For advisors, the challenge is clear: With numerous sub-categories of asset classes to choose from (large, small, mid-cap, growth, value, core, domestic, international, etc.), multiple macroeconomic issues (such as Social Security reform, exchange rates and the budget deficit) to analyze, and thousands of individual stocks and bonds to consider, choosing the best investments for your clients has never been more difficult.

For clients whose needs can be met through mutual funds, the process may be simplified somewhat, but the investment approaches available still may be perplexing. Historically, most funds were managed using a ‘bottom up’ or ‘fundamental approach.’ The other alternatives, largely, were index funds.

But now, advisors have an increasingly popular investment option at their disposal: disciplined, quantitatively managed funds. While most people are familiar with fundamentally managed funds, financial advisors who can explain the advantages of quantitatively managed funds are in a strong position to better meet their clients’ needs with a more comprehensive range of investment products.

A quantitative management approach actually combines the benefits of fundamental analysis with the speed and accuracy of computer-based mathematical models. These funds–which use sophisticated models to synthesize and evaluate a broad range of investment information–are based on a straightforward concept. Quantitative managers visit the numbers, not the company!

Fundamental managers analyze company data, talk with analysts, speak to company senior management, consider macroeconomic trends, scrutinize balance sheets and weigh the relative merits of many companies’ stocks to develop an in-depth assessment of an organization’s overall potential. Of course, it’s next to impossible for fundamental managers to consider every micro and macroeconomic relationship; there is neither the time nor the human resources to do so.

Alternatively, quantitative analysis systematically examines the relationships that portfolio managers must consider and offers a highly effective way to address a broad range of macro and microeconomic considerations. Quantitative models can analyze thousands of pieces of data daily, helping portfolio managers identify buying and selling opportunities, and target asset allocations. The success and strength of a quantitative strategy resides in its ability to analyze all of these hundreds of relationships, allowing the portfolio manager to make informed decisions in a relatively short amount of time.

Quantitative analysis involves significant human input–it isn’t just computer-crunched data. Custom designed computer models do the “heavy lifting” by crunching the numbers; portfolio managers examine the resulting data and make the final investment decisions within a given portfolio. Quantitative managers use the same raw data–such as earnings and cash flow, analyst projections, profit and loss statements–as fundamental managers. The principal difference is that quantitative managers are able to sift through this enormous volume of data–each and every day.

A focus on the data affords portfolio managers objectivity in security selection. In addition, the discipline’s built-in ‘forensic accounting’ methods that factor in historical data can help identify inconsistencies or abnormalities in a company’s financial statements that could affect future stock performance.

Fundamental research can uncover valuable insights into a specific company and/or its management team. However, for investors looking to combine microeconomic insights with objective rigor, a balanced portfolio of both quantitative and fundamental funds is an increasingly popular option, and can be an ideal solution for their investment needs.

Financial advisors who can communicate the advantages effectively of such a broadly diversified portfolio will truly add value.

Tony Elavia is Chief Investment Officer of NYLIM Equity Investors, a division of New York Life Investment Management LLC.

A quantitative management approach combines the benefits of fundamental analysis with the speed and accuracy of computer-based mathematical models