Our scorecard gives you a quantative view of a specific company by combining the most powerful factors and presenting them in an easy to understand presentation. You can get instant answers on the following key questions:
You will find answers to all these questions in our scorecard. Here's an overview of the main parts:
Our scorecard makes peer comparison very easy by calculating percentiles on each factor, relative to the sector, industry group and industry. It does this by ranking the companies on this factor and then creating 100 groups (percentile) with the same number of companies.
Often it's quite useful to get a list of the other companies in the sector, industry group or industry and see the factors for each company. To make this very easy, we added links to the sector, industry group and industry at the top of the screen. Clicking on any of these links will open the screener and include only the companies belonging to this group.This list will also apply the filters specified in the Filter Menu for your last screen. The filters taken into account are: countries, markets, market cap, trading value, results age and currency. By applying these filters it's easy to see which companies were used in the industry, industry group and sector calculations on the scorecard. Please note that this will overwrite your industries filter.
You can use the functionality of the screener to add additional filters, change the sorting etc...
This field allows you to quickly navigate to the scorecard of another company. When you start typing the name of a company, a list of options will show up. You can select a company from the list
After selecting the company, the scorecard will be reloaded with the values for this company.
Our stock scorecard is fully dynamic and calculates all scores based on the filters of your last screen. If the selected stock is not included in this screen, dynamic scores such as the Magic Formula, ERP5 and VCs will be blank.
Click on the link provided at the top of the scorecard to get an overview of the filters used in your last screen. The next column in this grid shows the results of a test on each individual filter and whether the company passed or failed the test. The last column displays the value for this specific company.
It's one thing to find undervalued stocks, but what tells you that this stock will return to intrinsic value in the near future? Companies might have relatively low earnings, cash flow or book value multiples, but often this low valuation is justified if for instance the company is getting hammered by competition and doesn't have enough economic moat to create a profitable business model.
Some of the most powerful screens like the O'Shaughnessy trending value screen avoid value traps by using a momentum factor. It looks for relatively undervalued stocks but tries to avoid value traps by only selecting the companies with the highest stock price increase over the last 6 months. If the stock price has been going up during the last months, something must be evolving in the right direction.
Our own backtests have shown that you get even better results by using momentum as a primary factor in your model, i.e. look for stocks that have gone up in the recent past but are still cheap.
We think momentum is important so we positioned it in the top left corner of our scorecard. You can see the price increase during the last 3, 6 & 12 months as well as the price range, i.e. where the stock price is now compared to its 52 week high and low.
Joel Greenblatt, one of the most successful hedge fund managers with a spectacular track record, created a very simple and effective formula that can easily be understood by even the most novice investors. He wrote a book about it called 'The little book that beats the market' where he explains the formula in detail. His advice sounds very simple: buy good companies at bargain prices and repeat this process every year.
Not exactly rocket science, but it works. To calculate the magic formula, we rank companies based on ROIC and Earnings Yield and then we rank them again based on the sum of the 2 rankings. A company with a magic formula score of 100 is in 100th position out of the stock universe of your last screen. The score is also displayed as a star rating.
New! The Magic Formula score is now fully dynamic and is calculated on a filtered stock universe using the filters specified in the Filter Menu. The filters taken into account are: countries, markets, industries, market cap, trading value, results age and currency.
If you want to get a better understanding of the components used in the Magic Formula calculation, you can click on the 'details' link on top of the chart. This opens the following screen:
First of all, this screen shows a small introduction to the Magic Formula and a useful link to the little book. It then displays a few visualisations that should help get a better understanding of the score:
We think this new scorecard provides a much more intuitive assessment of the relative cheapness and quality of a company. And since it uses the screener filters of your last screen, you can play around with these filters in the screener and see the effect on the histograms. This way you can see the histograms for one or more sectors, countries or for selected company sizes.
The ERP5 ranking is our home-brewed ratio based on the magic formula and ideas by the father of value investing and stock screening in general, Benjamin Graham. We developed this formula as we didn't want to find companies that just performed well during the last year, but also the 4 years before that. We also wanted to remove companies with a high price-to-book value. This formula worked very well in our backtests and it has become a favorite factor of many of our members.
The visualization is very similar to the magic formula. We display both the ranking and a star rating.
New! The ERP5 score is now fully dynamic and is calculated on a filtered stock universe using the filters specified in the Filter Menu. The filters taken into account are: countries, markets, industries, market cap, trading value, results age and currency.
The next section on the scorecard provides a very quick health assessment based on balance sheet data. Joseph Piotroski designed the f-score as the sum of 9 binary health signals and he used this on low price-to-book companies to separate the dogs from the good prospects.
Our scorecard shows the Piotroski F-score on a bullet chart. This has different components:
Below the chart we also show a quick interpretation of the F-score.
If you want to understand how the score was calculated, you can click on the 'details' link on top of the chart. This opens the Piotroski F-Scorecard which consists of 2 parts.
This scorecard shows all 9 signals and an explanation in plain English. You can drill down on any particular signal and see how it's calculated.
The Piotroski F-Score is a point-in-time health assessment and a high score indicates that the company's prospects are improving. The score also changes over time and even if there are no studies proving any predictive value, it can be quite interesting to see how the Piotroski F-Score evolved over time. Open the 'History' tab to see the Piotroski F-Score and the 9 signals during the last 11 periods.
The Z-score was built to predict whether a company is likely to go bankrupt in the next 2 years. It was developed by finance professor Edward I. Altman and is based on multiple corporate income and balance sheet values. The Z-score will signal 70% of bankruptcies of publicly listed companies and predicted the demise of Enron, Worldcom and other disasters.
