Trading Strategy Walk-through: CAPE Pt: 1
May 13, 2020, 6:49 p.m.
In the first post of this series I described the general concpet of this trading strategy, and described how Cyclically Adjusted Price-to-Earnings ratio's were a primary component of the strategy. However, what I didn't say much about was the specific calculation of CAPE's I use and my reason for doing so. In this post, I'm not acutally going to provide any code snippets for the CAPE component of the trading strategy, and instead I'll discuss my reasoning for the specific variation of CAPE I'm utilizing. If you're unfamiliar with CAPE's or the normal calculations, I highly recomend reviewing [Robert Shiller's great work](https://www.econ.yale.edu/~shiller/data.htm "Robert Shiller's great work"), or if you're really keen on learning about CAPE's, give these books a read: - [The Intelligent Investor by Benjamin Graham](https://www.amazon.com/Intelligent-Investor-Definitive-Investing-Essentials/dp/0060555661 "The Intelligent Investor by Benjamin Graham") - [Margin of Safety by Seth Klarman](https://www.amazon.com/Margin-Safety-Risk-Averse-Strategies-Thoughtful/dp/0887305105 "Margin of Safety by Seth Klarman") In summary, CAPE is normal defined as price divided by the average of ten years of earnings (simple moving average), adjusted for inflation. As such, it's most widely used to assess the likely future returns of equities over timeframes of 10 - 20 years. It works as a leading indicator, gauging whether a particular equity is undervalued or overvalued by comparing its current market price to its inflation adjusted historical earnings, with higher than average CAPE's implying lower than average long-term annual average returns. While the primary components, and the signals, remain the same, the calculations I use for my CAPE's differ from the normal calculations. Unlike Graham, Buffet, Shiller, or other value investors/researchers, I utilize an exponential moving average for my CAPE calculations, and base it on quarterly data (rather than annual) for 8, 9, or 10 year time-frame(s). The timeframe parameter depends on the desired responsiveness and ones own thoughts on business cycles. The reason for these changes is straightforward. An Exponential Moving Average (EMA) is preferred over a Simple Moving Average (SMA) because, as an EMA is front-weighted, it reacts more strongly to the more recent data and therefore 'hugs' recent price action more closely. Keeping a closer eye on recent price action is an especially important factor when using these fundamental criteria to try to determine the appropriate time to enter & exit positions. Secondly, by utilizing quarterly data rather than annual data, I have 4X as many reference points for the time-period in question. These extra data points allow for a much more responsive indication of the financial performance/health of an equity based on its CAPE. While introducing more data points into a calculation like this may, in some cases, make it slightly more difficult to find the 'signal in the noise', it has not been an issue for me yet. Thats all I've got for now. In the next post I'll actually dive into the coding implementation. Thanks for reading! >**DISCLAIMER:** To be brutally honest, I do not know if this strategy can consistantly generate abnormal risk-adjusted rates of return. I am simply sharing this idea so others can contest or explore its potential usefulness. The information in this post does not constitute investment advice. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from use of or reliance on such information.
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