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Corporate Governance in the Age of AI: New Challenges and Opportunities in the Waves of Changes
The remarkable advancements in information and communication technology have brought about vast changes in modern society. Individuals can now share information faster and more efficiently than in the past, which has dramatically impacted communication between individuals and between companies and consumers. Such changes have not been limited to mere changes in the speed or manner in which information is delivered. The advances in big data and artificial intelligence (AI) have fundamentally transformed how we process information and make decisions.
In the age of big data, we are exposed to a tremendous amount of information. However, all this data is not always helpful on its own. This is where AI technology comes into play. AI analyzes a vast amount of data and identifies patterns to extract the most important information. In the process, AI emphasizes specific data points or opinions to make them more visible and enable people to form a consensus.
Reinforcement learning techniques, widely used in the finance sector, also display a phenomenon known as “tacit collusion,” through which algorithms with different starting points eventually converge to take similar forms. The U.S. Securities and Exchange Commission (SEC) has raised the concern that such unintended but potentially pervasive coordination effects of automated decision-making could lead to market inefficiency and lower liquidity (Dou, Goldstein, and Li, 2024).
Such changes also affect corporate governance. While it was common for the corporate governance structure to be swayed by a few majority shareholders or institutional investors in the past, the situation has now changed. The influence of retail investors has increased as information has become more accessible, as illustrated in the GameStop episode in 2021. Retail investors in online communities, such as Twitter and Reddit, shared information, opinions, and strategies at an unprecedented speed and made investment decisions in groups, which caused a massive ripple in the stock market.
Retail investors’ participation in the stock market has steadily increased and exploded since the COVID-19 pandemic. The trading volume of retail investors in the Korea Exchange (see Figure 1.a) clearly illustrates this phenomenon. These investors mostly use mobile smartphone trading applications and trade stocks anywhere at any time (see Figure 1.b). The trading behaviors of such retail investors create an environment where shareholders can have significant influence over the management. If the individual voices of investors congregate in online communities through data and AI algorithms, this would significantly change traditional corporate governance practices.
[Figure 1]
(a)
The figure above shows the time series trend of the trading volume of retail investors in the Korea Exchange over the last 18 years.
(b)
The figure above shows the volume proportion (%) traded by retail investors in the Korea Exchange by trading method. It displays the time series trend over the last 18 years.
Against this backdrop, it is pivotal for companies to prepare for new forms of shareholder activism. As it is becoming increasingly likely for retail investors to act in blocks, their solidarity could directly impact corporate decision-making. This presents a new challenge to companies. Now, they cannot tackle problems by negotiating with a handful of important shareholders but should also consider the opinions of many retail investors.
In this regard, the tension among investors is also worthy of note, as tensions could rise between the Dumb Block (investor groups that move randomly) and the Smart Block (investor groups that move strategically). Because retail investors are more likely to display short-term bias, this tension would become a new variable in corporate governance. For companies, managing such variables to accomplish mid- to long-term growth and enhance company value has become a critical issue.
How well retail investors learn compared to institutional investors and how rational they are in investments would be pivotal in determining whether the voices of retail investors could provide information that would impact a company’s financial decisions when they act as a group. The prospect, however, is not bright.
According to a joint research project I am conducting with Professor Dong-woo Roh at the Hong Kong University of Science and Technology and Professor Shu-Feng Wang at Ajou University, retail investors have continued to show biases in their investment decisions for decades. For example, regarding the phenomenon called the “disposition effect,” more retail investors tend to sell stocks in a rush during a long period of underperformance after buying at a high price and experiencing loss, despite the fact that they could have greater returns in the future by retaining their assets (see Figure 2). As a result, the impact of collective decision-making by small retail investors, who will play a vital role in the Korean capital market, on price efficiency and the informativeness of the capital market raises concerns.
