Selected Working Papers
Are Managers 'Under-the-Weather' During Earnings Conference Calls?
with Bill Francis and Wenyao Hu [SSRN]
Abstract: Earnings conference calls represent an important communication channel for investors to observe managerial behavior. We examine the impact of executive mood using seasonally-adjusted weather conditions at corporate headquarters during these calls. Using 66,724 earnings calls from 2006 to 2017, we find that managers speak more negatively and with less (more) quantitative information (uncertainty) when local weather conditions are poor. Managers also exhibit more (less) extreme negative (moderate) language and executive mood persistently influences returns after earnings calls. Our results remain significant after adding controls for investor mood, separating firms from big and small states, mediation tests, firm and executive fixed effects, and propensity score matching. Our findings suggest exogenous effects of weather significantly impact managerial mood and how management communicates in unprepared corporate disclosures, which influences firm value in a manner not fully captured by fundamental financial accounting information.
Gender and Analyst Reports
with Bill Francis, Gilna Samuel, Kate Suslava, and Daqi Xin [SSRN]
Abstract: We examine gender differences in characteristics of sell-side analyst reports. We find that female analyst reports are shorter and more readable. Consistent with an “ethical standard” explanation, the textual sentiment of female analyst reports is less optimistic. Moreover, female analyst reports contain less financially oriented content, are more long-term oriented, and are less likely to be issued in response to coverage firm earnings announcements. Readability, length, and sentiment of female analysts’ reports induce different market reactions than their male counterparts, yet female analysts improve report readability more and increase objectivity over their career than male analysts do. Our results provide evidence of gender stereotyping in the analyst profession.
Gender and Earnings Conference Calls
with Nerissa Brown, Bill Francis, Wenyao Hu, Tengfei Zhang, and Daqi Xin [SSRN]
Abstract: Using quarterly earnings conference call transcripts, we investigate gender issues in interactions between sell-side analysts and executives. We find that women are generally less “visible” on conference calls. Specifically, female analysts appear on calls less frequently, speak less, and ask fewer follow-up questions. Female analysts and executives exhibit less uncertainty, less numerical information, and fewer hesitations in their dialogue than their male counterparts. Female executives (analysts) are interrupted more (less) frequently. The above relations are mitigated with greater state-level attention to #MeToo. Further, we introduce a new measure of firm gender equality/similarity using the first principal component of nine gender-related features on conference calls. We find that gender equality is associated with positive market reaction and lower bid-ask spreads. Earnings call gender equality is positively associated with analysts’ following stock recommendation, forecast accuracy, and forecast speed, and negatively associated with analysts’ following dropped coverage and the number of revisions. Overall, our results suggest that gender equality on earnings conference calls improves market efficiency and benefits analysts’ forecasts by enhancing calls’ information content and flow.
How Does Payment for Order Flow Influence Markets? Evidence from Robinhood Crypto Token Introductions
with Tom Boulton and Michael Walz [SSRN]
Abstract: In contrast to equities and options markets that have SEC Rule 606 reports and reported wholesaler trades, payment for order flow (PFOF) in crypto asset markets is subject to widespread noncompliance with securities law. PFOF rates for crypto assets, however, are 4.5 (45) times higher than options (equities) markets. We use staggered Robinhood Crypto token introductions (i.e., when crypto tokens are made available for trading on their platform) as a shock to PFOF to examine their effects on crypto asset trading platform activity. We find that volume shifts away from other trading platforms but increases for the largest crypto assets (i.e., BTC and ETH). Order imbalances shift to net sales and average trade sizes increase for transactions made in USD terms. Implied spreads and return volatility both increase but the largest crypto assets are unaffected. Overall, our results show PFOF introduction changes trading activity and increases costs for participants at crypto asset trading platforms by approximately $4.8 million daily.
