Revolutionizing the Analyst Workflow with AI

In today's fast-paced financial world, investment analysts are tasked with processing an immense amount of information to derive actionable insights. From analyzing financial statements to understanding market dynamics, the amount of data can often be overwhelming. But what if there was a way to make this process more efficient, faster, and, most importantly, insightful? Enter Artificial Intelligence (AI).

In this blog, we will explore how AI, specifically through tools like ChatGPT, can transform the workflow of investment analysts. We'll use Vedanta Limited as a case study to demonstrate how prompts and interactions with AI can yield a detailed equity research note, effectively showcasing the true potential of AI-driven analysis.

The Traditional Analyst Workflow

The traditional analyst workflow typically involves collecting and reviewing data from multiple sources, such as financial statements, industry reports, and recent developments. Analysts then consolidate this information to provide meaningful insights about a company's valuation, growth prospects, and risks. This entire process, while effective, is extremely time-consuming and labor-intensive.

How AI Enhances the Analyst Workflow

AI, through natural language models like ChatGPT, can act as a personal research assistant that reduces the time spent on manual tasks, allowing analysts to focus on more value-added activities, such as interpreting data and making strategic decisions. Here's how ChatGPT helped me in writing comprehensive research note on Vedanta Limited:

1. Prompting the AI for Data Gathering I started by breaking down the different aspects I wanted to cover for Vedanta. I provided ChatGPT with prompts such as: "What is the current business structure and core operations of Vedanta Limited?" "Provide a summary of Vedanta Limited's major business verticals and their contributions to revenue, including percentage contributions." "What are the recent financial highlights from Vedanta's earnings report for FY24?" This enabled the AI to gather and compile detailed responses for each question. Instead of scouring multiple annual reports, press releases, and analyst commentaries, I could focus on refining and tailoring the responses.

Mapping Commodity Prices to Segment Revenue: Vedanta's revenue is highly sensitive to commodity prices. I used prompts like "Map the segment to the associated commodity prices and provide the performance of these commodities over 1 Year, YTD, and QTD." The AI provided a table that helped visualize how fluctuations in commodity prices impacted Vedanta's major business verticals. This analysis showed the sensitivity of Vedanta's revenue to the underlying commodities, providing a clearer understanding of the risks and opportunities associated with price volatility.

1. Analyzing Market Position and Competitors To understand Vedanta's competitive positioning, I asked the AI: "Who are Vedanta Limited's main competitors in the natural resources sector?" "How does Vedanta compare with its competitors in terms of market share, profitability, and growth prospects?" The AI provided me with a concise analysis of competitors like Hindalco Industries, ONGC, and Tata Steel. By cross-referencing the data, I could quickly understand Vedanta's market strengths and weaknesses.
2. Recent Developments and Strategic Moves I used prompts like "What are the key takeaways from Vedanta's recent demerger announcement?" and "What strategic initiatives has Vedanta undertaken recently to enhance its market position?" The AI reviewed the demerger details, highlighting Vedanta's plan to split into six different entities to unlock shareholder value. It summarized how these strategic moves are expected to impact the business and provided an assessment of upcoming projects such as the world's largest alumina refinery in Lanjigarh.
3. Creating a Detailed Valuation Analysis Valuation is one of the most complex aspects of equity research. I used prompts like: "How does Vedanta's current valuation (P/E, EV/EBITDA) compare with its peers?" "What is the value of the company per share using DCF and reverse DCF methods?" By leveraging the AI's understanding of valuation methodologies, I was able to extract a detailed valuation comparison that included metrics such as Price-to-Earnings ratio (P/E), Enterprise Value to EBITDA (EV/EBITDA), and Sum-of-the-Parts (SOTP) valuation. This saved significant time, which would traditionally be spent running financial models manually. Analyst Recommendations and Target Prices: To ensure accuracy and prevent any potential hallucinations from the AI, I directed ChatGPT to look up specific sources for current analyst recommendations. I used prompts like: "What are the current analyst recommendations and target prices for Vedanta's stock? Lookup websites like Moneycontrol, ETMoney, EconomicTimes, and provide analyst-level recommendations and target prices. Look for the latest reports published by sell-side brokerage firms like ICICI Securities, Kotak Securities, etc." "Lookup reuters(.)com for the consensus level recommendation and the target price and report it." This ensured that the AI provided reliable and up-to-date information based on recent sell-side research reports and consensus estimates, giving me more confidence in the output.
4. ESG and Sustainability Analysis With the increasing importance of ESG, I prompted the AI for Vedanta's sustainability practices: "What are the key sustainability initiatives outlined in Vedanta's most recent sustainability report?" The AI produced a well-rounded response, mentioning Vedanta's carbon neutrality target by 2050, its investment in Nand Ghars, and a summary of the company's CSR activities. This allowed me to have a holistic view of the company beyond financial metrics.

The Final Research Note

After combining all the generated content, I had a detailed equity research note on Vedanta Limited. It covered everything from a general overview, financial performance, market position, recent developments, ESG practices, to valuation metrics. The AI significantly reduced the time needed for data collection and allowed me to focus more on the narrative and insight generation.

Benefits of Using AI for Analysts

• Enhanced Efficiency: Instead of spending hours combing through multiple data sources, AI can provide quick, concise responses that analysts can validate and refine.
• Data Consolidation: AI brings together information from disparate sources, offering a unified perspective that is often difficult to achieve manually.
• Improved Insight Generation: By using AI prompts to drive research, analysts can focus on creative and analytical aspects of their work, leading to deeper insights and a better understanding of the subject.
• Reduced Errors: AI can help reduce errors in data extraction and initial analysis by automating repetitive processes.

The Power of Professional Data Sources

If AI can do so much with open data available on the web, just imagine the wonders it can achieve with access to professional data sources like LSEG. With real-time Reuters news, corporate disclosures from filings, real-time reports from sell-side analysts, conference call transcripts, and knowledge graphs that link entities, the potential for AI-driven analysis is virtually limitless. Leveraging these data sources would allow AI to provide even more accurate, timely, and comprehensive insights, making it an indispensable tool in the analyst's toolkit.

Call to Action

If you want to explore the possibilities of integrating AI into your organization—leveraging professional data sources like LSEG, real-time Reuters news, corporate filings, sell-side analyst reports, conference call transcripts, and proprietary data—to conduct a proof of concept, feel free to reach out to me.

Disclaimer

Vedanta has been taken purely as an example, as this company has been in the news significantly. This blog is not intended as investment advice. The example is solely to demonstrate the capabilities of AI.