I looked at the S&P 500 Index long term trend in this post. That trend analysis revealed that the S&P Index price closely follows an exponential curve over 95 years of history. Amazingly, 96% of the variance in the S&P Index price is explained simply by experiencing exponential growth over time. That growth rate is 0.0013 per week which equates to a 6.76% annual return.
What about another key investment index, the NASDAQ? Most investors know the NASDAQ Index focuses primarily on the technology sector. There are around 3,500 companies in the NASDAQ, but it is weighted by market capitalization so the top 10 companies ranked by market cap account for about 50% of the index performance. The common understanding is that the NASDAQ is inherently more volatile than the S&P 500 and has a higher level of risk. Younger, fast growing companies dominate the NASDAQ, which makes it more sensitive to forward projected risks such as interest rate increases or other economic concerns.
Rather than rely on common understanding, let's see what the price data looks like. I pulled the weekly NASDAQ price form Yahoo Finance from February 1971 through November 2022. This amounts to 2705 weekly data points as displayed on this chart:
The price data is shown on the left axis with the corresponding week number on the bottom axis. Similar to the S&P 500, it is clear that this data follows an exponential growth pattern. Adding an exponential trend shows this:
The red line is the "line of best fit" to the data. For more on trend lines and linear regression I will write a future post that delves into these concepts in more detail.
To make the trend a bit easier to work with, a log transformation can be applied to the weekly price data using the LN function in Microsoft Excel to convert the exponential trend to a linear trend. The red trend line is now a straight line rather than a curve:
The red box shows the formula and the R-Square value of the red linear trend line. The R-Square value tells us what percent of the variance in the NASDAQ price data over time is explained by the trend line. This value ranges from 0 to 1. An R-Square of 0 would mean that the trend line does not fit the data at all and a value of 1 means the trend line fits the data perfectly. We can see here that the NASDAQ Index very closely follows this trend line. In fact, the R-Square value is 0.96 which implies that 96% of the variance in the price of the NASDAQ Index since 1971 can be explained simply by the passage of time alone (the week number) and exponential growth. The remaining 4% of the variance is due to market fluctuations, speculation, bubbles, crashes, and all other market phenomena. Surprisingly, this trend line R-Square is the same as the S&P 500. The common understanding that the NASDAQ is inherently more unstable and risky compared to the S&P 500 is false. In fact, it behaves similarly and an investor should feel confident that over the long term, the NASDAQ will follow the historical trend line.
You can see around week #1560 on the bottom axis, the NASDAQ rose far above the trendline and then collapsed. This is the famous DOT-COM bubble that burst in 2000. You can see on the chart that the price crashed below the trend line and then followed the trend upwards through about week #2000, where it crashed again in 2008. The 2008 crash drove it far below the trend line, however, and it has rather consistently increased since then.
Compared to the S&P trend's weekly growth rate of 0.0013 or 6.76% annual return, the NASDAQ trend sports a 0.0019 weekly growth rate or 9.88% annual return. This increased return makes sense knowing the NASDAQ is more largely composed of young fast growing companies compared to the S&P. Overall, this analysis reveals that the NASDAQ is similar to the S&P and is overwhelmingly predictable and consistent over the long term. Investors with a long term view should feel comfortable investing in the NASDAQ and expect a 9-10% annual average return.
留言