Summary: The arbitrage pricing theory shows that in a complete market, an asset can be priced by first identifying and pricing indices and then replicating the asset with a sufficiently large number of these indices. In practice, however, it appears favorable to focus on a small set of indices that captures most of an asset’s price movements. The selection of factors is therefore crucial for the model’s quality. Ideally, the selection is based on fundamental and economically reasonable relationships between asset and factors. If gathering or implementing these economic fundamentals is too costly or impossible, a selection based on statistical grounds might be considered. This concept is applied to the stocks of the S&P 100 where for each stock the bundle of 5 out of 103 MSCI indices is chosen that statistically explains most of the volatility. The optimization is done with a heuristic search method, memetic algorithms, which basically combines simulated annealing and evolutionary principles. Our results indicate that stock prices are merely influenced by industry factors, whereas country or regional indices seem to have less effect on asset returns.