Tail dependence structure of the foreign exchange market: A network view

Tail dependence of financial entities describes when the price of one financial asset has an extreme fluctuation (e.g., price sharply rises or falls), the degree of its effect on the price fluctuation of another asset. Under the background of the global financial crisis, tail dependence structure of financial entities plays an important role in financial risk management, portfolio selection, and asset pricing. In this paper, we propose a concept of tail dependence networks to investigate the tail dependence structure of the foreign exchange (FX) market. Lower- and upper-tail dependence networks for 42 major currencies in the FX market from 2005 to 2012 are constructed by combing the symmetrized Joe-Clayton copula model and two filtered graph algorithms, i.e., the minimum spanning tree (MST) and the planar maximally filtered graph (PMFG). We also construct the tail dependence hierarchical trees (HTs) associated with the MSTs to analyze the currency clusters. We find that (1) the two series of lower- and upper-tail dependence coefficients present different statistical properties; (2) the upper-tail dependence networks are tighter than the lower-tail dependence networks; and (3) different currency clusters, cliques and communities are respectively found in the two tail dependence networks. The key empirical results indicate that market participants should consider the different topological features at different market situations (e.g., a booming market or a recession market) to make decisions on the investing or hedging strategies. Overall, our obtained results based on the tail dependence networks are new insights in financial management and supply a novel analytical tool for market participants.     

A double-threshold GARCH model of stock market and currency shocks on stock returns

International integration of financial markets provides a channel for currency movements to affect stock prices. This paper applies a four-regime double-threshold GARCH (DTGARCH) model of stock market returns to investigate empirically the effects of daily currency movements on five stock market returns, namely in Taiwan, Singapore, South Korea, Japan and the USA. The asymmetric reactions of the mean and volatility stock returns in five markets to stock market and foreign exchange news are investigated using linear and nonlinear models. We discuss a four-regime DTGARCH model, which allows for asymmetry in both the conditional mean and conditional variance simultaneously by using two threshold variables to analyze stock market reactions to different types of information (that is, positive and negative news) that are generated from stock and foreign exchange markets. By applying the four-regime DTGARCH model, this paper finds that the interactions between the information of stock and foreign exchange markets lead to asymmetric reactions of stock returns and their associated variability. The empirical results show that international fund managers who invest in newly emerging stock markets need to evaluate the value and stability of domestic currencies as part of their stock market investment decisions.     

An iterative agent bidding mechanism for responsive manufacturing

In today's market, the global competition has put manufacturing businesses in great pressures to respond rapidly to dynamic variations in demand patterns across products and changing product mixes. To achieve substantial responsiveness, the manufacturing activities associated with production planning and control must be integrated dynamically, efficiently and cost-effectively. This paper presents an iterative agent bidding mechanism, which performs dynamic integration of process planning and production scheduling to generate optimised process plans and schedules in response to dynamic changes in the market and production environment. The iterative bidding procedure is carried out based on currency-like metrics in which all operations (e.g. machining processes) to be performed are assigned with virtual currency values, and resource agents bid for the operations if the costs incurred for performing them are lower than the currency values. The currency values are adjusted iteratively and resource agents re-bid for the operations based on the new set of currency values until the total production cost is minimised. A simulated annealing optimisation technique is employed to optimise the currency values iteratively. The feasibility of the proposed methodology has been validated using a test case and results obtained have proven the method outperforming non-agent-based methods.     

The Interplay Between Two Stock Markets and a Related Foreign Exchange Market: A Simulation Approach

There is still much that is unknown about the interactions among financial markets, and about the relationships between stock prices and exchange rates. This topic gains attention during financial crises, and many papers try to find empirical regularities emerging from financial data, or to study contagion processes. In this paper we present a study on the interplay between two stock markets and one foreign exchange market extending the framework provided by the Genoa Artificial Stock Market. There are four different trading strategies, and the agents are divided into two groups: those who trade in the stock markets and those who trade in the FOREX. We studied three market conditions: the FOREX dynamics, the behavior of the two stock markets together with the FOREX, and finally we conducted a what-if analysis for testing the effects of a inflationary monetary shock of one currency affecting all of the three markets.     

