The Tech Industry or the Regulated Industry: Which One has the True Glamour Stocks?

We provide the first comprehensive analysis of the properties of investment recommendations generated by “Robo-Analysts,” which are human-analyst-assisted computer programs conducting automated research analysis. Our results indicate that Robo-Analyst recommendations differ from those produced by traditional “human” research analysts across several important dimensions. First, Robo-Analysts produce a more balanced distribution of buy, hold, and sell recommendations than do human analysts and are less likely to recommend “glamour” stocks and firms with prospective investment banking business. Second, automation allows Robo-Analysts to revise their recommendations more frequently than human analysts and incorporate information from complex periodic filings. Third, while Robo-Analysts’ recommendations exhibit weak short-window return reactions, they have long-term investment value. Specifically, portfolios formed based on the buy recommendations of Robo-Analysts significantly outperform those of human analysts. Overall, our results suggest that automation in the sell-side research industry can benefit investors.

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Predictabilty of Aggregate Stock Market Returns: Evidence Based on Glamour Stocks

SSRN Electronic Journal ◽

10.2139/ssrn.243187 ◽

2000 ◽

Author(s):

Venkat R. Eleswarapu ◽

Marc R. Reinganum

Keyword(s):

Stock Market ◽

Evidence Based ◽

Market Returns ◽

Stock Market Returns ◽

Glamour Stocks

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Value-Glamour and Accruals Mispricing: One Anomaly or Two?

The Accounting Review ◽

10.2308/accr.2004.79.2.355 ◽

2004 ◽

Vol 79 (2) ◽

pp. 355-385 ◽

Cited By ~ 216

Author(s):

Hemang Desai ◽

Shivaram Rajgopal ◽

Mohan Venkatachalam

Keyword(s):

Cash Flow ◽

Abnormal Returns ◽

Cash Flows ◽

Sales Growth ◽

Working Capital ◽

Operating Cash Flows ◽

Accruals Anomaly ◽

Glamour Stocks

We investigate whether the accruals anomaly is a manifestation of the glamour stock phenomenon documented in the finance literature. Value (glamour) stocks, characterized by low (high) past sales growth, high (low) book-to-market (B/M), high (low) earnings-to-price (E/P), and high (low) cash flow-to-price (C/P), are known to earn positive (negative) future abnormal returns. Note that “C” or cash flow is operationalized in the finance literature as earnings adjusted for depreciation. Sloan (1996) shows that firms with low (high) total accruals earn positive (negative) future abnormal returns. We find that a new variable, operating cash flows measured as earnings adjusted for depreciation and working capital accruals, scaled by price (CFO/P) captures mispricing attributed to the four traditional value-glamour proxies and accruals. Interpretation of this finding depends on the reader's priors. If the reader believes that value-glamour phenomenon can be operationalized only as C/P, and not CFO/P, then one would conclude that CFO/P is a parsimonious variable that captures the mispricing attributes of two distinct anomalies, value glamour and accruals. However, if a reader views the value-glamour anomaly broadly as a fundamentals-to-price anomaly, then (1) the CFO/P variable can be considered an expanded value-glamour proxy and; (2) our results are consistent with Beaver's (2002) conjecture that the accruals anomaly is the glamour stock phenomenon in disguise.

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Glamour Brands and Glamour Stocks

SSRN Electronic Journal ◽

10.2139/ssrn.1571491 ◽

2012 ◽

Cited By ~ 4

Author(s):

Matthew T. Billett ◽

Zhan Jiang ◽

Lopo L Rego

Keyword(s):

Glamour Stocks

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Disagreement and the Stock Market

The Journal of Economic Perspectives ◽

10.1257/jep.21.2.109 ◽

2007 ◽

Vol 21 (2) ◽

pp. 109-128 ◽

Cited By ~ 393

Author(s):

Harrison Hong ◽

Jeremy C Stein

Keyword(s):

Stock Market ◽

Stock Returns ◽

Stock Prices ◽

Trading Volume ◽

Market Value ◽

Limited Attention ◽

Heterogeneous Agent ◽

Value Stocks ◽

Glamour Stocks ◽

Heterogeneous Agent Models

A large catalog of variables with no apparent connection to risk has been shown to forecast stock returns, both in the time series and the cross-section. For instance, we see medium-term momentum and post-earnings drift in returns—the tendency for stocks that have had unusually high past returns or good earnings news to continue to deliver relatively strong returns over the subsequent six to twelve months (and vice-versa for stocks with low past returns or bad earnings news); we also see longer-run fundamental reversion—the tendency for “glamour” stocks with high ratios of market value to earnings, cashflows, or book value to deliver weak returns over the subsequent several years (and vice-versa for “value” stocks with low ratios of market value to fundamentals). To explain these patterns of predictability in stock returns, we advocate a particular class of heterogeneous-agent models that we call “disagreement models.” Disagreement models may incorporate work on gradual information flow, limited attention, and heterogeneous priors, but all highlight the importance of differences in the beliefs of investors. Disagreement models hold the promise of delivering a comprehensive joint account of stock prices and trading volume—and some of the most interesting empirical patterns in the stock market are linked to volume.

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