Profiting from changes to the Nasdaq 100 index

Nasdaq Building, Times Square, New York

Nasdaq Building, Times Square, New York

Yuanbin Xu (2012), writing his masters degree thesis at Brock University, in St. Catherines, Ontario, Canada, examined stock market reactions around the Nasdaq-100 index reconstitutions from 1997 to 2010.

The Nasdaq-100 index changes have been performed in two ways: regular index changes and irregular index changes. Regular annual index changes are primarily based on market capitalizations. The public announcement is usually released via a press release in early December and changes become effective after market close on the third Friday of December. The market capitalizations are calculated using the Nasdaq official closing price on the last trading day of October, and shares outstanding from a public SEC document available on EDGAR as of the end of November. Given that this data is generally accessible to all investors, regular index changes have been predictable.

In contrast, irregular index changes occur when securities no longer meet the eligibility criteria. Neither the specific time nor the reasons are publicly known until they are released, so irregular index changes have usually been unpredictable.

In Xu’s sample included 205 additions and 136 deletions from the index. 136 additions and deletions were regular while 69 additions were irregular. Nearly all of the irregular deletions were involved in confounding events and therefore were not included in the sample studied.

For the regular additions, Xu found a significantly positive, abnormal return of 0.76% on the announcement day. The cumulative abnormal return from the first day in December to the day before the announcement day averaged 3.66%. This was also statistically significant. However, he also found that prices reversed completely, on average, by the effective date of the third Friday in December. The results were mixed for regular deletions from the index.

On average, the irregular additions to the index experienced an abnormal return of 1.28% on the announcement date and 1.73% on the following day. And, another gain of 1% occurred on the effective day. The price reversal started the day after the effective date, and that was a significantly negative 1.37%.

In summary, Xu noted, individual investors could could profit by purchasing stocks added to the Nasdaq-100 index, and shorting stocks deleted from the index, on the announcement date, and then closing the position on or before the effective date.

Posted in scientific understanding of financial markets, Uncategorized Tagged with: , , , , , , , , , , , , , , , , , , , , ,

Leave a Reply

Your email address will not be published. Required fields are marked *


Book Three: Trading With The News

Learn about a news-based trading system that yielded a back-tested, average annualized, compounded return from 2000 to 2011 of 58.6%.

“Only once you’ve done your homework will you be able to understand how the stock market works and learn to distinguish between news and noise.” Maria Bartiromo, Use The News

Book Two: Technical Analysis

Learn about the "trend recalling" algorithm that yielded researchers a simulated annual return of greater than 400% in multiple tests.

“The scientific method is the only rational way to extract useful knowledge from market data and the only rational approach for determining which technical analysis methods have predictive power.”
David Aronson, Evidence Based Technical Analysis

Book One: Analysts’ Forecasts

Learn the strategy, based on analysts' revised forecasts, that yielded researchers an average of 1.13% - 2.19% profit per trade, for trades lasting one to two days?

Learn how certain analysts' recommendations, following brokerage hosted investment conferences, yielded profits of over 3% during a two-day holding period?

Learn how researchers found an average profitability of 1.78% for two-hour trades following an earnings announcement?

"This set of tools can help both ordinary and professional investors alike to re-think and re-vitalize their stock picking, timing and methods. A young, aspiring Warren Buffet could put this book to good use."
James P. Driscoll, PhD, investor

Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments by David Aronson (software included)

Evidence-Based Technical Analysis by David Aronson

Archive of Earlier Posts