A multi-agent system using iterative bidding mechanism to enhance manufacturing agility

The global market has become increasingly dynamic, unpredictable and customer-driven. This has led to rising rates of new product introduction and turbulent demand patterns across product mixes. As a result, manufacturing enterprises were facing mounting challenges to be agile and responsive to cope with market changes, so as to achieve the competitiveness of producing and delivering products to the market timely and cost-effectively. This paper introduces a currency-based iterative agent bidding mechanism to effectively and cost-efficiently integrate the activities associated with production planning and control, so as to achieve an optimised process plan and schedule. The aim is to enhance the agility of manufacturing systems to accommodate dynamic changes in the market and production. The iterative bidding mechanism is executed based on currency-like metrics; each operation to be performed is assigned with a virtual currency value and agents bid for the operation if they make a virtual profit based on this value. These currency values are optimised iteratively and so does the bidding process based on new sets of values. This is aimed at obtaining better and better production plans, leading to near-optimality. A genetic algorithm is proposed to optimise the currency values at each iteration. In this paper, the implementation of the mechanism and the test case simulation results are also discussed.     

ENMX: An elastic network model to predict the FOREX market evolution

The foreign exchange (FOREX) market is a financial market in which participants, such as international banks, companies or private investors, can both invest in and speculate on exchange rates. This market is considered one of the largest financial markets in the world in terms of trading volume. Indeed, the just-in-time price prediction for a currency pair exchange rate (e.g. EUR/USD) provides valuable information for companies and investors as they can take different actions to improve their business. This paper introduces a new algorithm, inspired by the behaviour of macromolecules in dissolution, to model the evolution of the FOREX market, called the ENMX (elastic network model for FOREX market) algorithm. This algorithm allows the system to escape from a potential local minimum, so it can reproduce the unstable nature of the FOREX market, allowing the simulation to get away from equilibrium. ENMX introduces several novelties in the simulation of the FOREX market. First, ENMX enables the user to simulate the market evolution of up to 21 currency pairs, connected, and thus emulating behaviour of the real-world FOREX market. Second, the interaction between investors and each particular quotation, which may introduce slight deviations from the quotation prices, is represented by a random movement. We analyse different probability distributions like Gaussian and Pseudo-Voigt, the latter showing better behaviour distributions, to model the variations in quotation prices. Finally, the ENMX algorithm is also compared to traditional econometric approaches such as the VAR model and a driftless random walk, using a classical statistical and a profitability measure. The results show that the ENMX outperforms both models in terms of quality by a wide margin.     

移动Agent系统Mogent的安全设施分析

安全问题是移动Agent技术应用发展中的一个重要主题。本文详细分析了南京大学计算机软件新技术国家重点实验室研制的基于Java的移动Agent系统Mogent(Mobile Agent)的安全设施，主要包括密写操作与密钥管理、安全传输通道、访问控制、授权和电子货币。     

An Heterogeneous, Endogenous and Coevolutionary GP-Based Financial Market

Stock markets are very important in modern societies and their behavior has serious implications for a wide spectrum of the world's population. Investors, governing bodies, and society as a whole could benefit from better understanding of the behavior of stock markets. The traditional approach to analyzing such systems is the use of analytical models. However, the complexity of financial markets represents a big challenge to the analytical approach. Most analytical models make simplifying assumptions, such as perfect rationality and homogeneous investors, which threaten the validity of their results. This motivates alternative methods.In this paper, we report an artificial financial market and its use in studying the behavior of stock markets. This is an endogenous market, with which we model technical, fundamental, and noise traders. Nevertheless, our primary focus is on the technical traders, which are sophisticated genetic programming based agents that co- evolve (by learning based on their fitness function) by predicting investment opportunities in the market using technical analysis as the main tool. With this endogenous artificial market, we identify the conditions under which the statistical properties of price series in the artificial market resemble some of the properties of real financial markets. By performing a careful exploration of the most important aspects of our simulation model, we determine the way in which the factors of such a model affect the endogenously generated price. Additionally, we model the pressure to beat the market by a behavioral constraint imposed on the agents reflecting the Red Queen principle in evolution. We have demonstrated how evolutionary computation could play a key role in studying stock markets, mainly as a suitable model for economic learning on an agent- based simulation.     

