Finance Dataset Research Articles

Web Intelligent for Forecasting Exchange Rate Currency using Clever Extraction Agent Combine with Financial Data Mining

From the current economic climate results in fluctuations of currency exchange rates in all countries. Since the most countries use USD as the reference exchange rate. The exchange rate will change from day to day so variety of factors which affect the exchange rate forecasting in the exchange rates in advance are critical to evaluate for the impact of the economic system of each country. It is important for investment decisions, exports, and profitability in the money market. It was reported on website (www) in the daily exchange rate changes. We use clever search agent (CSA) gather information from financial website generate to financial data mining. Kohonen Neural Networks is the method to determine similarity of internet documents using pattern index of financial document. And Ontology Structure of Sentence is the method to determine keyword using pattern index of financial content. Both are important components of Financial Data Mining. It is analyzed for exchange rate forecasting about USD/ Pounds. Our experimental forecast exchange rates for currency's USD / Great Britain Pounds by compare three algorithms as fallows GA, Meiosis Genetic Algorithms (MGA). This research propose new algorithm is called Dash Predator Swarm Optimization (DP2SO) which are accurate in prediction than other methods in generation of Genetic algorithm (GA) 35.83-41.52% which it depend on the accuracy of the information in each factor which are important finance dataset. It will present the future trends of exchange rate to the individual website.

Securities Finance: Data applications for beneficial owners

We use the IHS Markit Securities Finance dataset and beneficial owner consulting team to map the evolution of industry practices. Securities lending plays a critical role in global capital markets: the practice is broad, encompassing borrowing securities to cover a failed trade, borrowing to sell short, liquidity and financing. With the number of investors participating in securities lending continuing to increase, there is a need to have guiding principles in place with regard to lending programme controls and the evaluation of performance and risk. Part of the challenge from a beneficial owner’s perspective is being able to access quantitative and qualitative data to properly compare themselves to similar entities in order to make changes to existing parameters and perform a thorough review of their programmes. Having more transparency and a better understanding of the data allows beneficial owners to identify opportunities that may increase the profitability of their programmes, while allowing for changes to programme parameters within their risk/reward appetite. The increased focus on ESG (environment, social and governance) for many beneficial owners has resulted in new evaluations of securities lending programmes as they relate to ESG policies for respective investments. As internal priorities evolve for beneficial owners, regulation has also become an important factor of consideration for securities lending participants. It has affected the complete chain from execution of initial trades to monitoring and reporting. We provide evidence of these changes from academic research, industry working groups as well as the IHS Markit Securities Finance dataset and consulting business.

Estimating Chinese foreign health aid: an analysis of AidData's Global Chinese Official Finance dataset

BackgroundChinese global health aid has expanded tremendously since the 2000s. Unlike many donors, China has no official aid reporting obligations, nor does it voluntarily disclose detailed aid information. Therefore, several third parties have attempted to estimate China's health aid footprint. However, current estimates use varied definitions of health aid, geographic regions, and time spans. These distinct methodological approaches make comparisons of Chinese aid to other aid donors difficult. Our study builds on previous tracking efforts and improves on them by creating a standardised estimate using commonly accepted definitions of aid and frameworks for categorising health projects. MethodsWe categorised AidData's Chinese Official Finance Dataset health-related projects according to health aid frameworks from the Organization for Economic Co-operation and Development (OECD) and the Institute for Health Metrics and Evaluation (IHME). Only projects that fitted the definition of official development assistance were included. We analysed data by both total project count and financial value to assess priority health-aid focus areas for China. We also provide an updated estimate for projects with missing financial values in AidData's database by applying the median cost of similar projects to projects with missing financial values, allowing for comparison with other donors. FindingsBetween 2000 and 2014, China funded 620 health-related aid projects, which made up more than 20% of its total aid project portfolio. Most of these projects were located in Africa. According to the OECD framework, the priority focus areas of these 620 projects were: basic health care, such as medical teams, drugs, and medicine (n=244, 36%); malaria control (118, 19%); medical services, such as specialty equipment, infrastructure and services (108, 17%); and basic health infrastructure (78, 13%). According to the IHME framework, health-systems strengthening accounted for 70% (n=434) of total projects, primarily due to China's contributions to human resources for health, infrastructure, and equipment. The only other significant allocation under the IHME framework was malaria (n=118, 19%). When we estimate missing financial values, we noted that China was the fourth largest health aid donor to African countries from 2008–2014, after the USA, UK, and Canada. InterpretationThese findings enable a better understanding of Chinese health aid in the absence of transparent aid reporting. Such understanding could lead to better coordination, collaboration, and resource allocation for both fellow donors and recipient countries. FundingHuang Fellows Program, Duke University Science & Society (PK).

