Portfolio Optimizer lets quantitative analysts and portfolio managers analyze an initial or model portfolio to suggest potential trades that maximize portfolio utility in a risk/retun construct. Fully customize variables within an optimization while relying on FactSet to seamlessly manage underlying data.

Key Features:

• Select models: select global, regional, or single-country models from suppliers such as Barra, Northfield, APT, R-squared, or your own custom risk models
• Set dates: choose any date for optimization; this flexibility makes it possible to iterate an optimization through time
• Create factor constraints: establish penalties for falling below or exceeding any user-defined constraints
• Identify sector and industry constraints: specify minimum or maximum concentration in particular sectors and industries using third-party or proprietary definitions
• Define alphas: define alphas of each security or upload proprietary alpha scores to FactSet's mainframes
• Find portfolios and benchmarks: select any FactSet formulas, Universal Screen, portfolio, or third-party benchmark as the starting point for analysis

Analyze Risk of Non-Normal Portfolios with Monte Carlo VaR

Equity options don’t exhibit normal distribution in their returns. When introduced into an equity-only portfolio, these securities change the distribution of the portfolio’s return. As a result, tracking error and other mean/variance statistics no longer describe the portfolio’s risk. To meet this need, FactSet offers Monte Carlo VaR in Portfolio Analysis.

Key Features:

• Access data from Portfolio Analysis: tap into any of the portfolios, benchmarks, and composites available to the Portfolio Analysis application
• Simulate different factor return outcomes: apply a Monte Carlo technique from the same risk models that you would select for a tracking error analysis
• Compute option returns: select the the appropriate options pricing model (Barone-Adesi, Whaley for American options, and Black-Scholes for European options)  to compute option returns
• Aggregate resulting security returns: compile your results into a single, simulated portfolio return for each outcome in the Monte Carlo Simulation
• View results in a chart and add statistics: analyze the resulting distribution as a chart or run statistics like VaR or Expected Tail Loss to any Characteristics report