Light-Weight Currency Management Mechanisms in Mobile and Weakly-Connected Environments

This paper discusses the currency management mechanisms used in Deno, an object replication system designed for use in mobile and weakly-connected environments. Deno primarily differs from previous work in implementing an asynchronous weighted-voting scheme via epidemic information flow, and in committing updates in an entirely decentralized fashion, without requiring any server to have complete knowledge of system membership.We first give an overview of Deno, discussing its voting scheme, proxy mechanism, basic API, and commit performance. We then focus on the issue of currency management. Although there has been much work on currency management in synchronous, strongly-connected environments, this issue has not been explored in asynchronous, weakly-connected environments. We present currency management mechanisms, based on peer-to-peer currency exchanges, that enable light-weight replica creation, retirement, and currency redistribution while maintaining the correctness of the underlying consistency protocol. We also demonstrate that peer-to-peer currency exchanges can be used to exponentially converge to arbitrary target currency distributions, without the need for any server to have global system information.     

基于动机分析的区块链数字货币异常交易行为识别方法

当前区块链数字货币被众多恶意交易者利用,导致了"粉尘"注入、"空投"操作、勒索、骗局等一系列异常交易行为.因此,研究区块链数字货币异常交易行为的识别方法对于规范交易行为、保障网络空间安全具有重要意义.在众多区块链数字货币中,比特币市值超过所有区块链数字货币市值和的一半,具有高代表性.比特币系统的用户数量多、交易规模大、...     

Robust hedging strategies

While investing in foreign assets may bring additional benefits in terms of risk diversification, it may also expose the portfolio to a further source of risk derived from changes in the value of the foreign currencies. Hedging strategies for international portfolios have usually focused on the use of forward contracts to mitigate the currency risk. We propose an alternative formulation aimed at the reduction of the overall portfolio risk by assuming the returns are uncertain and maximizing the portfolio return for the worst possible outcome of the returns. This technique known as robust optimization provides a first guarantee on the portfolio value thanks to the non-inferiority property. We further complement our approach with forward contracts on the foreign exchange rates and options on the assets. Because the total return on any asset will be the product of its local return and currency return, the models proposed are bilinear and non convex. A reformulation of both the uncertainty set and the objective function as a semidefinite problem will yield an approximate tractable model. We compare the hedging alternatives proposed with simulated and historical market data and conclude on their relative benefits.     

Regional smart logistics economic development based on artificial intelligence and embedded system

Increasing the demand for smart regional logistics in terms of quality and quantity demands industrialization and strengthening economic sectors. The Internet of Things is a future technology that can provide many innovative solutions for the modernization of economic sectors. Research institutes and research groups continue to work hard to provide solutions and products to solve various financial problems using the Internet of Things. The Field-Programmable Gate Array (FPGA)-based embedded Internet of Things (IoT) system is the preferred communication source for the regional intelligent logistics labor market balancing system to balance marketability and flexibility in the economic field. In the local smart logistics market, labor can be received as a commodity. The financial sector has achieved a balance between these labor markets in various uses of the non-logistics labor market, usually with intermediaries pushing labor to market groups and relocating it elsewhere. Workers and employers work with the labor market. Guiding regional intelligent logic economic market analysis, currency boundaries affect the low and high performance of registrations. The logistics market has established equilibrium conditions to reveal the swirling effects of currency fluctuations caused by reserves use. The balancing method in the smart logistics market shows the immediate and bypassing impact of rural sector profits on revenue performance, revenue value, and investment in use, revenue, and salary strategies. The traditional MIP structure is modified to avoid resource conflicts with Application-Specific Integrated Circuit (ASIC) components. The essential concepts for designing this type of Million Instructions per Second (MIPS) include core, and its implementation effects are analyzed. Second, ECOMIPS also seeks to create customizable and reusable structures to bridge the gap between microprocessors and ASICs.     

A Forex trading expert system based on a new approach to the rule-base evidential reasoning

Currently FOREX (foreign exchange market) is the largest financial market over the world. Usually the Forex market analysis is based on the Forex time series prediction. Nevertheless, trading expert systems based on such predictions do not usually provide satisfactory results. On the other hand, stock trading expert systems called also “mechanical trading systems”, which are based on the technical analysis, are very popular and may provide good profits. Therefore, in this paper we propose a Forex trading expert system based on some new technical analysis indicators and a new approach to the rule-base evidential reasoning (RBER) (the synthesis of fuzzy logic and the Dempster–Shafer theory of evidence). We have found that the traditional fuzzy logic rules lose an important information, when dealing with the intersecting fuzzy classes, e.g., such as Low and Medium and we have shown that this property may lead to the controversial results in practice. In the framework of the proposed in the current paper new approach, an information of the values of all membership functions representing the intersecting (competing) fuzzy classes is preserved and used in the fuzzy logic rules. The advantages of the proposed approach are demonstrated using the developed expert system optimized and tested on the real data from the Forex market for the four currency pairs and the time frames 15 m, 30 m, 1 h and 4 h.     

