Featured faculty: Cen Ling and Jo Chanik
Written by Putro Harnowo

“Data is the new oil,” according to British mathematician Clive Humby in 2006. Many have rushed to get their hands on data since then, and none are more eager to turn numbers into profits than financial institutions. Just like oil, crude data needs to be extracted and refined, which brings the dawn of new alchemists called data scientists.

A data scientist uses a blend of skills in statistics, computer science and domain knowledge to draw meaningful insights from abundant data available. The fast-growing demand for this profession outpaces the growth of talent supply, and financial institutions found themselves competing not only among themselves but also against tech companies to attract the best data savants.

“Financial institutions have significantly increased their recruitment of data scientists in the past two decades,” says Jo Chanik, Assistant Professor in the Department of Finance at the Chinese University of Hong Kong (CUHK) Business School. “Institutional investors strategically adjust portfolio allocation and recruitment decisions to maximise the benefits generated by their data scientists.”

Along with Associate Professor Cen Ling from the same department, Professor Jo observes a clear pattern of the surge of data scientists on Wall Street. The number rose from 11,799 in 2008 and slightly dipped after the subprime crisis, but since then, it has quadrupled to reach 57,050 data scientists in 2021, with the top three employers by numbers being big names like Morgan Stanley, Credit Suisse and Goldman Sachs. Some firms recruit more than others, leading to variations in the concentration of data scientists.

However, the study finds that if a small group of investors has significantly more data and analysis on certain stocks, powered by employing more data scientists, they can uncover additional information unknown to others. Having exclusive information will make these investors trade less aggressively and frequently, which can slow down the rate at which useful information is reflected in the stock price.

“Competition among data scientists speeds up the production and trade of private information, but the concentration of data scientists covering a stock reduces its price informativeness in the capital markets,” Professor Jo adds.

“Competition among data scientists speeds up the production and trade of private information, but the concentration of data scientists covering a stock reduces its price informativeness in the capital markets.”

Professor Jo Chanik

Are data scientists the modern Midas?

As explained in a paper titled, Data scientists on Wall Street, Professor Jo and Professor Cen, as well as Han Bing of the University of Toronto and Han Yanru of Stevens Institute of Technology, collected detailed resumes from the Revelio Lab, which gathers career history data from various sources, including LinkedIn and Indeed.

They then merged the dataset with data from Refinitiv and Thomson Reuters Global Ownership to link with the actual stock holdings. Data scientist roles were further identified with a US-based occupations classification system called the ONET code, resulting in 326,627 unique data scientists employed by 3,126 institutional investors from 2008 to 2021.

Data scientists were mainly classified into three groups: data collectors for those gathering and organising data, data analytics for those evaluating data for making business decisions, and data maintenance refers to storing and protecting data with proper hardware. Data analytics has the most substantial impact on trading profitability, implying that data analysis provides the most direct and valuable insights for investment decisions.

Institutional investors with more data scientists also achieve higher profits. Each additional data scientist hired leads to a 0.004 percentage point increase per quarter in “abnormal” profits, which refer to returns earned that exceed predictions by standard asset pricing models. One extra data scientist translates to a 13 per cent increase in the average trading profitability.

“Data scientists assist financial institutions in earning abnormal profits by collecting, maintaining, and analysing large datasets, particularly alternative data, to identify mispriced assets,” says Professor Jo.

“Their insights allow institutions to make profitable trading decisions, with data analysts having the strongest impact on generating investment returns.”

Unequal access creates unequal outcomes

As multiple institutional investors can hold the same stock, the researchers then focus on the concentration of data scientists. To illustrate, stock A is held by 10 investors, with one having ten data scientists and the rest having none. The same number of investors holding stock B, each owning one data scientist. While the total number of data scientists is the same, stock A has a higher data scientist concentration than stock B.

In stock A, the investor with more data scientists has an analytical edge as it has more resources focusing on one stock. In stock B, each investor has similar resources, making the race to extract and leverage information more intense, eventually diminishing the informational advantage.

When a handful of investors with higher data scientist concentration strategically tilt their asset allocation to a smaller set of stocks, they enjoy an “information monopoly,” where the generated insights are concentrated within a few. Such a privilege is not for sharing.