The Value of Data: Analyst Vs. Machine
with Stefano Bonini, Majeed Simaan, and Guofu Zhou [SSRN]
Abstract: Our paper is the first to study the role of analysts’ recommendations in predicting cross-section of stock returns when there are a large number of firm characteristics that provide competing information. Utilizing a rich dataset spanning 25 years and encompassing 2,515 stocks, along with 1,309,335 sell-side analyst reports, we find that a baseline benchmark deploying various machine learning (ML) algorithms on 94 factors from Gu et al. (2020) generates annual six-factor model alphas ranging from 10% to 20%. Analyst-generated information adds value to portfolio formation only when the representative ML agent fails to efficiently process the rich set of the firm’s characteristics. That is, with comprehensive factors and complex ML, the incremental value of analyst-generated data is small. Our findings suggest that analysts have investment value for investors who cannot process all publicly available information efficiently without sophisticated ML tools. Further analysis around the Global Research Settlement supports this view, showing a diminishing pattern of analysts’ value after 2003 to virtually zero.
with Bill Francis and Wenyao Hu [SSRN]
Abstract: Earnings conference calls represent an important communication channel for investors to observe managerial behavior. We examine the impact of executive mood using seasonally-adjusted weather conditions at corporate headquarters during these calls. Using 66,724 earnings calls from 2006 to 2017, we find that managers speak more negatively and with less (more) quantitative information (uncertainty) when local weather conditions are poor. Managers also exhibit more (less) extreme negative (moderate) language and executive mood persistently influences returns after earnings calls. Our results remain significant after adding controls for investor mood, separating firms from big and small states, mediation tests, firm and executive fixed effects, and propensity score matching. Our findings suggest exogenous effects of weather significantly impact managerial mood and how management communicates in unprepared corporate disclosures, which influences firm value in a manner not fully captured by fundamental financial accounting information.
Gender and Analyst Reports
with Bill Francis, Gilna Samuel, Kate Suslava, and Daqi Xin [SSRN]
Abstract: We examine gender differences in characteristics of sell-side analyst reports. We find that female analyst reports are shorter and more readable. Consistent with an “ethical standard” explanation, the textual sentiment of female analyst reports is less optimistic. Moreover, female analyst reports contain less financially oriented content, are more long-term oriented, and are less likely to be issued in response to coverage firm earnings announcements. Readability, length, and sentiment of female analysts’ reports induce different market reactions than their male counterparts, yet female analysts improve report readability more and increase objectivity over their career than male analysts do. Our results provide evidence of gender stereotyping in the analyst profession.
Gender and Earnings Conference Calls
with Nerissa Brown, Bill Francis, Wenyao Hu, Tengfei Zhang, and Daqi Xin [SSRN]
Abstract: Using quarterly earnings conference call transcripts, we investigate gender issues in interactions between sell-side analysts and executives. We find that women are generally less “visible” on conference calls. Specifically, female analysts appear on calls less frequently, speak less, and ask fewer follow-up questions. Female analysts and executives exhibit less uncertainty, less numerical information, and fewer hesitations in their dialogue than their male counterparts. Female executives (analysts) are interrupted more (less) frequently. The above relations are mitigated with greater state-level attention to #MeToo. Further, we introduce a new measure of firm gender equality/similarity using the first principal component of nine gender-related features on conference calls. We find that gender equality is associated with positive market reaction and lower bid-ask spreads. Earnings call gender equality is positively associated with analysts’ following stock recommendation, forecast accuracy, and forecast speed, and negatively associated with analysts’ following dropped coverage and the number of revisions. Overall, our results suggest that gender equality on earnings conference calls improves market efficiency and benefits analysts’ forecasts by enhancing calls’ information content and flow.
How Does Payment for Order Flow Influence Markets? Evidence from Robinhood Crypto Token Introductions
with Tom Boulton and Michael Walz [SSRN]
Abstract: In contrast to equities and options markets that have SEC Rule 606 reports and reported wholesaler trades, payment for order flow (PFOF) in crypto asset markets is subject to widespread noncompliance with securities law. PFOF rates for crypto assets, however, are 4.5 (45) times higher than options (equities) markets. We use staggered Robinhood Crypto token introductions (i.e., when crypto tokens are made available for trading on their platform) as a shock to PFOF to examine their effects on crypto asset trading platform activity. We find that volume shifts away from other trading platforms but increases for the largest crypto assets (i.e., BTC and ETH). Order imbalances shift to net sales and average trade sizes increase for transactions made in USD terms. Implied spreads and return volatility both increase but the largest crypto assets are unaffected. Overall, our results show PFOF introduction changes trading activity and increases costs for participants at crypto asset trading platforms by approximately $4.8 million daily.