Chinese Development Finance and its determinants: Does global governance matter?

AbstractMotivationWhat motivates China to offer development finance in recipient countries? Research on Chinese development finance focuses on China’s political and economic interests in allocating development finance. Few studies, however, have assessed the impact of international norms and regulation—a neo‐institutional perspective—on China’s development finance.PurposeThe article tests the three sets of hypotheses on Chinese development finance, positing that countries will receive more Chinese aid if: (1) they have a UN General Assembly voting alliance with China and do not recognize Taiwan; (2) the more oil and gas reserves they have; and (3) the more memberships of intergovernmental organizations (IGOs), such as the UN agencies, and international non‐governmental organizations (INGOs), which include non‐profit organizations and charities, such as Oxfam, as well as foundations and professional associations.Approach and MethodsThis study models the amount of Chinese aid received by developing countries as a function of its integration to IGOs/INGO networks. It draws on data from AidData’s Global Chinese Official Finance Dataset, 2000–2014. The model used is ordinary least square regression using fixed effects.ConclusionsChinese aid allocation does not only reflect its political and economic interests. Taiwan recognition continues to be the dominant factor for Chinese development finance. IGO and INGO memberships of recipient countries increase the chance of receiving more Chinese development funding, especially for least developed countries.Policy ImplicationsChinese development finance provides many developmental opportunities for less developed countries. A key aspect of getting Chinese development finance has been alliance with China. Linkages to IGOs and INGOs also matter. Countries aiming to get Chinese development finance should consider increasing their memberships to transnational networks of IGOs and INGOs.

A Comparison of Two Data Sets Used to Measure Expenditure Trends for Local Public Park and Recreation Services

Six studies published between 1978 and 2017 used data from the Census Bureau’s annual publication, Government Finances, to identify trends in local park and recreation expenditures. This paper evaluates the validity of the Government Finances data and conclusions of analyses drawn from it by comparing annual data on park and recreation expenditures collected annually by the Trust for Public Land (TPL) from 2008 through 2017 from the same agencies in the same years published in the U.S. Census Bureau’s Government Finances.Results revealed several key discrepancies between the datasets. First, the Government Finances average total, operating, and capital expenditures for the 100 cities were substantially higher than those reported by TPL. Second, the TPL data showed a gradual and incremental decrease in total expenditures over the decade, whereas the Government Finances data showed a substantial increase in total spending, especially after 2012. Third, the declines in the TPL data in total and operational spending from 2008 to 2009, which marked the onset of the Great Recession were 11.31% and 17.69%, respectively, whereas the Government Finances data showed increases of 1.05% and 5.96%, respectively. Fourth, the Government Finances data showed increases from 2008 to 2016 of 22% in total spending and 6% in operational spending, suggesting park and recreation agencies had fully recovered from the Great Recession, but the TPL data showed declines of 20% and 16% in those expenditures, respectively. Fifth, averages masked substantial variations among cities, as there were dramatic differences in expenditures in the two data sets for many individual municipalities. Our analyses confirmed that concerns relating to the Government Finances dataset were legitimate. Applying those data to monitor trends in expenditures of “mainline” park and recreation agencies is inappropriate and is likely to mislead rather than inform. For the Government Finances data to have utility, the Census Bureau should divide them into “core” and “non-core” services. If that is not done, then we recommend that state associations use the TPL definitions and questionnaire to collect primary information about park and recreation expenditures either annually or biennially to improve monitoring accuracy and inform policy and decision-making. Subscribe to JPRA

The decrease in life insurance ownership

Based on our analyses of Survey of Consumer Finances datasets, the proportion of households owning a life insurance policy decreased from 72% in 1992 to 60% in 2016. We estimated logistic regressions on the likelihood of ownership of any, term, and cash value life insurance. We conclude that changes in household characteristics accounted for the decrease in term life insurance ownership, but not for the decreases in any and in cash value life insurance ownership. We also found a positive association between use of a financial planner and life insurance ownership. We discuss implications for financial planning.

