Fundamental Analysis (FA)

Fundamental analysis (FA) is a way of judging what an asset is worth by examining the economic, financial and qualitative facts behind it, rather than the pattern of its recent price. For a company, that means studying its revenue, profits, debts, management, competitors and industry. The goal is to estimate an intrinsic value and compare it with the market price, so that an investor can decide whether the asset looks cheap, expensive or fairly priced.

Fundamental analysis is usually contrasted with technical analysis, which studies charts, trading volumes and price patterns to anticipate market moves. Many investors combine the two. FA is the basis of value investing and of most professional equity and credit research, and in recent years analysts have tried to adapt it to cryptocurrencies, where there are often no profits or accounts to examine.

Imagine buying a small café. A careful buyer would not just look at what the last café on the street sold for. They would count the customers, check the rent, read the accounts and ask whether a new chain is opening next door. From that, they would work out a fair price and refuse to pay much more.

Fundamental analysis applies the same thinking to shares, bonds or tokens. It asks what the thing actually earns or does, how secure that is, and what it is reasonably worth. If the market price is well below that estimate, the analyst sees an opportunity; if it is far above, a warning.

The method is most closely linked to Benjamin Graham and David Dodd, whose 1934 book Security Analysis argued that investors should base decisions on careful study of a business's financial statements and a margin of safety between price and value. Graham's later work, and the long record of investors influenced by him, made the approach a cornerstone of modern investing. Over the following decades, academic finance added tools such as the discounted cash flow model and formal ways of measuring risk.

Analysts usually work at two levels. Top-down analysis begins with the wider economy, such as growth, inflation and interest rates, then narrows to industries and companies. Bottom-up analysis starts with an individual company and its prospects.

• Financial statements. The income statement, balance sheet and cash flow statement show profits, assets, debts and how cash moves through the business.
• Ratios. Measures such as the price-to-earnings ratio, debt-to-equity and return on equity allow comparison with rivals and with the company's own past.
• Valuation models. Discounted cash flow, dividend discount and comparable-company methods turn forecasts into an estimated value per share.
• Qualitative factors. Management quality, brand strength, regulation, competitive advantages and governance can matter as much as the numbers.

A simple example uses the price-to-earnings ratio:

If a company's shares trade at 60 and it earned 4 per share last year, its P/E is 60 ÷ 4 = 15. If similar firms trade at a P/E of 20 and the company's prospects are comparable, an analyst might conclude it is undervalued, although a lower ratio can also reflect higher risk or slower growth. Earnings per share itself is described under earnings per share.

Most cryptocurrencies do not publish audited accounts or pay dividends, so analysts look for other evidence of value and use. Common factors include:

• Network activity. Daily active addresses, transaction counts and fees paid indicate how much a network is used.
• Value in applications. Total value locked measures assets deposited in DeFi protocols on a chain.
• Token supply. Tokenomics, including issuance schedules, unlocks and burns, affects how much new supply reaches the market.
• Team and governance. The track record of developers, the spread of token ownership and how decisions are made.
• Documentation and code. The project's whitepaper, audits and open-source activity.

Some analysts also adapt equity ratios, for instance comparing a network's market value with the fees it generates, in the manner of a price-to-sales ratio. Data drawn directly from blockchains, often called on-chain data, is a major source, because it is public and updated continuously.

• Forecasts are uncertain. Valuations depend on assumptions about growth and discount rates; small changes can produce very different answers.
• Markets can stay mispriced. A stock or token judged undervalued may stay cheap for years, and the efficient market hypothesis argues that publicly available information is already reflected in prices.
• Data quality. Company accounts can be restated or manipulated, and crypto metrics can be inflated by wash trading or automated activity.
• Thin crypto fundamentals. Many tokens have little usage or revenue, so their prices depend mostly on sentiment, which fundamental methods struggle to capture.

Fundamental analysis is a framework for reasoning about value, not a guarantee of returns, and nothing here is investment advice.