08 Financial Accounting 1: Stock Analysts/Equity Valuation
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ItemPublic Disclosure of Private Meetings: Does Transparency of Corporate Site Visits affect Analysts' Attention Allocation?( 2020-08-16)We investigate the consequences of increased transparency of corporate site visits on financial analysts' attention allocation. Using the timely disclosure requirement by the Shenzhen Stock Exchange (SZSE) in China since July 2012 as a setting, we find that non-visiting analysts reduce attention allocated to visited firms relative to non-visited firms. These results are consistent with the conjecture that such transparency reveals the information advantage of visiting analysts relative to non-visiting analysts, who then reallocate attention across different firms. Cross-sectional analyses suggest that the effects are more pronounced when the information advantage is larger. Further evidence suggests that such transparency has positive spillover effects on peer firms' informational efficiency by influencing analysts' attention allocation. Thus, despite the potential disclosure costs directly imposed on firms, firms collectively can benefit from this disclosure requirement due to the positive spillover effects.
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ItemScenario-based systematic risk in earnings( 2020-08-15)We develop a scenario-based measure of risk in earnings. We start by forming a range of scenarios, or plausible states of the economy, represented by earnings persistence parameters from cross-sectional earnings prediction regressions. We then combine out-of-sample earnings components with these scenarios to predict earnings. The standard deviation of predicted earnings of all scenarios is our firm-year risk measure, sigma. We demonstrate that $\sigma$ captures systematic risk in earnings and that our measure complements firm characteristics that have been proposed as measures of risk in predicting cross-sectional returns. The differential one-year returns of the interquartile range for $\sigma$ is 1.9\%. We contribute to the literature by developing a priced scenario-based risk measure using only fundamental information.
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ItemWhy Don't Analysts Always Value Earnings Conference Calls?( 2020-08-15)We compare analyst forecasts before earnings releases, between earnings releases and conference calls, and after conference calls, and unexpectedly find that the forecasts do not become more accurate or less dispersed around conference calls. We propose and show that analysts ignore potential information in conference calls if they got prior access to private information. Analyst forecasts between earnings releases and conference calls are associated with less market movement during conference calls. Our results suggest that some analysts have superior information access before a few open conference calls. We show that public disclosure is sometimes preempted by private information channels and implicitly question the effectiveness of disclosure regulation.
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ItemDo Analysts Cater to Investor Beliefs? Evidence from Dual-Listed Chinese Firms( 2020-08-14)We take advantage of a unique setting in China to provide novel evidence on a catering theory for analyst optimism. Our study utilizes the Stock Connect programs that allowed foreign investors to invest in Chinese stocks as an exogenous shock to investor beliefs. We further focus our study on a subset of Chinese firms with both "A shares" (listed in mainland China) and "H shares" (listed in Hong Kong) to provide a clean test of our hypotheses. We find that A share analysts become less optimistic in their recommendations following the introduction of less optimistic investors through the Stock Connect programs. In addition, catering theory predicts that when investors hold heterogeneous beliefs, analysts tend to segment the market and slant toward extreme positions in order to attract target investors. Consistent with this prediction, we find that A share analysts with buy or strong buy (sell or underperform) recommendations of a given firm become more optimistic (pessimistic) in their forecasts and research report tone after the Stock Connect programs. Finally, we show that in updating their earnings forecasts, analysts are more (less) responsive to earnings surprises that are consistent (inconsistent) with their stock recommendations. Overall, the results suggest that analysts cater to investors' opinions.
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ItemGoing Digital: Implications for Firm Value and Performance( 2020-08-13)We examine firm value and performance implications of the growing trend of non-technology companies adopting digital technologies, using a measure based on the disclosure of digital words in the business description section of 10-Ks. Digital adoption is associated with a market-to-book ratio 8-26% higher than industry peers. Part of the differences in market-to-book is explained by accounting capitalization restrictions, which we estimate to explain roughly 15% of the differences. Portfolios formed on digital disclosure earn a DGTW-adjusted return of 36% over a 3-year horizon and a monthly alpha of 57-basis-points. We also find significant increases in asset turnover conditional on digital activities, while also finding significant declines in margins and sales growth.
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ItemDiffusers of Entrepreneurship( 2020-08-13)We examine an emerging phenomenon that talented employees leave successful entrepreneurial firms to join younger ones. Using a unique person-level dataset and a comprehensive sample of private firms from the U.S. Census Bureau, we find that such "entrepreneurial diffusers", by passing on entrepreneurial spirit, innovative culture, and institutional wisdom, enhance younger startups' innovation productivity and likelihood of successful exits. Further, these diffusers are motivated by the entrepreneurial spirit of risk-taking instead of monetary gains. Finally, we find that the departure of entrepreneurial diffusers contributes to the long-run IPO underperformance documented in the prior literature. Our paper offers new insights into a labor market channel of the cross-firm diffusion of entrepreneurship, which is critical to the sustainability of a vibrant entrepreneurial ecosystem.
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ItemWhat Can Analysts Learn from Artificial Intelligence about Fundamental Analysis?( 2020-08-09)The accounting-based valuation model developed by Nissim and Penman (2001) presents both increasingly disaggregated equity-value drivers (financial statement ratios) in a structured hierarchy and descriptive evidence on the over-time and cross-sectional properties of these ratios. Nissim and Penman do not use their model to forecast residual income, either as an end-objective or as an input to valuation, primarily because of estimation difficulties arising from the highly non-linear structure of their framework. We revisit their theory, taking the perspective of an equity investor seeking to maximize risk-adjusted returns through fundamental analysis. We address the estimation difficulty described by Nissim and Penman using a machine learning algorithm, Deep Learning, designed to approximate arbitrarily-complex higher-order interactive relationships. We derive firm-level profitability forecasts using their structured hierarchy, use these forecasts to estimate intrinsic values and compare the estimated values to price. We find that risk-adjusted returns to a trading strategy based on buying (selling) firms with high (low) value-to-price ratios are substantial, and larger when the underlying valuation model is based on greater disaggregation and long-horizon forecasts of operating activities. We find only weak evidence that incorporating historical information beyond the information in the current period financial statements or focusing on core items is beneficial, and that taking account of non-linearities is especially important in valuing small, loss-making, technology and financially distressed firms.