Study guide
The Series 86 begins where real research begins: with the economy and the industry, not the individual company. Function 1 tests your ability to gather and interpret macroeconomic data, government policy signals, and industry-level statistics before you ever open a 10-K. This chapter covers the indicators, frameworks, and basic statistical tools an entry-level research analyst is expected to command.
Macroeconomic Indicators and the Business Cycle
Every industry forecast rests on a view of the overall economy, so Function 1 expects you to know the major macroeconomic data series and what they signal. Gross domestic product measures the total value of goods and services produced; analysts watch real GDP, which strips out inflation. Inflation itself is tracked through the consumer price index and the personal consumption expenditures index, while the labor market is read through nonfarm payrolls and the unemployment rate. Business surveys such as the purchasing managers index are diffusion indexes: a reading above 50 signals expansion, below 50 signals contraction. Indicators are grouped by timing. Leading indicators, such as building permits, initial jobless claims, new orders, and the slope of the yield curve, tend to turn before the economy does. Coincident indicators, such as payrolls and industrial production, move with the cycle. Lagging indicators, such as the unemployment rate and the average prime rate, confirm turns after the fact. An inverted yield curve, where short-term Treasury yields exceed long-term yields, has historically preceded many recessions, though it is not a perfect predictor. The practical skill is matching indicators to the industry you cover. An analyst following Halverson Freight, a trucking company, would track industrial production, retail inventories, and diesel prices, because freight volumes follow goods production more closely than they follow services spending. Choosing the right drivers is the first step in building a defensible industry forecast.
Fiscal and Monetary Policy
Fiscal policy is set by Congress and the President through taxation, government spending, and transfer programs. Expansionary fiscal policy, such as tax cuts or increased spending, adds to demand and tends to lift cyclical industries first, but large deficits require heavier Treasury borrowing, which can push interest rates higher over time. Monetary policy is conducted by the Federal Reserve, mainly through the Federal Open Market Committee. The Fed's traditional tools include setting a target range for the federal funds rate, conducting open market operations, adjusting the discount rate, and paying interest on reserve balances; in recent cycles it has also used large-scale asset purchases and balance sheet runoff. Policy matters to analysts through several channels. Interest rates feed directly into the discount rates used in valuation models, so a rising-rate environment compresses the value of long-duration growth stocks. Rate-sensitive industries such as housing, autos, and banks respond quickly to policy shifts: an analyst covering Casabella Homes, a homebuilder, would treat mortgage rates as a primary demand driver. Currency is another channel, because tighter U.S. policy tends to strengthen the dollar, which hurts exporters and companies with large foreign revenues when results are translated back. Finally, remember that policy operates with a lag; the economy often feels a rate change several quarters after it happens, so analysts must distinguish between what policy has already done and what is still working through the system.
Market Size, Growth Rate, and Capital Intensity
Industry analysis starts with size. The total addressable market, or TAM, is the full revenue opportunity if a product reached every potential customer; the serviceable market narrows that to segments a company can realistically reach. Analysts size markets top-down, starting with macro data and applying penetration assumptions, and bottom-up, building from unit counts and average prices, and they check that the two approaches roughly agree. Growth rate places the industry in its life cycle. Embryonic and growth-stage industries expand faster than GDP, maturity brings growth roughly in line with the overall economy, and declining industries grow more slowly or shrink. The stage matters because it drives the appropriate valuation framework and the durability of margins; growth stages attract new entrants, while shakeouts consolidate share among survivors. Capital intensity measures how much investment is required to produce a dollar of sales, often gauged by capital expenditures to sales or total assets to sales. Semiconductor manufacturing, airlines, and telecommunications are capital intensive: a company like Orbix Semiconductor must commit billions to fabrication plants before the first chip ships. High capital intensity creates high fixed costs and operating leverage, meaning profits swing sharply with volumes, but it also builds barriers to entry. Asset-light industries such as software or staffing scale with little incremental investment, which supports higher returns on capital but invites more competition. On the exam, be ready to connect capital intensity to both the cyclicality of earnings and the height of entry barriers.
