Artificial intelligence (AI) is right here and the world as we knew is altering quickly. AI tools have advanced significantly and are complementing workflows in all disciplines. The software program business is an effective instance. Gone are the times the place data of syntaxes of programming languages was a talent an engineer can be pleased with. With AI within the image now, immediate engineering has turn into the extra sought-after talent—whether or not they can assemble sensible prompts to leverage the facility of AI and carry a few leap in effectivity.
How concerning the discipline of non-public finance? Can AI tools assist a humble retail investor leapfrog his capabilities? This is a tough query to reply, because it entails one’s hard-earned cash. While there isn’t a doubt that AI tools have turn into extra mature and highly effective than earlier than, they may nonetheless hallucinate and generate unsuitable outputs. We tried experimenting with free AI tools accessible. In quick, we discovered them to be extraordinarily helpful in producing fast analyses, compiling knowledge accessible on the internet, constructing stock-screeners and making complicated calculations. This is a boon for traders who’re crunched for time. At the identical time, these tools are susceptible to be inaccurate that it isn’t protected to guess actual cash based mostly purely on an AI-generated evaluation.
The finest method then, is to use AI-generated outcomes to acquire a fast perspective and deal with them as a launchpad for your additional analyses—and this alone, in our view, makes case for AI tools to be a part of one’s private finance arsenal. In this story, we share a couple of easy use-cases for elementary traders to get began and additionally spotlight the constraints of AI tools so to keep away from these pitfalls.
Sector primers
Often you’d end up new to a sector and would respect a crisp primer on the sector to get began. We prompted Google’s Gemini to generate a primer on the tyres business. We suggest a pointed immediate as beneath to information the mannequin in getting the solutions you want.
Assume you’re a veteran of the Indian tyres business. Generate a sector primer. Focus on components such because the incumbents and their market share, the segments they serve, the uncooked supplies, enter price volatility, margins that may be anticipated and the sector’s cyclicality.
Here are a couple of pointers we learnt from the output.
* MRF, Apollo, CEAT, JK Tyre and Balkrishna Industries collectively management over 85 per cent of the organised market
* MRF has the deepest distribution community, Apollo has a presence in Europe via the Vredestein model and Balkrishna Industries is a pacesetter in off-highway tyres
* Replacement/ after-market gross sales generate increased margins than gross sales to OEMs
* Natural rubber and carbon black (a by-product of crude oil) are key uncooked supplies and their costs affect margins, which may vary between 10 per cent and 17 per cent
* The business is semi-cyclical, given the OEM demand is tied to new car gross sales (cyclical) and alternative demand is tied to put on and tear (non-cyclical)
AI may compile such a primer in seconds, which may have taken days in any other case. You also can use the ‘Deep Research’ characteristic for a thoroughly-researched output, nevertheless it takes a couple of hours to generate the output relying on the immediate.
Looking up paperwork
As a elementary investor, you can not shrink back from studying verbose paperwork like annual experiences and earnings name transcripts. AI tools are right here to make it easy, and that is the use-case we discovered to be essentially the most helpful.
We have been curious to understand how Maruti Suzuki’s price construction and margins have been impacted in FY23, when crude oil and commodities’ costs spiked after the outbreak of the Russia-Ukraine battle. Apart from increased logistics prices, suppliers of solid/ solid and plastic elements are depending on crude derivatives and typically move on price inflation again to OEMs like Maruti. Since we’re in the same scenario at present, studying the FY23 annual report would give significant perspective.
We uploaded the annual report into NotebookLM, a Google product. The USP of this product is that it largely sticks solely to the paperwork uploaded to generate its solutions and additionally provides citations. We learnt that the corporate did bear commodity price pressures. Yet, it managed to develop its EBIT margin from 3.5 per cent in FY22 to 7.3 per cent, pushed by working leverage (19 per cent quantity development) and calibrated worth hikes.
Now, going into FY27, can FY23 repeat itself for Maruti? Back then in FY22, gross sales quantity was impacted due to the semiconductor scarcity, giving the corporate a low base to work with in FY23. The similar can’t be stated about FY27, as OEMs have had 12 months in FY26 publish the GST cuts. Hence, to defend margin, the corporate is left with the only real possibility of climbing costs/ scaling reductions again. This leaves us with carefully monitoring worth hikes and any resultant affect on demand as key monitorable.
For earnings name transcripts, readers can use Perplexity Finance, the place transcripts of the current 10 quarters are available (beneath ‘Earnings’ tab). It additionally segregates them into subjects. You can select a subject from the drop-down record. For occasion, the administration’s ready remarks, platform synergies and scaling, margins and dangers in modules and polymers phase, technique for aerospace and semiconductor enterprise are a number of the subjects we present in Samvardhana Motherson’s Q3 FY26 earnings name.
Here’s one other extrapolated use-case. Some ratios resembling EBITDA margin and mounted property turnover ratio will not be available in quarterly earnings releases. You can simply add these PDFs into the AI mannequin of your alternative and get them calculated in a jiffy. We prompted Claude to calculate mounted property turnover ratio of Samvardhana Motherson for FY23-25. The screenshot of the output might be present in Image 1.
