In 2005, legendary investor and money manager Joel Greenblatt wrote the best selling book “The Little Book That Beats the Market.”
The book is still considered one of the great classics in investing and it’s principles have stood the test of time.
We asked, can we take the principles taught by Greenblatt, overlay technical analysis and quantitative analysis onto it, and improve its results?
As you will see here in this issue of the Connors Research Traders Journal, the answer is yes.
Making Good, Better
The combination of Fundamental Analysis, Technical Analysis and Quantitative Analysis is known as “Quantamentals”.
In our opinion, and backed by systematic data-driven results, Quantamentals is the future of trading and investing.
Today, we are going to share with you a very simple example of how combining factors (fundamental, technical and quantitative) often improve the returns of portfolios, while at the same time lowering risk.
Systematically Improving On the Concepts From
“The Little Book That Beats the Market”
For the trading strategy in this issue, we are going to use factors that are well known and have been in the public domain for decades.
Specifically, we are going to use the techniques described in The Little Book That Beats the Market written by the highly successful hedge fund manager Joel Greenblatt.
All backtests here were created using Python and the Quantopian platform. The stock universe used is the “Q500US” universe, containing the 500 most liquid US stocks.
If you would like to be able to code these strategies yourself using Python, the most popular coding language for professional quant finance, click here for a free webinar about our upcoming “Python Programming for Traders” course.
Step #1 – Combining the Fundamental Factors
The first step in our model is to combine the fundamentals factors.
Here we will use return on invested capital (ROIC) to measure the “quality” of a company.
We will use EBIT/EV to measure the “value” of a company.
These are the same measures used by Mr. Greenblatt in “The Little Book That Beats the Market”.
- In this backtest, we first rank our 500 stocks by the value metric, with 500 being the cheapest and 1 being the most expensive.
- We then rank our 500 stocks by the quality metric, with 500 being the firm with the highest quality and 1 being the firm with the lowest quality.
- We then simply sum the rankings, rebalancing into the 50 firms that have the highest combined rank – firms with both high quality and low valuations. We rebalance our portfolio on a monthly basis.
Step #2 – Add in Low Vol
We will now add in the low vol factor to the mix.
The methodology here is the same.
- We will rank each stock by quality and value, the same as before, but this time add in a ranking for low volatility.
- We will apply a 100-day lookback to calculate volatility, sticking with the standard deviation of daily percent returns over this time frame. The stock with rank 500 will be the lowest volatility stock and the stock with rank 1 will be the highest volatility stock.
- We will then simply sum our three rankings – quality, value, and low volatility, and invest in the top 50 stocks with the highest combination rank. We again rebalance the portfolio monthly.
Two things stand out.
1. The annual return rises.
2. The volatility and the risk of the portfolio drops significantly.
Higher returns plus less risk is what we are all seeking!
Step #3 – Add in Absolute Momentum To Further Lessen the Risk
The next factor we will add to our model is absolute momentum or trend following.
We will do this in the form of a trend following “regime filter.” This well-known technique will first check if the overall market is trending higher. Only then will the model take new entries in our monthly rebalance. If the overall market is trending lower, no new entries are taken.
We now have to introduce a “risk-off” asset, something to rotate into when the trend of the overall market is down and we aren’t buying more stocks. For this purpose, we will simply use SHY (1-3yr US Treasuries).
There are many ways we can measure if the overall market is trending higher or lower. Instead of looking for the optimal way to measure this, we will instead use a simple moving average with a 100-day lookback. We will use the ETF “SPY” to represent the market.
- If the price of SPY is above its 100-day moving average, we will conclude that the market is trending higher and our model will take new entries.
If the price of SPY is below its 100-day moving average, we will conclude that the trend of the market is lower and our model will not take new entries.
We see two things here.
- The returns are lower, but…
- The risk is now far lower. Notice the lower annual volatility, lower drawdowns, and a rise in the Sharpe Ratio.
On a risk-adjusted basis, as measured by the Sharpe ratio, adding a trend filter makes the portfolio better.
Step #4 – Adding in Momentum Leads to Higher Returns.
The last step in our model is to incorporate cross-sectional, sometimes called relative strength, momentum.
- We first rank our stocks by our quality, value and low volatility factors, taking the top 50 stocks with the highest combined metrics.
- We will then rank those top 50 stocks using cross-sectional momentum. Our momentum measure will be the trailing 6-month total returns, skipping the last month.
- We will then take the top 20 stocks with the highest momentum scores. We are left with 20 stocks, which will be our final portfolio.
- We continue applying the time-series momentum rule, only taking new entries if the price of SPY is above its 100-day moving average.
Here are the results:
The end result is a trading strategy which
- outperformed buy and hold by over 3.4% a year,
- had a drawdown of less than half the S&P 500,
- had over 30% less volatility than the market
- And had a Sharpe Ratio of 1.14, well above pretty much any stand-alone strategy applying only fundamentals (or technicals, or quant) that we know of in print from 2003-2019.
Improvement Every Step of the Way
Notice how our trading strategy improved the risk-adjusted results with each factor we added.
We started with fundamental factors applied by many great investors including Joel Greenblatt. – quality and value.
We then added a quant factor – low vol.
We then moved on to adding technical analysis with the trend-following filter.
Finally, we added a cross-sectional momentum rule via a double sort, leading to higher returns.
This is the power of Quantamentals! By combining Fundamental and Technical analysis in a quantitative way, we were able to build a trading strategy that handily beat the market over the last 16+ years.
The following table displays the improvement every step of the way.
Specifically, focus on the Sharpe ratio, a measure of risk-adjusted returns. Notice that each factor added either increased returns or decreased risk (or both) leading to an increase in the Sharpe Ratio.
This strategy is only for illustrative purposes. We go much deeper into Quantamentals, including presenting strategies with much higher performance, in our upcoming Quantamentals course. By combining the three factors together we’ve seen non-optimized improvements vs buy and hold of up to 10% a year since 2003 while assuming less risk.
Furthermore, if you would like to gain the programming knowledge to be able to build and test high-level, portfolio-based trading and investing strategies like this one using the most popular programming language utilized by the top Hedge Funds and Investment Banks in the world – Python, click here.
We hope you enjoyed and learned from this issue. We’ve been writing about the concept of combining technical analysis, fundamental analysis, along with quantitative analysis since last year and as you have repeatedly seen with data-driven results, it’s a superior way to building higher performing strategies and portfolios to trade for yourself.
Larry Connors and Chris Cain, CMT
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