The S&P 500 finished February with a gain of more than 5% for just the 11th time in the index's history since 1928. Below is a look at prior 5%+ gains in February along with the S&P 500's performance in March and for the remainder of each year. The last time we had a 5% February was 2015. That March, the S&P fell 1.74%, and the index fell 2.88% from the end of February through year-end. Let's hope we don't see that type of action for the remainder of 2024, although a repeat of 2015 would be a lot better than what investors experienced in 1931 when the S&P rallied 11.37% in February only to fall 54.96% for the rest of the year!

Below is a look at our key ETF performance matrix highlighting total returns across asset classes in February, year-to-date, and year-over-year. Mid-cap growth (IJK) was the best area of US markets in February with a 9.58% gain. Dividend stocks (DVY) were the weakest area with a gain of just 1.16% during the month. Looking at sectors, Consumer Discretionary (XLY), Industrials (XLI), and Materials (XLB) actually performed the best in February, beating out Tech's (XLK) gain of just 4.7% even though the semis (SMH) were up 14%.

Outside of the US, China (ASHR) finally had a big month along with Israel (EIS), which gained 8.6%. Mexico (EWW) and Spain (EWP) were the only two country ETFs that fell in February.

Natural gas (UNG) once again fell sharply, adding to its year-over-year decline of more than 50%. Finally, fixed-income ETFs were broadly lower in February, led down by the 20+ Year Treasury ETF (TLT).

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Within the S&P 500, there are 67 stocks that show up in at least one of eight "AI" ETFs traded here in the US. As shown below, the 67 "AI" stocks in the S&P were up an average of 5.7% in February compared to a gain of 3.6% for the 433 non-AI stocks. On the year, the 67 "AI" stocks are up 5.9% versus a gain of 2.6% for the non-AI stocks. Given how much some of the most well-known AI names like NVIDIA (NVDA) are up so far in 2024, it's surprising to us that there isn't even more separation in performance between the AI and non-AI groups.

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