by Naman Agarwal
Published On Feb. 14, 2025
The increasing role of AI in the stock market presents both potential benefits and significant risks. On the one hand, AI can provide financial advice at reduced costs, and minimize the need for human advisors . Additionally, AI can streamline processes such as KYC, digital marketing, customer profiling, while also excelling in data analysis and summarization.
However, the adoption of AI in the stock market also introduces notable risks. For instance, deepfakes can be used to create fake videos of leading personalities recommending stocks or impersonating big brands, leading to mass impersonations and financial frauds. AI-driven trading algorithms and high-frequency trading can also increase market volatility, causing rapid price swings and potentially creating market bubbles or crashes.To mitigate these risks, it is essential to establish regulation, transparency, and risk management frameworks.
The total number of patent filings for algorithmic trading has increased significantly over the last decade and the proportion of these patents that incorporate AI elements has grown over time especially from year 2020. There is no doubt that AI has transformed the stock market .
AI's role in trading algorithms, investment analysis, customer service are discussed below :
Algorithmic trading, which involves using computer systems in automating stock trading processes, has become prevalent, accounting for a large percentage of the overall trading volume. The key driver of this is AI, with algorithms these days having the ability to execute trades at speeds greater than those of humans in record time.
High-frequency trading (HFT) is a form of algorithmic trading where huge amounts of stocks are bought and sold at a very high speed. These algorithms react to the market data and news in real time, causing fast-moving price swings and higher volatility. For instance, algorithms can respond to the release of Federal Reserve meeting minutes faster than human traders.
AI identifies trends and executes trades according to real-time data analysis. For instance, AI systems can also analyse sentiment on social media and news articles, giving investors a sense of the mood of the market. It can also analyse technical indicators such as exponential moving average (EMA), relative strength index (RSI), bollinger bands, fibonacci retracement, stochastic oscillator, and average directional index with respect to future price movement predictions.
The use of AI in trading triggers "herd-like selling" behavior during periods of stress, enhancing market instability.
When many investors track similar AI-derived signals, then it creates the possibility of the formation of either a market bubble or crash based upon algorithmic trend rather than its fundamental value.
Image Source - https://blog.quantinsti.com/algorithmic-trading-finance-grads-fundamental-traders/
The integration of Artificial Intelligence (AI) in investment analysis has revolutionized the field, offering unparalleled efficiency and accuracy. AI tools can swiftly summarize and analyze vast amounts of data, enabling investors to make informed decisions. For instance, AI can rapidly analyze complex documents such as bond indentures or corporate earnings releases, leading to improved price discovery across various asset classes.
AI's technical analysis capabilities allow it to identify trading opportunities, while its ability to capture real-time trends in news, company financial announcements, and social media discussions enables investors to gauge market sentiment accurately. This, in turn, facilitates more precise predictions. Furthermore, AI's risk assessment capabilities enable it to evaluate and mitigate market risks more effectively than traditional investment strategies.
AI models can analyze historical market data and volatility, making real-time adjustments to portfolios to align with changing market conditions. Additionally, AI-driven algorithms can suggest diversification strategies to minimize potential risks. By taking into account individual preferences, financial goals, and risk tolerance, AI provides hyper-personalized recommendations to investors and financial institutions.
Ultimately, AI's capacity to process vast amounts of data at unprecedented speeds enables it to identify patterns and trends that may elude human analysts, thereby providing a unique edge in investment analysis.
AI is gradually making customer communications easier through chatbots and KYC processes automated. AI-powered customer services, such as Sebi's Virtual Assistant- Seva for general information pertaining to the securities market, new circulars and grievance redressal mechanism.
AI-powered bots can quickly respond to queries, providing users with the latest stock prices, market trends, and relevant news and perform specific actions automatically such as executing buy and sell orders, notifying users about significant events affecting their holdings, and tracking portfolio performance in real-time.
AI enables 24/7 availability and reduced processing times, enhancing customer service and streamlining operations.
However, AI's contextual understanding of the Indian broking industry is currently limited, which means that there are still risks associated with not addressing all potential use cases effectively.
