Human Versus Machine: A Comparison of Robo-Analyst and Traditional Research Analyst Investment Recommendations

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.

Book-to-Market Ratio and Skewness of Stock Returns

ABSTRACT: This study demonstrates that stocks with low book-to-market ratios, also known as glamour stocks, have significantly more positive skewness in their return distributions compared to the return distributions of value stocks with high book-to-market ratios. The premium (discount) investors apply to these glamour (value) stocks also correlates significantly with the difference in return skewness. These findings suggest that the value/glamour-stock puzzle is partially explained by investor preference for positive skewness in stock returns. Such preference for skewness, which is consistent with investors having inverse S-shaped utility functions, is observed in such consumer behaviors as lottery purchases and gambling. This paper further documents significant predictive power of accounting-based measures, such as the book rate of return, with respect to the skewness of stock returns. Data Availability: Data are available from sources identified in the paper.

Analyst Coverage Of Small Cap Firms In A Period Of Brokerage Firm Retrenchment

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">We examine analysts&rsquo; incentives to cover small cap firms in the year 2002, a period following stock market declines and brokerage firm retrenchment.<span style="mso-spacerun: yes;">&nbsp; </span>Brokerage companies were losing a substantial number of sell-side analysts during this period and small firms were having unusual difficulty in attracting analyst coverage.<span style="mso-spacerun: yes;">&nbsp; </span>Consistent with analysts&rsquo; normal economic incentives and earlier research, we find that firm size, trading volume, and beta are all positively related to the number of analysts that cover a firm, whereas firm complexity is negatively related to analyst coverage.<span style="mso-spacerun: yes;">&nbsp; </span>In contrast to some earlier research, we find no evidence that analysts were more likely to follow glamour (or growth) stocks.<span style="mso-spacerun: yes;">&nbsp; </span>Specifically, price-to-book and revenue growth are not related to analyst coverage, and recent stock performance (price momentum) is negatively related to analyst coverage.<span style="mso-spacerun: yes;">&nbsp; </span>Our interpretation of this evidence is that analysts had reduced incentives to cover glamour stocks following the severe stock market declines in the early 2000s, the increased regulatory scrutiny of securities firms, and the resulting brokerage firm retrenchment.<span style="mso-spacerun: yes;">&nbsp; </span></span></span></p>

Disagreement and the Stock Market

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.