[Figure 2]
The figure above shows the probability of retail investors gaining profit (Gain) and losing profit (Loss) using the Cox Hazard regression analysis. This estimate is based on a random sample of 200,000 retail investors. The widening gap between the two probabilities indicates that the “disposition effect” is becoming stronger, demonstrating that the behavioral biases of retail investors have worsened over the past 18 years.
Corporate governance in the age of AI should not rely on the traditional model but become more complex and multilayered. Companies should consider more stakeholders and data analysis using AI technology and innovation in decision-making are pivotal in this process. Companies should opt for a more transparent and open governance structure that can reflect the opinions of various shareholders.
I have recently researched governance in Web3 environments that aim for decentralized governance (Han, Lee, and Li, 2024). In this study, we conducted a theoretical and empirical analysis of conflicts of interest between block holders in extreme organizational structures, such as Web3. Applications that run on computer codes called Smart Contracts without any management staff are called decentralized Web3 application services. In these services, users use their token shares to vote without any control from the management, and automated services are provided by reflecting the agreed code changes. Under such an organizational structure, the consensus formation process between token owners becomes central to governance. If a few block holders can sway half of the voting rights, the governance of this organization might reflect the interest of a few rather than the whole. Without checks and a response system to prevent such phenomena, there is a danger that the organization might go in an unintended direction. Such research findings on the Web3 environment have important implications for how corporate governance should be re-established in a technological environment where decisions based on the consensus between shareholders are becoming as frequent and important as internal decisions made through the board of directors.
To conclude, advancements in AI and information and communication technology present new opportunities for corporate governance but also call for new approaches that depart from the existing frameworks. Big data and AI provide individuals with more access to information, but at the same time, it has become more likely to be swayed by biased opinions. The collective shares of retail investors might collide with those of institutional investors that have traditionally improved corporate governance, resulting in new conflicts of interest. Amidst such changes in technology and information environment, companies face the challenge of adjusting to the new environment and devising innovative ideas and responses for the future. We now stand at the starting point.
(References)
Dou, Winston Wei, Itay Goldstein, and Yan Ji. “Ai-powered trading, algorithmic collusion, and price efficiency.” Jacobs Levy Equity Management Center for Quantitative Financial Research Paper (2024).
Han, Jungsuk, Jongsub Lee, and Tao Li. “Dao governance.” Seoul National University and University of Florida, Working Paper (2024).
In the age of big data, we are exposed to a tremendous amount of information. However, all this data is not always helpful on its own. This is where AI technology comes into play. AI analyzes a vast amount of data and identifies patterns to extract the most important information. In the process, AI emphasizes specific data points or opinions to make them more visible and enable people to form a consensus.
Reinforcement learning techniques, widely used in the finance sector, also display a phenomenon known as “tacit collusion,” through which algorithms with different starting points eventually converge to take similar forms. The U.S. Securities and Exchange Commission (SEC) has raised the concern that such unintended but potentially pervasive coordination effects of automated decision-making could lead to market inefficiency and lower liquidity (Dou, Goldstein, and Li, 2024).
Such changes also affect corporate governance. While it was common for the corporate governance structure to be swayed by a few majority shareholders or institutional investors in the past, the situation has now changed. The influence of retail investors has increased as information has become more accessible, as illustrated in the GameStop episode in 2021. Retail investors in online communities, such as Twitter and Reddit, shared information, opinions, and strategies at an unprecedented speed and made investment decisions in groups, which caused a massive ripple in the stock market.
Retail investors’ participation in the stock market has steadily increased and exploded since the COVID-19 pandemic. The trading volume of retail investors in the Korea Exchange (see Figure 1.a) clearly illustrates this phenomenon. These investors mostly use mobile smartphone trading applications and trade stocks anywhere at any time (see Figure 1.b). The trading behaviors of such retail investors create an environment where shareholders can have significant influence over the management. If the individual voices of investors congregate in online communities through data and AI algorithms, this would significantly change traditional corporate governance practices.
