07 Financial Accounting 1: Stock analysts/equity valuation (FAR1)
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ItemCan Machines Understand Human Decisions? Dissecting Stock Forecasting Skill( 2021)Human decisions are important but difficult to understand or predict. This paper uses machine learning models, which are adept at capturing nonlinear and complex relations, to analyze analysts’ forecasts and determine their skill. Machine-identified skilled analysts persistently outperform expert-picked star analysts. Machines rely on nonlinear interactions of analyst characteristics, such as past skill and efforts, to make predictions, in contrast with human experts, who lean more on relation-based information such as brokerage size. The puzzle of post analyst-revision drifts can be explained by our model in that such drifts are concentrated in machine-picked skilled analysts. Our approach also allows the formation of a “smart” analyst consensus that aggregates the forecasts of machine-picked skilled analysts. Investment strategies based on revisions of machine-identified skilled analysts and the smart analyst consensus both generate significant abnormal returns. Overall, we propose an interpretable machine learning framework that can be used to analyze and predict human decisions. We also provide a new, improved way to obtain the wisdom of the crowd applicable to other settings such as online forums, political opinions, and macroeconomic outlooks.
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ItemDo Managers Learn from Analyst Participation in Conference Calls?( 2021)While research finds that conference calls are informative to the market and analysts, they can also be informative to managers as analysts’ questions can provide a feedback effect. Using a sample of conference call transcripts from 2002 to 2018, we find that greater analyst participation, as measured by the number of words spoken by analysts relative to the number of words spoken by managers during conference calls, is associated with higher accuracy in managers’ subsequent earnings forecasts. Cross-sectional tests show that this positive association is more pronounced when managers use more uncertain words in conference calls, when analysts use a more negative tone to question management, and when participating analysts have higher industry expertise. We also employ a topic modeling approach and find that managers are more likely to benefit from conference calls when analysts question management about the company’s revenues, margins, customers, or business outlooks. Overall, our results are consistent with analyst participation in conference calls contributing to managerial learning.
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ItemBusiness Groups and the Value Implications of Ownership Transparency( 2021)We examine Korean business groups firms’ transitions from circular-shareholding to pyramidal shareholding structures between 2011-2019. With the removal of circular-shareholdings, neither the chaebol families’ degree of control of group firms nor the separation between their cash flow and ownership rights changed. Nevertheless, the removal of circular-shareholdings corresponded to a 10% decline in Tobin’s Q and market returns relative to other group firms. This relative value decline is not explained by an increase in observed expropriation or erosion of access to internal capital markets. Instead, our evidence is consistent with shareholding transparency allowing investors to better identify agency issues among business group firms.
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ItemAnalyst Information about Peer Firms During the IPO Quiet Period( 2021)The SEC limits sell-side analysts’ research activities on IPO firms both before and immediately after going public (the IPO quiet period). We examine whether, in spite of these restrictions, investors uncover information about the IPO firm during the quiet period indirectly through analyst research of peer firms. Our evidence suggests peer firm research is informative about the IPO firm during the quiet period. In particular, we find that analysts’ recommendation revisions issued for peer firms are more frequent around IPOs and that these revisions are predictive of future IPO performance. We also find that IPO investors trade on the information in analysts’ revisions of peer firms on the IPO date. However, only institutional investors make full use of this information, while retail investors are generally inattentive to IPO-relevant information in peer firm research, except when analyst revisions are particularly salient. Our findings suggest that investors infer relevant information about IPO firms through analyst research of peer firms during the IPO quiet period.
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ItemContrast Effects and Analyst Forecasts( 2021)Contrast effects take place when decision makers unconsciously interpret a signal by contrasting it with the signal preceding it. Using analyst forecast revisions in response to earnings announcements on consecutive days as the setting, we find evidence of contrast effects when the analyst covers the announcing firms on both days. The effects are driven by scenarios where the analyst is relatively inexperienced, where the firms announcing earnings on day t-1 are large relative to the firm announcing earnings on day t, and where more than one firms announce earnings on day t-1. Unlike investors who contrast earnings news against the prior day’s earnings announcements from bellwether firms, analysts do not benchmark against these firms if they are not in the coverage portfolio. Additional analyses suggest that our findings cannot be explained by information transfer or analyst limited attention.
