Get to know the Least Speculative - Highest Return stocks

Build easy to replicate winning portfolios based on a 'Best-in-Class' approach. Learn about this intuitive strategy and start investing in stocks with attractive returns and lower risk.



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95% of all Asset Managers work with Risk Metrics that are not precise enough. They calculate with formulas that may contradict investor's risk preferences.

Basically, investors cover 3 aspects before buying a stock:
1. Assessment of return-to-equity, financial stability and management quality.
2. Assessment of market valuation and screening of historical values.
3. Assessment of future risks and price volatility.
The last aspect is the hardest to estimate and maybe for many investors the most critical one. Investors desire more precision.

A more accurate method is required. We call it the "Warren Machine Measure". It calculates Risk exactly how investors perceive Risk: as the duration and magnitude of intermittent losses in relation to realized returns.

Risk Fallacy

When calculating Risk of individual stocks or portfolios the concept of "standard deviation" or "downside deviation" is commonly used. The formulas contain assumptions about the "real world" which are not always correct and in certain circumstances this can lead to sub-optimal decisions.

Sharpe ratio*: average return / standard deviation
Sortino ratio*: average return / downside deviation
Warren Machine Measure: average return / effective drawdown

Sharpe:   
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Sharpe:   
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Sortino:   
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Sortino:   
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WMM:   
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WMM:   
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* risk-free rate and target return are set to 0%

A higher ratio should indicate a better return-to-risk constellation, but this is not always true for Sharpe or Sortino, whereas the Warren Machine Measure ranks return-to-risk correctly.


Reality Check

Click the images* to enlarge

* Dividends are re-invested.

The reason for the better precision of the Warren Machine Measure is the fact that the chronological distribution of returns is taken into account and there is empirical evidence that volatility is more predictable than prices, i.e. risk is rather clustered in regimes of low or high volatility.*

* Benoit Mandelbrot and Richard L. Hudson: The Misbehavior of Markets, 2004.


New Opportunities

A precise Risk concept like the Warren Machine Measure allows for an unambiguous ranking of stocks in terms of their Stability.

Due to a more precise measurement of Stability the transition to a more unstable state is much better predictable and this fact adds a new dimension to selection and timing of individual stocks.


An investment in high-stability stocks is not necessarily pursuing the best possible returns but rather reduces the downside volatility to a large extent. A Best-in-Class approach to select stocks creates superior portfolios, as demonstrated below for a universe of 500 US large cap stocks.

The track record below is calculated using adjusted closing prices, i.e. dividends are constantly re-invested. At the beginning of a month, the 10 stocks with the highest Warren Machine Measure (Best-in-Class) are selected for the portfolio. This portfolio is held for an entire month without any transactions. Transaction costs of 1% per trade are deducted.
To zoom in, drag a rectangle with the mouse pointer or click the 'Zoom' buttons or move the orange handles at the bottom.

Past Performance Is No Guarantee of Future Results

10 Top Stability Stocks

* The price is the dividend adjusted closing price. Source: Yahoo Finance.

Additional Information

Above, you see the hypothetical result derived from 500 US stocks. It is a sample portfolio and by no means optimized. With very few transactions (around 20 per year) major downturns can be mitigated to a large extent. Other stock universes lead to very similar outcomes. Cross-check results with the tool Portfolio Visualizer.

For more opportunities, Best-in-Class portfolios can currently be provided for:
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About Me

Martin Rothe, CTA

I am an academic who fell in love with data. I learned programming software when the first desktop PCs appeared in the business world. Applying logic on raw and boring data turned out to be a fun and intriguing exercise. I have developed algorithmic trading tools and programs and built a hedge fund company around it. The financial success of objective decision making especially in times of stress and uncertainty in the financial markets directed my attention to the more all-encompassing hot topics of machine learning and AI. My aim is to provide intuitive and fact-oriented solutions for portfolio managers, investors and people who seek advice for self-directed wealth management.

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