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FAQ Answers: General
 

What is the difference between ‘quantitative’ research and ‘qualitative’ research?  

Qualitative research relies on an analysis of a stock by a single analyst who attempts to synthesize his or her view of fundamental data, market conditions, interviews with company executives, and other factors that will affect a stock’s future performance. Qualitative research, by its very nature, will reflect the particular biases of the individual analyst. 

Quantitative analysis looks at the mass of fundamental data that is publicly available and uses computer techniques (algorithms) to massage that data into 'filters' (‘see What is the difference between a factor and a filter?’ below) that have been backtested for their predictive power with regard to the future performance stocks. Because an analyst’s opinion does not enter into the equation, quantitative research is without bias or personal prejudice. 

Is quantitative analysis the same as technical analysis? 

No. Technical analysis generally is based on patterns observed in a chart of intraday and/or closing stock prices, with no reference to the fundamentals of the company in question. In technical analysis you will hear phrases like moving average, Fibonacci bands, and head and shoulders. 

In quantitative analysis, computers sift through the publicly available fundamental data that is available on essentially every stock traded in America. You will hear phrases like balance sheet health, insider trading, earnings velocity, and value index.  

How can quantitative research be based on a stock’s fundamental data? 

Fundamental data can be ‘crunched’ by computers, thereby turning it into numerical form. There are several criteria that underlie a good quantitative fundamental model. First, it must reflect parameters that make good business and market sense. Second, it must not be 'over-fit,' that is, consisting of so many parameters that a good fit is almost assured, even when there are no causal links between the model and the performance results. 

What do you mean by ‘alpha’? 

Alpha is the excess return over and above the return of the relevant benchmark that results from following a particular investing strategy or an individual manager’s results. Sabrient’s goal -- which has been achieved consistently since we began our stock rankings -- is 6% alpha per year. 

What do you mean by 'investing style'? 

There are three major equity investing styles value, growth and momentum.

Growth investing is a strategy whereby an investor seeks stocks with strong earnings and/or revenue growth or growth potential. It utilizes such metrics as high (and accelerating) earnings growth and upward analyst earnings revisions.  

Value investing is a style whereby investors buy companies whose shares appear cheap when compared to current earnings or corporate assets. Value investors typically buy stocks with high dividend yields, or ones that trade at a low PE (price-to-earnings) ratio or low price-to-book ratio (P/B).  

Momentum investing is an investment style that favors buying stocks that have had high returns over the past three to twelve months and selling those that have had poor returns over the same period. Momentum investors believe that such stocks will continue to head in the same direction because of the momentum that is already behind them.

What is a portfolio strategy? 

A portfolio strategy is a quantitative, computerized approach for defining the buy and sell rules in a portfolio over time.  

What does ‘adaptive rank’ mean? 

An adaptive rank is a computer algorithm that recognizes and accounts for changes in the market’s priorities as it establishes rankings of the stocks it is evaluating.  

It results from our quantitative analysis which has shown that markets value (and thus reward) different stock characteristics at different times. For example, during the internet boom, revenue growth was valued while earnings were considered unimportant. Shortly after the dot.com bubble burst, earnings again became a major focus, reducing the impact of revenue growth. Adaptive rank allows us to take into account these factors.  

What is the difference between a factor and a filter?  

A factor is a single data item or criterion (such as, P/E, stock price, or number of shares). A filter represents a combination of factors. Sabrient filters are created for the purpose of finding stocks that meet certain criteria and combine factors in such a way as to describe business and/or market concepts, rather than being just 'grab bags' of random factors. A filter must be proven to have predictive power to remain in the Sabrient system. In some cases, there can be a one-factor filter, but the majority of our filters have between 5 and 10 factors.