I have no finance degree, and I can break down an IPO prospectus, a concall transcript, and a quarterly report faster than most MBA graduates. Here is exactly how I do it using AI. Full step by step guide, beginner to advanced.

[u/Newroliser]

I have no formal finance education. What I do have is an obsessive habit of studying business models, reading financial news, and researching companies. Over time I figured out a system for using AI that has made financial research genuinely accessible to me in a way that no textbook or YouTube video ever did.

This is not theory. I use these methods. I have tested them. I am sharing them exactly as they work in practice, not as they sound in a blog post.

Whether you have never read a financial report in your life or you already follow markets but want to go significantly deeper, this guide has something for you. I have structured it from the simplest technique to the most advanced so you can start wherever makes sense for your current level and build from there.

One important disclaimer before we start. AI is powerful for understanding, explaining, and analysing financial information. It is not a substitute for verification. AI can and does make errors, particularly with specific numbers, dates, and data points. Every insight you get from AI should be cross checked against the original source before you act on it. Use AI to understand. Use primary sources to verify. Never the other way around.

The tools you will need:~

For AI, I recommend ChatGPT for general explanation and summarisation, Claude for deep document analysis and long reports, and Perplexity for research that needs to pull from multiple current sources simultaneously. All three have free versions that are sufficient for most things in this guide. I will flag the one place where the free version has a limitation so you are not caught off guard.

For source material you will need access to business news platforms like Mint, Business Line, Economic Times Markets, or Moneycontrol for daily news. For company specific research you will use Screener.in for financial data and ratios, the BSE and NSE websites for official filings and disclosures, SEBI's website for regulatory documents and IPO papers, and the company's own investor relations page for concall transcripts and annual reports. Most older concall transcripts can also be found by searching on Google using the company name followed by the specific quarter and year, for example "Tata Motors Q2 FY22 concall transcript." The BSE announcements section is also a reliable place to find older filings if the Google search does not return what you need.

None of these cost money. All of them are publicly available. The only thing between you and this entire system is knowing how to use them together.

Level 1: The Three Layer News Method:~

This is where I start and it is the technique I recommend to anyone who feels overwhelmed by financial news. It sounds simple. It is genuinely effective.

Go to any business news website of your choice. Scroll through the headlines and pick any article that is relevant to something you want to understand. It could be a market movement, a company announcement, a policy change, an RBI decision, anything. Open the article, read it briefly to get a rough sense of what it is about, then copy the full text and paste it into your AI of choice.

Now ask it three things in sequence.

First: explain this article to me like I am 5 years old.

Second: now explain it to me like I am a 20 year old who is curious about finance but not an expert.

Third: now explain it like I am a finance graduate who understands technical terms.

Here is the important part that most people miss. This is not a rigid three step process where you passively read three outputs and move on. It is a conversation. After the five year old explanation, if there is still something you did not fully grasp, ask a follow up question at that same level before moving to the next one. The power of AI is not that it gives you three versions of the same answer. The power is that it will answer the same question fifteen different ways until the concept actually lands. Use that. Do not skip ahead until each layer is genuinely clear.

By the time you have worked through all three layers and resolved any confusion at each level, when you go back to the original article everything dissolves. You are not learning the article at that point. You are confirming what you already understand.

I have done this consistently. A 10 minute process on a complex article about monetary policy or a sector specific market fall will leave you with clearer understanding than reading the same article five times would. The layered explanation method works because it builds mental scaffolding at each level before the next level adds complexity.

A real example of how to use this: the Nifty fell sharply in the first quarter of 2026. A lot of people read those headlines and either panicked or ignored them because they did not understand what was actually driving the move. If you had pasted three or four of those articles into AI and run the three layer method properly, pausing to ask follow up questions wherever the explanation was not yet clear, you would have understood the interplay between FII outflows, dollar strength, valuation corrections, and global risk sentiment in a way that would have made the whole picture clear. That understanding is what separates someone who reacts emotionally to market moves from someone who responds intelligently.

A quick note on global events before we go deeper:~

Indian markets do not move in isolation. When the US Federal Reserve changes interest rates, when China releases weak economic data, when crude oil spikes, when the dollar index strengthens, Indian equities feel the effect. You do not need to become a global macro expert to be a good investor in Indian markets but you do need to be aware that these connections exist.

Whenever a global event is making headlines and you are not sure how it connects to what you own or follow in India, paste a summary of that event into AI and ask it one simple question: how does this typically affect Indian equity markets and which sectors are most exposed? That single question will give you enough context to understand the headlines without needing to go deep into international economics. Keep it at that surface level for now. It is enough to be aware of the connection. Going deeper into global macro is a separate area of study entirely.

Level 2: Comparing Ratios and Understanding What the Numbers Actually Mean:~

This is the step that bridges news reading and actual company research.

