimed for data interpolation and extrapolation, in particular, for prediction of further system dynamics. The used neural networks have option of self-learning and self-development. At last, the third set is a set of user interfaces, which provides market actors by complete and adopted information and is organized in accordance with ideas of ergonomics.

Abstract: The Efficient Market Hypothesis has been well explored in terms of daily responses to market movements and financial reports. However, there is lack of evidence about information efficiency after the popularization of intraday trading. We investigate the time series properties of information adopted in the intraday market, in particular the causality effects. We use 30-min market price and news data to represent the past market data and the public information respectively, so that our analysis is in line with the EMH framework. Traders’ responses to such information are associated with the financial crisis. There was strong overreaction to market data right before the 2008 crisis and traders tend to rely more on news data during the crisis. We confirm that, in terms of the intraday information efficiency, it is worthwhile to adopt both types of information. Furthermore, there is still room for improving the price discovery process to reveal such information more effectively.

Abstract: In the theory and practice of real estate valuation, in analytical studies of the dynamics of real estate markets there is a problem of tracking changes in market prices. The apparent simplicity of this task leads to the fact that in everyday practice both market participants and professional analysts are satisfied with observations of average prices. The advantage of this traditional approach is computational simplicity. However, in the conditions of presence of a large number of special software and extensive statistical material can be used more complex research methods. The purpose of this article is to research big current market data of real estate objects and compare these data with the cadastral value determined in accordance with Russian legislation as the market value at the specified date. In this regard, there are problems associated with the multidimensional distribution of market prices and cadastral values. The article presents the method of calculation of changes of the real estate market prices on the basis of comparison of two-dimensional prices distributions of offers and cadastral prices for two periods. The main problem in studying the dynamics of real estate market prices is the inability to track the change in market prices for each property, as objects are constantly put up for sale and removed from it. The work carried out in the Russian Federation in 2014 toestablish the cadastral value of real estate opens opportunity to analyze two-dimensional distributions of current market and cadastral prices and to assess the dynamic characteristics of the market for any real estate objects. The main result of article is the method which allows to apprise the market value of real estate in real time when new market data come by their comparison with the previously established cadastral value. Cadastral value is assumed to be defined as market value at the valuation date.

Abstract: We introduce a new system of stochastic differential equations which models dependence of market beta and unsystematic risk upon size, measured by market capitalization. We fit our model using size deciles data from Kenneth French’s data library. This model is somewhat similar to generalized volatility-stabilized models. The novelty of our work is twofold. First, we take into account the difference between price and total returns (in other words, between market size and wealth processes). Second, we work with actual market data. We study the long-term properties of this system of equations, and reproduce observed linearity of the capital distribution curve. In the “Appendix”, we analyze size-based real-world index funds.

Abstract: The world of trading and market has evolved greatly. With the aid of technology, traders and trading establishments use trading platforms to perform various transactions. They are able to utilize several effective algorithms to analyse the market data and identify the key points required to carry out a successful trading operation. High-frequency trading (HFT) platforms are capable of such operations and are used by traders, investors and establishments to make their operations easier and faster. To accommodate high processing and high frequency of transactions, we integrate the concept of parallelism and combine the processing power of GPU using general-purpose graphics processing unit (GPGPU) to enhance the speedup of the system. High processing power without involving further costs in hardware upgradation is our approach. Methods of deep learning and machine learning also add a feature to provide help or assistance for several traders using this platform.