AI can be and is destructive if used by wrong people. It has become a very convenient tool for scammers to trick people using AI . Here are a few things to be taken care by investors :
Deep Fakes are artificially generated videos or audio recordings that are so realistic they are very difficult to distinguish from genuine content. They use artificial intelligence (AI) to superimpose a person's face or voice onto another person's body or speech.Deepfakes can be used to spread misinformation and manipulate stock prices by creating false narratives or endorsements. The low cost of creating these deep fakes makes large-scale impersonation possible.
In April, a deep fake video of Ashishkumar Chauhan, the MD and CEO of the National Stock Exchange (NSE), recommending stocks went viral on social media. The video, which included the NSE logo, appeared genuine but was a deepfake created using sophisticated technologies to replicate Chauhan’s voice and face. The NSE clarified that its employees are not authorised to recommend or deal in stocks.
In January, fake videos of Nimesh Shah, MD of ICICI Prudential, promoting stocks, were found on Facebook Ads and the same was the case with Mr. Anil Singhvi from Zee Business . ( https://youtu.be/4qvt_SEIE_A?si=YxT3NL4a8kWRUkf6 )
A fake image of smoke rising from a building triggered a panic-driven stock market sell-off in 2023. In May 2023, the U.S. stock market dipped due to a fabricated image of an explosion near the Pentagon, which turned out to be fake.These examples demonstrate how easily AI-generated content can influence investors and cause market instability.
Image Source - https://edition.cnn.com/2023/05/22/tech/twitter-fake-image-pentagon-explosion/index.html
Deepfake videos of CEOs or other influential figures endorsing a particular stock can create false hype and lead to a surge in buying, inflating the price beyond its real value. For Example :
Link1 : https://youtube.com/shorts/q1voh5ag8KU?si=MQv7yQUOJZouXv_f
Link 2 : https://youtu.be/kQwUNQ0tiDY?si=DO6TSgcGyMbLhGSj
Conversely, a deep fake video showing a CEO accepting a bribe or confessing financial fraud can destroy trust and shareholder value within minutes.For instance, a deep fake video of a CEO announcing a fake product recall could cause panic and a subsequent stock sell-off.Deepfake technology can clone senior executives' voices to create fake speeches or interviews, destroying carefully built trust.
Creating duplicate apps, websites, and social media to look exactly like trusted financial institutions and will trick the investment. These fake platforms promise investors a quick return and high returns.
Victims added to WhatsApp or Telegram groups, in most cases, with a name that is a resemblance of a reputable financial institution and share fictitious stories of success.
They convince the victim to download an app to make investment in fictitious IPO and stocks by making a money transfer to bank accounts of shell companies.
For example : Scamsters add a Noida-based businessman to WhatsApp group GFSL Securities Official Stock C 80, and take away ₹9.09 crore from him. ( https://www.ndtv.com/india-news/fake-apps-investors-and-stocks-scamsters-con-faridabad-woman-of-rs-7-crore-6483057 )
The success stories and testimonials of scammers are so convincing, made by AI. Fake testimonials can be seen on social media or even in messaging groups.In just seconds, AI can produce thousands of fake reviews that could be used to manipulate the trust of a customer.
The criminals animate stolen ID photos with AI to get loans using these fraudulent applications.With AI, it becomes hard for the would-be victims to notice the scam since the content is very convincing.
AI-driven trading algorithms can significantly contribute to market volatility, especially during events like Initial Public Offerings (IPOs). Here's how:
Speed and Reaction Time: AI algorithms respond to market data and news much faster than any human trader. Such a quick response leads to price swings very quickly. For example, AI can process complex information such as the release of Federal Reserve meeting minutes faster than any human trader, and this is already happening.
Amplification of Price Movements: The early trading after the IPO may be dominated by algorithmic trading. These strategies can amplify price movements based on very limited data inputs, thereby generating extreme volatility. If most investors use similar AI-generated signals, it may lead to the formation of bubbles or crashes based on algorithmic trends rather than intrinsic values.
Increased Trading Volume: AI rebalances portfolios so quickly, resulting in a high trading volume. High-frequency AI-driven trading will likely continue to dominate in liquid asset classes. Higher trading volume is likely to add to increased volatility during stressful periods in the market. For instance, AI-driven ETFs exhibit a significantly higher turnover ratio compared to actively managed equity ETFs.
