First, it is important to understand related terminology and background information of AI-accelerated data processing. This will be important to know in order to understand the more advanced techniques of AI that are revolutionizing research and portfolio management in the market. For instance, data processing, in short, is the collection of information to produce a meaningful output ( UAGC Staff, 2024 ). On a mass scale, it can be used to perform a variety of tasks; for example, in many hospitals, data processing is being utilized to track patient well-being and manage their records ( Freedman, 2018 ). In quantitative trading, a form of investing based on the implementation of historical figures in hopes of making more accurate decisions in the future, big data analytics is changing the way investors make decisions.

Big data analytics is characterized by processing high volumes of high-velocity data from a multitude of sources and is powered by machine learning, a subset of AI ( Wang, Xu, & Pedrycz, 2017 ). Through big data, firms like Amazon have improved efficiency substantially, reaching close to 40% of sales based on big data-enhanced personalized recommendations ( Shabbir & Gardezi, 2020 ). As evidenced by its technical aptitude, big data analytics consists of four aspects: volume, velocity, variety, and veracity ( Marr, 2021 ). Volume refers to the massive amounts of data generated daily, such as stock prices, financial reports, and media coverage. Velocity is the speed at which this data is produced and how fast it must be to remain relevant. In the stock market, velocity is especially important for fast-moving strategies and short-term trading. Variety refers to the format of the data for processing structured data like spreadsheets and unstructured data like news articles. Lastly, veracity focuses on data reliability, a key aspect in the stock market due to the high volume and velocity of information.

Machine learning (ML) incorporates the four aspects of big data analytics to identify key patterns and trends ( Goodell, Kumar, Lim, & Pattnaik, 2021 ). It uses these patterns to create its own algorithms whilst building on its knowledge by continuously processing more data in real-time. In the stock market, big data analytics and machine learning are used to predict stock prices and create algorithms ( Rundo, Trenta, di Stallo, & Battiato, 2019 ). For example, big data analytics can track millions of transactions in stock markets worldwide, enabling investors to leverage on external factors impacting their regional market and find patterns. It also helps investors to go through high volumes of information and make the most accurate decisions. ML also plays an important role, as it accelerates big data by processing even more data faster. It’s also the base for large language models (LLMs) and natural language processing (NLP) ( Vaniukov, 2024 ). These processes are used in big data for AI to analyze human writing and in generative AI platforms, like ChatGPT, to replicate human text ( Stöffelbauer, 2023 ).

Big data and ML are transforming research by enabling investors to use real-time and sentiment analysis ( Awan, Rahim, Nobanee, Munawar, Yasin, & Zain, 2021 ). With further development, they can play a massive part in research, automation, and prediction ( Sivarajah, Kumar, Kumar, & Chatterjee, 2024 ). The upcoming section explores the impact of these analysis methods on the research by explaining how they work and why they are so effective.

Analysis models often focus on specific market factors and delve into them comprehensively. With AI, analysis models can conduct even deeper research to a scale beyond human capabilities by using advanced data processing techniques like ML ( Keylabs, 2024 ). This paper explores two key features of AI analysis models: a deeper understanding of market dynamics and more accurate insights.

To improve the scope of research, AI programs can amass and process high volumes of data to deliver quick insights, which otherwise takes a trained group of specialists ( Friedenthal, Moore, & Steiner, 2015 ). Remaining aware of market dynamics and having access to the latest news is necessary for consistently successful investment decisions. This is because the stock market often has massive shifts in public sentiment and global events that occur suddenly and cause significant stock fluctuations ( Kareem, Fayed, Rady, El-Regaily, & Nema, 2023 ). Understanding the current situation can significantly improve investment decision-making, and AI-accelerated big data analytics and machine learning can be incredibly useful in developing that understanding. These evaluations require a lot of time and effort, especially for amateur or individual investors. Through AI, analysis can be much deeper without taking as much effort on the investor’s side ( Na, 2024 ). ML can even further AI’s prowess by producing more sources for the input data, showing how AI integration could transform research depth ( MLM Team, 2022 ). This can benefit amateur investors, in particular, who don’t have the resources to learn how to professionally analyze market factors and fluctuations. Instead, in-depth AI analysis can form the basis of their research and guide them to make better investment decisions.

Overall, AI-enhanced analysis outperforms traditional approaches in this area by leveraging its ability to handle vast amounts of unstructured data efficiently. For example, while traditional methods may rely on small, structured datasets to predict trends, AI models can analyze massive amounts of data in real-time, improving accuracy and scope. It also forms the basis of more advanced analysis models like real-time and sentiment analysis, which procure analysis in specific aspects of the stock market ( Wable, 2023 ). These models can revolutionize market research and need analysis to be performed comprehensively.

