A big part of trading is about making decisions with incomplete information in an ambiguous environment and then managing those decisions towards the best possible favorable outcome. I don't mean just trade entries, exits, money management or trailing stops etc. The above applies to all other aspects of trading like market selection, trading research, setups to explore or employ, retrospective analysis etc.
Given the prevalence of decision making in trading/investing and our great ability to sabotage our decision making with psychological biases, I think having few structured techniques in our toolbox for good decision making is highly useful.
The purpose of this post is to put forth the idea of utilizing structured decision making techniques available in other fields in trading and to present an overview of 5 simple and effective decision making techniques.
Update: The post is updated with annotated charts. Also few changes to the content of the post.
The core idea of this post is to analyze fundamentals just like price action and see if that provides any additional value. Recently I added this functionality to my software for fun but now I think it might be worthwhile to investigate further for longer term trades.
So which fundamental metrics do we use? Following are two fundamental metrics this post uses -
AMZN chart is interesting i.e., Rolling EPS trend is down (since ~ 2011 Q1) but the price trend is sideways to up. In AMZN past history (and other charts included in this post) there were no instances where EPS downtrend is down and price has sustained uptrend.
MSFT chart is also interesting i.e., its Rolling EPS trend was doing pretty well from 2006-2012 but its price trend is sideways. Market not recognizing its value? I can imagine reaction from TA folks to this question :-).
Note - Readers proficient in fundamental analysis are welcome to suggest other metrics for future posts. The requirement is I should be able to construct the metric from the data available in the Annual/Quarterly reports.
Question - What would be a good metric(s) to analyze markets (like SP500) from fundamental perspective? Any suggestions are welcome.
Wish you all good health and good trading!
Disclaimer: The above is not a recommendation. Please do your own due diligence. Also I change my trading opinions often as new information/insights roll in.
The core idea of this post is to analyze fundamentals just like price action and see if that provides any additional value. Recently I added this functionality to my software for fun but now I think it might be worthwhile to investigate further for longer term trades.
So which fundamental metrics do we use? Following are two fundamental metrics this post uses -
- Rolling (4 Quarters) Earnings Per Share
- Rolling (4 Quarters) Price/Sales Ratio.
- When EPS trend is down, the price action is either down or side ways. So if EPS trend is down (like in AMZN) but price trend is up, something funny could be going on like the stock running on stories/perception.
- At bottoms, the EPS trend seems to act as leading indicator. Near tops, the price is the leading indicator.
- Inclusion of EPS trend in price action chart provides a better picture of the context.
- Caveat - I have not yet verified quantitatively the above assertions are valid.
AMZN chart is interesting i.e., Rolling EPS trend is down (since ~ 2011 Q1) but the price trend is sideways to up. In AMZN past history (and other charts included in this post) there were no instances where EPS downtrend is down and price has sustained uptrend.
MSFT chart is also interesting i.e., its Rolling EPS trend was doing pretty well from 2006-2012 but its price trend is sideways. Market not recognizing its value? I can imagine reaction from TA folks to this question :-).
Note - Readers proficient in fundamental analysis are welcome to suggest other metrics for future posts. The requirement is I should be able to construct the metric from the data available in the Annual/Quarterly reports.
Question - What would be a good metric(s) to analyze markets (like SP500) from fundamental perspective? Any suggestions are welcome.
Wish you all good health and good trading!
Disclaimer: The above is not a recommendation. Please do your own due diligence. Also I change my trading opinions often as new information/insights roll in.
Often interesting ideas pop up when we look at same data but with different lenses. Many readers of the blog probably might be aware of Low Volatility Anomaly in markets. If you are not familiar with it and interested on that topic, then do a Google search for "Low Volatility Anomaly". You will find many articles, academic journal papers and explanations on that anomaly.