Herd Behavior: When a large number of investors follow the same AI-generated signals, it can cause fast buying and selling, which may amplify price movements. This may lead to a "monoculture" scenario, where many market players rely on the same datasets, thereby amplifying the risks of widespread impact if those models fail. This means that when AI models respond to similar patterns, they can all move in the same direction, which may create a buying or selling spurt and, hence, increase volatility. A few AI-driven ETFs had higher turnover during the March 2020 market turmoil, indicating potential for more herd-like selling in times of stress.
Flash Crashes: Highly Programmed and Rapid Trade Execution Algorithms can cause extremely short-period market price movements, sometimes called "flash crash" events. The event can be started whenever algorithms identify a downward trend and a spiking increase in volatility, triggering a selling event.
While the fraudsters and scammers go on to exploit AI , it becomes necessary for the regulatory bodies to act immediately and protect the general public . Here are a few steps taken by these bodies :
India (SEBI): The Securities and Exchange Board of India (SEBI) is embracing AI-backed technologies to enhance its regulatory framework, while also implementing stricter regulations to prevent over-reliance on AI that could pose significant threats to capital markets. SEBI is leveraging AI for various applications, including fraud detection, risk assessment, and customer service enhancements. Additionally, it plans to expedite the IPO approval process using AI. SEBI's Virtual Assistant, Seva, provides investors with valuable information about the securities market, circulars, and grievance redressal.
To ensure transparency and accountability, SEBI has proposed increased disclosure requirements for investment advisors and research analysts who utilize AI tools. These entities must disclose the extent of their AI use to clients. Moreover, SEBI has emphasized that investment advisors and research analysts are solely responsible for data security and regulatory compliance, regardless of their reliance on AI tools.
United States (SEC): The US Securities and Exchange Commission (SEC) Chair, Gary Gensler, has stated that without swift intervention, AI could trigger a financial crisis within a decade.The SEC has approved the world's first AI-powered order type by Nasdaq.
Regulation of AI-Generated Content: Regulations such as watermarking AI-generated content are being considered to prevent misuse.The Indian government has warned social media platforms to repeatedly remind users that local laws prohibit them from posting deep fakes and content that spreads obscenity or misinformation.
The increasing sophistication of deepfakes makes it crucial for regulators to invest in supervisory technology (sup-tech) that can use AI to process information and spot fraud.
Importance of Awareness:
Investors need to be more aware and skeptical about information they encounter online, particularly regarding stock recommendations.They should not rush into investment decisions based solely on videos or social media content.
Verification of Information:
Investors should verify the source of any financial information, checking if it comes from a reputable news organization or the company's official website.They should look for inconsistencies in videos, paying attention to facial expressions, body language, and voice patterns.It’s crucial to conduct thorough research on a stock before investing, considering the company's fundamentals, financials, and market trends.
Avoiding Investment Scams:
Investors should be wary of promises of high returns, as they are often a sign of a scam.They should avoid installing apps or transferring money based on information from unofficial sources such as WhatsApp or Telegram groups.Investors should be cautious of apps that mimic well-known companies.
Source : ANI News
AI for Deep Face Detection:Modern cybersecurity solutions include specialized deepfake detection tools and AI-enabled systems that can spot abnormal communication patterns.
Advanced Cybersecurity:AI-enabled systems can spot abnormal communication patterns, and combined with robust encryption and multi-factor authentication, can create a barrier against impersonation attempts.
AI for Market Monitoring:AI can be used to track developments in changing markets and identify the entities acting in them.AI algorithms are used by trading firms to monitor price changes, identify reasons for price shifts, and conduct trades, while adapting to the constantly evolving market environment.
Increased Liquidity: AI-driven trading is likely to increase market liquidity, especially in less liquid asset classes such as emerging markets and corporate debt, by lowering barriers to entry for quantitative investors. AI's ability to quickly rebalance investment portfolios leads to higher trading volumes, which can deepen markets.