Post-market transient
It is just not sensible for retail traders to watch the market via the day. Perplexity Finance and Google Finance (Beta) may also help remedy this to an extent. Once buying and selling for the day closes, you will get a abstract of how your shares carried out. All you want to do is create a customized watchlist. While Perplexity provides you a readymade abstract (beneath ‘Watchlist’ tab), you have to a immediate to do the identical on Google Finance (Beta). We had created a watchlist with shares from the auto sector on Perplexity Finance. A screenshot of the market transient for March 27 is given for reference (see Image 2).
Screening shares
Building screeners to filter shares is inevitable in bottom-up investing. Though there are free screeners accessible on-line, they work with mounted syntaxes. It takes a while to learn the way precisely they work and can be troublesome for some traders who will not be savvy sufficient. For this particular use-case, an investor can use the ‘Screener’ characteristic of Perplexity Finance. It allows you to body your situations/ parameters in pure language itself. Here is an easy screener we tried. To determine quick rising auto ancillary shares, we gave the next immediate. The output is given in Image 3.
Prompt: Filter auto ancillary firms that had income development of over 20 per cent in FY25
DCF simulations
If you like the discounted money flows (DCF) mannequin for valuation selections, AI tools can deal with that too. Simply put, beneath DCF methodology, future free money flows are discounted for time worth to estimate an funding’s price. The methodology requires you to assume values for the variables concerned and the mannequin itself is just pretty much as good because the logical accuracy of the assumptions. But as soon as achieved with this step, you may use any of the AI fashions to run simulations. This use-case may very nicely discover favour with traders who will not be acquainted with spreadsheets.
Prompt:
Calculate Infosys’ market worth of fairness per share utilizing discounted money circulate mannequin. Also point out if there may be any upside left from present market worth of ₹1,275.
Assume the next variables.
Free money circulate to agency (FCFF) for FY26 = ₹30,000 crore.
FCFFs to develop at 5 per cent between FY27 and FY30.
Perpetual development price for FCFFs at 2 per cent.
The firm is net-debt free and so, price of debt might be taken as nil.
For price of fairness, assume danger free price at 4.4 per cent, fairness danger premium at 7.1 per cent and beta of 0.9 instances.
Number of shares excellent at 406 crore.
We replicate the output in brief right here, with out the calculation steps.
Output: Expected per-share fairness worth = ₹951
At present worth, there isn’t a margin of security. Instead, the market worth is about 34 per cent above the DCF‑derived intrinsic worth.
Readers can mess around with the identical immediate by altering variables to simulate a bull case, a bear case and a impartial case.
Caveat emptor
We have barely scraped the floor on this story, and we admit that the probabilities are countless. However, the know-how is in its infancy and has some tough edges to be smoothened. Once you determine to undertake AI for your funds, you need to totally know its limitations as your portfolio could also be one hallucination away from catastrophe. Here are a number of the limitations we noticed throughout our quick time testing the fashions.
Charts with valuation multiples (P/E, P/B for instance) in a time-series are actually helpful. A P/E time-series, based mostly on every day costs, is a traditional instance. Such charts assist put present valuations in perspective, relative to the previous. We tried constructing a every day P/B chart for SBI with Perplexity. What we bought as an alternative was a P/B chart as of economic year-ends, additionally not with out errors (see Image 4).
Note that whereas P/B values for FY21-24 are largely correct, that of FY25 and the present P/B will not be. Actual P/B ratio as of FY25 finish is 1.4x and the present P/B ratio is 1.6x – that are 2x, in accordance to Perplexity..
Next, we tried evaluating income development (12 months on 12 months) of TCS, Infosys and HCLTech over the past 4 quarters. Perplexity does have quarterly monetary knowledge beneath the ‘Financials’ tab. Ideally, the mannequin ought to have picked up knowledge from right here to construct the output desk. Instead, it tapped internet sources to do it. This meant some values have been incorrect (see Image 5).
Note that TCS’ income in Q2 FY26 truly grew 2.4 per cent and HCLTech’s income grew 8.2 per cent and 13.3 per cent in Q1 FY26 and Q3 FY26 respectively.
Next, have a look Image 3 once more. It contains Olectra Greentech, which is an OEM and not a provider of auto parts.
Moving on, we tried prompting Google Finance (Beta) to compute internet revenue margin of Maruti Suzuki, Hyundai and Mahindra & Mahindra for the final 5 quarters. Similar to Perplexity, Google Finance too, natively homes quarterly monetary knowledge. However, it sourced knowledge from internet sources to construct the output desk. Though some numbers come shut to actuals, some differ extensively (see Image 6 together with the infographic desk).
Similarly, different niggles do exist. However, for the use-cases we talked about earlier and past, AI is just too highly effective a instrument for retail traders to not benefit from. We recommend readers to use these AI platforms as a supplementary supply of their common course of investing and preserve experimenting till simpler, dependable workflows are found.
Attribution to AI merchandise within the story are meant neither as endorsements nor as criticisms
Published on March 28, 2026