Visual predictions of currency crises using self‐organizing maps

Throughout the 1990s, four global waves of financial turmoil occurred. The beginning of the 21st century has also suffered from several crisis episodes, including the global financial crisis of 2008–2009. However, to date, the forecasting results are still disappointing. This paper examines whether new insights can be gained from the application of the self‐organizing map (SOM) – a non‐parametric neural‐network‐based visualization tool. We develop a SOM‐based model for prediction of currency crises. We evaluate the predictive power of the model and compare it with that of a classical probit model. The results indicate that the SOM‐based model is a feasible tool for predicting currency crises. Moreover, its visual capabilities facilitate the understanding of the factors and conditions that contribute to the emergence of currency crises. Copyright © 2011 John Wiley & Sons, Ltd.     

VaRSOM: A Tool to Monitor Markets' Stability Based on Value at Risk and Self-Organizing Maps

We introduce a variant of self-organizing maps (SOMs) termed VaRSOM that evaluates the similarity among inputs and nodes of the map employing value at risk (VaR). In this way we embed risk measurement within a machine-learning architecture, thus becoming particularly well-suited to analysing financial data. We tested the visualization capabilities and the explicative power of VaRSOM on data from the German Stock Exchange; we then evaluated the results in a comparative perspective, opposing the VaRSOM outcomes to those of SOM trained with more conventional similarity measures. The results lead to the conclusion that VaRSOM is a tool particularly well suited to visualize and exploit critical patterns in financial markets. This, in turn, opens perspectives for a general machine-learning framework sensitive to financial distress and contagion effects. Copyright © 2015 John Wiley & Sons, Ltd.     

经济结构转型与“双支柱”调控框架

理解经济运行环境的变化才能更有针对性地制定和实施货币政策和宏观审慎政策。中国经济在2012年前后迈过工业化的高峰期,家庭部门发生从制造到服务的消费升级,企业部门出现从资本密集型向人力资本密集型的产业结构转型,中国的经济结构转型进程与高收入国家类似发展阶段的经历高度一致。经济结构转型过程中,传统资本密集型行业信贷需求大幅下降,新兴人力资本密集型行业信贷需求相对较低且在债务主导的金融服务体系中面临融资供给制约。由于市场内生的企业信贷供给和需求双双下降,总需求不足问题凸显。宏观经济稳定方面,宏观经济运行特征由此前的“易热难冷” 转向“易冷难热”,“债务—通缩”风险加大。这要求货币政策在执行中更加注重温和通胀目标,充分运用各种政策工具确保总需求和总供给的平衡。金融稳定方面,防范系统性风险的重点要与时俱进地调整。时间轴上要特别关注经济周期和金融周期下行叠加带来的系统性风险;空间轴上不仅要关注“大而不能倒”的系统重要性机构,还须加强“小广散”金融机构的稳定性,注重与其相关的风险传染。     

Information-sharing in services for military personnel in transition to civilian life

An analysis of policy and practice documents on information-sharing in the transition of military personnel into civilian life finds (1) a complex transition pathway along which the responsibility for co-ordination of transition shifts from service providers to those in transition; (2) a lack of attention to operationalizing information-sharing for different service circumstances; (3) the potential for developing a framework for managing information-sharing; and (4) a need for further research to draw on the evidence of transitions and information-sharing in other policy sectors.     

Do Not‐For‐Profits Need Their Own Conceptual Framework?

This paper raises the issue of whether not‐for‐profit (NFP) oganisations require a conceptual framework that acknowledges their mission imperative and enables them to discharge their broader accountability. Relying on publicly available documentation and literature, it suggests the current Conceptual Frameworks for the for‐profit and public sectors are inadequate in meeting the accountability needs of NFPs. A NFP‐specific conceptual framework would allow the demonstration of broader NFP‐specific accountability and the formulation of NFP‐appropriate reporting practice, including the provision of financial and non‐financial reporting. The paper thus theoretically challenges existing financial reporting arrangements and invites debate on their future direction.     

