NISM RA Chapter 4 — Fundamentals of Research: the four ways to analyse a stock
This is my note on Chapter 4 of the NISM-Series-XV Research Analyst workbook — “Fundamentals of Research.” After two chapters of definitions and terminology, this is where the book starts talking about the actual craft: what investing really is, what a research analyst actually does, and the four different lenses through which people analyse stocks. It also has a surprisingly meaty commodity section, with gold and crude oil as case studies. Here’s everything, in plain words.
What investing actually is (and what it isn’t)
Investment means committing money upfront to earn returns over an investment horizon, based on thorough analysis of the security’s safety, income, and growth potential. The chapter is at pains to separate this from the things it gets confused with:
A trader tries to profit from the spread between buying and selling price, without any necessary change in the underlying value of the asset. Traders are motivated by historical price patterns repeating — their time horizon is short. Within this family: those betting on short-term price moves based on calculated guesses are speculators; those relying on price patterns and charting techniques are chartists; and those whose entire buy-and-sell cycle fits within one day are day traders.
An investor, by contrast, focuses on the potential of an asset’s value to increase over time. Value increases when the asset can generate higher cash flow without a proportionate increase in risk, or when its risk falls without a proportionate fall in cash flow. That’s the real dividing line: traders profit from price patterns and anomalies (typically correcting within 3–6 months) without any change in fundamental value; investors profit from changes in the value itself.
The workbook’s framing (which also appears in its sample questions): speculation is a short-term call made with leveraged funds, while investment is a long-term, disciplined activity for creating wealth.
Investing splits further into two styles:
Active investing — identifying specific securities to buy or sell. It means constantly evaluating every security in the portfolio: selling what’s priced above intrinsic value, buying what’s priced below it. More effort, more transactions. The active investor’s goal is to beat the return of the broader asset class.
Passive investing — investing in a broad set of securities that fairly represents the asset class, typically by indexing (buying everything in an index). The passive investor’s goal is simply to earn the asset class’s return; the analysis stops at the asset-class level, not individual securities.
What a research analyst actually does
The role of a fundamental research analyst has two parts: research (obtaining all the necessary information to answer a defined question) and analysis (working through that information to reach a conclusion).
An annual report is a treasure trove — but it’s published once a year, gets dated with time, and rarely covers the industry or economy in depth. So the analyst has to go further: talking to industry experts, accessing market research reports, doing secondary research on the economy and competitors, and often primary research — visiting company facilities, speaking to customers, suppliers, and employees.
The line you cannot cross: insider information vs mosaic analysis
This part matters enormously for anyone heading into this profession, so I’m giving it its own section.
Insider information is material, non-public, price-sensitive information — the kind that, if published, would immediately affect an investor’s decision to buy or sell. It sits with a handful of people closely connected to the company (or their relatives). Whether something counts as insider information depends on the source (how reliable), its impact, and its certainty. The research work of an analyst must never involve collating insider information.
The workbook’s example draws the line beautifully: a CEO talking about an unpublished acquisition proposal IS insider information. An employee mentioning increasing workload in the purchase department (which might hint at higher business activity) is NOT.
Mosaic analysis is the legitimate alternative: collating pieces of information from different sources — each individually insignificant, some public, some non-public — which, put together, yield a critical insight. Mosaic analysis is acceptable. The analyst’s duty is to be careful about whether an insight genuinely came from assembling the mosaic, or from being privy to one specific piece of non-public price-sensitive information. The first is skill; the second is a violation.
Approach 1: Technical Analysis
Technical analysis assumes that everything that can affect a share’s performance — company fundamentals, economic factors, market sentiment — is already reflected in the stock price. So instead of studying the business, it forecasts the direction of prices by studying patterns in historical market data: price and volume. Practitioners are called technicians or chartists.
Three essential elements of price behaviour:
- The history of past prices indicates the underlying trend and its direction.
- The volume accompanying price movements signals the strength of the trend.
- The time span over which price and volume act carries the impact of long-term factors.
These get integrated into price charts (line charts, bar charts, candlestick charts), with support levels (where there’s lots of buying interest) and resistance levels (where there’s lots of selling interest). Practical readings: if a stock approaches an established resistance level, a holder may book profits since prices tend to retract there. If a support or resistance is broken on strong volumes, the trend may have accelerated — the supply-demand situation has changed. Volumes confirm trends: an up or down move without volume behind it suggests a weak trend. Chartists also use moving averages to smooth out day-to-day fluctuations that obscure the trend (this exact point appears in the workbook’s sample questions).