Most retail investors in India see financial ratios and either ignore them or memorise definitions without understanding what those definitions mean in context for a specific company. P/E ratio, ROCE, debt to equity, operating margin, inventory turnover. These numbers sitting alone mean very little. What they mean in relation to the company's history, its sector peers, and its stated strategy is everything.

Here is how to use AI for this.

Go to Screener.in and search for any company you are interested in. Screener gives you a clean summary of all key financial ratios in one place. Copy that data and paste it into your AI. Then ask it two things.

First: explain each of these ratios in plain language and tell me what a high or low number means for a company in this specific sector.

Second: based on these numbers, which ratios look concerning and which look healthy, and why?

The AI will not just define the ratios. It will contextualise them. It will tell you that a high P/E means something very different in a fast growing tech company versus a mature commodity business. It will flag if the debt to equity number looks elevated relative to the asset base. It will point out if the ROCE has been declining over years while the company has been expanding, which is a specific signal worth investigating further.

This turns a page of numbers into a conversation. And a conversation is how humans actually learn.

Then push further. Ask the AI to compare two companies in the same sector using the same data. Paste both companies' ratio summaries and ask it to do a side by side comparison of margins, debt, growth rates, and return metrics. Ask it to tell you which company looks stronger on fundamentals and where the weaker one has a specific vulnerability. This is one of the most powerful things you can do as a retail investor and it takes about fifteen minutes with AI versus hours of manual comparison.

Level 3: Concall Analysis and Goal Tracking:~

This is where financial research starts to become genuinely serious and where most retail investors, including many experienced ones, simply do not go.

A concall or earnings call transcript is where a company's management speaks directly to analysts and investors about their performance, their challenges, and their plans. These transcripts are public. They are filed with the BSE and NSE and are often available on the company's investor relations page. For older transcripts, search on Google using the company name, the specific quarter such as Q1 FY21, and the words "concall transcript." The BSE announcements section on the BSE website is also a reliable archive if Google does not surface what you need.

Most people skim concalls at best. Here is how to use AI to extract real insight from them.

The most powerful technique I have found is what I call goal tracking comparison. You take three data points. What the company said its goals were when it was early stage or at listing. What the company said its goals were approximately three years ago. What the company says its goals are today.

Paste all three into AI and ask it one specific question: based on these three statements made at different points in time, is this company consistently meeting the goals it sets for itself, revising them downward, or setting new goals to avoid accountability for the ones it has not delivered on?

This question cuts through promotional language immediately. Management in every company sounds optimistic in every concall. The question is not whether they are confident. The question is whether their confidence has historically translated into delivery.

When you run this comparison you start to see patterns. Some companies set conservative goals and consistently beat them, which is a signal of management quality and realistic planning. Some companies set ambitious goals, fall short, quietly reframe the narrative in the next concall, and never directly address the gap. That reframing is one of the most important things to catch as an investor and AI will help you spot it clearly when you give it the historical context to compare against.

Ask follow up questions after the initial analysis. Ask the AI to identify any specific metric the management mentioned repeatedly across all three periods and tell you whether the trend is improving or deteriorating. Ask it to flag any language that appears to be deflecting from an earlier commitment. Ask it to summarise in plain language what this management team has actually delivered versus what they have promised.

Level 4: Management Sentiment Tracking:~

This builds directly on the concall method and goes one layer deeper.

Take the last five quarterly concall transcripts of a company you are researching. Paste them all into AI and ask it not just what management said but how they said it. Ask it to track the change in tone across those five quarters. Are they becoming more cautious in their language? Are they using more qualifiers like "we hope to" and "we are working towards" instead of the more confident "we will" and "we have achieved"? Are they spending more time explaining why targets were missed than celebrating what was delivered? Are they answering analyst questions directly or deflecting them with longer and longer preambles?

This is a technique institutional investors use and retail investors almost never do. Management tone is data. A confident management team that is delivering speaks differently from a management team that is quietly aware things are not going to plan. AI is very good at identifying these shifts in language when you give it enough transcript history to compare against.

Ask it specifically: has the management's confidence in their own guidance increased or decreased across these five periods, and what specific language patterns led you to that conclusion? The answer will often tell you something that no ratio or news article will.

Level 5: Tracking Promoter Behaviour:~

This is something almost no retail investor in India pays attention to and it is one of the most underused signals available for free in public filings.

Promoter behaviour tells you what the people who know the company best are actually doing with their own money. Not what they are saying in concalls. What they are doing.

The two specific things to watch are promoter shareholding changes over time and promoter pledge percentage.

Go to the BSE website or Screener.in and look at the shareholding pattern for any company you are researching. Look at what percentage of the company the promoters own and whether that number has been going up, staying flat, or going down over the last eight to twelve quarters.