Toward a conceptual framework of the harmonization of bank financial reporting standards in transition economies: The case of Mexico

In recent years banks in emerging and transition economies throughout the world have been compelled to change lending and financial reporting practices in response to the economic transition of their countries. Correspondingly, on the basis of the theory of soft budget constraints (SBC), economists have studied the relationship between economic development, banks' lending practices, and banks' penchant to overstate loans. However, very little research has been performed by accounting researchers regarding the nature of bank loan overstatement during times of financial crisis in emerging markets. Accordingly, this paper uses the theory of soft budget constraints to develop a four stage conceptual framework of the harmonization of bank financial reporting standards during times of economic transition. Emphasis is placed on using the theory of soft budget constraints to characterize the harmonization of standards and practices with respect to bank loan overstatement. The framework is applied to the harmonization of Mexican bank financial reporting during the country's economic transition of the late 1990s. The Mexican harmonization process during this period generally followed the framework.     

影子银行体系监管改革的国际经验及其对中国的启示

全球金融危机过后,影子银行体系的监管改革问题前所未有地成为各国金融当局普遍关注的热点,以美国为代表的发达经济体掀起了一场旨在规范影子银行体系发展的金融监管体制改革。本文的研究表明,过度金融创新以及金融监管的缺失是刺激美国影子银行体系迅速发展的直接原因。然而,危机过后,美国并未简单地采取抑制金融创新的办法加强对影子银行体系的监管,而是通过金融监管机构改革以及提高微观金融数据的采集与分析的办法,规范和引导影子银行体系的健康发展。这种市场化的监管改革思路值得我们借鉴。从影子银行体系的运行机制以及发展来看,中国的影子银行体系与美国存在较大差异,但全面强化宏观审慎金融监管是我国金融监管体制改革的大势所趋。在此情况下,美国金融监管改革的经验值得我们认真总结和研究。最后,本文认为,中国应当继续深化金融体制改革;积极探索现行金融监管框架的改革;加强微观金融数据的收集与整合,完善宏观金融风险预警机制。     

金融消费者概念探析

本文在对金融消费者概念的不同观点进行梳理、分析的基础上,提出金融消费者应该具备如下特征:第一、金融消费者仅限于自然人;第二,金融消费者应该包括个人投资者;第三,因信息不对称导致的弱势地位是判断金融消费者的根本标准.     

Enterprise Risk Management Through Strategic Allocation of Capital

This article presents a conceptual framework for operationalizing strategic enterprise risk management (ERM) in a general firm. We employ a risk‐constrained optimization approach to study the capital allocation decisions under ERM. Given the decision maker's risk appetite, the problem of holistically managing enterprise‐wide hazard, financial, operational, and real project risks is treated by maximizing the expected total return on capital, while trading off risks simultaneously in Value‐at‐Risk type of constraints. This approach explicitly quantifies the concepts of risk appetite and risk prioritization in light of the firm's default and financial distress avoidance reflected in its target credit rating. Our framework also allows the firm to consider a multiperiod planning horizon so that changing business environments can be accounted for. We illustrate the implementation of the framework through a numerical example. As an initial conceptual advancement, our formulation is capable of facilitating more general ERM modeling within a consistent strategic framework, where idiosyncratic variations of firms and different modeling assumptions can be accommodated. Managerial implications are also discussed.     

Regime Change and the Role of International Markets on the Stock Returns of Small Open Economies

We examine the influence of US, UK and German macroeconomic and financial variables on the stock returns of two relatively small, open European economies, Ireland and Denmark. Within a nonlinear framework, we allow for time variation via regime switching using a smooth transition regression (STR) model. We find that US (global) and UK and German (regional) stock returns are significant determinants of returns in both markets. Further, global information represented by oil and US asset price movements drive changes between states in each market. Significantly, the role of country‐specific domestic variables is typically confined to a single state while global and regional variables pervade all states.     