The low-volatility anomaly basically says portfolios of low-volatility stocks have produced higher risk-adjusted returns than portfolios with high-volatility stocks in most markets studied. Now often most of these low-volatility anomaly studies take one of the following two approaches -
Ranking-Based Approach:
In rankings based approach, the market or target segment (like large cap, small cap, emerging etc) is divided into deciles/quintiles based on a volatility measure. The division is such that securities in the lowest decile/quintile will be of low volatility. The portfolio is then invested in these low volatility deciles (or weighted heavier) and re-balanced monthly.
Minimum Variance Approach:
Another scheme is constructing minimum variance portfolios with the understanding minimum variance portfolios will have lowest risk. Then a weighting algorithm is used to determine the weights and limits for the selected securities & sectors belonging to that minimum variance portfolio. Then the portfolio is re-balanced monthly.
While the idea is good, I am not sure either of the above approaches are practical for individual traders unless one has large account and time. Also my personal preference when it comes to academic papers on trading is to generally pick the concept, understand the authors viewpoints, discard rest and figure my own way to incorporate those concepts for profitable outcome.
Capturing Volatility Premium:
IMO often good ideas come from simple rearrangement of concepts picked in various contexts over time. Applying that here, what do we know when it comes to volatility and these approaches -
If that is true, then why not simply pick few broad market (liquid) ETFs, buy when their volatility is low and sell when their volatility is high?
Let's put above hypothesis to test. The broad market ETFs chosen for the test are - Emerging Markets (EEM, Europe (EFA), Asia & Pacific (EPP), US Small cap (IJH) and US Mid cap (MDY).
Some Notes:
Following annotated images provides various performance stats. One can glean several insights both at individual market level as well as at portfolio level. Some highlights:
In the below image, notice the horizontal areas in the equity curve (black line) and the behavior of benchmark (red line) in those periods. The system goes into sidelines or has position only for a short time when the volatility is high in the benchmark. That is what we want.
The following image provides various performance stats and ratios both for individual markets and for portfolio. The pie-chart provides the color notation. Notice anything of interest in "Annualized Sharpe", "Sortino Ratio" and "Rolling Correlation" bar plots?
The below scatter plot shows where individual markets and equal weighted portfolio fall in annualized Risk-Return spectrum.
The last image provides detailed performance stats, calendar returns and draw downs etc. Looks like US Small cap has better returns of all whereas on risk-adjusted basis, the account seems to do better.
Now one idea doesn't make a system. The purpose of the test is basically to check for myself whether the hypothesis (i.e., buying and selling based on volatility level and price action )has legs and worth investigating further. The results are better than I expected for first round. The hypothesis seems to be worth investigating further. Thoughts?
I have not seen any low volatility anomaly studies on net that approach it this way. If you know of any studies/articles that discuss low volatility anomaly using approaches (besides ranking into deciles or using minimum variance) then please let me know.
Side Note: Like other tests on the blog, formulation of test rules, back-testing, analysis and visualizations are done using a proprietary software I developed over time. The software was built using R language and C#.
The low-volatility anomaly basically says portfolios of low-volatility stocks have produced higher risk-adjusted returns than portfolios with high-volatility stocks in most markets studied. Now often most of these low-volatility anomaly studies take one of the following two approaches -
Ranking-Based Approach:
In rankings based approach, the market or target segment (like large cap, small cap, emerging etc) is divided into deciles/quintiles based on a volatility measure. The division is such that securities in the lowest decile/quintile will be of low volatility. The portfolio is then invested in these low volatility deciles (or weighted heavier) and re-balanced monthly.
Minimum Variance Approach:
Another scheme is constructing minimum variance portfolios with the understanding minimum variance portfolios will have lowest risk. Then a weighting algorithm is used to determine the weights and limits for the selected securities & sectors belonging to that minimum variance portfolio. Then the portfolio is re-balanced monthly.
While the idea is good, I am not sure either of the above approaches are practical for individual traders unless one has large account and time. Also my personal preference when it comes to academic papers on trading is to generally pick the concept, understand the authors viewpoints, discard rest and figure my own way to incorporate those concepts for profitable outcome.
