By FactSet. Originally published at ValueWalk.
In his previous blog post Playing the Oracle: What will Berkshire Hathaway Buy Next?, FactSet’s Bryan Adams tried to anticipate Warren Buffet’s next move by screening for companies that might be on his shopping list. Certainly, it was an ambitious task to predict not just any investor’s actions, but an investor that goes by the nickname “the Oracle.” In this post, I have lower ambitions; I’m putting on my quantitative glasses and will be looking at the past rather than the future. What I am trying to find out is how the strategy described by Bryan would have performed historically when carried out in a systematic way.
- Buffett Screener Based On The Book “Buffettology”
- Warren Buffett – How to Calculate Intrinsic Value
- Warren Buffett’s Top 20 High Dividend Stocks
The Approach
In his post, Bryan used screening to narrow down his universe to a small set of securities to define the potential shopping list. By doing so, however, we might lose potentially useful information, or breadth, in quant language; put simply, we could use the same criteria to find companies that are unattractive by looking at the bottom of the list. When I looked at a way to convert the same criteria in a more continuous signal, another investment icon came to mind: Joseph Piotroski.
Piotroski developed a scoring system to determine the strength of a firm’s financial position. Similar to Piotroski’s famous F-Score, for each of our criteria, companies that pass will score a one, those that do not, a zero. We can then simply add up these scores to come to an overall score of 0-6.
I used the same criteria as in the previous analysis; however, I put in a cap on P/E since we don’t want to overpay. Here are the criteria:
- Size: Market Cap between $5 and $150 billion
- Pretax Income: Income of more than $500 million
- P/E: Minimum P/E in the last three years between 12 and 25
- ROE: Return on Equity above 8% in the last three years
- DE: Debt to Equity ratio less than 33%
- Concentration: Active in fewer than three industries
For the backtest, I looked at the MSCI All Country World, and I rebalanced my universe on a monthly basis between 2007 and 2017.
The Results
As can be seen in the chart above, the companies with the maximum score of six outperform all others. More comforting, when putting on my quant glasses again, we also observe a nice monotonically decreasing pattern, where companies with a score four or above outperform and two or below underperform the benchmark.
Playing My Own Devil’s Advocate
Relying purely on historical results to infer conclusions about the future is as scientific as we can get when it comes to quantitative investing. In my view, this also means that a good quant is very critical of the results, effectively playing his/her own devil’s advocate.
We did see good performance, but we conveniently ignored transaction costs. Taking the analysis a step further, for anyone familiar with Buffet’s style, monthly rebalancing might seem at odds with his approach, where he generally buys to hold. Therefore, a strategy with very high turnover would not be appealing.
| Universe | Information | % Total | Number of | ||||
| Score (Flags) | Return | Sharpe Ratio | Alpha | Beta | Ratio | Turnover | Securities |
| Benchmark | 0.35 | 0.061 | 0.00 | 1.00 | 1.24 | 2,471.81 | |
| Score = 6 | 0.81 | 0.189 | 0.55 | 0.68 | 0.47 | 10.20 | 30.69 |
| Score = 5 | 0.44 | 0.091 | 0.13 | 0.83 | 0.09 | 10.66 | 169.42 |
| Score = 4 | 0.49 | 0.095 | 0.16 | 0.89 | 0.39 | 12.37 | 439.17 |
| Score = 3 | 0.37 | 0.065 | 0.02 | 0.98 | 0.08 | 14.03 | 658.65 |
| Score = 2 | 0.27 | 0.046 | -0.08 | 1.04 | -0.50 | 16.67 | 607.74 |
| Score = 1 | 0.20 | 0.031 | -0.17 | 1.10 | -0.36 | 10.17 | 566.15 |
Table 1: Portfolio Characteristics
As we can see in the table above, the strategy is actually rather “stable”; there is relatively low turnover in all of the buckets, meaning we don’t have to churn the portfolio very often and transaction costs are manageable.
| Universe Return | Annualized Universe Return | |||||
| Score (Flags) | 1 Month | 3 Month | 6 Month | 1 Month | 3 Month | 6 Month |
| Benchmark | 0.35 | 1.19 | 2.33 | 4.30 | 4.83 | 4.72 |
| Score = 6 | 0.81 | 2.20 | 4.18 | 10.18 | 9.11 | 8.54 |
| Score = 5 | 0.44 | 1.46 | 2.70 | 5.46 | 5.96 | 5.48 |
| Score = 4 | 0.49 | 1.58 | 3.06 | 6.03 | 6.48 | 6.21 |
| Score = 3 | 0.37 | 1.16 | 2.19 | 4.51 | 4.73 | 4.42 |
| Score = 2 | 0.27 | 1.01 | 2.07 | 3.33 | 4.09 | 4.18 |
| Score = 1 | 0.20 | 0.81 | 1.76 | 2.41 | 3.27 | 3.56 |
Table 2: Equal weighted portfolio returns
Given Buffet’s buy to hold philosophy, I also looked at different holding periods and there we see that with quarterly or semiannual rebalancing the signal is very persistent. We give up some performance when we look at the annualized returns, 8.54% vs. 10.18% when rebalancing on a semiannual basis, but this is still significantly higher than the benchmark return of 4.72%.
The above results assume an equal weighted portfolio of stocks, which could introduce a small cap tilt that might explain some of the outperformance as well as introduce additional turnover. Therefore, I also looked at value (market cap) weighted returns:
| Value Weighted | ||||||
| Universe Return | Universe Return | |||||
| Score (Flags) | 1 Month | 3 Month | 6 Month | 1 Month | 3 Month | 6 Month |
| Summary | 0.35 | 1.16 | 2.28 | 4.27 | 4.73 | 4.62 |
| Score = 6 | 0.88 | 2.32 | 4.45 | 11.14 | 9.62 | 9.11 |
| Score = 5 | 0.47 | 1.66 | 3.25 | 5.77 | 6.82 | 6.61 |
| Score = 4 | 0.49 | 1.55 | 3.03 | 6.04 | 6.34 | 6.15 |
| Score = 3 | 0.34 | 1.17 | 2.30 | 4.10 | 4.77 | 4.65 |
| Score = 2 | 0.22 | 0.70 | 1.27 | 2.70 | 2.85 | 2.55 |
| Score = 1 | 0.06 | 0.47 | 1.22 | 0.77 | 1.87 | 2.45 |
Table 3: Value weighted portfolio returns
It seems that the weighting by market cap actually brings a small improvement in performance, but more importantly, the results are in line with the original results.
In order to test the robustness of the strategy, I ran additional versions of this test:
- To get a more continuous signal, I converted the criteria into decile scores instead of flags and added these together
- To see whether the performance was driven by large sector tilts, I ran a sector neutral version of the Decile Score approach
- Similarly, I ran a region neutral version
It would be too much detail to highlight of all of these tests, but in all of these variations the signal held up and we observed very similar results.
Finally, I could not resist comparing Berkshire Hathaway’s performance to that of our quantitative approach.
A good quant acknowledges her/his limitations and admittedly, this is not a 100% apples to apples comparison. Berkshire the company consists of more than its investment portfolio, we ignored transaction costs and looked at a relatively short period of
The post A Quant Approach to Buffett’s Investment Style appeared first on ValueWalk.
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