China’s 21st century geopolitics and geo-economics: An evidence from the Western Balkans

This article advocates the thesis that the People?s Republic of China, in accordance with a strong geopolitical strategy, is using geo-economics tools in the context of reshaping global powers? influence in diverse parts of the world. One of these spatial dimensions of Chinese emerging domination refers to the Western Balkans, primarily through the economic presence of China in this region. To address this issue first will be considered concepts of geopolitics and geo-economics, followed by highlighting China?s presence in the Western Balkans region and flourishing infrastructure projects in the domain of transport and energy. The research is conducted by relying on the existing databases on China?s economic involvement in different economy sectors worldwide, mainly from China Global Investment Tracker (CGIT) and AidData?s Global Chinese Official Finance Dataset for the WB countries in the period 2013-2018. Special attention is given to China?s benefits in terms of realizing those projects, even though the Western Balkans countries? market is not attractive per se. Finally, some concluding remarks have been made in terms of the potentials of China?s strategy to reconsider political and security dynamics in the Western Balkans.

What LED to the Boom? Unpacking China's Development Cooperation in Latin America

What led to the boom of Chinese development cooperation in Latin America? This article provides a systematic analysis of China's foreign behavior, motives, and policies regarding development cooperation toward the region between 2000 and 2014. I propose a comparative framework that defines Chinese development cooperation as a tool of economic diplomacy. Drawing on empirical evidence from AidData's Global Chinese Official Finance Dataset and Chinese white papers on foreign aid, the findings evidence that China was motivated by multiple and conjunctural factors in providing development cooperation. In the realm of theory, the article contributes to the literature on economic statecraft—filling in gaps in understanding the relationship between economics and politics. Empirically, it provides a set of tools for understanding the important role that development cooperation plays in a nation's statecraft. Regarding Chinese foreign policy studies, it offers insight into the financial dimension of China's international economic relations.

What Factors Are Important in Aversion to Education Debt?

This study examined the factors related to attitude toward education debt. Based on the conceptual framework of the multidimensional nature of attitude toward debt, empirical models were constructed with variables focused on debt utility and debt fear. Using the 2016 Survey of Consumer Finances dataset, this study found that debt utility variables, such as having a higher educational level and holding a student loan balance, were negatively associated with an aversion to education debt. However, debt fear variables, such as unfavorable attitudes about debt in general and not having applied for credit, were positively associated with aversion to education debt. Subgroup analyses across different income and educational levels showed that the negative relationship between education level and aversion to education debt was more evident in the high‐income group and that most of the debt utility and debt fear variables were statistically significant only in the lower education level. These results suggest that aspects of debt utility and fear of debt contribute to attitudes about education debt, but there is inconsistency across socioeconomic status.

Chinese development aid to Asia: Size and motives

This research note empirically examines China’s development assistance to Asia, a region critical to China’s geostrategic ambition, from 2000 to 2014. It uses AidData’s Global Chinese Official Finance Dataset, one of the most reliable publicly available data sources on Chinese aid, which systematically collects and classifies different types of China’s official development finance. It is found that, despite a recent surge, China’s development assistance to Asia remains highly limited compared to that of Japan, Asia’s top donor, and that the economic sectors of energy, transport, and mining dominate financial flows. Econometric analysis results suggest that China’s aid allocation in Asia is influenced by its export relations, but, more importantly, that foreign policy considerations do not play a significant role, which is in contrast to the findings of recent work on Chinese aid to Africa. The results suggest that better measures may be needed to properly capture China’s strategic interests in Asia, including its involvement in the territorial dispute in the South China Sea.

What can we learn on Chinese aid allocation motivations from available data? A sectorial analysis of Chinese aid to African countries