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ItemRetail Investor Trading and Market Reactions to Earnings Announcements( 2021)This paper uses holdings and outage data from Robinhood and transaction-level data from U.S. exchanges to examine how retail investors affect the pricing of public earnings information. We find that retail trader activity is associated with prices that are more responsive to earnings surprises, and earnings announcements affected by seemingly random retail trading outages experience weaker price responses. These results are concentrated in firms that are smaller and have less robust informational environments. Additional evidence shows that the retail activity is associated with more volatile returns during the earnings announcement window, which can slow the incorporation of public information and contribute to larger bid-ask spreads. Overall, our results suggest that retail investors can facilitate the incorporation of public information into price over the 2-day earnings announcement window despite the potential to increase volatility and impose risk on other market participants.
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ItemFrom Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses( 2021)An AI analyst we build to digest corporate financial information, qualitative disclosure, and macroeconomic indicators is able to beat the majority of human analysts in stock price forecasts and generate excess returns compared to following human analysts. In the contest of “man vs machine,” the relative advantage of the AI Analyst is stronger when the firm is complex, and when information is high-dimensional, transparent and voluminous. Human analysts remain competitive when critical information requires institutional knowledge (such as the nature of intangible assets). The edge of the AI over human analysts declines over time when analysts gain access to alternative data and to in-house AI resources. Combining AI’s computational power and the human art of understanding soft information produces the highest potential in generating accurate forecasts. Our paper portraits a future of “machine plus human” (instead of human displacement) in high-skill professions.
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ItemCan Machines Understand Human Decisions? Dissecting Stock Forecasting Skill( 2021)Human decisions are important but difficult to understand or predict. This paper uses machine learning models, which are adept at capturing nonlinear and complex relations, to analyze analysts’ forecasts and determine their skill. Machine-identified skilled analysts persistently outperform expert-picked star analysts. Machines rely on nonlinear interactions of analyst characteristics, such as past skill and efforts, to make predictions, in contrast with human experts, who lean more on relation-based information such as brokerage size. The puzzle of post analyst-revision drifts can be explained by our model in that such drifts are concentrated in machine-picked skilled analysts. Our approach also allows the formation of a “smart” analyst consensus that aggregates the forecasts of machine-picked skilled analysts. Investment strategies based on revisions of machine-identified skilled analysts and the smart analyst consensus both generate significant abnormal returns. Overall, we propose an interpretable machine learning framework that can be used to analyze and predict human decisions. We also provide a new, improved way to obtain the wisdom of the crowd applicable to other settings such as online forums, political opinions, and macroeconomic outlooks.
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ItemDo Managers Learn from Analyst Participation in Conference Calls?( 2021)While research finds that conference calls are informative to the market and analysts, they can also be informative to managers as analysts’ questions can provide a feedback effect. Using a sample of conference call transcripts from 2002 to 2018, we find that greater analyst participation, as measured by the number of words spoken by analysts relative to the number of words spoken by managers during conference calls, is associated with higher accuracy in managers’ subsequent earnings forecasts. Cross-sectional tests show that this positive association is more pronounced when managers use more uncertain words in conference calls, when analysts use a more negative tone to question management, and when participating analysts have higher industry expertise. We also employ a topic modeling approach and find that managers are more likely to benefit from conference calls when analysts question management about the company’s revenues, margins, customers, or business outlooks. Overall, our results are consistent with analyst participation in conference calls contributing to managerial learning.
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ItemTo Share or not to Share? Financial Analysts’ Questioning in Conference Calls( 2021)We study whether superior financial analysts strategically reveal information in earnings conference calls. To the extent that analysts’ relative information advantages translate into desirable professional outcomes, we expect superior analysts to be mindful of safeguarding their information advantages when interacting with peers. Consistently, we find that superior analysts (i.e., analysts with a higher ex-ante relative forecast accuracy) share less information in their questions during conference calls. Hereby, analysts more likely maintain their information advantages. In additional analyses, we underscore the strategic motives of information sharing. We find that analysts ask more (less) informative questions when they are exposed to a higher information uncertainty (competition). Moreover, our analyses indicate that informative questions trigger informative answers, which are not only valuable for the respective analyst but also peer analysts and capital markets. Collectively, our results shed light on the role of analysts as information intermediaries in shaping firms’ information environments.