Through AI and ML integration, analysis models also improve accuracy with big-data analytics. Consistently maintaining accuracy is difficult because the relevance and quality of data collected aren’t always consistent. There are two main problems: the high velocity of data and its unstructured format ( Big Data Framework, 2019 ). Firstly, with quintillion bytes of data produced every day, screening data that matches the speed of the information’s production is crucial ( Duarte, 2023 ). This poses a logistical issue as big data analytics need to be expedited to filter all irrelevant information. The second fundamental issue is the lack of standardization in the information format, an unavoidable challenge in data processing. Unstructured data, which is information that’s not organized in any particular manner or format, is the root of the issue ( Baig, 2024 ). This data type includes text, images, and video. For corporations who want to get a truly comprehensive understanding of market dynamics like public sentiment and economic trends, processing as much data as possible, including information that isn’t in any standardized format, can make a significant difference. To combat these issues, the processing of high volumes of information, referred to as big data analytics, is necessary. This is because ML processes will exponentially improve as more data is processed ( Pugliese, Regondi, & Marini, 2021 ). Over time, as AI is trained to filter out irrelevant data, findings will be increasingly more reliable. As accuracy increases, processes like real-time analysis and sentiment analysis will be much more effective as only relevant data is used as input.

Analysis models have a range of applications that can further improve market research. These models align with the research question by demonstrating how AI applications, such as real-time and sentiment analysis, address inefficiencies and data overload in the stock market. In the next part of this section, I detail the two most important advanced AI models and their applications. I analyze how real-time and sentiment analysis provides answers to the hypothesis that AI can significantly enhance market research and decision-making, starting with real-time analysis.

Real-time analysis, the process through which data is collected, processed, and used as soon as it’s accessible, has copious applications, especially for investors looking to leverage small price fluctuations ( Sisense, 2018 ). This analysis model analyses the data it receives immediately to provide quick and actionable insights. The data can be both structured and unstructured, making real-time analysis models effective at gauging the overall market at a given time ( Adlakha, Ridhima, & Katal, 2021 ). In the stock market, these models monitor live feeds of stock prices, market news, and even social media activity to help investors make informed decisions faster. It also helps substantially in creating more accurate short-term investments. Through AI acceleration, real-time analysis models can help spot patterns and register the smallest changes in price ( Striim Team, 2024 ).

Additionally, real-time analysis models can help investors track the market at all times. Although human researchers cannot stay attentive to the market during all hours, AI is free from physical limitations and can continuously run processes in the background. Traditional strategies also rely on periodic fundamental and quantitative updates, while AI-enhanced models provide up-to-the-minute information at all times. This can improve the depth of research for both institutional and individual investors significantly. Furthermore, AI can filter out unessential information and alert users only when something significant occurs, which minimizes time wasted and improves efficiency. This can happen because of ML processes, which train AI to filter high-velocity data to get only high-quality, relevant information ( Mahesh, 2020 ). This overall improves research accuracy, which, in the incredibly competitive stock market, can provide a significant edge for investors using the tool. Over time, AI-accelerated real-time analysis may get increasingly more accessible to the general public, revolutionizing the stock market as more people leverage minute price changes.

A prominent example of the possibilities of real-time analysis models is AlphaSense, an AI-powered platform that uses real-time analysis models to gather and produce financial insights for its users. Through big data analytics, the models can process a wide array of data as it is produced, enabling investors to respond faster to emerging opportunities or risks and giving them a significant edge in volatile markets ( Taba, Tolan, & Meering, 2024 ). Traditional analysis methods rely on pre-compiled datasets and periodic updates, which results in delayed responses. For example, while AlphaSense can flag market shifts within minutes of social media or news activity, traditional models may take hours or even days to analyze similar insights. AlphaSense is being utilized in the market, especially for its ability to reduce the time spent on manual analysis and improve decision-making accuracy.

