Abstract: Although the terminology of Big Data has so far gained little traction in economics, the availability of unprecedentedly rich datasets and the need for new approaches – both epistemological and computational – to deal with them is an emerging issue for the discipline. Using interviews conducted with a cross-section of economists, this paper examines perspectives on Big Data across the discipline, the new types of data being used by researchers on economic issues, and the range of responses to this opportunity amongst economists. First, we outline the areas in which it is being used, including the prediction and ‘nowcasting’ of economic trends; mapping and predicting influence in the context of marketing; and acting as a cheaper or more accurate substitute for existing types of data such as censuses or labour market data. We then analyse the broader current and potential contributions of Big Data to economics, such as the ways in which econometric methodology is being used to shed light on questions beyond economics, how Big Data is improving or changing economic models, and the kinds of collaborations arising around Big Data between economists and other disciplines.

Abstract: We provide a framework for empirical analysis of negotiated-price markets. Using mortgage market data and a search and negotiation model, we characterize the welfare impact of search frictions and quantify the role of search costs and brand loyalty for market power. Search frictions reduce consumer surplus by $12/month/consumer, 28% of which can be associated with discrimination, 22% with ineifficient matching, and 50% with search costs. Large consumer-base banks have margins 70% higher than those with small consumer bases. The main source of this incumbency advantage is brand loyalty; however, price discrimination based on search frictions accounts for almost a third.

Abstract: Techniques for prediction of financial instrument returns, identifying statistical history, the discovery of pricing anomalies, and financial instrument visualization are disclosed. In one particular exemplary embodiment, the techniques may be realized as a method for identifying financial instrument returns and pricing anomalies including matching, using at least one computer processor one or more portions of current market data associated with a financial instrument with historical market data, averaging outcomes of matched historical market data, and providing a probabilistic outcome for financial instrument returns, pricing anomalies, or other metrics based on the matched historical market data and the current market data. Techniques for financial instrument analysis may also include processing event data, correlating the event data using a large volume of historical market data to identify a predicted impact on returns of a financial instrument and/or pricing anomalies, and presenting the predicted impact to a user (e.g., in near real time).

Abstract: Motivated by the need to produce accurate forecasts of the demand for electric vehicles (EV), this paper discusses the problem of predicting the market shares of new products. Forecasting with choice models requires at least recalibrating the model’s alternative specific constants (ASC) to reflect the fact that unobserved factors can be different in the design year than in the base situation. As most studies for new technologies rely on stated preference (SP) data, the ASC do not reflect the true market and there are no clear rules about model recalibration, even if real market data is available. The paper proposes a method to forecast EV demand using SP data. The authors use a choice model estimated on such data to simulate the EV market share in Denmark in 2030. The results show that if they simply calibrate the ASC based on aggregate real market data for the base year, the model is unresponsive to future changes in the attributes, due to the major adjustment of the constants. To overcome this, the authors suggest a method which combines the choice model with a diffusion model to take into account that new products often need time to obtain a significant market share and as a result of this method a better prediction is obtained.

Abstract: We construct a new systemic risk measure that quantifies vulnerability to fire-sale spillovers using detailed regulatory balance sheet data for U.S. commercial banks and repo market data for broker-dealers. Even for moderate shocks in normal times, fire-sale externalities can be substantial. For commercial banks, a 1 percent exogenous shock to assets in 2013-Q1 produces fire sale externalities equal to 21 percent of system capital. For broker-dealers, a 1 percent shock to assets in August 2013 generates spillover losses equivalent to almost 60 percent of system capital. Externalities during the last financial crisis are between two and three times larger. Our systemic risk measure reaches a peak in the fall of 2007 but shows a notable increase starting in 2004, ahead of many other systemic risk indicators. Although the largest banks and broker-dealers produce – and are victims of – most of the externalities, leverage and linkages of financial institutions also play important roles.

