Earnings and valuation analysis evaluates a company's financial performance and market pricing to determine whether its stock is undervalued, fairly valued, or overvalued. By examining revenue growth, profit margins, earnings per share, and valuation multiples such as price-to-earnings and price-to-sales ratios, traders and investors can assess whether the current market price is justified by fundamentals.
Revenue trends indicate whether a company's business is growing or contracting. Earnings per share (EPS) is the primary metric analysts use to measure profitability. Price-to-earnings (P/E) ratio compares market price to earnings to gauge relative valuation. Price-to-sales (P/S) ratio is useful for companies not yet profitable. Discounted cash flow (DCF) models estimate intrinsic value based on projected future cash flows. Earnings surprises — beating or missing analyst estimates — drive significant short-term price movements.
Enter long when valuation multiples are below historical averages and earnings growth is accelerating. Buy after a positive earnings surprise when the stock pulls back to support on healthy volume. Enter when the DCF-derived intrinsic value exceeds the current market price by a margin of safety. Initiate positions when sector rotation favours undervalued segments.
Exit when valuation multiples reach historically elevated levels relative to growth. Sell after a negative earnings surprise, especially if guidance is lowered. Reduce exposure when insider selling increases alongside stretched valuations. Exit value positions when price reaches or exceeds the calculated intrinsic value target.
Daily, Weekly, Quarterly (aligned with earnings cycles)
Fundamental analysis works best on longer time horizons where earnings growth ultimately drives share price. For shorter-term trades around earnings events, combine fundamental analysis with technical levels to time entries. Remember that crypto assets require different valuation frameworks since they lack traditional earnings — network fundamentals and tokenomics serve as substitutes.
Qualitative factor analysis evaluates the non-numerical attributes of an asset or company that influence long-term value — management quality, competitive moats, brand strength, regulatory positioning, and industry trends. While quantitative metrics measure what has happened, qualitative analysis assesses the strategic factors that will drive future performance, providing context that numbers alone cannot capture.
Intermarket analysis studies the correlations and divergences between asset classes — equities, bonds, commodities, currencies, and crypto — to gain a macro edge. When relationships that normally hold start breaking down, it often foreshadows major market regime shifts. Traders use these cross-market signals to confirm or contradict setups in their primary market.
Macro trading uses macroeconomic data — GDP, inflation, interest rates, employment, and central bank policy — to establish directional biases across currencies, equities, bonds, and commodities. Traders position for large economic regime shifts rather than intraday noise, using fundamental catalysts as the primary edge.
Event-driven trading capitalises on price dislocations caused by scheduled and unscheduled catalysts — earnings announcements, FOMC meetings, governance votes, protocol upgrades, token unlocks, mergers, and geopolitical shocks. The edge comes from correctly anticipating the event's impact or exploiting the volatility expansion around it.