Advanced social media sentiment analysis for short-term cryptocurrency price prediction

In recent years, the scrutiny of bitcoin and other cryptocurrencies as legal and regulated components of financial systems has been increasing. Bitcoin is currently one of the largest cryptocurrencies in terms of capital market share. Therefore, this study proposes that sentiment analysis can be used as a computational tool to predict the prices of bitcoin and other cryptocurrencies for different time intervals. A key characteristic of the cryptocurrency market is that the fluctuation of currency prices depends on people's perceptions and opinions, not institutional money regulation. Therefore, analysing the relationship between social media and web search is crucial for cryptocurrency price prediction. This study uses Twitter and Google Trends to forecast the short-term prices of the primary cryptocurrencies, as these social media platforms are used to influence purchasing decisions. The study adopts and interpolates a unique multimodel approach to analyse the impact of social media on cryptocurrency prices. Our results prove that people's psychological and behavioural attitudes have a significant impact on the highly speculative cryptocurrency prices.     

Market Appraisal and Accounting of the Cost of Production Capital

It is determined that the cost of sold goods and services in the market of goods, taken into account by the statistics on the nominal gross domestic product and the price index allows estimating the market value of production capital in the national currency. National accounts also take into account the costs of productive capital and fixed capital stocks, which are valued by their market value. It is shown that when the discounted capital is underestimated as compared to the market value, the money in circulation serves turnover only and does not ensure the reproduction of capital. It is also shown that economic growth and technological progress is possible when the value of the recorded production capital is no less than its market value.     

基于区块链的数字货币演化

区块链是数字货币研究的主流技术和重要前提。作为一种去中心化的分布式计算技术,区块链具有共同维护、防窜改、可追溯等中心化技术所不具备的优势。以区块链的基本原理为基础,主要分析了哈希加密、共识机制以及智能合约方面的关键技术;以区块链中的首个应用比特币为基础,分析了以太坊、达世币、卡尔达诺、比特股等数字货币的发展演化历程。根据数字货币的研究现状及其所面临的诸多挑战,展望了区块链未来在数字货币的发行与监管、交易跟踪和海量交易数据分析方面的研究趋势。     

From chaining blocks to breaking even: A study on the profitability of bitcoin mining from 2012 to 2016

Bitcoin is a widely-spread payment instrument, but it is doubtful whether the proof-of-work (PoW) nature of the system is financially sustainable on the long term. To assess sustainability, we focus on the bitcoin miners as they play an important role in the proof-of-work consensus mechanism of bitcoin to create trust in the currency. Miners offer their services against a reward while recurring expenses. Our results show that bitcoin mining has become less profitable over time to the extent that profits seem to converge to zero. This is what economic theory predicts for a competitive market that has a single homogenous good. We analyze the actors involved in the bitcoin system as well as the value flows between these actors using the e³value methodology. The value flows are quantified using publicly available data about the bitcoin network. However, two important value flows for the miners, namely hardware investments and expenses for electricity power, are not available from public sources. Therefore, we contribute an approach to estimate the installed base of bitcoin hardware equipment over time. Using this estimate, we can calculate the expenses miner should have. At the end of our analysis period, the marginal profit of mining a bitcoin becomes negative, i.e., to a loss for the miners. This loss is caused by the consensus mechanism of the bitcoin protocol, which requires a substantial investment in hardware and significant recurring daily expenses for energy. Therefore, a sustainable crypto currency needs higher payments for miners or more energy efficient algorithms to achieve consensus in a network about the truth of the distributed ledger.     

人民币冠字号码识别预处理算法研究

近年来,人民币冠字号码的识别受到越来越广泛的关注,其在打击经济犯罪,维持市场稳定和社会和谐等方面都具有很强的实用性和广阔的应用前景。一个稳定高效的人民币冠字码识别系统在很大程度上依赖于图像预处理的结果。提出了一套完整的人民币冠字码识别预处理方案,其中包括图像采集、倾斜校正、采集方向识别、冠字号码区域定位和二值化、字符提取等算法,并对三种冠字码区域二值化方法进行了比较和分析。实验结果表明,所提出的预处理方法精度很高,为后续的冠字码字符识别工作提供了可靠的技术保障。