Abstract: In recent years, data mining techniques have been used to identify companies who issue fraudulent financial statements. However, most of the research conducted thus far use datasets that are balanced. This does not always represent reality, especially in fraud applications. In this paper, we demonstrate the effectiveness of cost-sensitive classifiers to detect financial statement fraud using South African market data. The study also shows how different levels of cost affect overall accuracy, sensitivity, specificity, recall and precision using PCA and Factor Analysis. Weighted Support Vector Machines (SVM) were shown superior to the cost-sensitive Naive Bayes (NB) and K-Nearest Neighbors classifiers.

Abstract: This article argues that the policy uncertainty generated by elections encourages private actors to delay investments that entail high costs of reversal, creating pre-election declines in the associated sectors. Moreover, this incentive depends on the competitiveness of the race and the policy differences between the major parties/candidates. These arguments are tested using new survey and housing market data from the United States. The survey analysis assesses whether respondents’ perceptions of presidential candidates’ policy differences increased the likelihood that they would delay certain purchases and actions. The housing market analysis examines whether elections are associated with a pre-election decline in economic activity, and whether any such decline depends on electoral competitiveness. The results support the predictions and cannot be explained by existing theories.

Abstract: This study compares 10 widely used financial performance measures of stock return in the Australian stock market. The five financial measures are calculated on information provided in publicly available financial reports (accounting based financial measures) and the other five are calculated using market information as one of the key variable (market based financial performance). The sample includes companies from all major industries from 2001 to 2010. The panel data analysis shows that market based financial performance measures can better explain stock price variance compare to accounting based measures of financial performance. It has significance for researchers and practitioners seeking to select measures that can empirically explain the performance of company. It has also importance for shareholders tracking performance of companies in order to make profitable investments.

Abstract: A general concept prevails that the financial leverage is helpful to enhance the financial performance of the companies. For measuring the impact of financial leverage on the financial health of the companies, it is essential to know whether a positive relationship exists between financial leverage and financial performance or not? So, this study is intended to test the hypothesis and to measure a relationship between the financial leverage and the financial performance of the John Keells Holdings plc in Sri Lanka during the periods of 2006-2012. The findings of the study show a negative relationship between the financial leverage and the financial performance of the John Keells Holdings plc. But the financial leverage has a significant impact on the financial performance of the John Keells Holdings plc in Sri Lanka.

Abstract: The idea of Corporate Social Responsibility is one of the most inspiring ideas in modern business, also even in financial business. CSR, already widely implemented in economy, contains significant ecological components. Nowadays, around the world many leading banks, investment funds, pension funds, insurance companies and public companies, use environmental aspects in their business. The growing and diversified ecological engagement of different financial institutions could be even called ecological evolution of financial business. The same process has started also in Poland. The financial institutions support natural environment in many ways: saving resources, financing proecological organizations, maintaining adequate PR and IR communication and even offering special financial products such as: deposits, payment cards, shares, bonds, investment funds and specialized stock index Respect. Especially, the new investment possibilities are clear evidence of positive changes of the Polish financial market. The aim of the elaboration is to indicate the phenomenon of environmental rules implementation in Polish financial business. The considerations in the article are focused on financial offer related to natural environment protection. The issue is presented in two aspects: theoretical and empirical. In the article, critical analysis of literature and reports, analysis of financial market data, induction method and comparison method have been used. Especially, selected financial products offered on Polish financial market were analysed in the period of 2008–2014 in respect to ecological criteria. The problem of financial market “ecologization” is a very important subject of scientific research. The process is not only very inspiring, but also controversial.

Abstract: Hierarchical analysis is considered and a multilevel model is presented in order to explore causality, chance and complexity in financial economics. A coupled system of models is used to describe multilevel interactions, consistent with market data: the lowest level is occupied by agents generating the prices of individual traded assets; the next level entails aggregation of stocks into markets; the third level combines shared risk factors with information variables and bottom-up, agent-generated structure, consistent with conditions for no-arbitrage pricing theory; the fourth level describes market factors which originate in the greater economy and the highest levels are described by regulated market structure and the customs and ethics which define the nature of acceptable transactions. A mechanism for emergence or innovation is considered and causal sources are discussed in terms of five causation classes.

