HARNESSING THE POWER OF AI IN INVESTMENT SELECTION DECISIONS

336 CHAPTER-31 HARNESSING THE POWER OF AI IN INVESTMENT SELECTION DECISIONS Dr. Lalee Sharma Asst. Professor, Govt. VYT P.G. Autonomous College, Durg (C.G.) Anjali Kashyap Research Scholar, Govt. VYT P.G. Autonomous College, Durg (C.G.) Abstract The chapter explores the application of AI into investment selection processes, emphasizing its powerful influence on financial investment decision-making. AI is enabling investors to analyze big data, identify patterns and make informed investment choices through use of Machine Learning, Natural Language Processing and Robo-advisory services. The chapter also highlights the limitations and ethical considerations of using AI in investment selection. Thus, the chapter aims to provide a balanced overview of the benefits and potential risks presented by AI through a thorough literature review of academic papers, examples from the industry and, reports from major financial companies. Keywords: Artificial Intelligence, Investment Selection, Investment Automation, Smart Investment Platforms, Data-Driven Decision Making. Introduction The world of investing is going through big changes and a lot of it is because of the Artificial Intelligence (AI). In the past, people used to pick stocks based on their own research or simple strategies. But, now AI tools like Machine Learning, Deep Learning, Natural Language Processing, and Reinforcement Learning are being used in many parts of investing. According to a 2023 report by PwC, more than 54% of investment companies around the world are already using AI. They say it helps them to be more accurate and give better service to their clients. Another study by Deloitte in 2022 found that 75% of top investment managers believe that AI and data analysis will be very important for staying ahead in the future. This chapter looks at how AI is being used in investing, what it can do, what problems it might cause, and why it is important to use it responsibly and follow the rules. The Evolution of Investment methods 1. Traditional Investment 1.1 Fundamental analysis: This includes investors looking at things like the company’s financial reports, industry’s state and the overall economy, and also how good the leaders of the company are. There was usage of P/E ratio, EPS, ROE, etc. to decide whether to invest in a stock or not. 337 1.2 Technical analysis: This is more focused on the stock’s prices and trading activities. It uses tools like moving averages, Bollinger Bands, and the Relative Strength Index (RSI), especially by the short-term stock investors. The traditional methods were not perfect because the people’s personal opinions and emotions could affect their decisions. Researchers like (Gao, 2023) showed that many investors make mistakes because of common thinking errors like being too confident, sticking to first impressions or being afraid of losing money than excited about gaining it. 2. Move towards smarter tools: Back in 1990s and 2000s, the finance world used math-based models and fast computer programs to help with trading. But such systems can not learn or improve on their own. Things like sudden economic changes, geo-political events can affect investments quickly, so, decisions need to be made in real-time. AI tools can go through tons of data, find patterns and learn new information, and make decisions way faster than any human can. 3. From Human-driven investing to AI-based Investing 3.1 1980s to 2000s: Using Mathematics and Statistics: People started using numbers, formulas, and data to help them make smarter investment choices. 3.2 2000s to 2010s: Fast Computer Trading: Computers began to take over, using pre-set rules to buy and stocks super quickly. 3.3 2015s to now: Smart AI Investing: Today, AI helps investors by learning from huge amounts of data and adjusting to changes in the market on its own. Some examples of AI tools used in finance today are ChatGPT which can understand and explain financial information, BERT which can read and analyze financial language, and XGBoost which helps with things like credit scoring. AI Tools used in Investment Selection AI is changing the way people invest their money. It helps both big financial companies and regular investors choose, track, and manage their investments more easily. AI uses smart computer programs that can go through huge amounts of data and find useful information. This makes it easier to make smart investment choices. 1. Reinforcement Learning (RL): In investing, RL helps improve trading strategies by learning from how the market reacts to each decision. RL is also used to manage investment portfolios. The system can automatically adjust which stocks or assets to keep or sell, depending on how the market is behaving. (Moody & Saffell, 2001) 338 Example: Google DeepMind created Alpha Portfolio. It is an experimental system that uses reinforcement learning to manage investments and change strategies based on what is happening in the market. 