Account-level Data Research Articles

Attention of women's liberation and investor herding behavior

In recent decades, the women's liberation movement is garnering attention on a global scale. To investigate the impact of women's liberation attention on herding behavior among male and female investors in the stock market, we obtain a unique account-level data set of retail investor transactions in the Chinese stock market. Our results document that investors in Chinese cities with higher women's liberation attention demonstrate less herding behavior than other investors. Moreover, by facilitating investors' confidence and risk tolerance, women's liberation attention indirectly reduces herding behaviors. Our results remain solid in robustness tests, and additional tests confirm that women's liberation attention helps narrow the gender gap in herding behavior. Moreover, women's liberation attention is particularly significant in reducing investor herding behavior under volatile market conditions. Our paper contributes to the studies of gender equality in the stock market and provides empirical evidence by using account-level data.

Retail Trading and Return Predictability in China

Abstract Using comprehensive account-level data, we separate Chinese retail investors into 5 groups and document strong heterogeneity in trading dynamics and performances. Retail investors with smaller account sizes cannot predict future returns correctly, display daily momentum patterns, fail to process public news, and show overconfidence and gambling preferences, while retail investors with larger account balances predict future returns correctly, display contrarian patterns, and incorporate public news in trading. Using performance measures established in previous literature, we find that smaller retail investors suffer from poor stock selection abilities and trading costs, while large retail investors’ stock selection abilities are offset by trading costs.

U.S.-U.K. PETs Prize Challenge: Anomaly Detection via Privacy-Enhanced Federated Learning.

Privacy Enhancing Technologies (PETs) have the potential to enable collaborative analytics without compromising privacy. This is extremely important for collaborative analytics can allow us to really extract value from the large amounts of data that are collected in domains such as healthcare, finance, and national security, among others. In order to foster innovation and move PETs from the research labs to actual deployment, the U.S. and U.K. governments partnered together in 2021 to propose the PETs prize challenge asking for privacy-enhancing solutions for two of the biggest problems facing us today: financial crime prevention and pandemic response. This article presents the Rutgers ScarletPets privacy-preserving federated learning approach to identify anomalous financial transactions in a payment network system (PNS). This approach utilizes a two-step anomaly detection methodology to solve the problem. In the first step, features are mined based on account-level data and labels, and then a privacy-preserving encoding scheme is used to augment these features to the data held by the PNS. In the second step, the PNS learns a highly accurate classifier from the augmented data. Our proposed approach has two major advantages: 1) there is no noteworthy drop in accuracy between the federated and the centralized setting, and 2) our approach is flexible since the PNS can keep improving its model and features to build a better classifier without imposing any additional computational or privacy burden on the banks. Notably, our solution won the first prize in the US for its privacy, utility, efficiency, and flexibility.

Does price limit improve price discovery? Evidence from IPO market in a quasi-natural experiment

The two major Chinese stock exchanges imposed a new trading rule with a price-move limit of 44% on the first trading day for IPOs after 2013. Following this quasi-natural experiment, market sentiment on IPOs drastically rises, resulting in extreme price run-up in initial trading days. Newly listed stocks under the trading rule experience greater illiquidity, higher turnover and volatility than earlier ones. Account-level data from the Shanghai Stock Exchange shows that following the new rule, net buyers of IPO change from institutional to retail investors. The evidence suggests that this price limit delays price discovery by arousing sentiment and crowding out informed investors.

Asset Complexity and the Return Gap

Abstract Existing research finds that investors’ returns vary with their wealth and level of sophistication. We bring a new perspective from the supply side by showing that return heterogeneity can be magnified as assets offered by the market become more complex. Using detailed account-level data, we examine the trading of B funds—complex, structured products in the Chinese market. During a 3-year market cycle, the return gap between the naive and sophisticated is an order-of-magnitude greater when trading B funds than when trading simple, non-structured funds. In an event study, we confirm that this disparity is driven by differences in investors’ understanding of product complexity.