Similarly, Kensho, developed by S&P Global, is another real-time analysis tool that specializes in processing large data sets. Through ML, Kensho is able to uncover hidden correlations in data at a rapid rate so investors can react proactively instead of reactively ( S&P Global, n.d. ). In comparison, traditional quantitative analysis techniques might miss these subtle patterns entirely or take significantly longer to recognize their relevance. For example, if a major geopolitical event like a trade agreement or conflict is announced, Kensho can instantly process the information and predict its potential impact on specific industries or stock prices. However, it could take a human analyst hours to accurately identify the pattern, resulting in possible missed opportunities. In the market now, investors and larger players are capitalizing on the real-time analysis capabilities of models like AlphaSense and Kensho, including hedge funds, investment firms, and even government agencies ( CBM Team, 2024 ). By leveraging these tools, users can stay ahead of the curve, developing an edge that enables them to react swiftly and effectively.

Overall, through its numerous benefits, such as improving depth, accuracy, and even accessibility, AI-accelerated real-time analysis can revolutionize research in the stock market. These findings align with the research question by showing how AI addresses inefficiencies, mitigates volatility, and enables investors to act proactively. Real-time analysis models like AlphaSense and Kensho illustrate the hypothesis that AI can significantly enhance decision-making for a range of investors. By addressing these core challenges, real-time analysis provides a concrete example of how AI can revolutionize market research, providing many more opportunities for investors willing to integrate the tool into their strategy.

Besides real-time time analysis, AI can also accelerate sentiment analysis. Sentiment analysis models help understand public demeanor towards specific stocks or the market in general by analyzing news, social media, and other indicators of sentiment ( van Attelveldt, van der Velden, & Boukes, 2021 ). Due to its research utility, sentiment analysis is used by many corporations for various purposes. For example, it’s used by Bloomberg for creating effective news feeds ( Welson-Rossman, 2019 ), by Apple to enhance their products, and by Google to create personalized ads ( The App Solutions, 2023 ). AI boosts this analysis by mitigating bias, making analysis models more reliable. Sentiment analysis is all about understanding the tone and connotations of human text. This is achieved through natural language processing (NLP), which allows AI to form connections between words and ideas and makes it possible for machines conducting sentiment analysis to link synonyms and like phrases correctly ( Kleinings, 2024 ). Through various NLP preprocessing techniques, AI is able to process large volumes of unstructured data efficiently ( Vaniukov, 2024 ). These techniques include tokenization, which breaks text into words and phrases, and named entity recognition, which identifies names of companies and stocks ( UbiAI, 2023 ). They are imperative in ensuring accuracy and extracting solely relevant information from a large range of sources. Sentiment scoring, which is assigning positive, negative, or neutral values to text, is another vital technique as it allows models to gauge tone by simply analyzing tweets or news headlines about a company ( Kannan et al., 2016 ). Words can be deriving from different languages as well, as advanced NLP programmes can translate and utilize varying languages; in the European Parliament, the Europarl parallel corpus is created from the group’s conferences and comprises of 21 languages ( Khurana et al., 2022 ).

Traditional strategies, including human researchers and rule-based computing systems, have a series of drawbacks that AI integration can solve. For example, human analysts might sift through selected news articles or social media posts to gauge market sentiment, but this approach is time-consuming, prone to human error, and cannot be used to process the vast volume of data generated daily. Rule-based systems, meanwhile, rely on predefined keyword dictionaries that may miss nuanced language and slang. For instance, traditional models might flag a tweet containing the word “bullish” as positive but fail to recognize sarcasm. NLP-driven AI models can accurately interpret this nuance, improving the accuracy of these models.

Furthermore, it’s likely for human analysts to make mistakes in gauging these factors, especially due to implicit bias. Though we may not always be aware, our subconscious feelings and ideas affect our everyday decisions, particularly our judgment. Through ML, sentiment analysis models could be trained to apply the same criteria across all text it has to analyze ( Mehta, Pandya, & Kotecha, 2021 ). Additionally, the continuous expansion of AI’s databases helps reduce inaccuracies and anomalies, while simultaneously updating sentiment markers with up-to-the-minute data. This helps form a more accurate and less biased judgment on sentiment, considerably improving the accuracy of research. (This can be argued against, however, as algorithms created by humans will inherently carry human bias, an important issue explored later in Section 4.)

Overall, AI-boosted sentiment analysis in the stock market can help form a better understanding of stocks and companies, improving market research substantially. Public sentiment is a predominant factor impacting stock prices, which makes AI-accelerated sentiment analysis very important to utilize ( Tiwari, Abakah, Bonsu, Karikari, & Hammaoudeh, 2022 ). Furthermore, there are a large number of sources for models to analyze, as most news channels and media platforms provide daily updates and reports on market dynamics, trends, and even public sentiment. These all impact public sentiment and, therefore, the stock market as a whole. Firms and influential individuals often also impact sentiment; research from Maryland Smith has shown that 92% of firms use their Twitter accounts more than once a day, with some tweets having a tremendous impact on stock prices ( Kannan, 2021 ). This creates a large pool of sources that big data analytics can process to get very accurate results. Because of this, ML can power generative AI and NLP large language models (LLMs) like ChatGPT used to understand human text and speech ( Harshvardhan, Gourisaria, Pandey, & Rautaray, 2020 ).