Abstract: Research in the field of corporate social responsibility (CSR) has grown exponentially in the last few decades. Nevertheless, significant debate remains about the relationship between CSR performance and corporate financial performance (CFP). This is particularly true for the case of Chinese state-owned enterprises (SOEs). The purpose of the current study is to empirically test the relationship between CSR and CFP. We use data for 66 Chinese SOEs listed on the Shanghai and Shenzhen stock exchanges. The results are interesting in that they are not consistent with similar studies using US and other Western market data. We find a significant negative relationship between CSR performance and CFP. The results are discussed in light of the preferential government treatment afforded to Chinese SOEs, and social welfare requirements imposed on such entities. Implications for Chinese policy-makers are discussed.

Abstract: The main objective of this study was to examine the effect of financial information on investment in shares for Kenyan retail investors, applying the behavioral finance theory. The traditional Efficient Market Hypothesis is becoming deficient to explain investor behaviors in the capital markets. Hence behavioral factors are being considered as possibly playing a role in the securities market activity. Primary data was collected from 311respondents randomly sampled form the population of 836.250 investors participating at the Nairobi Securities Exchange as at March, 2013. Data analysis was done applying descriptive andlinear regression statistical data analysis. The results revealed that financial information variable had significant influence on decisions to invest in shares with p-value .000 (p<0.05).Acquiring financial information therefore has the potential to improve investors’ decision making resulting on improved overall portfolio performance. On formulating policy, both the stock market regulators and financial advisers should make strategic frameworks toeducate investors to improve their financial analysis knowledge, economic, and commercial skills as a means to encourage more participation in the securities markets.

Abstract: This data report presents a dataset on residential real estate prices in Germany provided by ImmobilienScout24 and introduces real estate price indices of labor market regions. The dataset consists of online adverts of houses and apartments that are available for rent or sale. The dataset complements already existing datasets in two ways: First, it is available almost without any time lag, allowing the analysis of most recent developments. Second, the high market share of ImmobilienScout24 results in a high number of observations, which gives the opportunity to use the data for analyses on a small regional scale.

Abstract: The SRISK measure is advertised as measuring the recapitalization needed by a financial institution in the event of a financial crisis. It is computed from the estimated reaction of the institution’s share price in the event of a sharp drop in market prices. This indicator relies both on an economic analysis and an econometric model. It is applied to a large set of international and domestic financial institutions, updated regularly and made available online. Although innovative, it stirred naturally debates among academics, supervisors and professionals, highlighting some limitations, in particular when considering the SRISK measure as a supervisory tool. First, the SRISK is based on market return data: consequently, it applies only to listed institutions and is exposed to criticisms as to which extent it can mirror fundamentals. Second, the SRISK seems to lack sound foundations for policy analysis: with a reduced-form approach, conclusions regarding causality are not obvious from an economic point of view. Moreover the SRISK is a conditional measure to an event whose likelihood is not integrated in the framework. Third, empirical analyses of SRISK as a supervisory tool, used for instance to identify systemic financial institutions (SIFIs) or as an early-warning indicator, have shown some limited perspectives.

Abstract: Cash balance pension benefits are accumulated at guaranteed crediting rates, usually based on yields on government securities. Viewed as a financial liability, the benefit is a form of interest rate derivative, which can be valued using financial models and theory. In this article, we derive the market value for a range of commonly used crediting rates, assuming the accrued benefit liability comprises the past contributions, allowing for full interest credits up to a known future retirement date. We use the Hull-White interest rate model to determine crediting rates and to determine the market value. We explore the risks associated with different crediting rate choices by evaluating the liability using market data from 1998 to 2013. We propose two other approaches to the accrued benefit. The first approach assumes the accrued benefit comprises past contributions with interest up to the valuation date, but no future interest credits. Future credits on past contributions are assumed funded through future co...

