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"FINANCIAL DATABASES"
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Improving access to finance for India's rural poor
Finance is an extraordinarily effective tool in spreading economic opportunity and fighting poverty. India has a relatively deep financial system and wide network of rural banks. But India?s financial markets and institutions have not served poor people well; despite improvements in the delivery of financial services over the past three decades, the vast majority of India?s poor households, who are concentrated in rural areas, do not have access to formal finance. Improving Access to Finance for India's Rural Poor examines the current level and pattern of access to finance for India?s rural households, evaluates various approaches for delivering financial services to the rural poor, analyzes what lies behind the lack of adequate financial access for the rural poor, and identifies what it would take to improve access to finance for India?s rural poor. Based on the analysis of a large-scale rural household survey, in combination with an evaluation of the role of financial markets and institutions, this title also examines different forms of financial service provision, including formal, informal and microfinance, raises questions about approaches used so far to address financial exclusion, and makes recommendations for policy advisors and financial service providers on how to scale-up access to finance for India?s rural poor, to meet their diverse financial needs (savings, credit, insurance against unexpected events, etc.), in a commercially sustainable manner. Its conclusions will be of interest to anyone involved in economic policy, finance or microfinance, poverty analysis, and poverty reduction.
eBook
Commercializing blockchain : strategic applications in the real world
The accessible, non-technical guide to applying and benefiting from blockchain technology. Blockchain has grown at an enormous rate in a very short period of time. In a business context, blockchain can level the playing field between small and large organisations in several ways: Exact copies of the immutable, time-stamped data is held by all parties, all transactions can be viewed in real time, data blocks are cryptographically linked, all raw materials are traceable and smart contracts ensure no middle-men, ease of audit and reduced friction. The trust, transparency, security, quality and reduced costs of blockchain make it a game-changing technology that crosses sectors, industries and borders with ease. Even though the technologies are ready for adoption, businesses remain largely unaware of their full potential and effective implementation. End users require accurate and up-to-date information on the practical applications of blockchain-Commercializing Blockchain provides it. A practical and easy-to-understand guide to blockchain, this timely book illustrates how this revolutionary technology can be used to transform governments, businesses, enterprises and entire communities. The author draws from his experience with global retailers, global technology companies, UCL Centre for Blockchain technologies, the government of the UK, Retail Blockchain Consortium and many other sources to present real-world case studies on the use and benefits of blockchain. Topics include financial transactions, tokenisation, identity management, supply chain transparency, global shipping and freight, counterfeiting and more. Provides practical guidance for blockchain transactions in business operations -Provides practical guidance for blockchain transactions in business operations -Demonstrates how blockchain can add value and bring increased efficiency to commercial operations -Covers all of the essential components of blockchain such as traceability, provenance, certification and authentication -Requires no technical expertise to embrace blockchain strategies Commercializing Blockchain: Strategic Applications in the Real World is ideal for enterprises seeking to develop and deploy blockchain technology, particularly in areas retail, supply chain and consumer goods.
Book
Deep Learning Algorithm Research and Performance Optimization of Financial Treasury Big Data Monitoring Platform
With the rapid development of information technology and the advent of the digital age, the management of fiscal treasury is facing unprecedented challenges and opportunities. In order to improve the efficiency and effectiveness of deep learning algorithms in the financial and treasury big data monitoring platform, this paper further studies the performance optimization methods of the model. This paper deeply studies deep learning algorithm research and performance optimization of financial Treasury big data monitoring platforms. This paper reviews the basic concepts, methods, and applications of deep learning and their application in the financial database big data monitoring platform. In the financial Treasury big data monitoring platform, deep learning algorithms are widely used in image recognition, natural language processing, recommendation systems and other fields. This article first conducts in-depth theoretical research on deep learning algorithms, including various neural network structures (such as convolutional neural network CNN, recurrent neural network RNN, etc.), optimization algorithms (such as gradient descent method and its variants), regularization techniques, etc. In addition, we also studied the practical applications of deep learning in fields such as image processing, natural language processing, and recommendation systems. In order to verify the effectiveness of deep learning algorithms in the financial and treasury big data monitoring platform, we designed corresponding experiments. These experiments include using deep learning algorithms for image recognition of financial documents, natural language processing, and building recommendation systems. We collected real fiscal treasury data as the experimental dataset and preprocessed and annotated the data.
Journal Article
Blockchain : the insights you need from Harvard Business Review
Can blockchain solve your biggest business problem? While news outlets are transfixed with Bitcoin's latest swings, your most forward-looking competitors are tuning out the noise and quietly making key bets on blockchain. They're effortlessly tracking every last link in their supply chains. They're making bureaucratic paper trails obsolete while keeping their customers' data safer. And they're imagining new ways to use this next foundational technology to sustain their competitive advantage. What should you be doing right now to ensure that your business is poised for success? These articles by blockchain experts and consultants will help you understand today's most essential thinking on what blockchain is capable of now, how to adopt it in your organization, and how the technology is likely to be used in the near future and beyond. Blockchain: The Insights You Need from Harvard Business Review will help you spearhead important conversations, get going on the right blockchain initiatives in your company, and capitalize on the opportunity of the coming blockchain wave. Catch up on current topics and deepen your understanding of them with the Insights You Need series from Harvard Business Review. Featuring some of HBR's best and most recent thinking, Insights You Need titles are both a primer on today's most pressing issues and an extension of the conversation, with interesting research, interviews, case studies, and practical ideas to help you explore how a particular issue will impact your company and what it will mean for you and your business.-- Provided by publisher.
