As I quest to backtest Tactical Asset Allocation (TAA) strategies to earlier time periods (pre-2003), I think I have uncovered an inherent accuracy issue when Yahoo adjusted price data is used for backtesting TAA strategies. The purpose of this article is to present my findings to investors who use Yahoo adjusted price data to backtest TAA strategies.
In TAA strategies, a ranking of ETFs (or mutual funds if you are using mutual funds as proxies) is determined by relative growth over a specified period. For illustrative purposes, let's say we use a 2-month growth period. So the ETF that has the highest percentage growth in a 2-month period would be selected in the TAA strategy. Rather than use actual price data, we use adjusted price data that includes the effects of dividends. The growth is thus based on total return over the 2-month period.
When we backtest to earlier years, we continue to use adjusted price data to determine the ranking of the ETFs. Thus, if the backtesting goes back to January, 2000, we would determine the top-ranked ETF (or mutual fund) by calculating the 2-month growth percentage of each ETF, and selecting the one with the highest growth. Pretty simple.
But there is an accuracy issue that creeps into the data (Yahoo data) if the adjusted price close data becomes much less that the close price data. And the inaccuracy becomes very significant as the adjusted price gets to less than $5.00. The ETF that would have been selected in 2000 (at that time) based on relative 2-month growth may not be the ETF that our backtesting selects. And therein lies the problem with using Yahoo adjusted price data.
The reason this problem occurs is because the Yahoo adjusted price data is rounded off to $0.01. $0.01 is not that important when the price of the ETF/mutual fund is $50.00 (only 0.02%). But $0.01 is very significant when the price (i.e. the adjusted price) is $1.00 (1.0%). When we are calculating relative growth between ETFs in a 2-month period, 1% can greatly affect the selection process.
As time moves away from the present and the effects of dividends are included in Yahoo adjusted prices, the adjusted prices drop and backtesting will have fidelity issues. Many mutual funds have adjusted prices near $1.00 and close prices near $10.00 before 2000. I will give one example of this inaccuracy based on real data. If I look at PREMX before 2000, I find the close price increases from $10.45 to $10.55 over a time period. This is a growth of 0.957%. The Yahoo adjusted price data increases from $1.92 to $1.93 over this same time period. This is a growth of 0.521%. Maybe PREMX would have been selected in the TAA strategy with a growth of 0.957%, and maybe it would not be selected with a growth of 0.521%.
To avoid this error, the adjusted price data needs to carry more significant digits. Instead of $1.92, the adjusted price needs to be something like $1.922. Relative growth based on adjusted price close data needs to duplicate relative growth based on price close data to maintain backtesting fidelity. My guess is that some datasets may be of higher fidelity, but they are not free (or may not be available to the public). And I would also guess that just because you pay for the use of adjusted price data, does not mean you will always get higher fidelity data.