When does it work? Short-term investors and traders lean on technical signals because fundamentals seldom change drastically in the short run. But for long-term investing it’s less suitable — fundamentals DO change over the long term, making past price trends unreliable guides.
Approach 2: Fundamental Analysis
Fundamental analysis is the long-term lens. Its premise: since an equity share is part-ownership of a company, over the long term its value should be driven by the profits and cash flows the company generates. When short-term price movements push the price far from fair value, that divergence is the profit opportunity.
The method: estimate the fair (intrinsic) value of the share from the expected performance of the business. Market price below intrinsic value = attractive investment. Market price above it = sell or avoid. So profit comes from two things together: identifying a good business AND buying it at the right price.
Worth noting: this whole approach contradicts the Efficient Market Hypothesis (EMH), which says share prices already incorporate and reflect all relevant information — leaving no mispricings to exploit.
Fundamental analysis studies the company’s business and governance comprehensively. The questions it asks: Is the macro trend (cyclical and secular) likely to help the industry grow or decline? How intense is competition within the industry? How is the company positioned versus competitors? What’s its cost structure and how does profit behave in different environments? Is its financial position strong enough to fund growth or withstand a crisis? Can the management identify and execute the right strategies? Is the governance structure aligned with shareholders’ interests?
All of these fall into three baskets — and this trio is the backbone of the next several chapters of the workbook:
- Economic analysis
- Industry analysis
- Company analysis
(This is the famous top-down structure: economy → industry → company.)
Approach 3: Quantitative Research
Some analysts approach equity purely through numbers. The quantitative approach can be applied to both technical and fundamental analysis. In the technical context, instead of reading charts, quants study the underlying data — relationships between up-moves and down-moves, volumes, and other parameters. In the fundamental context, they look for financial and operational metrics that could serve as leading indicators of company performance.
At the simplest level: time series analysis and regression of historical data to extrapolate future earnings. More sophisticated: econometric models, financial statement analysis to project future financials and growth rates, then sensitivity analysis and simulations to test how changes in assumptions affect valuations.
But pure econometrics in fundamental analysis has a major limitation (also flagged in the workbook’s sample questions): the availability of comparable information. Frequent changes in accounting standards and business models make past data less comparable with present conditions. That’s why pure quantitative research is not often employed in fundamental analysis.
Approach 4: Behavioural Finance
Investment decisions should be based on analysis of available information — but very often they’re influenced by the decision-maker’s behavioural biases, leading to less-than-optimal choices. The behavioural school assumes that security prices drift away from fair value — in both directions — because of the fear and greed of market participants. (The workbook parks the detailed catalogue of biases for a later chapter.)
Fundamental analysis of commodities
The chapter then pivots to commodities — and this section is bigger than you’d expect. Commodity fundamental analysis studies the economic, political, and natural factors that influence supply and demand, and hence prices: supply-demand factors, seasonality, macro conditions, news, currency movements, interest rates, weather, inventory levels, and government intervention.
Supply-side factors: production (farms, oil wells, mines); weather (floods, drought, cyclones); government policies (tariffs, levies, trade restrictions, subsidies); geopolitical events (sanctions, wars, trade disputes); and input costs (energy, wages, technology).
Demand-side factors: global economic growth (rising GDP means more demand for metals, energy, agri products); population growth and urbanisation (more food and energy consumption); the substitution effect (switching between commodities); seasonal demand (fuel in winter/summer, festive food); and consumer preferences (shifts to organic food, renewables, EVs).
Macroeconomic indicators: inflation (commodities, especially gold and silver, act as an inflation hedge); interest rates (higher rates strengthen the USD, which lowers commodity prices); and trade balance and industrial data (PMI and industrial production drive metals and energy demand).
Case study 1: Gold
Gold’s uses: jewellery, industry, investment, and central bank reserves. It’s one of the most traded commodities globally, seen as both an inflation hedge and a safe-haven asset because it stores value. When equities, bonds and currencies underperform in turbulent times, investors turn to gold — historically it has an inverse relationship with stocks, bonds and currencies.
Demand by sector: jewellery 47%, investment 24%, central banks 23%, technology 6%. Major producers: China, Australia, Russia, USA, Canada. Major consumers: China, India, USA, Germany, Saudi Arabia.
What drives the price up: expansionary monetary policy (falling rates), a weaker dollar, weaker economic data, higher inflation, lower supply, stronger demand, ETF buying, central bank buying, political instability, a bear stock market, and lower bond yields. Each of these reversed drives gold down. The pattern to internalise: gold thrives on fear, cheap money, and a weak dollar.