A risk-neutral approach to the RAROC method of loan pricing using account-level data

PurposeThis paper has used account-level data of corporate and retail borrowers, assessed their credit risk through the risk-neutral principle and examined its implication on loan pricing.Design/methodology/approachIt derives the capital charge and credit risk-premium for expected and unexpected losses through a risk-neutral approach. It estimates the risk-adjusted return on capital as the pricing principle for loans. Using GMM regression, the article has assessed the determinants of risk-based pricing.FindingsIt has been found that risk-premium is not reflected in the current loan pricing policy as per Basel II norms. However, the GMM estimation on RAROC can price risk premium and probability of default, LGD, risk weight, bank beta and capital adequacy, which are the prime determinants of loan pricing. The average RAROC for retail loans is more than that of corporate loans despite the same level of risk capital requirement for both categories of loans. The robustness tests indicate that the RAROC method of loan pricing and its determinants are consistent against the time and type of borrowers.Research limitations/implicationsThe RAROC method of pricing effectively assesses the inherent risk associated with loans. Though the empirical findings are confined to the sample bank, the model can be used for any bank implementing the Basel principle of risk and capital assessments.Practical implicationsThe article has developed and validated the model for estimating RAROC, as per Basel II guidelines, for loan pricing that any bank can use.Social implicationsIt has developed the risk-based loan pricing model for retail and corporate borrowers. It has significant practical utility for banks to manage their risk, reduce their losses and productively utilise the public deposits for societal developments.Originality/valueThe article empirically validated the risk-neutral pricing principle using a unique 1,520 retail and corporate borrowers dataset.

From Pro-environmental Behavior to ESG Fund Investing: Evidence from account-level data in China

From Pro-environmental Behavior to ESG Fund Investing: Evidence from account-level data in China

Who are the momentum chasers? New evidence from mutual funds in China

ABSTRACT Regarding the effect of past returns on the trading behaviour of individual investors, earlier studies focused on stocks, and we employ unique account-level data from 2005 to 2011 to analyse this effect in mutual funds. After considering both market, fund and personal characteristics, we find that mutual funds investors are momentum chasers. Furthermore, we find that the group of largest size individuals tends to behave like institutions, and do not chase near-term price performance. Finally, we discuss this behavioural difference between the fund and stock investors from the perspective of bounded rationality. Those results show that most individual investors in China’s fund market are not rational and prefer to chase funds with rising prices.

The convenience of electronic payments and consumer cash demand

How does the improved convenience of electronic payments affect consumer payment choice and cash demand? We study the staggered, quasi-random introduction of contactless debit cards by a retail bank. We use account-level data and compare transactions which are eligible for contactless authentication to transactions which are not. We identify a significant convenience effect on debit card use at the intensive margin. The convenience elasticity is strongest among younger clients. Treatment effects increase over time, coinciding with increasing merchant acceptance. The effect on cash demand is economically small and statistically insignificant. We also find no effect on consumer spending.

COVID-19 Vaccine Hesitancy on Social Media: Building a Public Twitter Data Set of Antivaccine Content, Vaccine Misinformation, and Conspiracies.

BackgroundFalse claims about COVID-19 vaccines can undermine public trust in ongoing vaccination campaigns, posing a threat to global public health. Misinformation originating from various sources has been spreading on the web since the beginning of the COVID-19 pandemic. Antivaccine activists have also begun to use platforms such as Twitter to promote their views. To properly understand the phenomenon of vaccine hesitancy through the lens of social media, it is of great importance to gather the relevant data.ObjectiveIn this paper, we describe a data set of Twitter posts and Twitter accounts that publicly exhibit a strong antivaccine stance. The data set is made available to the research community via our AvaxTweets data set GitHub repository. We characterize the collected accounts in terms of prominent hashtags, shared news sources, and most likely political leaning.MethodsWe started the ongoing data collection on October 18, 2020, leveraging the Twitter streaming application programming interface (API) to follow a set of specific antivaccine-related keywords. Then, we collected the historical tweets of the set of accounts that engaged in spreading antivaccination narratives between October 2020 and December 2020, leveraging the Academic Track Twitter API. The political leaning of the accounts was estimated by measuring the political bias of the media outlets they shared.ResultsWe gathered two curated Twitter data collections and made them publicly available: (1) a streaming keyword–centered data collection with more than 1.8 million tweets, and (2) a historical account–level data collection with more than 135 million tweets. The accounts engaged in the antivaccination narratives lean to the right (conservative) direction of the political spectrum. The vaccine hesitancy is fueled by misinformation originating from websites with already questionable credibility.ConclusionsThe vaccine-related misinformation on social media may exacerbate the levels of vaccine hesitancy, hampering progress toward vaccine-induced herd immunity, and could potentially increase the number of infections related to new COVID-19 variants. For these reasons, understanding vaccine hesitancy through the lens of social media is of paramount importance. Because data access is the first obstacle to attain this goal, we published a data set that can be used in studying antivaccine misinformation on social media and enable a better understanding of vaccine hesitancy.