2. Deep Leaning: It uses neural networks to understand really big and complicated data. Deep learning models like Recurrent Neural Networks and Long Short-Term Memory are used to guess how stock prices might change in the future or how risky the market might be. (Fischer & Krauss, 2018) Example: Big investment companies like BlackRock and Two Sigma uses deep learning to find hidden patterns between different types of investments and big economic events. 3. Natural Language Processing (NLP): In investing, NLP is useful because it can read things like news articles, company announcements, social media posts, and “forward-looking statements” to find what people are feeling or thinking about the market which can empirically affect investor sentiments and decisions. (Li, 2010) Example: Bloomberg uses NLP in its special software to quicky read news and give rea-time updates to professional investors so they make fast decisions. 4. Machine Learning (Gu et al., 2020) 4.1 Supervised Learning: This uses past data to teach the computer how to predict future results. Some examples include linear regression, decision trees, and support vector machines. 4.2 Unsupervised Learning: This method helps computer find patterns in data without needing answers in advance. For example, it can group investors who act in similar ways, which helps with planning and managing risks. 4.3 Reinforcement Learning: This is like learning through trial and error. The computer tries different strategies and learns which ones work best. It is often used to manage investments that change with the market. Example: JP Morgan Chase uses Machine Learning to help with trading and managing investments. Their smart systems help save money on trades and increase profits. (Annual Report | JPMorganChase) Applications of AI in Investment Selection 1. Understanding Sentiment Analysis and News-Based Investing: NLP is used to analyze news updates to spot important events that could affect stock prices, listening to how confident or unsure CEOs sound during announcements and also to check what people are saying about companies on social media(Tetlock, 2007). A 2022 report by Deloitte found that 42% of hedge funds use NLP to understand public mood and help decide when to buy and sell stocks. (Generating Value from Generative AI) Example: Thomson Reuters’ MarketPsych Indices uses NLP to find emotions like fear or trust in news and social media posts. 339 2. Exploring Alternative Data with AI: A 2023 report from the Economist Intelligence Unit said that 47% of big investment companies now use this type of AI-powered data to help them make smarter decisions. Such alternative data comes from social media posts, ESG reports of companies, satellite images showing how busy shopping malls are, review comments of popular products from websites and compare them, etc.(Cao et al., 2024) Example: Orbital Insight uses satellite images to guess how the economy is doing for instance, by counting the cars in parking lots. 3. Portfolio Optimization and Asset Allocation: Through usage of Reinforcement Learning, Deep Neural networks and, Genetic Algorithms, AI is making it easier for investors to create smart and balanced portfolios. These AI systems can adjust where the money goes like into stocks, bonds, or other assetsbased on how risky or rewarding each option is at that time. A 2023 report by PwC also said that more than half of investment managers around the world now use AI to help them choose where to invest and manage risks. (PricewaterhouseCoopers) Example: BlackRock’s Aladdin platform uses AI to check over 2,000 risk factors and test how portfolios might perform during different economic situations. 4. Risk Management and Compliances: AI is helping banks and financial companies to keep track of risks and adhere government rules in real-time. AI can predict risks of market crashes, spot unusual trades or price changes, read and understand long legal documents to help companies stay within the law and check for creditworthiness, etc. Example: SEBI supports using AI tools to ensure companies are not breaking rules like insider trading or market manipulation. 5. Robo-Advisors The Digital Investment Buddy: These are smart online tools that use AI to give financial advice and manage investments automatically. These robo-advisors can help build investment plan using smart math called Modern Portfolio Theory, help plan for goals like saving for college or retirement, use algorithms to reduce taxes and keep investments balanced. As per a report by Statista robo-advisors could be managing over $ 2.3 trillion by 2027 – more than double what they handled in 2023. (Robo-Advisors - Worldwide | Statista Market Forecast) Example: Apps like INDmoney and Groww use AI to help people choose mutual funds, stocks and, bonds based on how they are performing in the market. Benefits of AI in Investment Selection 1. Fast investment decisions: AI helps investors react to changes in the market in just a few milliseconds. Thu, they can be useful in high-speed trading, handling risk during big market changes, spotting unusual market activities, etc. A 2023 report by McKinsey said that financial companies using real-time AI tools improved their investment results by 20-25% because they could act 340 faster than others. (Generative AI in Banking and Financial Services | McKinsey) 2. Helps investor stay ahead: AI helps create new strategies faster and spot small price differences (Arbitrages) that do not last long helping take advantage of short-term chances before others do. 3. Avoiding Human biases: AI uses logic and data to make decisions, not feelings. If it is trained with good and diverse information, it gives fair and balanced advice. This helps avoid panic selling or getting excited about a stock without reason. 