The role of attribute selection in Deep ANNs learning framework for high‐frequency financial trading

In financial trading, technical and quantitative analysis tools are used for the development of decision support systems. Although these traditional tools are useful, new techniques in the field of machine learning have been developed for time‐series forecasting. This paper analyses the role of attribute selection on the development of a simple deep‐learning ANN (D‐ANN) multi‐agent framework to accomplish a profitable trading strategy in the course of a series of trading simulations in the foreign exchange market. The paper evaluates the performance of the D‐ANN multi‐agent framework over different time spans of high‐frequency (HF) intraday asset time‐series data and determines how a set of the framework attributes produces effective forecasting for profitable trading. The paper shows the existence of predictable short‐term price trends in the market time series, and an understanding of the probability of price movements may be useful to HF traders. The results of this paper can be used to further develop financial decision‐support systems and autonomous trading strategies for the financial market.     

Not‐For‐Profit Ratios for Financial Resilience and Internal Accountability: A Study of Australian International Aid Organisations

Not‐for‐profit (NFP) financial ratio research has focused primarily on organisational efficiency measurements for external stakeholders. Ratios that also capture information about stability, capacity (liquidity), gearing and sustainability enable an assessment of financial resilience. They are thus valuable tools that can provide a framework of internal accountability between boards and management. The establishment of an Australian NFP regulator highlights the importance of NFP sustainability, and affirms the timeliness of this paper. We propose a suite of key financial ratios for use by NFP boards and management, and demonstrate its practical usefulness by applying the ratios to financial data from the 2009 reports of ACFID (Australian Council for International Development) affiliated international aid organisations.     

Capital Structure and the Ex‐Dividend Day Return

We apply an option‐pricing framework to the ex‐dividend behavior of common stocks. The framework explains the observed behavior of positive returns on the ex‐dividend day and predicts that ex‐dividend day returns will be higher for firms with greater financial leverage. Empirical testing supports the prediction. In contrast to prior studies, we find that dividend‐capture activity has no significant impact on ex‐dividend behavior, and we offer an explanation based on the importance of tick intervals.     

The Puzzle of Financial Reporting and Corporate Short‐Termism: A Universal Ownership Perspective

This study considers the controversy surrounding financial reporting and corporate short‐termism as a puzzle. The question remains as to why corporate managers and investors persist in exhibiting behaviours that trade off long‐term value creation for meeting short‐term financial targets. Using inter‐temporal choice theory, the myopia characterising decision‐making is entirely rational, given the set of incentives faced. This study views the puzzle through the prism of universal owners (pension and superannuation funds), arguing that the investment policies or ‘mandates’ implemented by these financial behemoths is the source of the myopic behaviour. The paper explores a range of policies that universal owners may consider implementing to ensure that the payoffs to corporate managers and investors are optimised through the pursuit of long‐termism.     

The behavioral basis of policies fostering long-run transitions: Stakeholders,limited rationality and social context

Writings on sustainability transitions generally pay slight attention to the specific behavioral characteristics of individuals, groups and organizations. This paper examines how modern insights about bounded rationality, social interaction and learning can contribute to making transition polices more effective in addressing barriers and opportunities to realize a sustainability transition in the near future. We argue that the behavioral underpinnings of features like lock-in, surprises in innovation systems and network interactions have been insufficiently elaborated and connected to policy design. We identify and illustrate the most important behavioral features of relevant stakeholders in transition processes. By focusing on behavioral features at both individual and organizational levels, we arrive at recommendations for policy makers regarding important barriers to change and how to overcome these. Specific policy insights are offered at multiple levels, for different stakeholders, and associated with both behavioral biases and social interactions. The analysis combines insights from the literatures on sustainability transitions, “environmental–behavioral economics”, and behavioral foundations of learning and innovation. Our framework may serve as a basis for coherent behavior studies of transitions that otherwise run the risk of being ad hoc. This will improve conditional forecasting of system responses to transition policies.     

