Reflections

I've only been investing for about a month, and it's a very small sample to form opinions. Still, I've learned a few things and formed a basic philosophy. Here it is.

Luckily or unluckily, I started my journey right when the bear market hit. Stocks began breaking patterns, and corrections across markets. It was an overwhelming experience to figure out what to do.

Having the right network of people to share ideas goes a long way. Reading books also helps, as you learn from the past and build a mental map. I still lack that network, but I'm trying to find one. This website serves that purpose.

I was lucky in the sense that I could find good businesses at decent valuations. I wish I had started earlier. Investing is a function of (1) luck and (2) skill. Luck favors those who show up every day, keep a watchlist of companies, do their homework, and grab opportunities when they appear.

Markets are random. In a bull market, even stocks with zero fundamentals can soar due to euphoria. In a bear market, good results might not excite anyone, and panic can spread. That's where a prepared mind takes advantage.

Understanding the type of market we are in helps in setting the expectations right. You don't expect 30-40% CAGR in this market.

Below is a taste of how I approach finding the right businesses:

On screening:

• Of the entire universe of businesses, only a few fit your needs. Having a hard filter for screening is important, but not so rigid that you miss opportunities.
• Screening focus mainly on past trends? Having an idea about rate of change is important. When I examine a business, the first thing I ask is what's unique. Is there high sales growth? Deleveraging? A shift in product mix? I need to see what drives value creation.
• I use AI, stockscans.in, Screener, and read a lot of news and watch YouTube videos. Since it's results season, I have a quick summary of almost all the conference calls. If an idea clicks, I dig deeper (more on AI below).
• Good screens make a difference. It helps to have a strong taste for certain types of businesses. I believe in focusing on areas where I have competence. Since I know nothing about banks, insurance, or NBFCs, I stay away. I need some informational advantage, thinking same as others will yield average returns eventually.
• I don't take the efficient market hypothesis at face value, but it's an important mental model. I keep asking, "What am I seeing differently from others?" and "What if it's already priced in?" Those questions take time to answer but can filter a lot of ideas.

On analyzing a business:

• Every business is unique. The first thing I'm interested in is understanding the business model. If I don't even understand how the company makes money, how can I forecast its value creation? I follow the principle "best investors have no use for spreadsheets," because I see people doing ten-year DCFs, diving into extreme detail, only to realize they're wrong. I prefer the 80–20 principle: identify the key value drivers and forecast those. My horizon is usually three to four years—I can't predict much beyond that.
• Value is created from future cash flows, not past ones. Past growth rates can serve as a reference point, but they can't be the only factor you consider. This helps when deciding to sell, too. Once the drivers you identified have run their course, it's time to exit.
• Companies trading at already rich valuations create a negative cycle. I've seen stocks with a 120–130 P/E get punished for slow earnings. I generally avoid such companies because the odds can turn quickly when the cycle shifts. How do you justify a 130–150 P/E? In a bull market, maybe, but in a bear market? Hell nah.

Ultimately, everything is about earnings. The market loves earnings and their growth. Companies showing good growth get rewarded, what are we doing if not forecasting the earnings?

Sectors move together. Cycles come and go. The one in favor now shall be out of favor soon. Using market dashboard, screening for sectors showing more strength than the benchmark, tracking results across industries helps me in finding the favored ones.

Reading about a company in isolation is like the classic example of an elephant and blind people. Need to see the entire value chain. What are its value drivers? Which part of the value chain is most profitable? Does the sector itself make money for shareholders? Once you know that, you can focus on the specific company and see what makes it special from others.

One dilemma I face is how much research is too much like can't we just go with flow of the market assuming the markets are right? I don't have the answer to this yet.

But I try to keep my process intact:

1. Try to understand the business?
2. Understand the value drivers
3. Read the value chain and industry
4. Then go on to create a bull/base/bear case scenario
5. If the odds are in my favor in terms of forward PEG/growth rates, I might take a position.

Having a portfolio approach. I don't think in terms of each stock in my portfolio, but in terms of how my overall portfolio is doing? I follow 6-8-10% approach, 6 for idea I am not very confident, many ideas start at 6 and as I read can move higher. 8-10 businesses is what I think I am capable of tracking in my portfolio at any moment.

I face loss aversion bias a lot specially in a market like now where almost all the stocks are taking a hit. Having a disciplined approach and being objective about selling helps overcome the biases.

This is hard, and that's why investing is hard. Anyone who says otherwise is lying. If I have 0 interest in the field, I might work 6/10, and someone with more agency and who has a genuine interest in the field will outperform me by a greater margin, and chances of survival reduces specially in the field where you get paid to be right more than getting wrong.

The first thing is understanding if this is really the work I would like to spend my life on. What special advantage do I have in the field? If I'm not into it, problems with procrastination emerge.

AI is getting there. By the time I'm writing this, OpenAI has released Deep Research, which I think is a game changer- specially for research. I believe language models aren't great reasoners yet, but they're good curators. You can get about 60% of the good stuff from the answer and still be better off.

One example of how I use AI is: I use language models a lot to read conference calls. Instead of going through them one by one, I feed the PDFs into my own tool to generate detailed summaries, then I can just see what guidance or what different management has to say. I can easily get 40-50 concalls daily, and shortlist the ones I find interesting to research further.

As reasoning models get better and better, the line between an average analyst and AI will fade away. How do you differentiate then? The work I take 1 day, AI can do in minutes? I think judgement is where humans have an edge. Everyone will have their own opinions, ones having a better insight might have an edge? Or they won't... yet to see.

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