The Value of Data: Analyst Vs. Machine
with Stefano Bonini, Majeed Simaan, and Guofu Zhou [SSRN]
Abstract: Our paper is the first to study the role of analysts’ recommendations in predicting cross-section of stock returns when there are a large number of firm characteristics that provide competing information. Utilizing a rich dataset spanning 25 years and encompassing 2,515 stocks, along with 1,309,335 sell-side analyst reports, we find that a baseline benchmark deploying various machine learning (ML) algorithms on 94 factors from Gu et al. (2020) generates annual six-factor model alphas ranging from 10% to 20%. Analyst-generated information adds value to portfolio formation only when the representative ML agent fails to efficiently process the rich set of the firm’s characteristics. That is, with comprehensive factors and complex ML, the incremental value of analyst-generated data is small. Our findings suggest that analysts have investment value for investors who cannot process all publicly available information efficiently without sophisticated ML tools. Further analysis around the Global Research Settlement supports this view, showing a diminishing pattern of analysts’ value after 2003 to virtually zero.
Selected Publications
- Buy the Dip? (with Stefano Bonini and Majeed Simaan) European Financial Management (2024) Vol. 30, No. 4, pp. 2033-2070 [SSRN]
- Do Green Business Practices License Self-Dealing or Prime Prosociality? Cross-Domain Evidence from Environmental Concern Triggers (with Melanie I. Millar, Mason C. Snow, and Roger M. White) Accounting, Organizations, and Society, Accepted
- Fixed Income Conference Calls (with Gus De Franco, Da Xu, and Zhiwei (Vivi) Zhu) Journal of Accounting and Economics (2023) Vol. 75, No. 1, 101518 [SSRN]
- Does Native Country Turmoil Predict Immigrant Workers’ Honesty in Markets? (with Roger M. White) Journal of Economic Behavior & Organization (2022) Vol. 197, pp. 150-164 [SSRN]
- Do Sell-Side Analysts Play a Role in Hedge Fund Activism? Evidence from Textual Analysis (with Huimin (Amy) Chen) Contemporary Accounting Research (2022) Vol. 39, No.3, pp. 1583-1614 [SSRN]
- Do Sin Taxes Spur Cheating in Interpersonal Exchange? (with David Kenchington, Jared D. Smith, and Roger M. White) Accounting, Organizations, and Society (2022) Vol. 96, 101281 [SSRN]
- Unbridled Spirit: Illicit Markets for Bourbon Whiskey (with Conor J. Lennon) Journal of Economic Behavior & Organization (2021) Vol. 191, pp. 1025-1045 [SSRN]
- Which Buy-Side Institutions Participate in Public Earnings Conference Calls? Implications for Capital Markets and Sell-Side Coverage (with Andrew C. Call and Nathan Y. Sharp) Journal of Corporate Finance (2021) Vol. 68, 101964 [SSRN] [Internet Appendix: Participant Classifications] [Internet Appendix: Participant Data]
- Investor Awareness or Information Asymmetry? Wikipedia and IPO Underpricing (with Thomas J. Boulton, Bill Francis, and Daqi Xin) Financial Review (2021) Vol. 56, No. 3, pp. 535-561 [SSRN] [Internet Appendix]
- Bank Loan Renegotiation and Credit Default Swaps (with Brian Clark, James Donato, and Bill Francis) Journal of Banking & Finance (2020) Vol. 151, 105936 [SSRN]
- The Dark Side of Individual Blockholder Philanthropy (with Roger M. White) Financial Management (2020) Vol. 49, No. 3, pp. 741-767 [SSRN]
- Bulk Volume Classification and Information Detection (with Marios A. Panayides and Jared D. Smith) Journal of Banking & Finance (2019) Vol. 103, pp. 113-129 [SSRN] [Internet Appendix]
- Angels or Sharks? The Role of Personal Characteristics in Angel Investment Decisions (with Thomas J. Boulton and Pengcheng Zhu) Journal of Small Business Management (2019) Vol. 57, No. 4, pp. 1280-1303 [SSRN]