4. Smarter predictions: AI tools are good at finding patterns that older methods often miss. They can spot when a stock or asset is priced too high or low, guess how the overall economy might change in the future, etc. Deep Learning models like Long Short-Term Memory are especially good at predicting prices for things that change a lot like cryptocurrencies. 5. Managing and predicting risks: AI can predict losses using tools like VaR (Value-at-Risk) and CvaR (Conditional Variable) and can alert investors right away if it sees strange price changes. For instance, JP Morgan uses a system called LOXM that relies on ML to carry out big trades safely without causing too much risks in market. 6. Helps save money and time: AI helps financial companies save time and money by doing everyday tasks automatically. This includes collecting data, helping new customers sign up, checking if rules are being followed, and adjusting people’s investment plans. Because of this, companies can charge lower fees, and consequently, more people can afford good investment services. For instance, robo-advisors usually charge 0.25-0.50% p.a. while human advisors charge 1-2% p.a. 7. Personalized Investment Advice: AI factors in information such as a person’s earnings, spending habits, investment goals and risk appetite to create a custom investment plan for them. Apps like Wealthfront and INDmoney use AI to build personalized retirement plans and SIPs by studying their goals and tracking progress. Also, a report by Deloitte in 2022 stated that when financial companies use AI to give more personalized advice they are able to keep over 30% more of their clients. Limitations and Ethical considerations of using AI in Investing 1. Issues of Fairness: This can happen when the system favors certain groups like richer investors over other, especially in things like credit scores or investment advice. For instance, if AI is trained on data that mostly includes one type of group, it might not give good investment chances to people from underrepresented backgrounds. (O’Neil, 2016) 2. The “Black Box” Problem: The AI systems are often called “black boxes” because even experts cannot always explain how they make decisions. Investors and regulators might not know why the AI chose a certain stock or made a trade. (Doshi-Velez & Kim, 2017) 341 3. Job changes and need for human supervision: if we rely too much on AI, there might be less room for human thinking, creativity and ethical choices. That is why many experts believe we still need humans to be involved so they can step in or change or stop an I decision if something does not seem right. This is called “human-in-the-loop” system. 4. Problems with data in AI: Quality, Bias and Overfitting 4.1 If the past data includes unfair decisions by investors, AI might learn those same biasness. 4.2 Overfitting happens when AI does great on practice data but struggles in real life because it is too focused on the past and cannot handle new situations. 4.3 Confusing information such as unclear or non-standardized reports by companies can confuse AI and leads to quality issues in its reporting. Example: if an AI model was trained using market data before COVID-19, it might not work well after the pandemic because the market changed a lot. 5. Legal rules and regulatory challenges: If an AI system makes a bad investment and someone loses money, it is not always clear who is responsibleshould it be the company, the programmer or someone else? AI can also accidentally break privacy rules by learning too much about the people’s behavior without asking for permission. Using AI in Investing – Smartly and Responsibly AI is changing the way people invest their money. It makes investing more personal, reduces human mistakes and helps things run more smoothly for all kinds of investors. But even though AI is powerful, it comes with some risks. Because it works so fast and on its own, it can sometimes make unfair choices or mistakes that are hard to notice. That is why it is important to have humans checking on what AI is doing, and to make sure there are rules to keep things fair and safe. In the future, the goal is not to replace people with AI, but to help people make better choices using AI. To do this, everyone including governments, tech experts, finance professionals, and teachers need to work together to make sure AI is used in a way that is smart, fair and, easy to understand. References 1. Annual Report | JPMorganChase. Retrieved July 3, 2025, from https://www.jpmorganchase.com/ir/annual-report 2. Cao, S. S., Jiang, W., Lei, L. 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