“It’s not the number but the concentration of data scientists that reduces price informativeness. When data scientists are concentrated among a few institutional investors, these investors gain an information monopoly and have less incentive to trade on their information quickly,” says Professor Jo. “These investors trade cautiously to protect their advantage, delaying the incorporation of information into stock prices and limiting its spread across the market.”

Investors who hire more data scientists are found to hold more concentrated portfolios. A one standard deviation increase in data scientist concentration leads to an 11 per cent decrease in price informativeness. “This concentration creates a situation where valuable insights remain with a small group of investors rather than being reflected in stock prices, leading to less efficient price formation.”

Race to build the best data army

While institutional investors compete to be one step ahead of others, hiring more and more data scientists has become the new battleground. Investors finding themselves lagging behind their peers will react by increasing their recruitment efforts, especially for data analytics roles.

Financial institutions that frequently trade stocks, cover a broader range of industries, and have larger assets are more likely to expand their data teams, as evidenced by hedge funds employing more data scientists than pension funds and banks. These investors are strategically monitoring and responding to their competitors’ hiring activities, rather than blindly following general trends. Data scientists who worked for competitors are also sought after, leading to a tense catch-up in the talent race.

The number of data personnel recruited positively correlates with the number of data-science related undergraduate programmes at local universities, suggesting that the supply in the labour market affects financial institutions’ employment.

“Data science roles in financial markets will likely continue growing rapidly, with increasing specialisation and strategic importance,” Professor Jo adds. “As the competition intensifies among financial institutions and against tech companies, we may see wider wage premiums for data talent, more sophisticated analytical techniques, and potentially regulatory responses addressing the efficiency costs of concentrated information advantages.”

Although the study used data on American institutional investors, he believes the findings would likely be relevant to emerging markets like China. “The competition for data talent and the value of information advantages are universal market dynamics. However, regulatory differences, varying levels of market efficiency, and potential differences in data availability might affect the magnitude of these effects in emerging markets.”

The post The irresistible rise of financial data scientists first appeared on China Business Knowledge.

Featured faculty: Cen Ling and Wu Jing
Written by Pan Jingyi

In recent years, artificial intelligence (AI) has become a key tool for companies, helping with everything from demand forecasting and procurement to streamlining and optimising processes. For global supply chains that have been under pressure lately due to geopolitical uncertainties, trade conflicts, sanctions, and environmental concerns, AI could be a game changer.

According to a recent EY report, around 40 per cent of supply chain organisations are investing in generative AI for managing knowledge and information. Technologies tend to spread along economic networks, either horizontally among competitors or vertically along the supply chain. However, the diffusion of emerging technologies such as AI remains underexplored.

“We uncover a clear pattern of AI diffusion along supply chains, where AI adoption among downstream sectors leads to subsequent adoption among their upstream suppliers,” says Cen Ling, Associate Professor at the Department of Finance of the Chinese University of Hong Kong (CUHK) Business School.

Downstream industries are those closer to the final stage of production and delivery to end customers, including service and manufacturing sectors, while upstream industries refer to sectors providing raw materials or basic inputs, like mining and agriculture, for downstream industries.

Professor Cen highlights Foxconn, a key supplier for Apple and Nvidia, as a notable example. The rapid AI applications of its major customers have turned Foxconn from a labour-intensive company to one that produces AI-driven electric vehicles and AI servers to house Nvidia chips.

Along with Wu Jing, Associate Professor at the Department of Decisions, Operations and Technology of the School, as well as Han Yanru of Stevens Institute of Technology (A Graduated PhD student of CUHK Business School) and Qiu Jiaping of Shanghai University of Finance and Economics, Professor Cen conducted a study titled Artificial intelligence along the supply chain to delve deeper into critical questions like whether supply chain can serve as a diffusion channel for AI technologies and what economic mechanisms propel this diffusion.

Lead-lag patterns

To measure how American public companies are adopting AI, the team tracked the hiring of AI-skilled employees using data from Revelio Labs, an analytics firm that gathers workforce information from employment platforms like LinkedIn and Indeed, from 2009 to 2019.

The results show a significant increase in AI employees over the past decade, with AI use growing in almost all industries. However, considerable variation exists among various sectors, with downstream industries adopting AI technologies much faster than upstream industries. This is because, Professor Cen explains, downstream companies in the supply-chain data are typically large and reputable industry leaders with direct access to big data of customer profiles.