Market Volatility: The speedy adoption of AI without regulation creates new risks and amplifies existing ones. AI-based trading algorithms speed up the markets, and therefore amplify price movements, thus further exacerbating market volatility. Algorithmic trading tends to create rapid price swings and "flash crash" events when market prices swing wildly within short periods. High-frequency trading can be very disruptive during IPOs because initial trading can be significantly influenced by automated strategies that amplify price movements.
Possibility of Financial Crisis: The swift proliferation of AI can eventually lead to a financial crisis as there are still no adequate regulatory measures. Algorithm-decision-based outputs can be those "black swan" incidents that create major market falls due to the fact that these decisions might be carried out within seconds, before the authorities may even realize this. The existence of systemic risk due to dependency on the banking sector from very few core models is another reason.
Rise of Non-Bank Financial Intermediaries: AI may be a catalyst for increasing investment in hedge funds, proprietary trading firms, and other types of non-bank financial intermediaries, where markets are much less transparent and harder to track. Nonbanks are more agile in applying AI and less regulated than big commercial and investment banks.
Ethical Issues: AI systems may perpetuate biases in the training data, leading to discriminatory practices in trading and related decisions, raising ethical concerns. The decisions of AI models are erratic, and it is difficult to assess whether these models prioritize the interests of brokers or companies that create them over their clients.
Job Displacement: The advent of AI in finance may lead to job displacement of human financial advisors and stock pickers as AI-powered tools are capable of delivering personalized financial recommendations and portfolio management. There is apprehension that the Robo advisor will gradually replace the human advisor in actively managed equity funds.
Innovation and Efficiency: AI will spur economic growth through innovation in financial markets and improved efficiency. Its ability to automate processes and analyze data can help in more effective investment strategies and better capital allocation.
Increased Investment: AI may make investment more accessible and attractive to a larger section of the people, thus enhancing the inflow of capital into the market, which further creates more growth in the economy.
Lower Costs: AI is also likely to bring down the cost of financial advice to the common investor.
Improved Price Discovery: AI would therefore improve the processing of unstructured data, leading to efficiency in the marketplace and enhanced decisions in terms of investments.
Regulation: Regulatory bodies need to come up with rules and guidelines on AI in finance, such as increased disclosure requirements for investment advisors using AI tools. This is to prevent fraud and market manipulation. Regulations may include watermarking AI-generated content and clear guidelines on the use of AI in trading. The regulators must ensure that market participants comply with regulatory provisions and that the financial ecosystem remains secure.
Investor Education: Investors need to be more aware and skeptical about the information they encounter online. They should verify sources, look for inconsistencies, conduct their own research, and be cautious of promises of high returns to avoid falling victim to deep fake scams.
Risk Management Framework: Comprehensive risk management frameworks will have to ensure the AI system's rigorous testing and validation; therefore, transparency in decision making through explainable means is ensured. Stress tests, as well as dynamic risk adjustment methods, are highly recommended for analyzing the risk posed in various market conditions. Data mapping of interdependencies between the models, technological infrastructure, and supporting data shall also be taken into consideration to meet the risks with AI.
Artificial intelligence (AI) is indeed a potent force that is rapidly reshaping the stock market landscape, and it's essential to recognise that this transformation brings both opportunities and risks. The key takeaway is that while AI offers numerous benefits, including increased efficiency and speed, enhanced liquidity, and the democratisation of trading, its implementation requires continuous learning, adaptation, and responsible deployment.
AI is revolutionising stock market operations by automating tasks and processing vast amounts of data at unprecedented speeds. This automation extends to various aspects of the financial industry, from back-office operations to customer interactions and the creation of analytical models. AI-driven systems are able to analyse complex financial documents quickly and efficiently, improving price discovery across various asset classes.
However, the rapid adoption of AI also poses significant challenges. There is a risk of increased market volatility due to the speed at which AI-driven trading algorithms can react to market data, potentially leading to rapid price swings and "flash crashes". The potential for AI to amplify existing risks in financial institutions and create new ones is also a concern. The lack of regulatory oversight can lead to systemic risks if many market players rely on a limited number of core AI models, as well as ethical issues if AI models perpetuate biases from their training data.
The misuse of AI is a serious threat, especially with the proliferation of deepfakes and disinformation. AI-generated fake videos and audio can be used to manipulate stock prices by impersonating reputable figures to endorse certain stocks, leading to significant losses for investors. The creation of fake apps mimicking well-known companies also poses a risk to investors.