[Figure 1]
(a)
(b)
Against this backdrop, it is pivotal for companies to prepare for new forms of shareholder activism. As it is becoming increasingly likely for retail investors to act in blocks, their solidarity could directly impact corporate decision-making. This presents a new challenge to companies. Now, they cannot tackle problems by negotiating with a handful of important shareholders but should also consider the opinions of many retail investors.
In this regard, the tension among investors is also worthy of note, as tensions could rise between the Dumb Block (investor groups that move randomly) and the Smart Block (investor groups that move strategically). Because retail investors are more likely to display short-term bias, this tension would become a new variable in corporate governance. For companies, managing such variables to accomplish mid- to long-term growth and enhance company value has become a critical issue.
How well retail investors learn compared to institutional investors and how rational they are in investments would be pivotal in determining whether the voices of retail investors could provide information that would impact a company’s financial decisions when they act as a group. The prospect, however, is not bright.
According to a joint research project I am conducting with Professor Dong-woo Roh at the Hong Kong University of Science and Technology and Professor Shu-Feng Wang at Ajou University, retail investors have continued to show biases in their investment decisions for decades. For example, regarding the phenomenon called the “disposition effect,” more retail investors tend to sell stocks in a rush during a long period of underperformance after buying at a high price and experiencing loss, despite the fact that they could have greater returns in the future by retaining their assets (see Figure 2). As a result, the impact of collective decision-making by small retail investors, who will play a vital role in the Korean capital market, on price efficiency and the informativeness of the capital market raises concerns.
[Figure 2]
Corporate governance in the age of AI should not rely on the traditional model but become more complex and multilayered. Companies should consider more stakeholders and data analysis using AI technology and innovation in decision-making are pivotal in this process. Companies should opt for a more transparent and open governance structure that can reflect the opinions of various shareholders.
I have recently researched governance in Web3 environments that aim for decentralized governance (Han, Lee, and Li, 2024). In this study, we conducted a theoretical and empirical analysis of conflicts of interest between block holders in extreme organizational structures, such as Web3. Applications that run on computer codes called Smart Contracts without any management staff are called decentralized Web3 application services. In these services, users use their token shares to vote without any control from the management, and automated services are provided by reflecting the agreed code changes. Under such an organizational structure, the consensus formation process between token owners becomes central to governance. If a few block holders can sway half of the voting rights, the governance of this organization might reflect the interest of a few rather than the whole. Without checks and a response system to prevent such phenomena, there is a danger that the organization might go in an unintended direction. Such research findings on the Web3 environment have important implications for how corporate governance should be re-established in a technological environment where decisions based on the consensus between shareholders are becoming as frequent and important as internal decisions made through the board of directors.
To conclude, advancements in AI and information and communication technology present new opportunities for corporate governance but also call for new approaches that depart from the existing frameworks. Big data and AI provide individuals with more access to information, but at the same time, it has become more likely to be swayed by biased opinions. The collective shares of retail investors might collide with those of institutional investors that have traditionally improved corporate governance, resulting in new conflicts of interest. Amidst such changes in technology and information environment, companies face the challenge of adjusting to the new environment and devising innovative ideas and responses for the future. We now stand at the starting point.
(References)
Dou, Winston Wei, Itay Goldstein, and Yan Ji. “Ai-powered trading, algorithmic collusion, and price efficiency.” Jacobs Levy Equity Management Center for Quantitative Financial Research Paper (2024).
Han, Jungsuk, Jongsub Lee, and Tao Li. “Dao governance.” Seoul National University and University of Florida, Working Paper (2024).
| Professor Jongsub Lee received his Ph.D. in finance from NYU Stern and is currently the Finance professor at the Seoul National University Business School. He has served as the Vice President of the Korean Securities Association and currently serves as the editor-in-chief of the association’s flagship journal, Asia-Pacific Journal of Financial Studies. He also advises on finance policy for the Ministry of Economy and Finance and financial regulatory reform for the Financial Services Commission. His main research interests concern corporate finance, digital financial innovation, and the analysis and management of credit risks. |