All things considered, the integration of AI with analysis models exemplifies the revolutionary impact it can have on the stock market. It supports the hypothesis of how AI can transform the scope and depth of research through the vast amount of information analyzed every second in real-time analysis and the complex insights made with NLP in sentiment analysis. Together, these models highlight how AI-driven tools transform the stock market into a more efficient, accurate, and inclusive environment. It also reflects how AI dramatically improves accuracy through tools like ML, which helps filter out irrelevant data and minimize bias. In the future, as more investors start using these tools, the way people research and ultimately make decisions in the stock market could completely change.

To create a successful AI-run portfolio, the implementation of accelerated data processing techniques is crucial, especially ML ( Takyar, 2023 ). ML programs can revolutionize algorithmic trade through historical data analysis ( Strader, Rozycki, Root, & Huang, 2020 ). This analysis can help deduce patterns in price fluctuations and be developed in AI-accelerated portfolios to provide statistical insights on investment decisions, such as the probability of success and risk ( Bansal, 2024 ). ML can also be used to support AI-automated investment decisions and even create a completely AI-run stock portfolio.

Advanced ML systems, including deep learning (DL) and reinforcement learning (RL), can further improve these aspects. These factors increase reliability, enabling investors to trust and possibly apply their AI to their strategy. DL, for example, utilizes neural networks, developing algorithms that follow a logic similar to humans ( Sarker, 2022 ). Neural networks are computational systems inspired by the structure and function of the human brain, consisting of layers of interconnected nodes (or “neurons”) ( Hardesty, 2017 ). Each node processes input data and passes the output to the next layer, progressively identifying patterns and relationships. In stock market, these networks conduct deep learning (DL), analyzing historical data to identify trends and patterns for future forecasts ( Selvamuthu, Kumar, & Mishra, 2019 ). These systems make DL incredibly effective, detecting subtle correlations between factors and price changes that human analysts may overlook.

To further minimize errors, DL incorporates backpropagation. This algorithm compares each neuron to its findings, analyzing the level of fault in the information it used to build its knowledge and removing it, significantly improving the degree of accuracy and reliability of the output information ( Gillis, Hashemi-Pour, & Zola, 2024 ). When processing a data set with high volumes of data, there is a high chance of encountering errors, and in the stock market, even marginal inaccuracies can lead to major losses. Backpropagation solves this problem, making a substantial difference in the reliability of AI-generated results. This ability to learn complex, non-linear relationships makes DL incredibly effective in the stock market, where a multitude of complex factors all contribute greatly to market dynamics.

The advent of reinforcement learning (RL), another sub-application of ML, greatly contributes to improving ML algorithms and enhancing reliability. Through RL, AI platforms improve their decision-making skills, which can help portfolio construction and management ( Devika, Sunitha, & Ganesh, 2016 ). Reinforcement learning is based on the Markov Decision Process (MDP), a framework for modeling decision-making in environments where outcomes are partly random and partly under the decision-maker's control ( Kanade, 2022 ). It consists of states, which represent different scenarios and the AI’s actions ( Ye, 2022 ). At each time step, the AI machine, referred to as the “agent,” makes a decision, where it is rewarded if it makes the right choice. The agent is then stimulated to make another decision in a different time step based on its previous choice. If it makes the correct decision, it is rewarded yet again. This process is repeated many times, and through many trial-and-error processes, the computer learns which decisions are beneficial and which aren’t. For example, in portfolio management, states could represent market conditions (e.g., rising or falling stock prices), actions could be buying or selling a stock, and rewards could reflect the profitability of those actions.

Random forest is another system that utilizes decision trees to enhance the accuracy of AI. Slightly different from RL, it exemplifies how ML can be achieved with multiple AI agents. A decision tree is a model that splits data into branches based on certain criteria. This is accelerated by random forest, which utilizes a range of decision trees, each trained on slightly different subsets of the data, to reduce errors from overfitting ( Au, 2018 ). The most common result between the decision trees is designated as the overall classification. This technique improves accuracy as repeatedly incorrect classifications by an agent help identify faulty machines and specify the root of errors. Inaccurate agents are tweaked and improved, then added back into the random forest system for testing. In the context of the stock market, random forest can classify stocks as buy, sell, or hold by analyzing a wide range of financial indicators. This technique also enables AI technologies can be tested, making a substantial difference in the long term ( GeeksforGeeks, 2024 ).

