Every industry has its own seasons

1 · One number+9.13% vs. −0.68%

Cutting the returns of the US energy sector ETF (XLE) into half-years since 1999, the Nov–Apr average was +9.13% and the May–Oct average was −0.68%. The materials sector ETF (XLB) had the same shape, +7.63% versus −1.08%. In both industries, most of the yearly return was concentrated in the winter-and-spring half of the year.

Over the same period the S&P 500 returned +4.99% in Nov–Apr and +2.43% in May–Oct. This means the six-month cycle from Lesson 2 was mild for the whole market, but split by industry, steeper and reversed cases were mixed in.

2 · InsightEach industry has its own working season

Picture a four-season clothing store. Padded jackets sell in winter, swimsuits in summer. Even if the store's total sales are similar year-round, each item has its own peak. The stock market is similar. Even if the whole market's seasonality is mild, industries tied to real demand each have a different calendar.

The person who turned this idea into an investing rule is Sam Stovall, chief investment strategist at CFRA. His sector half-year rotation holds cyclical sectors such as consumer discretionary, industrials, materials and IT in Nov–Apr, and defensive sectors such as consumer staples and health care in May–Oct, changing only the industry without leaving stocks. CFRA data put the S&P 500's six-month averages since 1945 at +6.6% for Nov–Apr and +1.4% for May–Oct (CFRA-Stovall, 2019).

The reason industry seasonality is easier to explain than whole-market seasonality is that the cause lies in real demand. There is demand that returns at the same time each year: energy demand ahead of winter heating and the spring driving season, corporate capex plans bunched at the start of the year, and retail sales in the year-end shopping season. The general explanation is that stock prices reflect those expectations a few months before the actual demand. But this is an explanation, not a proven causal link, so this lesson treats it as a hypothesis.

Recalling the principle from Lesson 4, industry seasonality belongs to the kind where you can write 'why it happens' in one line. So it may hold up longer than a price pattern with no cause, but when the size and timing of demand change, the pattern changes too. The shale revolution changed the structure of crude oil supply, and electric vehicles and remote work are changing what the driving season means.

3 · DataThe scorecard of nine sectors and gold

The nine Select Sector SPDR ETFs were listed in December 1998. So the half-year comparison covers 27 observations each, from May–Oct 1999 to May–Oct 2025, and for Nov–Apr from Nov 1999–Apr 2000 to Nov 2025–Apr 2026. In the chart below, the blue dot is the Nov–Apr average and the gray dot is the May–Oct average. The farther apart the two dots, the more that industry's return was concentrated in one half of the year.

The industries with the largest gaps were energy (+9.82 percentage points) and materials (+8.71), followed by industrials (+5.56). The cyclical sectors Stovall picks for Nov–Apr gathered at the top. But the picture changes a little when you also look at win rates. Energy's Nov–Apr win rate was 70% (19 of 27), actually lower than the S&P 500's (74%), and its May–Oct median was +0.07%, higher than the average (−0.68%). This means a few years of big collapses, like 2008, dragged down the summer average.

When comparing industries, return relative to the market matters more than absolute return. In a half-year when the whole market rises together, most industries are positive. Energy's Nov–Apr excess return (vs. the S&P 500) averaged +4.14 percentage points, but it beat the market in only 14 of 27 cases. The large average excess return came from a few big wins; it did not win every time.

Commodities, not just industries, have calendars too. Calculating gold futures' (GC=F) monthly record from 2001 to September 2026 (25–26 observations per month), January's average of +2.95% (65% win rate) and August's +2.50% (73%) were high, and June was lowest at −0.87% (42%). The explanation often given is physical demand for precious metals ahead of the Chinese Lunar New Year and India's Diwali and wedding season. Brooke Thackray explains that the strength before the Lunar New Year has been clearer in the last 20 years (Wealth Professional, 2023-09-20).

Remember that the gold figures were calculated from a continuous futures series. Price gaps created when the front-month contract rolls to the next one at each expiry get mixed into the monthly returns. For gold the gap is small, but for commodities with high storage costs, like crude oil and natural gas, this distortion is large, so the same method should not be used.

5 · One thing to practiceThe sector half-year table and excess return

Today's assignment is to build a half-year return table for nine sector ETFs and calculate the excess return versus the S&P 500. The table below is pre-calculated from the same data. Move the start and end years and the table is recalculated; tap a row to see that sector's year-by-year record. Keep only the sectors that lean the same way even when you change the period as candidates for 'seasonality with a cause.'

Narrowing to the last 10 years, you can check for yourself whether the Nov–Apr edge of materials and industrials remains and how much energy's gap has narrowed. Also remember that for sectors with large distributions, such as consumer staples and utilities, price-basis figures come out lower than actual returns. This table is an educational calculation and does not recommend trading any particular sector or ETF.

The figures in this lesson are on a price basis, so distributions, trading costs and taxes are excluded. A strategy of switching sectors every half-year loses its edge once trading costs and taxes add up. The fact that things leaned one way in the past is no guarantee they will lean the same way going forward.