Abstract
Charles Schwab Inc. appears to expend a great deal of effort analyzing stocks from many perspectives and rating their potential for future returns over a 12-month investment horizon. This study examines whether their analysis explains returns beyond known variables. Schwab’s equity ratings are an interesting dataset to study because their ranking methodology appears to highly correlate with known academic research. Examining an original sample size of 3074 stocks and using the Year 2014 as a test sample, evidence is presented that Schwab’s ex-ante analysis does add explanatory value to ex-post returns, especially in the presence of firm Beta, firm size and a stock’s nearness to its 52-week high.
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Notes
In 2002, the Securities and Exchange Commission indirectly addressed the historical imbalance in “buy” versus “sell” recommendations by strengthening the disclosure of potential conflicts of interest between corporations and their investment banker.
See Malmendier and Shanthikumar (2014) and others for literature reviews.
The largest and smallest firms in the sample of 3074 firms are $494 and $0.075 billion respectively. Approximately 54 % of the sample firms have a market cap of $2 billion or larger.
See Sturm (2013) for a discussion.
See Ball and Brown’s (1968) seminal paper and many others who followed.
Such academic studies date back to at least the early 1900’s (see Cootner 1964 and others).
There is an overlap of about 4 trading days over the holiday period between SERP and the HPR. For purposes of this study and because it applies to the entire sample, the overlap is inconsequential.
None of the stocks were from the same SERP, so the excluded stocks should have no significant impact on the results.
See Sturm (2013) for a discussion of the overlap between technical analysis and market efficiency.
Of course, this relation has been shown to not always hold. See Fama and French (1992) and many other studies that followed.
Beta exhibits the highest and most statistically significant correlation with returns, results not reported. Orthogonal values are used for all multi-variate regressions while the unorthogonalized variables are used for all univariate regressions.
For Beta, numerically higher ratings are assigned to lower Beta stocks and for size, numerically higher ratings are assigned to larger firms.
Stocks further away from their 52-week high were assigned numerically higher ratings and vice versa.
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Sturm, R.R. Schwab’s equity ratings: value added or old news?. J Econ Finan 41, 257–275 (2017). https://doi.org/10.1007/s12197-015-9347-1
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DOI: https://doi.org/10.1007/s12197-015-9347-1