Explainable artificial intelligence modeling to forecast bitcoin prices

Forecasting cryptocurrency behaviour is an increasingly important issue for investors. However, proposed analytical approaches typically suffer from a lack of explanatory power. In response, we propose for cryptocurrency pricing an explainable artificial intelligence (XAI) framework, including a new feature selection method integrated with a game-theory-based SHapley Additive exPlanations approach and an explainable forecasting framework. This new approach, extendable to other uses, improves both forecasting and model generalizability and interpretability. We demonstrate that XAI modeling is capable of predicting cryptocurrency prices during the recent cryptocurrency downturn identified as associated in part with the Russian-Ukraine war. Modeling reveals the critical inflection points of the daily financial and macroeconomic determinants of the transitions between low and high daily prices. We contribute to financial operating systems research and practice by introducing XAI techniques to enhance the transparency and interpretability of machine learning applications and to support various decision-making processes.     

The Influence of Country,Industry, and Topic Factors on IFRS Policy Choice

This paper proposes a framework for understanding management's decision‐making on observable accounting policy choices. The framework is used to hypothesize how country, industry, and topic factors influence policy choice under International Financial Reporting Standards (IFRS). The hypotheses are tested on the choices made by the largest firms from 10 jurisdictions on a comprehensive set of IFRS policy topics, which are hand‐collected from the financial statements. The results are consistent with the framework: country factors are particularly influential when the choice does not affect an important accounting number; and industry and topic factors influence the choice on some topics. Overall, we find that country factors have the greatest influence on IFRS policy choice.     

Forecasting stock volatility with economic policy uncertainty: A smooth transition GARCH-MIDAS model

Given that policy uncertainty shocks in the economic environment can exacerbate financial market volatility and pose financial risks, this paper utilizes a smooth transition version of the GARCH-MIDAS model to investigate the impact of different structural state changes in economic policy uncertainty (EPU) on stock market volatility. The extended model explains the nonlinear effects of the macro variables and the structural break changes in regime transitions. The empirical results confirm that the EPU indicators provide effective prediction information for stock volatility from the in-sample and out-of-sample analyses, which reveals that the smooth transition model provides an effective method for detecting the possible regime changes between stock volatility and macroeconomic uncertainty. Additionally, we further confirm that some category-specific EPU indicators also have strong smooth transition behaviour with respect to stock volatility. More important, our new model provides significant economic value to investors from a utility gain perspective. Overall, the institutional changes present in EPU play a nonnegligible and important role in stock market volatility. Accurate identification of the structural features of financial data helps investors deepen their understanding of the sources of stock market volatility.     

Financial Stress Regimes and the Macroeconomy

Some financial stress events lead to macroeconomic downturns, while others appear to be isolated to financial markets. We identify financial stress regimes using a model that explicitly links financial variables to macro‐economic outcomes. The stress regimes are identified using an unbalanced panel of financial variables with an embedded method for variable selection. Our identified stress regimes are associated with corporate credit tightening and with NBER recessions. An exogenous deterioration in our financial conditions index has strong negative effects in economic activity, and negative amplification effects on inflation in the stress regime. These results are obtained with a novel factor‐augmented vector autoregressive model with smooth‐transition regimes (FASTVAR).     

A simulation estimator for testing the time homogeneity of credit rating transitions

The measurement of credit quality is at the heart of the models designed to assess the reserves and capital needed to support the risks of both individual credits and portfolios of credit instruments. A popular specification for credit-rating transitions is the simple, time-homogeneous Markov model. While the Markov specification cannot really describe processes in the long run, it may be useful for adequately describing short-run changes in portfolio risk. In this specification, the entire stochastic process can be characterized in terms of estimated transition probabilities. However, the simple homogeneous Markovian transition framework is restrictive. We propose a test of the null hypotheses of time-homogeneity that can be performed on the sorts of data often reported. We apply the tests to 4 data sets, on commercial paper, sovereign debt, municipal bonds and S&P-rated Corporates. The results indicate that commercial paper looks Markovian on a 30-day time scale for up to 6 months; sovereign debt also looks Markovian (perhaps due to a small sample size); municipals are well-modeled by the Markov specification for up to 5 years, but could probably benefit from frequent updating of the estimated transition matrix or from more sophisticated modeling, and S&P Corporate ratings are approximately Markov over 3 transitions but not 4.