Corporate actions and the manipulation of retail investors in China: An analysis of stock splits

We identify a group of “suspicious” firms that use stock splits, perhaps along with other activities, to artificially inflate their share prices. Following the initiation of suspicious splits, share prices temporarily increase, and subsequently decline below their presplit levels. Using account level data, we find that small retail investors acquire shares in firms initiating suspicious splits, while more sophisticated investors accumulate positions before suspicious split announcements and sell in the postsplit period. We also find that insiders sell large blocks of shares and obtain loans using company stock as collateral around the initiation of suspicious splits.

How Does Active Change Affect Investment Efficiency? Evidence from Monthly Account-level Data on Chinese Online Platform

ABSTRACT Using the monthly mutual fund transaction data of individual investors on a large Chinese Fintech platform, the paper studies how does active change in risk-taking affect investment efficiency. In this paper, active change is found to have a positive and significant effect on investment efficiency, which is measured by expected Sharpe ratio. The empirical evidence supports the “smart money” effect in fund market. One possible channel is that individual investors obtain higher investment efficiency by chasing fund market trends. And it’s interesting to find that the effect of active change on investment efficiency is heterogeneous. The increasing financial cognition of investors weakens the effect of active change, while the risk aversion strengthens it. Moreover, long-term investor education plays an important role in guiding investment behaviors.

Market Timing Skill and Trading Activity in Taiwan’s Retail-Dominated Futures Market

There has been substantial research on performance persistence among professional and retail investors. These studies typically employ infrequently sampled data on portfolio holdings and returns, making it difficult to distinguish skill from luck, let alone differentiate between superior security selection and market timing ability. Based on 10 years of comprehensive account-level data comprising 61 million trades made by investors in Taiwan’s retail-dominated futures market, the authors confirm that institutions consistently earn alpha at the expense of behaviorally biased individual traders. The authors introduce an approach for separating overconfident traders from skilled market timers. This novel skill measure interacts prior performance with past trading volume. Investors identified as highly skilled subsequently earn a net-of-cost average annual return of 115%. The authors’ results contribute in two ways: (1) introducing a new measure for assessing high-frequency trading skill and (2) validating the intuition that significant alpha can be harvested by active investors in retail-dominated markets. <b>TOPICS:</b>Futures and forward contracts, emerging markets, manager selection, performance measurement <b>Key Findings</b> ▪ The authors employ comprehensive data on 61 million individual trades made by investors in Taiwan’s financial futures market over a 10-year period to confirm that behaviorally biased retail investors consistently lose to institutional investors. ▪ They demonstrate weak performance persistence and a nonlinear relationship between past trading volume and future performance, characterized by overconfidence on the part of a vast majority of traders and rational learning about skill by the most active traders. ▪ The authors describe a method for inferring true market timing ability from a combination of data on prior performance and past trading volume and identify a small subset of skilled traders who subsequently earn extraordinary profits after accounting for transaction costs.

Contract Size Changes in the Options Market: Effects on Market Efficiency and Investor Behavior

We study options market participants’ trading behavior before and after an options multiplier increases. Using unique account-level data, we show that local retail and local institutional investors who trade in both options and futures markets trade more after the change in the multiplier. After the options multiplier increases, the options market becomes more efficient, and investors trade lottery stocks more actively.

Lessons from a utility-sponsored revenue neutral electricity conservation program

Using monthly account level data for over 27,000 households between 2007 and 2014, this study evaluates a revenue neutral municipal electricity conservation program. Rebates for the purchase of energy efficient appliances were financed via a small surcharge on high consuming households. The results demonstrate that the program mainly transferred money between residents with almost no effect on electricity consumption. Using variation in the timing of the rebate checks, none of the energy efficiency incentives yielded a statistically or economically meaningful reduction in electricity consumption compared with a counterfactual where no rebate was offered. Using a bunching estimator and exploiting changes in behavior around the high consumption threshold, a small reduction in electricity consumption is attributable to the surcharge, suggesting that prices are better than subsidies at reducing electricity consumption. Overall, the change in behavior attributable to the electricity conservation program is small, supporting recent evidence that many energy efficiency programs underperform in real-world settings.

Heterogeneity in Retail Investors: Evidence from Comprehensive Account-Level Trading and Holdings Data

Retail investors are heterogeneous, with vast differences in wealth, skills and demographics. Using comprehensive proprietary account-level data on trading and holdings from the Shanghai Stock Exchange from 2016 to 2019, we separate tens of millions of retail investors into five groups by their account sizes as well as other demographic variables, and we examine their trading behavior and return performance in Chinese equities. Retail investors with account sizes less than three million CNY follow momentum trading strategies, and the prices of stocks they buy experience negative returns next day, while the ones they sell experience positive returns. In contrast, retail investors with larger account balances follow contrarian strategies, and they buy and sell stocks in directions consistent with future price movements. In addition, retail investors with smaller account sizes fail to process public news and they incur losses from trading, while retail investors with larger account sizes incorporate public news in their trading and experience trading gains. These patterns are stronger for young male retail investors.