Case study 2: Crude oil
Crude oil is called the mother of the global financial market — and “black gold” — because of its role in global growth. Distilled at different temperatures it yields bitumen, lubricating oils, fuel oil, diesel, paraffin, naphtha and gasoline; it fuels vehicles, planes, boats and railways and goes into asphalt, lubricants and plastics. Any supply-demand imbalance creates inflationary concerns worldwide.
Quality is judged on two parameters: density and sulphur content. The two benchmarks: WTI (West Texas Intermediate) — high-quality US crude, API 39°, sulphur 0.24%, traded on NYMEX. Brent — the pricing benchmark for Europe and Africa, from the UK’s North Sea, API 38°, sulphur 0.4%, traded on ICE.
Supply is controlled by the United States and OPEC+ (13 OPEC members plus Russia). Numbers worth remembering: the Middle East holds 48% of known reserves; OPEC owns almost 40% of the world’s crude, accounts for 75% of proven reserves, and exports 55% of oil sold worldwide. Major producers: USA, Russia, Saudi Arabia, Canada, China. Major consumers: USA, China, India, Germany, Japan.
Price-positive factors: OPEC production limits, falling US production, falling US rig counts, falling US inventories, a weaker dollar, economic expansion, political instability (especially in the Middle East), extreme weather in the Gulf of Mexico (hurricane season), and a bullish stock market. The reverses push prices down.
Two pieces of history worth knowing
Negative oil prices, April 2020. On 20th April 2020, WTI crude went negative — a commodity priced below zero, ignoring its cost of production. The reason: the May contract was expiring the next day, and with 90% of the world in COVID lockdown there was no demand. WTI is a deliverable contract at Cushing, Oklahoma, and lockdown restrictions on oil movement meant contract buyers couldn’t take delivery. So buyers sold in a panic, and the price crossed below zero.
The 1973 oil crisis. The first major post-WWII oil crisis: OPEC members quadrupled prices to almost $12 a barrel and prohibited exports to the United States, Japan and Western Europe — which together consumed more than half the world’s oil.
How commodities move the equity market
The two markets are tightly linked because many listed companies use commodities as raw materials. Rising commodity prices raise input costs and squeeze profit margins; falling prices improve profitability and can boost stock valuations. The classic example: rising crude improves the profitability of oil-producing companies while hurting airlines and logistics companies through higher input costs.
And because commodities trade globally, their price moves are signals: changes in crude, gold or copper prices often flag shifts in global demand and supply, and growth-sensitive equity markets react. A fall in copper prices, for instance, may signal slowing industrial demand — dragging down metal and infrastructure stocks.
How this chapter is tested
Chapter 4 carries about 5 marks — mid-weightage, and it’s almost entirely conceptual. No formulas to practise here; the questions test whether you can distinguish and define.
The workbook’s own three sample questions show the style exactly: (1) speculation vs investment — short-term leveraged call vs long-term disciplined activity; (2) what smooths day-to-day price fluctuations in technical analysis — moving averages; (3) the limitation of the quantitative approach — changes in accounting standards, business structures and regulations limit its forecasting effectiveness.
The traps are the contrasts. Be able to separate: investor vs trader vs speculator vs chartist vs day trader (the definitions differ in horizon and motivation); active vs passive investing (beat the asset class vs earn the asset class’s return); technical vs fundamental (price patterns and short-term vs business value and long-term); and — the big one for a future registered analyst — insider information vs mosaic analysis. The CEO-acquisition vs purchase-department-workload example is exam-ready as it stands.
The commodity section is fact-recall: gold’s demand split (jewellery 47%), gold’s inverse relationship with stocks/bonds/currencies, WTI vs Brent (where each trades — NYMEX vs ICE — and their API/sulphur profiles), the OPEC numbers (40% of crude, 75% of proven reserves, 55% of exports), and the logic of why WTI went negative in April 2020. These make easy MCQs precisely because they’re concrete.
My approach: this is a “contrast table” chapter. I’m making a two-column table for each pair (investing/trading, active/passive, technical/fundamental, insider/mosaic) and flashcards for the gold and oil facts. An hour of that locks in most of these 5 marks.
Next up: Chapter 5 — Economic Analysis, the first leg of the top-down framework. See you in the next note.
Note: These are my personal study notes as I prepare for the NISM-Series-XV Research Analyst exam. They are for learning purposes only and are not investment advice.