Quantitative Stock Selection:

Research Projects: 2005

Group Code: Midas Group

Topic: Implementing the Black-Litterman Model

Our project provides an indepth guide to the Black-Litterman model along with a number of examples.

Powerpoint slides

Supplementary material

Group Code: Cyclical Analysts

Topic: Market Cycle Varying Multifactor Strategies

The goal of our project is to: 1) determine whether or not different factors behave better or worse during different stages of the economic cycle and 2) develop a time-varying multifactor strategy to take advantage of these differences in factor behavior.

Powerpoint slides

Supplementary results

Group Code: The Fuqua Four

Topic: Quantitative Stock Selection

Our project offers three innovations. First, we examine the benefit of factor industry rescaling. Second, we implement a dynamic factor weighting based on the slope of the yield curve. Finally, we show the benefit factor migration.

Powerpoint slides

Supplementary files

Group Code: Campbell Street Partners

Topic: Quantitative Stock Selection

The topics examined include the following: 1) Why Quant Selection is Attractive; 2) Examining FactSet; 3) Stock Selection Model; 4) Dynamic Weights/Regime Change; 5) Benchmarks; and 6) Next Generation Models.

Powerpoint slides

Group Code: Fuqua Investment Analytics

Topic: Quantitative Stock Selection

This research investigates a variety of modeling techniques towards building a long/short strategy driven by quantitative stock selection. We research (1) cross-sectional (time-invariant) factor selection: identification, specification, and diagnostics/evaluation; (2) refinement of factor specifications, including industry-normalization; (3) optimal fractile resolution and clustering; (4) integration of factor-based portfolios into a multivariate model based on mean-variance portfolio optimization with the imposition of custom constraints; (5) forecasting time series of returns to factor-based portfolios; (6) implementation of dynamic factor weights based on (time-variant) factor performance forecasts.

Powerpoint slides

FACTSET Dynamic Weights Implementation, by Stefan Gertsch

FACTSET Notes and Conventions, by Brian Wachob

Main spreadsheets

Group Code: Morse Asset Management

Topic: Refining the Long-Short

I extend the BA453 project Volatility Group by 1) Fix the Factors: Test additional screening criteria to develop a better model; 2) Optimize: First-cut model employed subjective scoring for multivariate screens. Optimization provides a more robust approach to weighting factors; and 3)Dynamic Weighting: Explore the benefits of time-varying factor weights to incorporate information from macroeconomic events.

Powerpoint slides

Group Code: Nuri Asset Management

Topic: TBA

TBA

Powerpoint slides

Group Code: Kurlat Asset Management

Topic: Implementing Dynamic Factor Weighting for Long-Short Portfolios

I use four factors (1st and 5th quintiles), plus the interaction with a macro variable with the 5th quintile of a factor to develop a dynamically weighted factor model for long-short portfolios.

Excel file

QSS Class of 2005 email