It is vital to ensure that the implementation of AI is handled responsibly. This means that financial institutions and regulatory bodies need to work together to create new regulations for the use of AI in finance. This can include mandating stress tests for AI models, enforcing transparency and explainability in AI-driven decisions, and developing comprehensive risk management frameworks. There is also a need to implement technological solutions such as deepfake detection tools and advanced cybersecurity measures. Additionally, investors must be educated to be more skeptical of online information and be aware of the potential for manipulation.
Identifying a deepfake video or image can be challenging, as these are created using sophisticated AI technology to be very realistic. However, there are several indicators you can look for to help determine if a video or image might be a deepfake:
Be suspicious: If a video or image pitching a stock or financial opportunity is too good to be true, then be wary of it. Do not make investment decisions solely based on the content of any video or image.
Verify source: Check the origin of the video or image. Is it from a reputable news organization or company website? If the source cannot be verified, then be suspicious.
Check for anomalies: Deepfakes are sophisticated, but they can have anomalies. Pay attention to these:
Facial expressions: Look for unusual or unnatural facial movements.
Body language: Observe if the subject's body language looks unnatural or inconsistent with what he says.
Voice patterns: Heed any anomaly or inconsistency in the voice. Deepfake audio can sometimes sound a bit off or unnatural.
Blinking unnaturally: AI-created faces blink unnaturally at times.
Light and shadows: Check for inconsistent lighting and shadows. If the lighting on the face does not match the background, it might be a manipulation.
Audio quality: Check for static, distortion, or unnatural inflections in the audio that might indicate manipulation.
Pixelation or blur: Be on the lookout for any pixelation, blurring, or unnatural blending around the edges of the subject or in the background of the image or video.
Official Stock Exchanges: The National Stock Exchange of India (NSE) is a major stock exchange, and its official website can be a source of market information, though it cannot recommend particular stocks. SEBI, or the Securities and Exchange Board of India, is the Indian stock markets regulator, though it's focused on regulatory matters. SEBI also uses an AI-based conversation platform called Seva for investors.
Financial News Outlet: This are online sources of information from Forbes India, The Economic Times, Business Standard, and the Financial Times. All these are publications that deal with business and finance news. The sources might offer market trends and analyses.
Investment Firms: There are investment firms like ICICI Prudential, Sharekhan by BNP Paribas, and m.Stock by Mirae Asset Capital Markets. Such firms may release research and analysis reports.
Research and Analysis: Utilize free tools from a variety of financial platforms such as stock screeners, moneycontrol, to identify stocks according to specific criteria.
While the detailed regulatory landscape around AI in the stock market continues to evolve, there are apparent signs that it is well perceived by regulators concerning the risks of AI and has been taken into place. These encompass increased disclosure, stricter oversight in AI models, and measures of combating misinformation/deepfakes. It goes without saying that the nature of AI requires continued review and changes in regulations over time to update with the best pace of emerging technology.
If you suspect that you have encountered an AI powered investment scam, caution is the word, and you must proceed to take immediate actions to protect yourself and your assets. Here's what you should do :
Cross-Reference Information: If a video or message makes a specific claim about a stock, cross-reference this information with other reliable sources, such as reputable financial news outlets, official company statements, or regulatory body releases.
Do Your Own Research: Always do your own research on the company, its fundamentals, financials, and market trends before investing. Never rely on a single video or message for information.
Contact Authorities: If one feels that they have encountered a scam, report it to the authorities concerned. Sometimes, it could be the authorities concerned with the stock market (SEBI in India) or other similar law enforcement agencies.
Protect Personal Information: Be extremely cautious about providing personal or financial information to unverified sources. Scammers often use this information to perpetrate further fraud or identity theft.
Cyber Security: Strengthen it Ensure you have a strong password and multi-factor authentication, keep your software updated, and so on, to prevent cyber-attacks and identity theft.
References
https://www.forbesindia.com/article/technology/ai-the-ghost-in-the-stock-market-machines/94256/1
https://www.nytimes.com/2023/05/23/business/ai-picture-stock-market.html
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