Successful portfolios are constructed based on quality information. Investment managers need data that is analyzed thoroughly to provide meaningful input into their decision-making.

With thousands of stocks to invest in, where do you begin?

IQS provides quantitative stock analysis for your US or International equity strategies, bringing to your desk each week, stocks that can enhance your portfolios. Unlike generic data feeds, IQS's Stock Ranking System builds on publicly available data to create powerful investment tools.

IQS's analytical research uses sophisticated algorithms to forecast the effectiveness of each financial factor, not relying on past performance to predict what stocks will do in the future. IQS uses its Market Environment Indicator (MEI) methods and its proprietary weighting scheme to better forecast stock success, providing investment managers with a targeted list of securities to validate their current positions and to consider for inclusion in their portfolios.

• Our model performance is highly robust yet consistent, supported by sound financial economics and advanced statistical techniques. IQS's approach is intuitive from an investment perspective and is grounded in fundamental investment concepts using publicly available financial and market data. We simply use the data more effectively through a very sophisticated analytical approach.
  
  
• IQS provides far more powerful forecasts than typically seen. IQS's proprietary Dynamic Factor Model (DFM) incorporates the current market environment into our process and uses that information to weight the models--providing outstanding results
  
  
• Research customized to your investment style and your universe of stocks.
  
  
• Effective for long or short portfolios, across all capitalizations.
  
  
• IQS is an independent research firm, avoiding conflicts of interest.
  

Paper portfolios run with live data and with historical data back to 1989 indicate that the IQS Dynamic Factor Model (DFM) could lead to significantly higher returns.

Historical Performance (download PDF file).