Where will the Dow Jones Industrial Index go from here?

Wrapping up my series of trend analysis for the three major indexes, this post will look at the Dow Jones Industrial Index. You can check out my previous analysis of the NASDAQ in this post and the S&P in this post. Both of these trend analyses revealed the indexes are almost perfectly predictable over the long term with an expected annual return of 9.88% for the NASDAQ and 6.76% for the S&P.

The Dow Jones Industrial Index is composed of just 30 stocks, versus over 3,500 for the NASDAQ and 500 for the S&P 500 index. The 30 stocks on the Dow are selected for having a large market cap, the stock value X the number of shares. A common belief about the Dow is that it is comprised of older, more established companies and therefore is a more conservative investment option. The companies that make up the Dow change as some companies that decrease in market cap are removed and growing companies are added. The current Dow lineup includes old stalwarts like McDonald's, Goldman Sachs, and Coca Cola but also includes tech companies such as Salesforce, Apple, and Microsoft.

Similar to the other trend analyses, I pulled the price data for the Dow Jones Industrial Index back to January 1970. The Dow is one of the oldest stock indexes, going back more than 100 years, but the available price data is more recent since the index has undergone some changes that would require adjustments to be made to older data in order for it to be comparable. The weekly price data from January 1970 to December 11, 2022 amounts to 2,763 data points. Plotted on a bar chart, it looks like this:

The price data is shown on the left axis with the corresponding week number on the bottom axis. Similar to the S&P 500 and NASDAQ, 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 Dow 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 Dow Jones Industrial 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 Dow Index since 1970 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 both the S&P 500 and the NASDAQ.

You can see around week #2000 on the bottom axis, the Dow declined substantially from above the trendline to far below the trendline. This is the 2008 financial crisis with the Great Recession following. You can see on the chart that after the 2008 crash, the Dow stayed below the trend line but closely tracked it to the present time.

Compared to the S&P trend's weekly growth rate of 0.0013 or 6.76% annual return and the NASDAQ weekly growth rate of 0.0019 or 9.88% annual return, the Dow weekly growth rate is 0.0015 which equates to a 7.8% annual return. This is in between the S&P and the NASDAQ expected returns. This makes sense knowing the NASDAQ is more largely composed of young fast growing companies, and the S&P is broader more diversified index as compared to the Dow. Investors with a long term view should feel comfortable investing in the Dow and expect a 7-8% annual average return.