Abstract: Chapters 1 and 2 presented various financial instruments in the form of market data familiar to Wall Street traders—namely, Bloomberg screens. Chapter 3 lays the mathematical foundation for the valuation of financial instruments, which depends in the first place on an analysis of the likelihood of future events using the tools of statistics and probability. This chapter shows you how to perform a statistical analysis of a given financial instrument by first identifying a suitable probability distribution and then calibrating it appropriately. Finally, this chapter discusses Risk measures such as value at risk, conditional value at risk, and the term structure of statistics.

Abstract: This article provides insight into the future of financial markets and regulation in order to define what would be the best strategy for Europe. To preserve financial stability, Europe has to choose between financial opening and independently determining how to regulate finance. Among the five scenarios we defined, three achieve financial stability both inside and outside Europe. In terms of market efficiency, the multi-polar scenario is the best and the fragmentation scenario is the worst, since gains of integration depend on the size of the new capital market. Regarding sovereignty of regulation, fragmentation is the best scenario and the multi-polar scenario is the worst because it necessitates coordination at the global level which implies moving further away from respective national preferences. However, the more realistic option seems to be the regionalisation scenario: (i) this level of coordination seems much more realistic than the global one; (ii) the market should be of sufficient size to enjoy substantial benefits of integration. Nevertheless, the "European government" might gradually increase the degree of financial integration outside Europe in line with the degree of cooperation with the rest of the world.

Abstract: At the end of 2011, the Indian power sector found itself in financial crisis, just a decade after the 2001 bailout of state electricity boards (SEBs) by the central government. Bankrupt state power distribution utilities in several states were unable to pay their bills or repay their debts. Despite the passage of the landmark 2003 Electricity Act and implementation of a broad set of reforms over the past decade, the sector today is looking at another rescue from the center, four times larger than before. This financial rescue scheme amounts to about Rs 1.9 trillion ($42 billion) and was instigated by the nonperforming assets of the banks and other financial institutions. The Electricity Act was envisaged to create independent companies functioning on commercial principles, but they are still far away from that goal. This report presents a diagnostic of the financial and operational performance of segments in the power sector value chain between adoption of the Electricity Act, 2003, and 2011, including analysis of the factors that contributed to the recent crisis. The report focuses on efficiency and productivity, whether performance has improved over time, and which states have emerged as performance leaders. Analysis of this kind is not new or unique, but this report aims to integrate historical performance, the current situation, future projections of the impact of worsening sector finances, and the actions that need to be taken to check the downturn. The report draws primarily from utility data collected by the Power Finance Corporation in successive years on utilities’ operational and financial performance. The Power Finance Corporation data were collated into a single database with the addition of various operational parameters at the plant level and the utility level from the Central Electricity Authority.

Abstract: Background: Given the rapid evolution of health markets, learning is key to promoting the identification and uptake of health market policies and practices that better serve the needs of the poor. However there are significant challenges to learning about health markets. We discuss the different forms that learning takes, from the development of codified scientific knowledge, through to experience-based learning, all in relationship to health markets. Discussion: Notable challenges to learning in health markets include the difficulty of acquiring data from private health care providers, designing evaluations that capture the complex dynamics present within health markets and developing communities of practice that encompass the diverse actors present within health markets, and building trust and mutual understanding across these groups. The paper proposes experimentation with country-specific market data platforms that can integrate relevant evidence from different data sources, and simultaneously exploring strategies to secure better information on private providers and health markets. Possible approaches to adapting evaluation designs so that they are better able to take account of different and changing contexts as well as producing real time findings are discussed. Finally capturing informal knowledge about health markets is key. Communities of practice that bridge different health market actors can help to share such experience-based knowledge and in so doing, may help to formalize it. More geographically-focused communities of practice are needed, and such communities may be supported by innovation brokers and/or be built around member-based organizations. Summary: Strategic investments in and support to learning about health markets can address some of the challenges experienced to-date, and accelerate learning that supports health markets that serve the poor.