These ML techniques increase the reliability of AI automation and prediction, effectively bringing AI-based portfolio management and predictive analytics closer to reality. To protect investors, it is important for AI systems to be reliable, as faulty autonomous and predictive systems could lead to significant volatility and even losses in the dynamic market ( Abbas, Cohen, Grolleman, & Mosk, 2024 ). Techniques like DL, RL, and random forest are exemplars of how AI is trained to create more reliable algorithms. The constantly improving nature of ML programs further enables AI algorithms to adapt to changing market conditions, done by processing even more real-time data to refine pre-existing algorithms and rules ( Vates, 2024 ). These technologies show how AI systems can be reliable and possibly even better than their human counterparts. With increasingly developing technology, human participation may reduce over time and even be replaced by AI ( Yalamati, 2023 ).

Overall, these structures enable AI to learn optimal strategies for navigating the stock market aligning with the research question by demonstrating how advanced ML systems like deep learning and reinforcement learning address inefficiencies and enhance decision-making accuracy ( Kenton, 2010 ). It also illustrates how AI technologies can significantly improve market reliability and adaptability, enabling investors to manage portfolios with greater confidence and precision. Through ML and its sub-applications, AI provides a solution to the challenges of complexity and volatility in the stock market, highlighting its potential. But first, it’s also important to understand: would investors use AI-powered algorithms or seek financial advice from AI-based platforms instead of using traditional alternatives? And if so, why? In this next part, I answer these important questions regarding the other advantages of AI.

Firstly, the stock market has significant fluctuations and a multitude of factors that are hard to predict ( Saraf & Kayal, 2023 ). With dynamic markets and ever-changing price fluctuations that cannot be predicted, AI-integrated portfolios need much development before being utilized by investors. Moreover, each market is different from another, with every industry and field having its own set of unique conditions ( Furhmann, 2012 ). The New York Stock Exchange (NYSE) is completely different than the Bombay Stock Exchange (BSE). With different stocks, trends, and regulations in each market, AI-integrated portfolios need to be able to adapt to the conditions and invest based on the situation. Other challenges specific to AI include the data quality inputted into machines and overfitting. Data quality, as mentioned in previous sections, is a fundamental problem of machine learning, as unsuitable or false data can lead to inaccurate results ( Cattaneo, Polenghi, Macchi, & Pesenti, 2022 ). In the stock market, this can mean substantial losses, which makes it super important for AI to be completely fine-tuned before practical use.

Additionally, AI-enhanced analysis must set countermeasures to mitigate the issue of biased training data. Bias directly impacts findings, distorting results and creating skewed insights. It also harms people, reinforcing negative stereotypes and disadvantaging groups. In the stock market, biased training data can also distort the analysis of certain sectors, markets, and regions. This can impact performance and exacerbate discrimination in the long run ( Kiritchenko & Mohammad, 2018 ). To mitigate this, techniques such as balanced sampling, synthetic data generation, and fairness-focused algorithms are used. Balanced sampling and synthetic data generation ensure that data proportionally represents all groups and supplement the dataset with underrepresented information ( Venugopal et al., 2024 ). Together, these techniques create a holistic view of the market. Firms and authorities also employ fairness-focused algorithms that gauge bias and underrepresentation. Overall, these methods provide a more impartial sentimental analysis of the market and bring AI closer to practical implementation.

Overfitting is another issue that also needs to be solved before AI can be practically applied in portfolio construction and management. Models can be trained to analyze and interpret historical data. However, real-time information can pose problems that hamper the AI’s predictive abilities. Overfitting occurs when advanced models begin analyzing minute details in data that aren’t relevant to the primary purpose the models are trained to do ( Moss, 2024 ). By analyzing irrelevant information, AI programs may lose sight of the major insights. To solve this issue, models must be trained on new data sets and recognize a variety of patterns so they don’t over-analyze and overfit new information ( CFI Team, n.d. ). Though difficult, these issues are solvable through regularization methods, including cross-validation and pruning techniques ( Kotsilieris, Anagnostopoulos, & Livieris, 2021 ). These techniques help machines to focus solely on essential components, providing solutions to data quality issues and overfitting. RL and diversification strategies can help hedge market risk by optimizing risk and reward through historical data analysis.

