Ainvestor

I published a new research article exploring a question that sits behind many investing decisions: why can a stock’s estimated fair value differ so much from its market price?

The short answer: they are produced in entirely different ways.

Fair value is a conditional estimate based on assumptions about future cash flows, growth, margins, and risk. Market price is a live consensus shaped by expectations, news, sentiment, liquidity, and the marginal buyer and seller.

That difference matters for AInvestor.

We treat the valuation gap as one signal—not as an automatic buy or sell decision. A stock below a fair-value estimate may offer a margin of safety, but it can also be deteriorating or temporarily unpopular. A stock above fair value may be risky, or the market may simply be pricing growth that a conservative model has not captured yet.

The practical takeaway is to use valuation alongside quality, growth, momentum, and financial-health signals—and to view fair value as a range rather than a precise target.

I wrote up the full framework, including the common drivers of the gap, why it can persist, and how investors can use it more carefully:

https://ainvestor.biz/research/articles/why-fair-value-differs-from-market-price

Curious: when you build or use investment tools, do you present a single “fair value” number, or a valuation range with explicit assumptions?

A lot of investing content is driven by narratives, opinions, and hot takes.

I wanted to build something more systematic.

So I built AInvestor — a platform that applies quantitative investing principles to stock analysis, scoring, and portfolio backtesting.

The core idea behind quantitative investing is straightforward: instead of making decisions mainly from intuition, you define rules upfront and apply them consistently.

In my case, that means:

• Collecting financial and market data

• Converting raw data into measurable signals

• Combining valuation, quality, risk, and macro factors into a scoring model

• Ranking stocks systematically

• Testing the framework through historical backtesting

One challenge I found while building this was that a scoring model alone is not enough. You also need portfolio logic, rebalancing rules, benchmarking, and ways to test behavior across different market environments.

That is why AInvestor includes not just stock scores, but also a backtest calculator that allows users to test different periods and compare results against the S&P 500.

The goal is not to claim certainty or “beat the market” with a magic formula.

The goal is to create a process that is:

• Transparent

• Repeatable

• Testable

• Easier to improve over time

When performance changes, you can analyze why — valuation assumptions, risk filters, factor behavior, or changing market regimes — instead of relying on post-hoc explanations.

I recently wrote a deeper article explaining how quantitative investing works in practice, how the framework is implemented inside AInvestor, and why backtesting matters.

If you’re interested in systematic investing, quantitative finance, or building data-driven investment tools, I’d love feedback.

Full article:
https://ainvestor.biz/research/articles/what-is-quantitative-investing

Hey everyone 👋

I’m Ivan, and I’m currently building AInvestor — a Data-powered stock analysis tool focused on long-term investing.

I have a background in mathematics, data science, and software engineering, and I’ve always been interested in finance and investing. AInvestor is my attempt to combine those areas into one practical product.

The goal is to make investing more data-driven and transparent by turning financial statements, valuation metrics, and other signals into clear insights that are easier to understand and compare.

I’m still actively building and improving it, especially the underlying models and scoring system, and I’d love feedback from other builders and investors here — especially on what actually matters when evaluating stocks long-term.

Would appreciate any thoughts or criticism.