Since the creation of the Forum on China-Africa Cooperation (FOCAC) in 2000, Chinese official development assistance (ODA) to Africa has increased drastically. Only few analyses on the determinants of Chinese ODA allocation to African countries are available. Moreover, existing literature mainly focused on total aid flows while Chinese motivations for aid allocation might differ depending on the ODA sector considered. Our objective is to study the factors associated with Chinese aid allocation to African countries by sector between 2000 and 2014. We consider three ODA broad sectors as defined by the Organisation for Economic Cooperation and Development (OECD): the social infrastructure and services sector, the economic infrastructure and services sector and the production sector. Chinese ODA is measured using AidData's Global Chinese Official Finance Dataset, 2000–2014, Version 1.0. Over the 2000–2014 period, China allocated 971, 218 and 138 ODA projects to African countries in the social infrastructure and services sector, the economic infrastructure and services sector and the production sector respectively. Between 2000 and 2014, the economic infrastructure and services sector was the first sector in terms of ODA amount with a total of US$18.9 billion ahead from the social infrastructure and services sector with US$7 billion or the production sector with US$3.1 billion. Results of our analysis suggest that the motivations of Chinese aid allocation to African countries differ by sector. Chinese ODA in the social infrastructure and services sector appears more responsive to the economic needs of recipient countries but is also more driven by foreign policy considerations. Chinese economic interest, in particular for natural resources acquisition, is associated with China's ODA allocation in the economic infrastructure and services sector. Finally, while governance quality in recipient countries is not related to Chinese ODA in the social infrastructure and services sector, we find that China allocates more ODA in the economic infrastructure and services sector and the production sector to African countries with weaker institutions. One of the strong conclusions of this study is to show that considering only China's overall aid to Africa can be misleading as to its underlying determinants, and therefore to point out the need to disaggregate the analysis by ODA sectors.

The effects of risk aversion on life insurance ownership of single-parent households

ABSTRACTThis study investigates the effect of risk aversion of single-parent households with at least one child under 18 on life insurance ownership. Analysing the 1992–2013 Survey of Consumer Finances datasets, we found that the likelihood of owning term life insurance decreases as risk aversion increases, but the likelihood of owning cash-value life insurance increases as risk aversion increases. Smokers were less likely to own term life insurance but more likely to own cash-value life insurance than comparable non-smokers.

Financial Knowledge and Household Saving: Evidence from the Survey of Consumer Finances

This study analyzed the relationship between financial knowledge and household saving behavior using the 2016 Survey of Consumer Finances (SCF) dataset. The results from a hierarchical model showed that both objective knowledge and perceived financial knowledge were positively related to the likelihood of being a saver. The explanatory power of the regression models increased significantly when financial knowledge variables were added. Furthermore, the results were robust across different measurements of household savings and additional analyses using the 2015 National Financial Capability Study (NFCS) dataset. Educators, policymakers, and financial institutions can benefit from these results as they develop programs, policies, and products to motivate and promote saving behavior.

Online Learning in Limit Order Book Trade Execution

In this paper, we propose an online learning algorithm for optimal execution in the limit order book of a financial asset. Given a certain number of shares to sell and an allocated time window to complete the transaction, the proposed algorithm dynamically learns the optimal number of shares to sell via market orders at prespecified time slots within the allocated time interval. We model this problem as a Markov Decision Process (MDP), which is then solved by dynamic programming. First, we prove that the optimal policy has a specific form, which requires either selling no shares or the maximum allowed amount of shares at each time slot. Then, we consider the learning problem, in which the state transition probabilities are unknown and need to be learned on the fly. We propose a learning algorithm that exploits the form of the optimal policy when choosing the amount to trade. Interestingly, this algorithm achieves bounded regret with respect to the optimal policy computed based on the complete knowledge of the market dynamics. Our numerical results on several finance datasets show that the proposed algorithm performs significantly better than the traditional Q-learning algorithm by exploiting the structure of the problem.

China's Development Finance to Asia

This study empirically examines China's development finance to developing countries with a focus on Asia from 2000 to 2012. It uses AidData's Global Chinese Official Finance Dataset, one of the most reliable and publicly available data sources that systematically collects and differentiates different types of China's official development financial flows, to produce descriptive and inferential statistics for Asia, a world region where the rise of China poses unique challenges. Descriptive statistical analysis indicates that South Asia was the largest recipient of China's ODA-like flows in Asia for the period under study while the majority of China's OOF-like flows to Asia went to Eastern Europe and Central Asia. In both types of flows, energy, transport, and mining sectors received the bulk of financing. The estimation results show that China's allocation decisions for its concessional flows in the region have strong motives of pursuing strategic interests while its less concessional flows are committed to more governance-challenged countries. This study also provides detailed discussion of the trends in China's development finance to Southeast Asia, a subregion which is critical to China's strategic and economic interests.

