The best for risk is the weekly “daily” indicator, which calculates returns for the whole year relative to the S&P (NYSEARCA:SPY). This indicator is especially helpful for those looking at what their money would achieve if it were to go into a specific portfolio or portfolio class.
The risk/reward ratio shows how the returns for a given period would have been compared to the S&P (NYSEARCA:SPY) since that timeframe is a few months long.
For example, if weekly returns are 10% and monthly returns are 30% and annual returns are 60%, the risk/reward ratio would be 100%.
It helps to evaluate this risk/reward indicator with an in-house market simulation tool. Below I have used the S&P (NYSEARCA:SPY) return simulation tool in Excel and shown the return scenarios that the S&P (NYSEARCA:SPY) could have achieved based on actual market data.
The red line shows how the market reacts to potential returns of a portfolio of stocks that would perform similarly on a weekly basis versus the S&P (NYSEARCA:SPY).
The blue lines show that a portfolio of stocks that perform similarly on a weekly basis would perform better after the market recoups some of the excess returns (i.e. a 5% return is higher than 10% for the blue line as we would expect after a period of 10%).
When using the weekly and monthly market data, you are comparing a market-cap-weighted index that may fluctuate for weeks to one that will be used daily.
There are additional advantages to using the data daily as opposed to weekly. A weekly market data point is more stable than one that may fluctuate during a week. This means that when the market moves sideways in the following weeks or months you may be able to buy low and sell high in less time than your investment would otherwise take.
Bottom line? If you are looking for a risk/reward indicator that will help you make decisions about the size to put into your portfolio you should focus on the weekly indicator. It is the best for risk/reward and you may get better results by simply looking for trends, rather than focusing on specific timeframes.
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