Seasonality of the US Information Technology Sector

Before investing in any stock market instrument, an investor asks himself a number of questions, such as:

• What is the expected rate of return?
• What are the possible risks?
• What are the growth prospects for the sector of the economy?
• When is the best time to buy and subsequently sell a financial instrument?

Let's consider the raised questions in a new series of our studies, in which we will study the seasonal patterns of changes in the value of financial instruments in general for individual sectors of the US stock market.

What sectors of the US economy exist and how many are there?

This question is answered by the global industry classification standard developed by Standard & Poor's and Morgan Stanley Capital International.

The standard reflects the current structure of the US stock market and identifies 11 sectors represented by the S&P 500 index:

• Consumer Discretionary;
• Consumer Staples;
• Energy;
• Financials;
• Health Care;
• Industrials;
• Information Technology;
• Materials;
• Real Estate;
• Telecom Services;
• Utilities.

The S&P 500 index structure is shown in the diagram (as of September 30, 2021):

And today we will reveal the seasonal trends of the index of the largest sector of the US economy in terms of capitalization - the information technology sector, and also examine the trading signal based on the seasonal patterns of the index.

The information technology sector index - S&P 500 Information Technology - includes 71 companies, the largest of which are known throughout the world:

• Apple
• Microsoft
• Nvidia
• Visa
• Mastercard

To identify the presence of seasonality, we will use the following criteria:

1) The share of cases of positive or negative monthly changes for the period under review is more than 53%.

2) The ratio of the average value of positive monthly changes to the average value of negative monthly changes:

• greater than 1, when the share of positive monthly changes is greater than 53% (see point 1);
• less than 1, when the share of negative monthly changes is greater than 53% (see point 1).

3) Linear trend in the distribution of monthly changes over the past 10 years:

• upward trend, when the share of positive monthly changes is greater than 53% (see point 1);
• downtrend, when the share of negative monthly changes is greater than 53% (see point 1).

If all three conditions are met, we will talk about the presence of seasonality in the price changes of a financial instrument.

The section of historical data from 1989 to 2010 will be the base period on which we will identify seasonal patterns in value changes.

If such patterns are identified, they will be used as signals to buy or sell in the tested section of history from 2011 to 2021.

Thus, we will check the effectiveness of the identified trading signals based on seasonal patterns.

Analysis of the obtained results 

Seasonal changes in the S&P 500 Information Technology index (price changes per month, in %) 

Explanation for the final lines:

1) In the first line, the share of cases of positive changes was calculated based on the analysis of monthly price changes. On the example of January, it looks like this: we take all the months "January" for 22 years, so we get 21. Of these 21 months, a price growth has been noted on the basis of 11 months. That is, the share of growth cases has been 52%.

2) In the second line, we compare the average value of all positive monthly changes with the average value of negative changes.

• If the value obtained is greater than 1, then the price passes more points in the growth months than in the months of declines.
• If the value obtained is less than 1, then in the months of decline the price passes more points than in the months of growth. In the case of the month "January", the value is less than 1.

So, six months (March, April, September, October, November and December) meet two of the three criteria for pronounced seasonality.

Now let's check these months according to the third criterion - the direction of the linear trend of the distribution of monthly changes for 2000-2010:

So, the linear trend of March, April and November changes over 10 years is directed upwards, which satisfies the last criterion for detecting seasonality.

The 10-year trend of changes in September, October and December is directed downward, which refutes the assumption of a seasonal trend in price growth.

Thus, with some confidence, we can say that the index of the information technology sector tends to grow in March, April and November.

These seasonal patterns we will use as a trading signal for testing on historical data sampling from 2011 to 2021.

We will evaluate the trading signal according to the following criteria:

• The rate of return reflects the relative change in the quotes of financial instruments in percentage. A positive value of the rate of return indicates the profitability of the strategy, negative - about the loss. 

The rate of return (R) of a financial instrument is calculated using the formula:

R = Σ P (%) / n,

where:

n is the number of transactions;

P (%) – the percentage of change in the quote of a financial instrument at the time of fixing a position, is calculated as follows:

for buy positions

P (%) = (position closing price - position opening price) / position opening price * 100%

for sell positions

P (%) = (position opening price - position closing price) / position opening price * 100%

• The average rate of return of profitable transactions (AR) includes the rate of return of only profitable transactions, as a percentage:

AR = Σ D (+) / n,

where:

n is the number of profitable transactions;

D (+) – rate of return of profitable transactions.

• Average drawdown (AD) reflects the average loss when closing losing transactions for the entire trading period, as a percentage. The lower the value of the average drawdown, the lower the losses, and the better the trading signal works.

AD = | Σ D (-) / n |

where:

n is the number of losing transactions;

D (-) – rate of return of losing transactions.

• Maximum rate of return (MaxR) is the maximum profit from closing successful transactions for the entire trading period, as a percentage. The higher the maximum rate of return value, the better the trading signal works.

MaxR = max (R)

• Max drawdown (MD) is the maximum of losses when closing unsuccessful transactions for the entire trading period, in percent (minimum profitability). The lower the value of the maximum drawdown, the better the trading signal works.

MD = | min (R) |

• Share of profitable positions (SPP) shows the share of profitable trading positions from the total number of positions, as a percentage. The higher the DPP, the more profitable trades are made.

SPP = number of profitable positions / total number of positions * 100

The results of the strategy of buying the information technology sector index (S&P 500 Information Technology) at the beginning of the months - March, April, November and selling it at the end of these months are presented in the diagrams:

So, the rate of return of buying the S&P 500 Information Technology index at the beginning of March and selling it at the end of the month has been 0.7% with an average rate of return of profitable transactions, with a drawdown of 3.8% and 4.7%, a maximum rate of return and drawdown of 9.1% and 8 .7% respectively.

For April purchases, the rate of return has been 2.4% with an average rate of return of profitable transactions and a drawdown of 3.8% and 3.7%, a maximum rate of return and drawdown of 13.7% and 5.5%, respectively.

For purchases in November, the rate of return has been 2.2% with an average rate of return of profitable transactions and drawdown of 3.8% and 1.5%, maximum rate of return and drawdown of 11.3% and 2.1%, respectively.

The share of profitable buying positions in March was 63.6%, in April - 81.8%, in November - 70%. 

Detailed results are shown in the Appendix:

XLSX (0.21 MB)Seasonality of the US information technology sector.xlsx

See also:

Seasonality of the US Health Care Sector

Seasonality of the US Consumer Discretionary Sector

Seasonality of the US Industrials Sector

Seasonality of the US Consumer Staples Sector

Seasonality of the US Financial Sector

Seasonality of the US Materials Sector

Seasonality of the US Energy Sector

Seasonality of the US Utilities Sector

Seasonality of the US Real Estate Sector

Seasonality of the US Telecom Services Sector

Bottom Line: Seasonality of the Broad US Stock Market