The Altman Z-Score is displayed on a bullet graph. A value below 1.81 indicates that the company is in significant distress and there's a high probability that the company will go bankrupt in the next 2 years. A value between 1.81 and 2.99 indicates that there's a good chance that the company will go bankrupt in the next 2 years. A value above 2.99 indicates that the company is in the "safe" zone.
Please note that we currently support only the original Altman Z-Score. This is only valid for manufacturing companies.
The M-Score was created by professor Beneish. It uses eight financial ratios to identify whether a company has manipulated earnings. In 1998, students from Cornell University University correctly identified Enron as an earnings manipulator using the M-Score, where experienced financial analysts failed to do so. (Enron filed for bankruptcy in late 2001.
The M-Score is displayed in a bullet chart. A score above -2.22 indicates a strong likelyhood of earnings manipulation.
If you want to understand how the M-Score is calculated, you can click on the details link at the top right corner of the Beneish M-Score panel. This opens the M-Scorecard.
This scorecard shows all 8 signals used to calculate the score and an explanation in plain English. You can drill down on a particular signal and see how it's calculated.
Value Composites were introduced by O'Shaughnessy in the 4th edition of 'What works on Wall Street'. O'Shaughnessy found that instead of looking at individual factors on their own, returns were considerably higher when combining them together. He called them 'value composites' and created 3 versions of them. You can find more details on the calculation by clicking here.
O'Shaughnessy calculates his value composites on the full universe of stocks. A VC of 1 means that the company is in the 1% cheapest stocks in the universe. This however does not take into account the different characteristics of industries or sectors. The average P/E, P/B and other ratios can vary considerably as you can see in the following overview.
For this reason we created 3 alternative calculations for each of the VCs. Instead of calculating percentiles for each component over the entire stock universe, we calculated the percentile within the sector, industry group and sector. You can see the results in the grid below:
This company is very cheap when looking at VC1 and VC2 (which adds shareholder yield as one of the factors). The VC3 is higher as the company buys back relatively little shares compared to all other stocks.
When we look at the industry column however, we see that the company is not that cheap after all and has a VC1 of 51. The VC3 increases to 70, which puts it in the red.
The overall conclusion is that the stock is cheap when comparing to all stocks but this is partly because of the fact that it operates in an industry where the ratios are typically lower than average. By making the VC industry, industry group or sector agnostic, you get a better view on whether it's a real bargain or not. Please note that we have not yet conducted any study on this so we don't know whether these group VCs have similar predictive capabilities as the pure VCs as designed by O'Shaughnessy.
New! All value composites are now fully dynamic and calculated on a filtered stock universe using the filters specified in the Filter Menu. The filters taken into account are: countries, markets, industries, market cap, trading value, results age and currency.
A company can be cheap overall, but how does it compare to the selected stock universe and to its peers? To make this as intuitive as possible we created the value factors grid. In the rows you can find the key value ratios. In the second column, we provide these ratios for the selected company.
In the third column, we provide the percentile to which the company belongs when using the complete stock universe. (percentiles divide all stocks in 100 groups with an equal amount of stocks. 1 = best, 100 worst) This data is displayed using a meter, which turns green (red) when the value is below 33 (over 66). We also provide the median value for this ratio calculated over the full stock universe. (We use median instead of average to avoid that the value is impacted by extreme values.)
In the next 3 columns, we provide the same percentiles and medians, but this time calculated only on the stocks belonging to the same sector, industry group and industry.
In the screenshot above you can see that while the company's P/S of 0.52 is quite low compared to all stocks, it is relatively high when looking only at the industry, where the average is 0.29. Another interesting information is that its FCF yield is 48 basis points lower than the median of all stocks, however when comparing to its peers in the industry the FCF yield is 92 basis points higher. If you want to see all other stocks in the industry, there's a quick link at the top of the scorecard. Click here for more information.
Finally, if you want to see how a particular measure is calculated, just hover over the value to see the formula tooltip:
If you want to read more about the selected measure, click on the 'Go To glossary' link. This takes you to the dynamic glossary where you can find background information about this measure, the formula and the calculation for the selected company. You can also find links to components of this calculation. Here's an extract of the glossary:
New! All value factors are now fully dynamic and calculated on a filtered stock universe using the filters specified in the Filter Menu. The filters taken into account are: countries, markets, industries, market cap, trading value, results age and currency.
The next grid in the scorecard shows the same information for the key quality factors. The first 2 columns show the ratio and the value for the company. The next 4 columns show an easy comparison to all stocks and its peers in its sector, industry group and industry. For each column it displays:
In the screenshot above you can see that the company has a ROC of 12.06%, which is lower than the median of 14.8% for all stocks. When we compare it to its peers in the industry, it's actually more than 100 basis points higher. Looking at shareholder yield, the company distributes almost double of the median of all stocks. When we compare this to the industry however, we can see that it's not great compared to its competitors. The next ratio seems to indicate that the company has a sustainable competitive advantage. Gross profitability of 68% is significantly above the industry median of 25.8%. If you want to see all other stocks in the industry we provided a quick link at the top of the scorecard. Click here for more information.
New! The quality factors are now fully dynamic and calculated on a filtered stock universe using the filters specified in the Filter Menu. The filters taken into account are: countries, markets, industries, market cap, trading value, results age and currency.
When evaluating a company, one should always check the latest news. Recent events might explain the reason why a stock is undervalued or has shown significant momentum. A company might be a takeover target, analysts might have up- or downgraded the stock, etc... By clicking on the 'News' tab, you get the latest 10 news items provided by the google finance news service.
The final tab on the scorecard provides a few useful links that are helpful to conduct further analysis on the company. We include the following links: