Ziggma stock scores.
Algorithm-based fundamental analysis delivers an unbiased ranking of stocks within an industry to make it easy for you to evaluate companies.
Why use our stock scores?
Move the odds in your favour
Picking the highest ranked stocks in each industry has proven to generate substantial outperformance.
Save time
Our Ziggma Stock Scores will help you pick the winners in each industry quickly while staying away from the losers. All you have to do is use the Scores as a screen setting in our free stock screener.
Unbiased
Our automated, quantitative approach provides 100% unbiased results.
Monitor your holdings
By keeping an eye on your holdings’ Ziggma Scores, you will be able to track the quality of your portfolio holdings over time.
Developed by finance professionals
Our team has decades of experience working for leading global asset managers.
Proven track record
Average annualised return of 18% vs. 10.3% for the S&P 500 (2011 – Nov 2020).
Our Approach
Algorithm based fundamental analysis.
- Combine the principles of fundamental analysis with algorithm-enabled, cutting-edge data processing technology to generate a relative ranking of stocks for each industry.
- Capture a total of 32 KPIs over a multi-year observation span across the evaluation categories growth, profitability, valuation and financial position.
- Apply various layers of weights to reflect the importance of a category, KPI or data point in time.
- Score stocks on a scale of 0 – 100 to provide an unambiguous ranking of worst to best.
Historic Performance
Strong historic outperformance.
The chart illustrates the performance of a portfolio comprising the top-rated 10% of stocks in each industry over the time period 2011 through 6 Nov 2020. While the exact replication of the tested investment strategy may only be available to investors capable of managing large portfolios, the track record clearly demonstrates that stocks with Ziggma Scores in the top 10% of an industry generated a substantial outperformance. By deduction, top ranking stocks by Ziggma Score have shown to produce higher long-term returns than stocks outside the top 10%.
Questions and answers.
Why is backtesting important and how we implement it?
Backtesting is the method for testing how well a strategy or a model would have done using historical data. To implement backtest, we create a hypothetical portfolio of the top 10% scored stocks in each industry using historical scores and stock returns from 01-01-2011 to 11-06-2020. The hypothetical portfolio is re-balanced once a month. In order to capture the income return, the returns are exclusive of paid dividends and are solely based on daily adjusted closing prices. The backtest results show that the strategy of Ziggma portfolio outperforms the relevant benchmark over the examined period.
Which companies do we cover?
We cover stocks trading in the US. In order to get the most robust data set, we apply a marketcap filter so that we select onlw companies whose market capitalization was greater or equal to $100 million at least once during the last 3 months. The reason for this is that below this threshold there are too many “special” cases, such as zombie companies, startups, special acquisition vehicles, etc. with aberrant ratios, income statements or balance sheets.
Why do some industries have more than one stock with the same score?
In mathematics, the pigeonhole principle refers to the situation when n pigeons are put into m containers, with n>m, then at least one container must contain more than one pigeon. Similarly, some industries incorporate more than 100 peers, so unavoidably some stocks in particular industries will have the same score/ranking (0 – 100). Although the raw score is usually a unique decimal number for each stock, using the empirical cumulative distribution we end up having two or three companies having the same score within large industries.
Why do some companies have two scores?
When more than one class of stock is offered by a company, they are usually designated as Class A shares and Class B shares. We have decided to score them both. Normally, the two have similar scores.