Evolutionary Approaches and the Construction of Technology-Driven Regulations

Innovation technologies have substantially changed commerce and society. A new financial industry in the form of financial technology (fintech) initiated the era of the digital economy. At the same time, inherent risks in technology-driven financial innovations, such as technical risks, information asymmetry, and even potential systemic risks, necessitate regulatory responses. However, insufficient regulatory techniques, outdated financial laws, and conservative regulatory concepts make it difficult for traditional regulations based on financial intermediaries to adapt to the current environment of decentralized financial transactions. Technology-driven regulations focused on data monitoring could be a remedy for the inefficiency and ineffectiveness of traditional financial regulations and enhance effective protection of financial consumers’ rights and interests. This new regulatory model aims to build a system that integrates equal access to information on blockchain transactions by both parties to it (i.e., the regulators and the financial institutions they regulate) for the purpose of oversight, intelligent real-time oversight, and an experimental sandbox for developing regulatory technology. This dynamic and flexible financial regulatory system could effectively address fintech risks.     

The roles of alternative data and machine learning in fintech lending: Evidence from the LendingClub consumer platform

There have been concerns about the use of alternative data sources by fintech lenders. We compare loans made by LendingClub and similar loans that were originated by banks. The correlations between the rating grades (assigned by LendingClub) and the borrowers’ FICO scores declined from about 80% (for loans originated in 2007) to about 35% for recent vintages (originated in 2014–2015), indicating that nontraditional data (not already accounted for in the FICO scores) have been increasingly used by fintech lenders. The rating grades perform well in predicting loan default. The use of alternative data has allowed some borrowers who would have been classified as subprime by traditional criteria to be slotted into “better” loan grades, allowing them to obtain lower priced credit.     

The fintech gender gap

Can fintech close the gender gap in access to financial services? Using novel survey data for 28 countries, this paper finds a large and ubiquitous ‘fintech gender gap’: while 29% of men use fintech products, only 21% of women do. This difference exceeds the gender gap in bank account ownership at traditional financial institutions. While country characteristics and individual-level controls explain about a third of the fintech gender gap, the residual gap declines by 60% when accounting for gender differences in the willingness to use new financial technology, the suitability of fintech products, and the willingness to use fintech entrants if they offer cheaper products. The paper concludes by discussing drivers of differences in attitudes and implications for policy to foster financial inclusion with new technology.     

中国金融科技监管沙盒机制设计研究

本文旨在从设立中国金融科技监管沙盒的必要性、可行性分析、机制设计等角度,探讨如何识别有价值的创新、缩短金融科技企业的创新周期并及早发现和化解潜在风险等问题.本文发现,金融科技新业态更具有颠覆性创新特征,采用传统金融监管框架可能会抑制创新,而金融科技监管沙盒是帮助平衡金融科技创新与风险的有效手段.建议参考中国香港的沙盒模...     

Insider trading and future stock returns in firms with concentrated ownership levels

We investigate the relationship between insider trading and stock returns in firms with concentrated ownership. To this end, we employ data from East Asian countries which span the period January 2003 to May 2012. Consistent with the previous literature, we find a significantly negative relation between the selling activity of insiders and stock returns. However, contrary to studies which focus on highly developed markets, we find that the buying activity of insiders is also inversely related to future stock returns. Our analysis shows that top directors with higher ownership levels drive this result, suggesting that the trading activity of insiders is not always associated with profit-making motives and can be explained by their level of ownership. Furthermore, we demonstrate that a trading strategy which focuses solely on purchases made by top directors with high ownership levels yields negative returns. The paper has important implications for outside investors who mimic the trading activity of insiders with the aim to realise profits.     

The impacts of investors' sentiments on stock returns using fintech approaches

This study uses fintech approaches, including web crawler technology with distributed architecture to select internet news messages largely and efficiently and a dictionary-based linguistic text mining to create sentiment variables, to explore the respective impacts of investors' optimism and pessimism on stock returns. The construction of sentiment variables in network- and dictionary-based messages is more precise and variable than that in traditional-based messages. Our results show that firms with investors' optimistic sentiments have significantly higher stock returns in the current month, whereas those with pessimistic sentiments have significantly opposite effects. The effect of both investors' optimism and pessimism on stock returns subsequently reverses. Then, the negative impacts of investors' largely pessimistic sentiments on stock returns are larger than the positive impacts of their largely optimistic sentiments within a quarter. Next, investors' optimistic sentiments significantly raise stock return volatility by approximately a quarter, but their pessimistic sentiments have the opposite effects. Furthermore, investors' high optimism more significantly and persistently raises stock return volatility than their general optimism, but the negative effects of their high pessimism on volatility become smaller and the persistence is shorter than general pessimism. In addition to the advantage of our methodology in creating sentiment variables, the simultaneous consideration of investors' optimism and pessimism to analyze the effects on the returns and the volatility of individual stocks in this study is more complete than previous related studies.     

