Stock Selection Model Research Articles

Improving Quantitative Models Through Optimization

Q uantitative active management and Markowitz-type optimization techniques have both become quite popular over the past twenty years. Bernstein and PradhumTn [1993] demonstrate that institutional equity managers appear to have increased their use of more quantitatively derived methods of stock selection such as earnings estimate revision or equity duration over the past five years. The increased popularity of index hnds and, in particular, tilted index funds, combined with the availability of computer optimization packages and data bases, has led to the widespread use of optimization techniques. With the exception of a relatively small group of managers that offer tilted index funds, however, few active equity managers regularly use optimization techniques in conjunction with quantitative stock selection models. Although most quantitative equity managers have been schooled on portfoliobased concepts developed by Markowitz [1959] and Sharpe [1970], such managers seem to focus on stock selection, and forget their quantitative portfolio-oriented “roots.” Many of today’s quantitative models attempt to forecast expected returns on an individual stock basis, and ignore the ramifications of combining those individual stocks into a portfolio. Most models have an implicit assumption that if one buys the model’s most attractive stocks, then one will be investing in the best possible portfolio. Active quantitative managers will often equal-weight the component stocks, market-weight the stocks, or pick individual stocks off the “best buy” list accord-. ing to their feelings regarding their own skill at stock selection. We demonstrate that optimizing a quantitative model’s “best buy” portfolio can provide superior performance when compared to more traditional weighting or stock selection schemes. In addition, the value-added that a mediocre manager can expect to achieve that is attributable to stock selection pales in comparison to that achievable through optimization techniques. In general, ignoring the correlation and covariant risk and weighting of a quantitative model’s “best” stocks can result in hundreds of basis points per year in lost performance. To support these contentions, we studied the results of a traditional quantitative equity technique, the dividend discount model (DDM), and found that returns could be enhanced significantly by simply

Consensus Expectations and International Equity Returns

Numerous studies of U.S. markets have shown that stock selection models have been able to isolate securities that earn returns above those predicted by a simple market model. Models examining the relation between analyst expectations and returns have been particularly useful. Unexpected earnings, changes in analysts' earnings-per-share forecasts, the number of analysts revising their forecasts-all these measures have been found to be useful in predicting abnormal returns in U.S. equity markets. At least two of these measures-changes in analysts' EPS forecasts and the number of analysts changing their forecasts-are also related to abnormal returns in several international markets.

The Development of Mean-Variance-Efficient Portfolios in Japan and the U.S.

Stock selection models have been, and can be, effectively employed in Japan to deliver excess returns. In 1992, in the initial year of this Journal’s publication, Guerard and Takano (1992) reported mean-variance efficient portfolios for the Japanese and U.S. equity markets, and show that the use of a regression-weighted composite model of earnings, book value, cash flow, sales, and their relative variables outperformed their respective equity benchmarks by approximately 400 basis points annually. Markowitz and Xu (1994) tested the composite model strategy and found that its excess returns were statistically significant from a variety of models tested, and the composite model strategy was not the result of data mining. Guerard (2006) updated the Guerard and Takano stock selection models are updated through 2003 using a similar Japanese-only database. The sophisticated regression model continued to produce the highest information coefficients in Japan and the US. Guerard included a consensus analyst derived forecast, revisions, and breadth variable, and show its contribution to stock selection. Several Japanese-only analyst forecasting model results are similar to US-only results. The use of the Global Compustat database allowed the construction of global variables that produce similar stock selection models. The effectiveness of these quantitative models had not lessened during the 1992-2003 period. In this update to celebrate 25 years of the Journal of Investing, the author uses a commercially available global database, FactSet, for the 2002-June 2016 time period to address stock selection composite models, mean-variance efficient portfolios in Japan and the U.S., and reports three results: (1) the original stock selection continues to be effective in Japan and the U.S. in 2002-6/2016; (2) the mean-variance efficient portfolios outperformed in Japan and the U.S. in 2002-6/2016; and (3) the Guerard and Takano stock selection model is not the result of data mining. The author reports three additional results: (1) the weighted latent root regression Guerard and Takano can be enhanced by using the Tukey optimal influence function; (2) the mean-variance efficient portfolios failed in Japan and the U.S. in 1996-2000 with the PCAP and Global Compustat databases and 2006-2007, 2012-2013, and YTD 2016 with the FactSet databases, but work in the vast majority of the years; and (3) the Guerard and Takano portfolios outperformed in 14 of 17 years in backtest, 82.4%, of the yeas whereas the Japan model portfolios outperformed in 9 of 14.5 years, or 66.7%, of the years, post-publication and the U.S. model outperformed in 9.5 of 13.5 years, or 70%, post-publication, respectively!

Ectopic Pregnancy after Pelvic Tuberculosis

This article addresses stock selection modeling and portfolio selection and implementation in EMs. The authors view EM investing as a special case of global investing, demonstrating how stock-selection models in EMs with price momentum and forecast earnings acceleration factors can enhance returns. The authors construct index-enhanced portfolios for EMs that offer superior return-to-risk ratios relative to domestic portfolios. On the basis of back test and in real-time performance, the authors show how an EM portfolio using forecast earnings and price momentum anomalies can be developed, estimated, and implemented to generate statistically significant excess returns.