Abstract: This research constructs and develops a financial stress index based on European financial markets. The integration of numerous sovereign states has created difficulty identifying stress in any one single financial component, but incorporating twenty-three headline European stress indicators across equities, bonds and currencies, in terms of both spreads and levels offer substantial explanatory benefits. The incorporation of a logistical framework specifically analysing the levels, volatility and co-movement of the included standardised series enables the creation of an index that adequately represents financial market stress in European. Using periods of pre-defined crisis in a logistic regression framework also aids the development of the index. The results provide evidence that the European-specific sovereign crises from 2010 to present, with particular emphasis on the mid-2011 period, have significantly over-shadowed any event that the financially-integrated Europe has previously experienced.

Abstract: The recent financial crisis and associated real estate bubble demonstrated the damage that can be caused by imperfect financial market pricing. On the basis of these imperfections, strong financial returns earned by financial institutions in the run-up to 2008 were, in fact, illusory. Executive Compensation in Imperfect Financial Markets explores the relationship between bank lending, real estate markets and stock market prices. Offering a heterodox view of financial market pricing and its relationship with executive pay, this book offers a competing interpretation of the recent crisis, which emphasises the role of bank leverage and investor expectations in generating instability - particularly through the interaction of financial institutions with the real estate market. In the process, it reveals that equity-based compensation incentivized increased bank leverage, which was a cardinal cause of the crisis. This timely book will be an essential read for all legal scholars and policy analysts operating in the field of banking and finance, as well as all those seeking a more rounded understanding of the financial crisis.

Abstract: This article probes into the distinct forces that influence the demand for financial services/products in Mauritius. The major contribution of this study is that a decomposed assessment is made with respect to the demand for financial services/products, namely, bank accounts, mobile banking, shares, life assurance policies, and treasury bills. Findings show that financial literacy matters significantly, let alone a pecking order presence with demand for bank accounts predominating over any other type of demand for financial services. Policy-wise, this article calls for ongoing efforts to improve on financial literacy for sophisticated financial products like stocks and treasury bills.

Abstract: Purpose – This paper aims to provide an overview of the market for corporate and sovereign credit default swaps (CDS), with particular focus on Europe. It studies whether the subprime crisis of 2007/2008 and, particularly, the European debt crisis 2009/2010 led to a differential development on corporate and sovereign CDS markets and investigates the primary use (speculative risk-trading or risk-hedging) of the two markets in recent years. Design/methodology/approach – The authors use aggregate market data on the size of the respective markets and on the structure of market participants and their changes over time to assess the main research question. They enhance existing data from public sources such as the Bank for International Settlements and Depository Trust and Clearing Corporation with their own statistics on European sovereign CDS and combine their conclusions with observations regarding standardisation efforts and regulatory changes in the CDS market. Findings – The authors show that after the su...

Abstract: Stock prediction with artificial neural network (ANN) techniques is one of the most important issues in finance being investigated by researchers across the globe. ANN techniques can be used extensively in the financial markets to help investors make qualitative decision. In this methodology a multilayer perception (M.L.P) neural network model is used to determine and explore the relationship between some variables as independent factors and the level of stock price index as a dependent element in the stock market under study over time. The results show that the neural network models can get better outcomes compared with statistical and parametric models like as multiple regression and other traditional statistical techniques. This study and test also show that useful predictions can be made without the use of extensive market data or knowledge, and in the data mining process, neural networks and some non algorithmic models can explore high level orders in complex time series which hide in the market structure and need very huge calculations in normal conditions. Our study was including of a relatively extensive range of indexes stock market prices in Iran. We've made two different predictions in Tehran Stock Exchange (TSE), and by help ANN and a new method of data mining, indexes stock market prices with about 1% error level, we predict.