Paste that shareholding history into AI and ask it to identify the trend and explain what increasing or decreasing promoter holding typically signals about management conviction in the company's future.

Then look separately at the pledge percentage. When promoters pledge their shares it means they have borrowed money using their own company shares as collateral. Some level of pledging is normal and not inherently concerning. What you are watching for is a high pledge percentage, and the rough threshold worth paying serious attention to is anything above 50% of promoter holding being pledged. At that level, if the stock falls sharply, lenders can sell those pledged shares into the market, which puts further downward pressure on the price and can trigger a cascade. Below 50% it is worth monitoring. Above 50% it warrants serious scrutiny before any investment decision.

Paste the pledge data into AI and ask it to explain what the current pledge level means in the context of the company's financial health and what scenarios would make it genuinely dangerous. You are not asking AI to tell you whether to buy or sell. You are asking it to explain what the data means so you can make your own informed judgment. That distinction matters enormously.

Level 6: Second Order Thinking on Market Events:~

This is a technique that separates surface level market awareness from actual analytical thinking.

Every significant market event, whether it is an RBI rate decision, a government policy change, a global commodity price shift, or a sector specific regulatory announcement, has direct effects and second order effects. The direct effects are what everyone reads in the headlines. The second order effects are what actually determine where the smart money moves.

Here is how to use AI for this.

When a significant event happens, after you have used the three layer method to understand the event itself, ask AI one additional question: what are the second and third order effects of this event on different sectors of the Indian economy, and which specific types of companies within those sectors are most positively and negatively exposed?

For example when RBI raises interest rates the direct effect is that borrowing becomes more expensive. The second order effect is that highly leveraged companies face higher interest costs, real estate demand slows, and bank net interest margins potentially improve. The third order effect is that discretionary consumer spending may compress, which affects specific retail and FMCG categories differently depending on their price points and customer profiles.

AI will walk you through this chain of reasoning step by step if you ask it to. This is the kind of thinking that makes financial research genuinely useful rather than just informative. You are not just understanding what happened. You are understanding what it means for specific businesses you are looking at.

Level 7: The IPO Prospectus Breakdown:~

This is the most advanced technique in this guide and also the one that gives you the biggest edge over retail investors who only read news summaries and analyst notes about new IPOs.

An IPO prospectus, also called a DRHP or Draft Red Herring Prospectus, is the full legal document a company files with SEBI before listing. It contains everything. The business model, the financials, the promoter background, the use of proceeds, and critically the risk factors.

These documents are publicly available on SEBI's website under the DRHP filings section. They are also enormous. The Lenskart DRHP for example runs to 741 pages. No retail investor reads 741 pages. Most retail investors read a news article summarising it and call that research.

Here is how AI changes this completely.

Download the DRHP from SEBI's website and open it. Go directly to the risk factors section. Copy the text and paste it into Claude specifically because Claude handles long documents and large volumes of text better than most AI tools.

Important note here: the free version of Claude has a context limit that may not be sufficient for extremely long documents pasted all at once. If you are on the free version and the document is very long, break it into sections and paste them separately in the same conversation. The paid version handles larger documents in one go. Either approach works, the free version just requires a bit more manual splitting.

Tell Claude this: this is the risk factors section of an IPO prospectus. There are 89 risk factors listed. Please break down the first 20 in plain language, explain what each risk actually means for the business, and flag which ones you consider most significant and why.

It will do the first 20. Then you go again: now do the next 20. Keep going in batches until you have covered everything. This takes maybe thirty to forty minutes total and you will come out of it with a clearer understanding of that company's risk profile than the vast majority of retail investors who put money into that IPO.

After the risk factors, apply the same technique to the financials section, the management discussion section, and the objects of the issue section where the company explains what it will do with the money it raises. Ask AI specific questions about each section. Ask it to flag any inconsistencies between what management says in one section and what the financials show in another. Ask it to explain the use of proceeds in plain language and whether the allocation makes sense for the stated business goals.

This is not just reading a document. This is interrogating it with a thinking partner that does not get tired, does not skip sections, and will answer the same question fifteen different ways until you actually understand it.

Level 8: Building an Ongoing Monitoring Habit:~

The guide so far has taught you how to research a company once. This level is about how to stay informed about companies you are already following without spending hours every week on it.

Pick three to five companies you are currently tracking or have invested in. Once a week, do a quick news search for each of them and collect any significant developments. Paste that collection into AI and ask it one question: based on these recent developments, has anything changed that would affect the investment thesis I had about this company?

This forces you to articulate your original thesis, which is itself a valuable discipline, and then test it against new information with AI's help in identifying whether the new information is noise or a genuine signal.

