Abstract
“Trading on Index Constituent Changes: Active vs. Passive Fund Management” studies mutual funds’ trading related to constituent changes in major Russell indices. The authors find an index inclusion premium of 84–208 basis points that helps explain active mutual fund returns, identify a subset of funds that follow a trading strategy on index constituent changes, and estimate the costs these funds impose on passive investors. “Measuring Price Elasticities in the US Stock Market around Quarterly IPO Inclusions” examines price elasticities in the US stock market using the shock to index weights triggered by quarterly IPO inclusions. The author confirms the finding of downward-sloping demand curves and concludes that markets are inefficient. The increase in passively managed assets seems to have no
impact on the efficiency of the stock market. “Predicting Index Inclusions Using Machine Learning Techniques” showcases the application of machine learning methods in finance. While a simple prediction replicating the index methodology performs equally well as the more advanced machine learning tools when forecasting index inclusions, the advanced predictions yield more profitable investment portfolios.