Wiki

Clear, well-sourced answers to common questions about investing, market data, and how the Indian stock market actually works. Each answer is written to be read in a few minutes, with links to the Financial Glossary for any term you want to look up.

Market Behaviour

Why prices move the way they do, and why the market often reacts to expectations rather than reported results.

Why Numbers Differ

Why the same company can show different valuations, ratios, and market caps across different data sources.

Data & Quality

How prices, statements, and datasets are constructed, revised, and validated, and what makes one dataset cleaner than another.

Company Analysis

How to read financial results with nuance, beyond the single headline number.

Derivatives & Structure

What derivatives data reveals about positioning and expectations, read as information rather than as a trade.

Investor Psychology

The predictable ways human psychology shapes investing decisions, and how discipline counters them.

AI & Investing

What AI can and cannot do for investors, and how to use it without outsourcing judgment.

Can AI replace investment research?

AI can accelerate investment research, but it does not replace it. Large language models are strong at synthesising, summarising, and screening large amounts of public information quickly, and weak at judgment, accountability, and reasoning about genuinely new events. The realistic role is augmentation: AI drafts and organises, while a person verifies the figures and makes the decision.

6 min read

Why do AI models sometimes disagree about the same company?

Two AI models can describe the same company differently because they were trained on different data with different cut-off dates, are given different questions and context, and generate answers with some built-in variation. Any of them can also state a figure that is simply invented. Disagreement is a signal to go and check the primary source, not to pick whichever answer you prefer.

6 min read

How should AI-generated financial information be verified?

Verify AI-generated financial information by treating every specific figure as unconfirmed until it is traced to a primary source. Check numbers against official filings, exchange data, and regulator or registrar records rather than against another AI. Confirm the figure is current, matches the exact definition you need, and refers to the right entity and period before you rely on it.

7 min read

What is an MCP server, and why does it matter for investors?

An MCP server is a small service that lets an AI assistant read data from a system you already use, through a fixed set of named tools rather than through text you paste in. For an investor it removes the copy-and-paste step: instead of describing your holdings to an assistant, you grant it permission to read them. The protocol decides what the assistant may ask for, and you decide what it is allowed to see.

6 min read

Research Skills

How to evaluate data, avoid common research traps, and focus on what actually matters over the long term.

How do you tell a reliable financial dataset from an unreliable one?

A reliable financial dataset comes from an authoritative source, states how it was adjusted and when it was revised, and covers the full universe without silently dropping companies. An unreliable one hides its methodology, changes past numbers without a trail, and shows gaps you notice only when the figures stop making sense. Judge data less by how polished it looks and more by whether you can trace each number to its origin.

7 min read

Why do stock screeners mislead if you ignore survivorship bias?

A stock screener usually shows only the companies that still exist today, which means every firm that was delisted, merged, or went bankrupt has silently dropped out of the list. Because the failures are missing, historical averages, win rates, and backtests look better than the market ever actually delivered. To read a screener honestly you have to remember the companies that are no longer there.

6 min read

Which company metrics change slowly enough to matter for long-term investors?

The metrics that matter over years are the ones that move slowly and describe the durable quality of a business: how much it earns on the capital it uses, its profit margins, and the direction of its debt. Fast-moving figures like a single quarter's earnings per share bounce around on timing, one-off items, and seasonality, so they say more about the last three months than about the company. Long-term research means weighting the slow, structural signals over the noisy ones.

7 min read

Funds & Indices

How mutual fund and index data is constructed, and how to research funds beyond a star rating.

Deposits & Fixed Income

What a fixed deposit really returns after tax, how much of it is insured, and why smaller banks pay more for the same money.

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