“We uncover a clear pattern of AI diffusion along supply chains, where AI adoption among downstream sectors leads to subsequent adoption among their upstream suppliers.”

Professor Cen Ling

Further analysis unveils that the increase in AI adoption by main customers precedes and potentially causes an increase in AI adoption among their suppliers in the following year. The researchers call this a “lead-lag” within firm-pair relationships and found that this pattern is not driven by market-wide or industry-specific trends.

In a controlled experiment where the team replaced the suppliers with those who have no prior connections to customers, the results showed that the AI adoption at customer firms does not impact that of suppliers. This suggests the diffusion is indeed driven by firm-to-firm interactions.

Learning and catering mechanism

Suppliers may enhance their AI adoption in response to their primary customers through two key channels: learning and catering. Under the learning scenario, primary customers, often large firms or industry leaders, typically adopt AI technologies before their suppliers, which then absorb and apply these technologies in their operations through routine supply chain interactions.

“Under this mechanism, close strategic relationships with customers equipped with AI technologies reduce suppliers’ costs of learning and adopting AI, which may improve suppliers’ own operational activities and performance,” Professor Cen says.

In the catering scenario, suppliers respond to the needs of major customers who have embraced AI technologies to sustain crucial partnerships. “The effectiveness of the catering channel depends on the relative bargaining power of customers against their suppliers,” he adds. “The learning channel is influenced by the relative size of supply-chain partners, which affects the applicability of the knowledge transferred.”

Based on the collected data, Professor Cen and his collaborators found that the learning mechanism is the primary driver. Suppliers mostly adopt AI to enhance their own capabilities rather than just to cater to customers’ demands.

What makes AI spread faster?

To examine the factors that can affect the suppliers’ learning mechanism, the team examined the employee mobility and geographic distance between customers and suppliers and found that when suppliers hire managers who previously worked for their main customers, the AI adoption is notably higher. Managers normally have a broader view of their company compared to rank-and-file employees, who may lack knowledge of AI advancements unless they work in AI-related roles.

“Our results validate that labour mobility from customers to suppliers, particularly employees with AI-related visions or skills, promotes the AI learning along the supply chain,” Professor Cen adds.

The diffusion of AI technology is also stronger when the geographical distances are shorter. Moreover, when customers relocate farther from suppliers, the suppliers’ AI adoption becomes less influenced by their customers.

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“A shorter distance between supplier and customer facilitates more frequent interactions and leads to faster transfer of knowledge and information,” Professor Cen says, highlighting the crucial role of geographic proximity in stimulating learning.

Positive economic outcomes

Finally, the team examined whether AI diffusion from customers to suppliers actually improves business outcomes. The result confirms that suppliers that have learned AI from their customers are more likely to enhance product quality and manage costs more effectively. More specifically, every standard increase in AI hiring is linked to a 0.74 per cent higher chance of boosting product quality.

As AI continues to spread across industries and borders, understanding how it diffuses along supply chains offers valuable lessons for both business leaders and policymakers.

Professor Cen suggests corporate managers can mitigate risks and maintain competitiveness by tapping into AI knowledge within their supply chain partners. For instance, they can identify the optimal point to acquire such knowledge from trade partners or hire AI experts from these partners.

Compared to traditional technologies, AI requires a significant initial setup cost. However, once established, the ongoing operational costs of using it are comparatively low. Professor Cen argues that government subsidies for downstream customer firms could help kickstart the adoption, “then the positive externalities will diffuse along economic networks to achieve a socially optimal level of AI adoption.”

The post Beyond cargo, supply chain also transfers AI first appeared on China Business Knowledge.

By Pan Jingyi, Principal Writer, China Business Knowledge@CUHK

Which would you choose: a bag produced by a firm known for its equal treatment of all staff or a bag produced by a company currently embroiled in a discrimination scandal? Conscious consumers, who prioritise ethical, social, and environmentally friendly products when making purchasing decisions, would likely choose the former.

A report indicates that 79 percent of respondents believe sustainability is either “somewhat important” or “very important” when making purchasing decisions. Additionally, 68 percent express a willingness to pay more for environmentally sustainable and socially responsible products.

“Our research delves deeper than merely looking at diversity and equality at the executive level. It gauges workplace equality practices for the broader workforce.”