The calm after the storm: implied volatility and future stock index returns

This article explores the predictive power of five implied volatility indices for subsequent returns on the corresponding underlying stock indices from January 2000 through October 2013. Contrary to previous research, very low volatility levels appear to be followed by significantly positive average returns over the next 20, 40 or 60 trading days. Rolling trading simulations show that positive adjusted excess returns can be achieved when long positions in the stock indices are taken on days of very low implied volatility. This may be a hint that market inefficiencies exist in some markets, especially outside the USA. The excess returns measured against a buy and hold benchmark are significant for the German and Japanese market when tested with a bootstrap methodology. The results are robust against a broad spectrum of specifications.     

印度金融科技“监管沙盒”制度借鉴与我国现实选择

近年来,随着金融科技的快速发展,世界各主要国家已将金融科技视为提升金融业核心竞争力的关键所在。印度金融科技产业的兴起得益于印度政府的多项改革举措和政策支持,包括"数字印度"战略和"废钞运动"等。但是,由于金融科技的跨界化、去中介、分布式等特征,原有的金融监管体系滞后于金融科技发展,监管能力无法匹配金融科技的发展水平。为了在鼓励金融科技创新和防控新型金融风险之间寻求平衡,印度储备银行、证券交易委员会、保险监管和发展局相继推出了面向各行业的"监管沙盒"机制,致力于实现金融科技创新、消费者权益保护和健全金融监管等目标。印度金融科技"监管沙盒"机制对于我国金融科技创新发展、"监管沙盒"制度完善和金融科技监管体系建设具有重要的启示意义。     

证券期货市场人工智能交易的刑法规制

证券期货市场人工智能交易具有强大的预测分析能力、投资策略制定能力以及敏锐的市场反应能力。与之相适应,刑法对证券期货犯罪的规制重点也应当从交易的行为方式向交易技术转移。鉴于利用人工智能交易可以实施滥用技术优势型市场操纵犯罪行为,刑法有必要对人工智能交易予以规制,并且这种规制利大于弊。规制应当明确区分人工智能交易的正当使用和滥用,并进一步完善操纵证券、期货市场罪的规定。     

The Check's in the Mail: Why the United States Lags in the Adoption of Cost-Saving Electronic Payments

The United States payment system costs around $225 billion annually and checks account for 75% of all noncash payments. Other electronic payment methods (debit cards, automated clearing house direct deposits, and debits) cost only around one third to one half as much as a check. This paper outlines the main reasons why the shift from checks to cheaper electronic payments has been slow, much slower here than in other countries. We also forecast the future use of checks and electronic payments and end by discussing policy initiatives that may speed up this substitution process.     

加拿大数字货币的发展实践与启示

分布式账本技术的应用使数字货币进入了新的发展阶段。中央银行数字货币是中央银行的电子化负债,其对计价、交易、支付以至货币政策传导等都存在深刻影响。以加拿大为例,重点分析加拿大银行数字货币的发展实践,着重讨论加拿大贾斯珀项目如何测试分布式账本技术在银行间大额支付系统的适用性,同时分析如何将数字货币支付结算拓展至证券和外汇领域,并与外部合作进行跨境、跨币种支付试验。加拿大央行数字货币实践取得的积极进展表明,数字货币发行能力是维系央行功能的基础保障,但仍需权衡中心化管理体系与去中心化技术系统的匹配问题,分布式账本技术及其在央行数字货币的应用仍需深入研究与评估。     

美国金融科技最新立法监管动态及对我国的启示

金融科技有助于降低交易成本、提高市场效率,但同时也因其“空白型金融创新”的特质给传统金融监管方式带来严峻挑战。美国在金融科技的立法方面,强调“建章立制,立法先行”,创制法律规范具有前瞻性;在监管方面,秉持“负责任的创新”的监管理念,创新监管范式,力求实现金融创新与合法合规之间的动态平衡。我国可合理借鉴美国的立法和监管经验,加强金融科技的顶层设计和立法建设,创制新的专门性立法,构建有利于金融科技发展的监管协调机制,研究以行为监管为导向的监管范式,构建包容性监管制度,探索完善中国式“监管沙箱”机制,最终建构“技术驱动型”的金融监管体系。     

交易透明度对证券场外交易信息效率的影响

黑池交易系统(Dark Pools)是美国近年发展较快的非公开交易平台,其信息透明度较低,可提供匿名交易的服务,并由此实现信息的非公开传递。通过扩展后验信念收敛速率的模型来描述不同交易透明度下场外市场的信息显示速率,发现私人信息与公共信息共存有利于信息更快地显示,因而私人信息渠道与公共信息渠道共存有利于提升信息效率,由此解释了非公开交易平台的价值,也为我国场外市场的分层发展提供可行建议.     