Capturing Volatility Premium:
IMO often good ideas come from simple rearrangement of concepts picked in various contexts over time. Applying that here, what do we know when it comes to volatility and these approaches -
- Volatility in markets is mean reverting i.e., low volatility begets high volatility and vice-versa.
- A big part of low volatility portfolio returns is due to periodic re-balancing of the portfolio.
If that is true, then why not simply pick few broad market (liquid) ETFs, buy when their volatility is low and sell when their volatility is high?
Let's put above hypothesis to test. The broad market ETFs chosen for the test are - Emerging Markets (EEM, Europe (EFA), Asia & Pacific (EPP), US Small cap (IJH) and US Mid cap (MDY).
Some Notes:
- Average True Range is used to measure volatility here. There are other ways to measure volatility. The choice of ATR as volatility measure is mostly a matter of convenience.
- The test results are frictionless i.e., no slippage and commissions.
- The test is done on weekly charts. Duration: 2000 - Current.
- The portfolio is weighted equally across the 5 major markets.
Following annotated images provides various performance stats. One can glean several insights both at individual market level as well as at portfolio level. Some highlights:
In the below image, notice the horizontal areas in the equity curve (black line) and the behavior of benchmark (red line) in those periods. The system goes into sidelines or has position only for a short time when the volatility is high in the benchmark. That is what we want.
The following image provides various performance stats and ratios both for individual markets and for portfolio. The pie-chart provides the color notation. Notice anything of interest in "Annualized Sharpe", "Sortino Ratio" and "Rolling Correlation" bar plots?
The below scatter plot shows where individual markets and equal weighted portfolio fall in annualized Risk-Return spectrum.
The last image provides detailed performance stats, calendar returns and draw downs etc. Looks like US Small cap has better returns of all whereas on risk-adjusted basis, the account seems to do better.
Now one idea doesn't make a system. The purpose of the test is basically to check for myself whether the hypothesis (i.e., buying and selling based on volatility level and price action )has legs and worth investigating further. The results are better than I expected for first round. The hypothesis seems to be worth investigating further. Thoughts?
I have not seen any low volatility anomaly studies on net that approach it this way. If you know of any studies/articles that discuss low volatility anomaly using approaches (besides ranking into deciles or using minimum variance) then please let me know.
Side Note: Like other tests on the blog, formulation of test rules, back-testing, analysis and visualizations are done using a proprietary software I developed over time. The software was built using R language and C#.
Wish you all good health and happy holidays!
Each week I come across lot of interesting research and analysis through WallStreetCurrents. So posting a list of articles I found interesting and informative.
Trading/Investing:
Wish you all good health and good trading!
Trading/Investing:
- Volatility based Asset Allocation - The title is bit misleading. It is not about position sizing based on asset volatility but rather it is about using VIX as one of the filters to determine selection of Assets. Clear explanation and lots of performance stats.
- Four Big Themes current going on - A summary of 4 big macro stories currently going on
- How to set profit targets and control losses - Interesting article from Futures Mag. Talks about walk forward optimization and using MAE, MFE etc for determining profit targets and losses.
- HP's Deal from Hell - Pretty insightful article from AswathDamodaran.
- How much profit will Amazon eventually make? - Interesting viewpoints from short perspective.
- This trend is very worrisome for Apple - Good analysis on Apple mobile market share and viewpoints.
- Augmented Light Bulb Turns a Desk into Touch Screen - Pretty interesting idea.
- Why Amazon thinks big data was made for the cloud
- The cleverest business model in Online Education - The article talks about a startup called Duolingo that taps into crowd to make learning a language free.
- Beyond Lithium Ion - ARPA E Places Bets on Novel Energy Storage - The article has also link to list of projects submitted to ARPA and grants they received.
- Why Google's Ingress game is a data gold mine
- MIT Researchers create tiny shape shifting robots - A interesting idea with 3 min video.
- 5 statistics problems that will change the way you see world - Interesting problems especially the 4th and 5th one.
- Weighing the Week Ahead - Summary of various stuff happening in markets.
- New generation investors betting on Americas housing market
- How does drawing improve children's mood?
Wish you all good health and good trading!
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