Prof. Cen Ling

Given the rise of conscious consumers in recent years, companies should look beyond product quality and consider ESG (Environmental, Social, and Governance) or CSR (Corporate Social Responsibility) factors. While previous research has shown that strong ESG or CSR practices can decrease a firm’s risk exposure, the direct cash flow implications of such practices are still unclear.

Researchers from The Chinese University of Hong Kong (CUHK) recently aimed to understand how the purchasing decisions of conscious consumers who value ESG and CSR can directly impact a company’s cash flow.

The study, The Rise of Conscious Consumers: The Impact of Corporate Workplace Equality on Household Spending, was conducted by Cen Ling, Associate Professor of Finance, and Wu Jing, Associate Professor of Decisions, Operations, and Technology, both at CUHK Business School. They were joined by Prof. Han Yanru from Stevens Institute of Technology, and Liu Chang, a PhD candidate from City University of Hong Kong.

“The market harshly punishes discrepancies between a firm’s declared commitment to and actual practices of workplace equality.”

Prof. Wu Jing

“There’s a significant uptrend in ESG investment. Do investors like firms with good ESG practices purely based on their preferences? Or, a company’s ESG strategy truly benefit investors in generating higher returns? If so, does ESG contribute to higher returns by affecting the risk component or the cash-flow component, or both?” asks Prof. Cen. He expressed interest in exploring potential long-term economic incentives for companies to adopt ESG strategies.

Existing studies offer varied insights on the influence of ESG strategy on corporate operating performance. This research hones in on corporate workplace equality to gauge its effect on consumers’ buying habits. “This approach demystifies how specific ESG strategies can impact a company’s bottom line. Cash flow is a direct barometer of a company’s financial health,” notes Prof. Wu.

Measures to Evaluate Workplace Equality

To address these inquiries, the researchers developed a measure of corporate workplace equality, known as the EEO Score. This metric is based on the textual analysis of Equal Employment Opportunity statements found in millions of U.S. online job listings.

“Our EEO Score delves deeper than merely looking at diversity and equality at the executive level. It gauges workplace equality practices for the broader workforce,” Prof. Cen states, emphasising the public’s greater concern for the rights of everyday employees over board members.

Prof. Wu adds, “Our EEO Score zeroes in on workplace equality for the average worker, filling a gap left by prior studies that centered on upper-tier corporate roles.”

The researchers highlight that their algorithm assigns higher EEO Scores to companies that make detailed, genuine, and enthusiastic EEO pledges in their job listings. While skeptics might dismiss online job listings as mere corporate rhetoric, the researchers have undertaken multiple validation steps to alleviate such concerns.

They underscored the EEO Score’s accuracy by demonstrating its foresight in predicting future discrimination-related litigation and headlines. They also pointed out the inherent costs that discourage companies with low equality standings from merely parroting those with higher commitments.

“The market harshly punishes discrepancies between a firm’s declared commitment to and actual practices of workplace equality,” Prof. Wu observes, emphasising the gravity of making an EEO commitment.

Consumers Favour Companies that Champion Equality

The next phase involved correlating household buying decisions with the EEO Scores of product manufacturers. They tapped into Nielsen Consumer Panel data that capture consumer behaviour of individual household.

“Instead of crafting a simulated consumer behaviour test in laboratories, we turned to genuine data, revealing the authentic spending habits of Americans,” Prof. Cen explains.

Findings were consistent with their hypothesis: U.S. households tend to spend more on products from companies that score high on workplace equality. However, Professor Cen highlights the presence of potential confounding factors in the positive correlation between household purchase decisions and EEO Scores, such as product quality. In other words, consumers might choose products from companies with high EEO scores solely because firms with better workplace equality practices also offer better product quality.

He adds, “To mitigate the endogeneity in this positive correlation between corporate workplace equality and household consumption, we looked at whether minority consumers displayed a heightened sensitivity in their spending patterns toward producers that championed workplace equality.”

As hypothesised, the pro-equality spending trend was more pronounced among racial or gender minority consumers.

Prof. Wu posits, “If product attributes unrelated to workplace equality were driving the correlation between spending and EEO Scores, it’d be challenging to explain the heightened sensitivity exhibited by minority consumers.”