There are many experts who point out the ethical and regulatory hurdles AI faces before it’s complete integration, such as Sam Altman, the CEO of OpenAI, who said: “I think, if this technology goes wrong, it can go quite wrong,” when referencing AI ( Zorthian, 2023 ). He is referring to the dangers of AI in general, which apply to the stock market as well. This final section is about the ethical and regulatory implications of AI in this industry before it can be truly trusted. There are many causes of concern regarding accountability, equality, and data protection. These issues may hamper the time it takes for implementation but are necessary to understand due to the high stakes of the stock market.

The main ethical concern is that the use of AI in algorithmic trading can blur the lines between who is truly in control and, when issues arise, who to blame. With a massive gap in understanding human input into machines and how AI outputs results based on the initial information, extensive research should be conducted before putting AI in the financial field ( Fletcher & Le, 2022 ). This is crucial for the stock market, where AI is starting to control larger funds and trades. Minimal errors in programming or a mistake in AI processing could cause massive losses for both large corporations and general investors alike. Furthermore, computers could exacerbate the damage done by market fluctuations as unfamiliarity with real-time market dynamics can prohibit AI from making accurate decisions. This could negatively impact investor decision-making, particularly among amateurs ( Ward & McVearry, 2024 ). Because of this, the use of AI in the stock market brings up the importance of accountability. Accountability is key in finance and is based on transparency and understanding the role of market participants in situations ( Franzel, 2013 ). Both AI and the human counterparts behind the technology should be held accountable for its consequences. This means transparency in how the technology operates and the establishment of legal frameworks that can regulate AI processes.

Another ethical dilemma of AI-powered trading is ensuring equality for all investors and providing a fair chance to those without the technology. As AI becomes increasingly effective, it will be integrated into the strategies of large investing corporations to help make the most optimal investments ( Abbas, Cohen, Grolleman, & Mosk, 2024 ). This could be problematic, however, as for a period of time, advanced AI technology will not be accessible to individual investors, creating a large skill gap and increasingly unequal capabilities between the groups. By concentrating further technology into the hands of powerful groups, AI can actually further economic disparity ( Sitaram, 2024 ). Additionally, AI algorithms must be trained to prevent discriminatory practices and recognize the bias of analyzed data. This is important in sentiment analysis, where human bias from the creator(s) of the model can transfer to the model’s ability to gauge sentiment. This can lead to extensive implicit bias in the model, interfering with analysis and accuracy. The social disparity may be exacerbated because of present bias in AI models through the promotion of companies in trading software based on characteristics unrelated to market performance ( Varsha, 2023 ). This is evident in an experiment by MIT, in which a biased generative AI model outputted discriminatory results based on the identity of the person mentioned in the query ( Nadia, 2022 ). This makes it necessary for bias to be minimized through regulatory action.

With data processing being a key aspect of AI applications in any field, data protection systems need to be completely secure for the safety of all investors and economic agents (Thabet & Soomro, 2023). This is crucial as malicious breaches of large data sets used for machine learning and output from AI programs can lead to significant problems due to the expansive data AI processes that produce results that people use in their investment decisions. Incorrect information can lead to inaccurate results, which may cause losses that are particularly damaging for individual investors ( Yanamala & Suryadevara, 2024 ). Another important aspect of data protection in the stock market is ensuring that investors’ personal information is not compromised and used for malicious intent ( ET Online, 2023 ). Data privacy needs to be regulated so that AI platforms follow ethical practices and don’t misuse or accidentally leak their users’ information if they’re using real-time investor data for training their AI.

To ensure ethical and legal guidelines are being adhered to, economic authorities such as the United States Securities and Exchange Commission (SEC) should implement a variety of strategies. Some important strategies include transparency requirements and bias mitigation. By ensuring that the creators of AI platforms understand how their work is able to process data and make decisions to hold them accountable, regulators can work towards documentation of processes and, therefore, regulation ( Khan & Mer, 2023 ). Another prominent issue that must be tackled through legislation is strictly enforcing bias mitigation processes in AI programs ( Ferrara, 2023 ). This can be done by standardizing certain procedures for each AI system and testing their validity before allowing implementation in the market ( Nazer, Zatarah, Waldrip, Ke, Moukheiber, Khanna, & Hicklen, 2023 ). Lastly, by setting regulations on the impact AI can have on investment decisions for large corporations and promoting the accessibility of the technology for individual traders, the skill gap between the groups can significantly decrease ( Kemme, McInish, & Zhang, 2022 ).