Corporate Actions and the Manipulation of Retail Investors in China: An Analysis of Stock Splits

We identify a group of “suspicious” firms that use stock splits—perhaps, along with other activities—to artificially inflate their share prices. Following the initiation of suspicious splits, share prices temporarily increase, and subsequently decline below their pre-split levels. Using account level data from the Shanghai Stock Exchange, we find that small retail investors acquire shares in firms initiating suspicious splits, while more sophisticated investors accumulate positions before suspicious split announcements and sell in the post-split period. We also find that insiders sell large blocks of shares and obtain loans using company stock as collateral around the initiation of suspicious splits.

Consumer defaults and social capital

Using account level data from a credit bureau, we study the role that social capital plays in consumer default decisions. We find that borrowers in communities with greater social capital are significantly less likely to default on loans, even after adjusting for different levels of income and other characteristics such as credit scores. The results are strongest for potentially strategic defaults on mortgages; a one standard deviation increase in social capital reduces such defaults by 12.4 %. These results can be generalized to any mortgage default. Our results also indicate that the effect of social capital is most prominent among more creditworthy borrowers, suggesting that when given a choice, the social cost of defaulting is an important factor affecting default decisions. We find a similar impact of social capital on consumer defaults in other datasets with more detailed information on borrowers as well. Our results are robust to modeling and methodology choices, as well as controlling for other drivers of default such as wealth, income and amenities from homeownership. Our results suggest that increasing social capital via measures to build community cohesion such as promotion of owner-occupied home ownership may be one avenue to deter consumer default.

Could Fund-dominant Accounts Take Advantage of Investors’ Attention? Based on Individual Investors’ Account-level Trading Data of the “Dragon-Tiger” Listed Stocks from a Security Company

Investors’ attention is limited. When investors have limited access to information resources, those events that attract investors’ limited attention in the stock market could usually affect their investment decisions. Some literature argues that, to some extent, if a stock is listed on today’s “Dragon-Tiger” Board, the listing event could create a short-term momentum in its price. Since this pattern can be noticed by anybody, the individual investor who possesses fund dominance could create this same kind of listing event artificially and manipulate other investors’ attention. To clearly test and examine this motivation, this paper firstly explains the theoretical mechanism of how fund-dominant accounts profit from pumping up the stock price and selling it in the next day by taking advantage of investors’ attention. And then, this paper chooses China’s A-share stocks which are listed on the “Dragon-Tiger” Board only because its price movement deviation hits 7% on that day as the sample, and the time span is from January 2015 to July 2019. Thanks to the availability of account-level data from a China security company, a name list of suspicious speculative fund-dominant accounts is found. Through the empirical test of the relationship between the speculation of these fund-dominant accounts and the selling decision of the next day, this paper finds that: First, there indeed exist speculation and price manipulation activities driven by fund-dominant accounts which can be concluded as a “one-day trip” trading pattern. Second, when the stock is listed on the “Dragon-Tiger” Board, the higher dominance a fund-dominant account speculates, the stronger the positive relationship between the speculation intention and the next day’s selling decision is. Third, the information disclosure mechanism of the “Dragon-Tiger” Board can capture the accounts that hype stocks and have a strong tendency of “one-day tour”. From the perspective of the causes behind the attention events in China’s stock market, this paper studies the speculation behavior of fund-dominant accounts, and provides micro evidence for the research on investors’ attention, asset pricing and trade-based price manipulation. Also, this paper contributes to the research on the public information disclosed by stock exchanges, and provides enlightenment for the supervisor and the stock exchange to further improve the governance system of the stock market.

Stock Price Manipulation: Corporate Actions and the Exploitation of Retail Investors in China

Using account level data from the Shanghai Stock Exchange, we find widespread evidence consistent with insiders manipulating share prices to exploit naive retail investors. We identify a group of “suspicious” firms that use stock splits—perhaps, along with other misleading activities—to artificially inflate their share prices. Retail investors are strongly attracted to these suspicious splits, providing insiders an opportunity to sell large blocks of shares or use their shares as collateral for loans. A group of large accounts exploits the positive feedback trading behavior of naive retail investors by accumulating positions before suspicious split announcements and selling in the post-split period.