证券交易者行为的进化博弈分析

运用进化博弈理论,构造证券交易者进化博弈模型,模拟市场中理性交易者与噪声交易者相互转换的过程,说明二者之间存在动态均衡,这种均衡受到交易者对证券收益的预期与噪声交易风险的影响。     

全球股票市场交易费用变动趋势研究

各国证券交易所为提高市场质量和增强国际竞争力,进行了股票市场交易费用结构与费率的频繁调整。本文首先总结了美国、英国、日本、香港等国家和地区交易所现行交易费用的结构特征,并以纽约证券交易所和伦敦证券交易所为案例估算了交易费率的调整比率。其次,本文运用2002年至2010年期间主要海外证券交易所的财务数据和市场数据,分析交易费用变动与交易所经营业绩的互动关系。最后,我们对欧美市场交易费用的大幅下调进行了成因分析,并探讨我国证券交易所内外部环境及经营特点与海外市场存在的差异,提出相关启示和思考。     

The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms

The impact of the industrial and digital (information) revolutions has, undoubtedly, been substantial on practically all aspects of our society, life, firms and employment. Will the forthcoming AI revolution produce similar, far-reaching effects? By examining analogous inventions of the industrial, digital and AI revolutions, this article claims that the latter is on target and that it would bring extensive changes that will also affect all aspects of our society and life. In addition, its impact on firms and employment will be considerable, resulting in richly interconnected organizations with decision making based on the analysis and exploitation of “big” data and intensified, global competition among firms. People will be capable of buying goods and obtaining services from anywhere in the world using the Internet, and exploiting the unlimited, additional benefits that will open through the widespread usage of AI inventions. The paper concludes that significant competitive advantages will continue to accrue to those utilizing the Internet widely and willing to take entrepreneurial risks in order to turn innovative products/services into worldwide commercial success stories. The greatest challenge facing societies and firms would be utilizing the benefits of availing AI technologies, providing vast opportunities for both new products/services and immense productivity improvements while avoiding the dangers and disadvantages in terms of increased unemployment and greater wealth inequalities.     

证券交易的基本类型、创新策略及实现模式

询价、报价和竞价是传统的证券交易业务类型。为了充分利用日趋发达的交易技术，交易所交易业务不应仅局限于证券所有权的交换领域。新的交易业务概念是指所有通过交易系统进行的、分配有专门证券代码，并且接收委托申报的业务。新概念实质上将很多原场外的服务纳入了交易系统。为了高效地实现日益增多的交易业务，本文提出了交易业务“流水线”的概念，以及结构化和柔性化的设计理念，以上证所为例，交易系统的每条“流水线”都能跑多项业务；通过结构化的调整，能快速响应业务的新需求。     

The use of digital footprints in the US mortgage market

We study the rise of digital footprint (DF) users in the U.S. residential mortgage market. The proportion of lenders that use a borrower's DF has witnessed remarkable growth from 6% in 2013 to 34% in 2018 in a short span – according to our analysis. We show that the use of DFs can significantly reduce a lender's overall risk and that the use of DFs can result in considerable societal benefits, by reducing the overall discriminatory forces. In sum, we provide evidence that the informational advantage associated with using a borrower's DF far outweighs that of existing traditional lending methods.     

The Effect of Investor Sentiment on Feedback Trading and Trading Frequency: Evidence from Taiwan Intraday Data

Abstract

Although extensive literature has suggested that investor sentiment may be one of the most important factors in explaining investor trading frequency and trading strategies, how individual investors are significantly influenced by sentiment remains underexplored. The feature of numerous individual investors in the Taiwan stock market provides an avenue to examine the relationship of investor sentiment to trading frequency and positive-feedback trading according to intraday data. Using a vector autoregression model to measure feedback trading in one-minute intervals, we find that trading frequency appears to increase in periods of rising market, suggesting that investor sentiment–driven trading increases market trading frequency without relying on past experiences to conduct trading behavior.     

The Inter-Temporal Behavior of Informed and Uninformed Traders

The purpose of this study is to investigate the inter-temporal trading behavior of informed and uninformed investors. We estimate a variation of the market microstructure model developed in Easley, Keifer, O'Hara, and Paperman (1996) and document the day-of-the-week pattern in informed and uninformed trading, as well as the probability of an information event and the probability of bad news. Using bootstrapped distributions, we show that the probability of trading against informed investors follows a U-shape pattern from Monday to Friday. Cross-sectional regression results suggest that inter-temporal patterns between informed and uninformed traders can generate observed patterns in liquidity provision costs.