Book
Analysts’ forecasts between last consensus and earning announcement date
Purpose
Thomson financial database reports a monthly consensus measure of analysts’ forecasts in the third week of every month, and firms’ earnings announcement dates are usually different from the last consensus calculation date. Thus, there is a gap between the last consensus calculation date and the earnings announcement date of firms. This study aims to address the question: “Do analysts issue forecasts that are slightly higher than the consensus number to increase the accuracy of their forecasts?”
Design/methodology/approach
This study is based on a sample of 91,172 quarterly earnings forecasts of various firms from 1990 to 2007 made between the last consensus calculation date and quarterly earnings announcement date. Descriptive statistics and statistical tests were used to analyze the data.
Findings
The findings propose that contrary to expectation, analysts’ forecasts between the last consensus calculation date and earnings announcement date are smaller than the consensus number. Also, the forecasts made between the last consensus and earnings announcement date is not as informative as forecasts made at other times as they could merely reflect the analysts’ herding behavior resulting from their career concerns.
Originality/value
This study provides a link between the literature that studies firms’ meet or beat analysts’ earnings phenomenon and analysts’ forecast decision-making context. This study also provides useful implications for the literature on the information content of analysts’ forecasts.
Journal Article
The Importance of Distinguishing Errors from Irregularities in Restatement Research: The Case of Restatements and CEO/CFO Turnover
Research on restatements has grown significantly in recent years. Many of these studies test hypotheses about the causes and consequences of intentional managerial misreporting but rely on restatement data (such as the GAO database) that contains both irregularities (intentional misstatements) and errors (unintentional misstatements). We argue that researchers can significantly enhance the power of tests related to restatements by distinguishing between errors and irregularities, particularly in recent periods when the relative frequency of error-related restatements is increasing. Based on prior research, the reading of numerous restatement announcements, and the guidance that boards receive from lawyers, auditors, and the SEC on how to respond to suspicions of deliberate misreporting, we propose a straightforward procedure for classifying restatements as either errors or irregularities. We show that most of the restatements we classify as irregularities are followed by fraud-related class action lawsuits as compared to only one lawsuit in the group of restatements classified as errors. As further validation of our proxy, we report that the market reaction to the restatement announcement for our irregularities sample (-14 percent) is also significantly more negative than it is for our errors sample (-2 percent). Finally, we demonstrate the importance of distinguishing errors from irregularities by showing the impact it has on inferences about the relation between restatements and CEO/CFO turnover over time.
Journal Article
Big tech in finance : how to prevail in the age of blockchain, digital currencies and web3
\"With Big Tech's breakthrough into finance with blockchain, it is imperative that finance players understand the ramifications and how they can defend their competitive advantage.Big Tech in Finance provides a cutting edge look at Big Tech's play for domination of the crypto economy, its ramifications and how finance is fighting back. The book analyses the motives behind Big Tech's break into banking and unpicks the strategies behind the use of blockchain, technology interfaces, infrastructure and investments into blockchain unicorns. The book then goes onto review how organizations in finance are countering these threats, with governments and banks driving their own strategies and use of centralized blockchains. Delving into the fight between Big Tech, Big Banking, start-ups, and regulators, Big Tech in Finance analyzes which actors have the best shot at succeeding. It explores the key tools in play, such as smart contracts, digital central bank currencies, decentralized autonomous organizations (DAOs) and the metaverse. The book also divulges the geopolitical dimensions underpinning the power struggle and its implications for the industry. Written by an internationally recognized expert on blockchain, the book draws on in-depth interviews with founders, investors, regulators, bankers and blockchain experts to provide valuable insider insights. This will be an essential read for finance and fintech professionals, bankers and investors and anyone else interested in the developments of fintech\"-- Provided by publisher.
Book
A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker)
COVID-19 has prompted unprecedented government action around the world. We introduce the Oxford COVID-19 Government Response Tracker (OxCGRT), a dataset that addresses the need for continuously updated, readily usable and comparable information on policy measures. From 1 January 2020, the data capture government policies related to closure and containment, health and economic policy for more than 180 countries, plus several countries’ subnational jurisdictions. Policy responses are recorded on ordinal or continuous scales for 19 policy areas, capturing variation in degree of response. We present two motivating applications of the data, highlighting patterns in the timing of policy adoption and subsequent policy easing and reimposition, and illustrating how the data can be combined with behavioural and epidemiological indicators. This database enables researchers and policymakers to explore the empirical effects of policy responses on the spread of COVID-19 cases and deaths, as well as on economic and social welfare.
The Oxford COVID-19 Government Response Tracker (OxCGRT) records data on 19 different government COVID-19 policy indicators for over 190 countries. Covering closure and containment, health and economics measures, it creates an evidence base for effective responses.
Journal Article