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To further solidify their findings, the researchers employed two methods based on exogenous social events. First, they observed a spending boost on products from California-based firms post the 2018 enactment of the California Gender Board Diversity Law. Next, they noticed that the linkage between household expenditure and a producer’s EEO Score generally strengthened post-societal events that heightened equality awareness, like the #MeToo movement in 2017 and the Black Lives Matter movement in 2020.

In summary, the research underscores the tangible cash flow benefits of corporate workplace equality. Prof. Cen admits that the current buying surge, led by racial and gender minority consumers, doesn’t ensure a direct short-term boost in spending as a result of improved workplace equality. However, he believes that as consumer consciousness around equality becomes more widespread, firms will invariably see the financial advantages of ESG strategy adoption.

The post Why Is Workplace Equality Good for Business? first appeared on China Business Knowledge.

By Jaymee Ng, Principal Writer, China Business Knowledge@CUHK

Identifying Expert Analysts

The researchers tested their hypothesis on the specific but important topic of supply chain. Using data from earnings conference calls of all U.S.-listed companies, they identified supply-chain expert analysts and examine whether their forecasts were more accurate than peers when the firms they covered experienced significant supply-chain-related structural shocks. To identify supply-chain expert analysts, they looked at whether the questions an analyst raises in all earnings conference calls are associated with special supply-chain terminology such as “customer”, “supplier”, “vertical integration”, “trade credit”, and “OEM”. Very intuitively, analysts who raised the highest number of supply chain-related questions in past conference calls were identified as supply-chain expert analysts.

They found that supply chain expert analysts showed a 6 percent reduction in forecast errors on average, compared to analysts who were not experts, when the firms they cover established relationships with large publicly listed customer firms. This effect is much stronger when the supplier firm suffers a higher level of information asymmetry (for example, suppliers that are smaller and less well-known) or customer switching risk (suppliers that have more product market competitors).

“The approach we applied in identifying expert analysts is very similar to Google’s dynamic advertising technology. For example, Google uses search and Youtube watching history to identify products that its users are potentially interested in,” says Prof. Cen. “This approach is definitely not confined to the analyst setting. Anyone can develop certain skills when their interest is combined with repeated practice. Our approach just takes advantage of the human nature that one is more likely to talk about something they are interested in and good at.”

Skill Transfer and Recognition

According to the study, topic-specific skills can also be transferred to other analysts in the same brokerage firm. Taking the example of supply chain topic again, an addition of one supply-chain expert analyst is linked to an 8 percent increase in the likelihood of a junior analyst employed by the same brokerage firm becoming a supply-chain expert in three years. In addition, the study highlights that the difference in the performance of expert and non-expert analysts gradually decreases over time, which suggests that non-experts also learn from their peers.

Surprisingly, the study suggests that market participants are more capable in recognizing topic-specific skills than employers. “It’s unfortunate, but our results show that brokerage firms don’t really pay sufficient attention to the different skills that their analysts have when making stock coverage assignments,” Prof. Cen says. “It’s really in their best interests to assign firm coverage according to the skills and interests of their analysts. Not only does this mean that they allow their experts to thrive, but it just makes sense from an economic point of view because it leads to an improvement in the accuracy of their analysts.”

On the other hand, investors recognise the expertise of specialist analysts and react to their recommendations. The study finds that, after a firm establishes relationships with important customer firms, a downgrade recommendation made by a supply chain expert analyst generated an extra negative 1.3 percent return over a three-day period, when compared to recommendations made by ordinary analysts. This finding suggests that investors pay more attention to opinions of supply-chain expert analysts when supply-chain risk carries a higher weight in future performance.

Generalization of the Approach

Most importantly, although the study mainly uses supply-chain-specific skill as an example, the authors suggest that the approach of identifying topic-specific skills is generalizable to other topics, such as new product development, mergers and acquisitions, payout policies, and tax strategies.

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However, Prof. Cen points out that three conditions must be met for this generalization: first, the topic that analysts are interested in must be relevant to company earnings. Second, to establish a causality, the opinions and expertise of analysts should not affect the type and timing of information arrivals. This can happen, for example, where the opinions of M&A expert analysts can affect the outcome (pass or withdrawal) of M&A deals. Finally, researchers must be able to identify analyst skills through textual analysis with a reasonable accuracy, for example through either keyword search or machine learning techniques.

The post Opening the Black Box of Analyst Forecasts first appeared on China Business Knowledge.