Aid, China, and Growth: Evidence from a New Global Development Finance Dataset

This paper introduces a new dataset of official financing — including foreign aid and other forms of concessional and non-concessional state financing — from China to 138 countries between 2000 and 2014. We use these data to investigate whether and to what extent Chinese aid affects economic growth in recipient countries. To account for the endogeneity of aid, we employ an instrumental-variables strategy that relies on exogenous variation in the supply of Chinese aid over time resulting from changes in Chinese steel production. Variation across recipient countries results from a country’s probability of receiving aid. Controlling for year- and recipient-fixed effects that capture the levels of these variables, their interaction provides a powerful and excludable instrument. Our results show that Chinese official development assistance (ODA) boosts economic growth in recipient countries. For the average recipient country, we estimate that one additional Chinese ODA project produces a 0.7 percentage point increase in economic growth two years after the project is committed. We also benchmark the effectiveness of Chinese aid vis-a-vis the World Bank, the United States, and all members of the OECD’s Development Assistance Committee (DAC). Our results indicate that Chinese, U.S., and OECD-DAC ODA have positive effects on economic growth, but we find no robust evidence that World Bank aid promotes growth. We also find that, irrespective of the funding source, less concessional and more commercially-oriented types of official finance do not boost economic growth. Finally, we test the popular claim that significant financial support from China impairs the effectiveness of grants and loans from Western donors and lenders. Our results do not support this claim.

Financial literacy, financial advice, and financial behavior

In this survey, we review the voluminous body of literature on the measurement and the determinants of financial literacy. Wherever possible, we supplement existing findings with recent descriptive evidence of German households’ financial literacy levels based on the novel Panel on Household Finances dataset, a large-scale survey administered by the Deutsche Bundesbank and representative of the financial situation of households in Germany. Prior research not only documents generally low levels of financial literacy but also finds large heterogeneity in financial literacy across the population, suggesting that economically vulnerable groups are placed at further disadvantage by their lack of financial knowledge. In addition, we assess the literature evaluating financial education as a means to improve financial literacy and financial behavior. Our survey suggests that the evidence with respect to the effectiveness of the programs is rather disappointing. We also review the role of individuals’ financial literacy for the use of professional financial advice and assess whether expert intervention can serve as a substitute to financial literacy. We conclude by discussing several directions for future research.

From Micro Behaviors to Macro Dynamics: An Agent-Based Economic Model with Consumer Credit

The paper develops an agent-based model populated by heterogeneous consumers, a productive sector and a banking sector. Taking a bottom up approach, the paper aims at providing a first tool to analyze households' borrowing dynamics in the different phases of the business cycle by relaxing some assumptions of mainstream consumption models and considering more realistic household borrowing behaviors. Although very simple, the model allows us to grasp the main implications of the interaction between consumers' wants (desired consumption), consumers' beliefs (their expectations about their future income), the behavior of the banking sector (rationing) and the behavior of the production sector (forecasting future demand). After presenting and discussing sensitivity analysis over a parameters' set, the paper reports results and the ex-post validation by comparing artificial and empirical distributions computed using the European Household Finance and Consumption Survey data set.

The Role of Self-Control on Retirement Preparedness of US Households

We examine the self-control problems of U.S households and their effects on households' retirement preparedness based on the Behavioral Life-Cycle Hypothesis. Using the 2010 Survey of Consumer Finances dataset, the level of retirement adequacy was estimated with income replacement ratio (IRR), and only 42% of households were adequately prepared for retirement. Results from logistic regression analysis indicated that households with loan payment and saving self-control problems were less likely to be prepared adequately for retirement compared to those without such problems. Age, education, race/ethnicity, marital status, employment status, retirement plans, expected retirement age and risk tolerance were significantly related to retirement preparedness. This study provides financial educator and researchers with suggestions on how to help household make a better retirement plan.

Accounting for Complex Sample Designs in Analyses of the Survey of Consumer Finances

We examined the effects of using bootstrap weights to account for the complex sample design in analyses of Survey of Consumer Finances (SCF) datasets. No article published in this journal that has used the SCF has mentioned the issue of complex sample designs. We compared results obtained without weights and with application of population and bootstrap weights in a logistic regression, and found no substantial differences between the unweighted and the weighted analyses. We also compared results for an ordinary least squares regression, and found few differences between unweighted and weighted models. Unweighted regressions produced more conservative significance tests than the counterpart, and some econometricians have suggested that unweighted analyses are better for hypothesis testing. If estimation of the magnitudes of effects is important, weighted regression may be better because it produces consistent estimators. Researchers should be cautious in drawing conclusions when weighted and unweighted effects are substantially different.