Secular Versus Cyclical Trends and Competitive Dynamics
A cyclical industry rises and falls with the business cycle: autos, housing, steel, airlines, and advertising all see demand contract in recessions. Defensive or non-cyclical industries, such as utilities, consumer staples, and much of health care, sell necessities and hold up relatively well in downturns. A secular trend, by contrast, is a long-term structural shift that plays out across multiple cycles, such as an aging population, electrification of vehicles, or the migration of commerce online. The analytical challenge is separating the two. When Bluewater Retail, a department store chain, reports falling sales, the analyst must decide whether the weakness is cyclical, and will reverse with the economy, or secular, meaning customers are permanently shifting to other channels. The answer determines whether the stock deserves a trough multiple or a shrinking one. Supply and demand dynamics complete the picture. Analysts track capacity utilization, inventory levels, order backlogs, and pricing to judge where an industry sits in its own cycle; in commodity industries, the low-cost producer sets the floor and excess capacity crushes prices. Competitive dynamics are commonly organized around rivalry among existing firms, the threat of new entrants, the threat of substitutes, supplier power, and buyer power. Concentrated industries with high entry barriers tend to sustain pricing power. Finally, weigh disruption risk: new technology, new business models, or regulatory change can convert a stable industry into a declining one faster than the historical data suggests.
Quantifying Key Drivers: Correlation and Regression
Series 86 expects basic statistical literacy, because analysts routinely test how closely an industry driver explains demand. The correlation coefficient ranges from negative 1 to positive 1 and measures the strength and direction of a linear relationship between two variables. A correlation near positive 1 means the variables move together; near negative 1, they move in opposite directions; near zero, there is little linear relationship. Correlation does not prove causation, and two series can appear related simply because both trend upward over time, so analysts often work with growth rates or changes rather than levels. Simple linear regression goes a step further, fitting a line that predicts a dependent variable, such as industry shipments, from an independent variable, such as housing starts. The slope tells you how much demand changes per unit change in the driver, and R-squared tells you the proportion of the variation in the dependent variable explained by the model. If an analyst regresses lumber demand on housing starts and finds an R-squared of 0.85, housing starts explain about 85 percent of the variation in lumber demand, making starts a credible forecasting anchor. Multiple regression adds several drivers at once, but be alert to multicollinearity, where overlapping drivers make individual coefficients unreliable. Also watch for structural breaks: a relationship estimated over one decade may not hold after a technology shift or regulatory change. Used carefully, these tools let you test management claims about demand drivers instead of accepting them at face value.
Key terms
- Gross domestic product (GDP)
- — The total market value of final goods and services produced in an economy over a period; real GDP is adjusted for inflation.
- Leading indicator
- — An economic data series that tends to change direction before the overall economy, such as building permits, new orders, or the yield curve.
- Lagging indicator
- — A series that confirms an economic turn after it has occurred, such as the unemployment rate.
- Purchasing managers index (PMI)
- — A diffusion survey of purchasing managers; readings above 50 indicate expansion and readings below 50 indicate contraction.
- Yield curve inversion
- — A condition in which short-term yields exceed long-term yields; it has historically preceded many recessions, though it is not a perfect predictor.
- Fiscal policy
- — Government use of taxation, spending, and transfers, set by Congress and the President, to influence the economy.
- Monetary policy
- — Federal Reserve actions, such as setting the federal funds target range and conducting open market operations, to influence money, credit, and interest rates.
- Total addressable market (TAM)
- — The full revenue opportunity available if a product or service achieved complete penetration of its potential market.
- Capital intensity
- — The amount of investment required to generate sales, often measured by capital expenditures to sales or total assets to sales.
- Secular trend
- — A long-term structural shift that persists across multiple business cycles, such as demographic aging or the move to online commerce.
- Cyclical industry
- — An industry whose demand rises and falls with the business cycle, such as autos, housing, or steel.
- R-squared
- — The share of the variation in a dependent variable explained by a regression model, ranging from 0 to 1.
Exam tips
- Per FINRA's current outline, the Series 86 has 85 scored questions in 4 hours 30 minutes and the Series 87 has 50 scored questions in 1 hour 45 minutes; the economic and industry material is the smallest Series 86 section, but it feeds the modeling and valuation questions, so learn the drivers, not just the definitions.
- Be able to classify an indicator as leading, coincident, or lagging on sight: building permits and jobless claims lead, payrolls and industrial production coincide, and the unemployment rate lags.
- If a question asks who does what, remember that Congress and the President run fiscal policy while the Federal Reserve runs monetary policy.
- Expect scenario questions asking whether a demand change is secular or cyclical; look for clues about whether the cause is the economy itself or a structural shift in behavior or technology.
- Know that correlation does not imply causation and that R-squared measures explained variation, not the slope or direction of the relationship.