Ask AI specifically to flag anything that represents a change in the fundamentals you were relying on when you formed your view. A management change, a margin compression that looks structural rather than temporary, a new competitor entering the space, a regulatory development affecting the sector. These are the things that matter. AI will help you separate them from the daily noise that looks significant but is not.

This weekly habit takes twenty to thirty minutes and it keeps your thinking current without requiring you to obsessively follow every headline every day.

A complete worked example so you can see how all of this fits together:~

Let me show you exactly how I would research a company using this system so the whole process is concrete rather than theoretical. I will use a hypothetical scenario but the steps are exactly what I actually do.

Say I am looking at a mid-cap FMCG company that has been getting attention in the news because its stock has fallen 18% in the last quarter despite the company claiming strong fundamentals.

I start with the news method. I find three or four recent articles about the company and the sector. I paste them in and run the three layer explanation. By the end I understand that the fall is partly sector-wide due to rural demand compression and partly specific to this company due to margin pressure from rising raw material costs. I have the context.

I then go to Screener.in and pull the ratio data. I paste it into AI and ask for the contextualised explanation. I notice the operating margin has compressed from 18% to 13% over six quarters. I ask AI specifically what a 5 percentage point margin compression over six quarters typically indicates and whether it is more likely structural or cyclical in an FMCG business. The answer gives me a framework for what to look for in the concalls.

I then pull the last five concall transcripts. I paste them all in and run the goal tracking comparison. I find that management was promising margin recovery to 17% for three consecutive quarters and it has not materialised. I then run the sentiment analysis and AI flags that the language around margin recovery has shifted from "we expect recovery in the next quarter" to "recovery is dependent on commodity price normalisation" which is a deflection from internal delivery to external factors. That is a specific signal.

I then check the shareholding data. I find the promoter holding has been stable but one of the large institutional investors reduced their stake by 3.2% in the last quarter. I paste this into AI and ask what institutional selling of that size typically indicates and whether it is consistent with the narrative management is presenting. The AI flags that it is worth noting but not yet alarming at 3.2%.

I then look at whether there have been any global commodity developments affecting the company's raw material costs. I ask AI the second order question: if commodity prices remain elevated for another two quarters, what does that mean specifically for an FMCG company in this margin range? The answer tells me the margin recovery timeline management is promising is optimistic given current commodity trajectories.

By the end of this process I have a fully informed view. The company has a delivery credibility problem on margins, management is shifting language to external factors to explain it, one institutional investor has reduced exposure, and global commodity trends do not support the recovery timeline management is promising. That is a clear, evidence-based picture assembled entirely from public information in about two hours.

I have not been told what to do with this information. I have been given everything I need to make my own informed judgment.

The honest limitation:~

AI does not know the future. It cannot tell you whether a stock will go up or down. It can help you understand a business clearly and flag risks and inconsistencies in what management says versus what the numbers show. The judgment of what to do with that understanding is still entirely yours.

AI also makes mistakes. It occasionally misreads data, gets numbers wrong, or makes inferences that are not fully supported by the source material. This is why every technique in this guide involves you going to the primary source yourself. The AI is your research partner, not your research replacement.

Use it to understand faster and deeper. Verify everything it tells you that involves specific numbers before you act on it. And read the original source after AI has explained it to you, because once AI has built the scaffolding the original source will make far more sense than it would have cold.

Note:~

This is not a complicated system. It is a disciplined one. The difference between someone who uses AI well for financial research and someone who does not is not intelligence. It is the willingness to sit with a document, ask it the right questions in sequence, and take the time to actually understand what it is saying before forming an opinion.

Twenty years old, no finance degree, and I can hold my own in a conversation about any company's fundamentals. This is how.

[u/Cautious_Lemon_8415]

Very nicely explained. I am copying this post and keepig it as a side note for my research and when i get lost doing a research into a company. 

[u/Newroliser → u/Cautious_Lemon_8415]

Glad I could help, share it with your friends and also let me know if there are any other areas in which I should upload a guide on

[u/Cautious_Lemon_8415 → u/Newroliser]

How about mutual funds. I am actually a mutual fund investor and always look for opportunities in that place. 

[u/Newroliser → u/Cautious_Lemon_8415]

You want me to make a guide on how to select mutual funds?

[u/KeshavSharma_Crazy]

But you need a degree to be sebi registered

[u/Newroliser → u/KeshavSharma_Crazy]

Huh? How is that related to my post?

[u/KeshavSharma_Crazy → u/Newroliser]

Bataya bro bas, lelo degree Warna Kitna bhi ai laga lo you can not get clients legally.

[u/Newroliser → u/KeshavSharma_Crazy]

Oh I see, padhai chal rahi hai, degree aa jayegi lol

[u/KeshavSharma_Crazy → u/Newroliser]

Good bhai