Information transfer and conference calls


A long-standing literature documents intra-industry capital market co-movements around earnings releases, yet the dynamics of these information transfers remain largely unexplored. We provide evidence on both the sources and channels of information transfers by separating two distinct events within the reporting window using intra-day data and by exploring potential mechanisms of information flows. We document that the co-movement of absolute and signed stock returns over the conference call windows of announcing firms and their industry peers are statistically and economically larger than the co-movement over the corresponding earnings announcement windows. Turning to mechanisms, we find that shared analyst coverage, coverage by analysts providing industry recommendations, shared institutional ownership, and joint financial media mentions are each individually and incrementally associated with higher rate of information transfer over both the earnings announcement and conference call windows. Textual analyses reveal that peer mentions and macroeconomic discussions both significantly contribute to conference call information transfers.

This is a preview of subscription content, access via your institution.


  1. 1.

    As an example, on a PepsiCo conference call, an analyst asked the management to address “Coke [saying] the pricing for carbonated soft drinks was up.” In another quarter, the analysts covering the Coca-Cola earnings conference call requested management’s view on the “competitive landscape, given Pepsi’s new strategic alliance with Tingyi.” (PepsiCo first quarter 2014 earnings call on April 17, 2014; Coca-Cola first quarter 2012 earnings call on April 17, 2012.) Similar exchanges are frequently seen for other high-profile rivals such as JC Penney-Macy’s and Intel-AMD and in more dispersed multi-competitor markets.

  2. 2.

    Throughout the paper, we use the term “rate” of transfer to designate the relative amount of the information transferred during an information event, rather than the speed at which it is transferred. We chose the term “rate” because in examining the coefficient of co-movement we express the transfer as a function of the conference call holding firm’s own information metric. We refer to the amount as relative because our coefficient of interest inherently captures the amount of transferrable information as a portion of total information disclosed in a given window. See section 4.1 for further discussion of our results in terms of rates and amounts.

  3. 3.

    We refer to the subsample with positive sales growth correlation as “true peers,” while retaining the term “peer” for the full sample.

  4. 4.

    Firms are not required to hold conference calls, and some choose not to do so (including, famously, Berkshire Hathaway, and many financial institutions). The number of conference-holding firms have grown from less than half of all analyst-covered firms in the early years of our sample to more than two-thirds in the later ones. Research has explored the initiation of conference calls in the 1990s and early 2000s, when it was a more discretionary disclosure choice, and found size, analyst coverage, institutional investor ownership, and market-to-book ratios among the significant determinants (Frankel et al. 1999; Bowen et al. 2002). We include these variables as controls in regression analyses, along with industry and time fixed effects.

  5. 5.

    Call et al. (2017) note a mean of 6.85 noncorporate participants in their conference call sample from 2007 to 2016, of which 6.17 are sell-side analysts and 0.25 buy-side analysts. Using data from 2002 to 2009, Jung et al. (2017) document a similar distribution, with 7.52 analysts on average, and 0.30 buy-side analysts.

  6. 6.

    In untabulated analysis, we supplement our aggregate results by estimating the models separately by calendar year to examine whether changes in trading and information channels over our examined period impact information transfer. We observe that our findings do not exhibit strong temporal variations and conclude that neither microstructure developments, such as algorithmic trading, nor reporting or dissemination evolution significantly affected the dynamics of conference call-related information transfers.

  7. 7.

    NIRI “Standards of Practice for Investor Relations: Disclosure” (updated 2016) available at

  8. 8.

  9. 9.

    Stop words are the most common words in the English language such as “and,” “the,” and “that.” We cannot obtain earnings announcement transcripts for about 15% of our conference call firms because they are in PDF format or included in an improperly labeled exhibit to Form 8-K filings.

  10. 10.

    See Appendix 3 for excerpts from conference call transcripts illustrating discussions apparently containing transferrable information.

  11. 11.

    Just like the choice to hold conference calls, the choice of timing appears to be a matter of firm-specific policy and not subject to strategic manipulation. The proportion of firms holding calls during trading hours did not change materially over the sample period (declining slightly from about 55 to 45%). Most firms hold their calls consistently during or outside of trading hours, with the remainder generally making one switch in policy over the sample period (consistent with stickiness reported by Matsumoto et al. 2011). Firms holding calls during trading hours are on average larger and have lower market-to-book value of equity. We include these variables as controls in the regression analyses. The practitioner investor-relations literature does not suggest that either approach offers incremental benefits (overall or for any subset of firms). Thus the choice of timing appears to be primarily a historical artifact, and, given the similarity of the two groups, we do not believe that our exclusion of calls held outside of trading hours biases our results.

  12. 12.

    We cross-reference the I/B/E/S earnings announcement timestamps with Dow Jones newswire timestamps (available from RavenPack) and find I/B/E/S timestamps to be highly reliable in identifying the earnings announcements taking place outside of trading hours. To retain a consistent overnight period, we exclude observations where an earnings announcement is released on a Friday evening and the conference call is held during trading hours of the following Monday. The number of dropped observations is small.

  13. 13.

    TAQ contains all intraday transactions data for securities listed on the NYSE, AMEX, and NASDAQ. We imposed the standard TAQ data requirements of having PRICE>0, SIZE>0, CORR<2, and COND not equal to A/C/D/N/O/R/Z.

  14. 14.

    We rely on the GICS classification as it has been shown to be a better technique for identifying peers (Bhojraj et al. 2003). In robustness section 5.5, we discuss the alternative specification of requiring only common GICS grouping but not analyst overlap.

  15. 15.

    As an example, if a conference call was held during trading hours on Wednesday, July 27, we excluded all peers releasing earnings announcements between 4 p.m. on Monday, July 25, and 4 p.m. on Thursday, July 28.

  16. 16.

    Even if the peer firm does not have an earnings announcement, it may disclose or experience significant news that induces a capital market reaction. For robustness, we examine whether the observed relations are sensitive to the presence of financial news (from RavenPack) for peer firms during the 24-h event window. We find that, while the signed and absolute returns of the peer firm itself are responsive to the presence of news, the coefficient of co-movement during the conference call window is not (not tabulated).

  17. 17.

    We examine three additional metrics of nondirectional information flows in the robustness section 5.2: trading volume, stock price volatility, and price range. The results are consistent with those reported for the absolute returns.

  18. 18.

    See section 5.1 for a discussion on overnight windows and robustness analyses.

  19. 19.

    Mean (median) total duration is estimated to be 41 (40) minutes with fifth (95th) percentile of 23 (63) minutes.

  20. 20.

    Focusing on market-adjusted returns is consistent with prior research (e.g., Thomas and Zhang 2008).

  21. 21.

    This is consistent with the result of Matsumoto et al. (2011) on their somewhat smaller sample.

  22. 22.

    Interestingly, the alternative nondirectional measures discussed in the robustness analysis in section 5.2 (volume, volatility, and range) exhibit an even higher difference. For example, the Spearman correlation between abnormal volatility for the announcing and peer firm is 0.061 on the earnings announcement window and 0.111on the conference call window (untabulated).

  23. 23.

    We exclude days on which the peer had an earnings announcement from the non-event period and require a minimum of 36 days of trading data.

  24. 24.

    We draw similar inferences when the standard errors are clustered by calendar date to allow for potential correlation across firms in various industries due to macroeconomic shocks. We also note that the inferences are not affected when the absolute return analyses are conducted using a Tobit, rather than an OLS, estimator.

  25. 25.

    We choose this definition since the majority of pairs share one sell-side analyst. This cutoff yields subsamples of 43% (56%) for the high (low) analyst overlap groups.

  26. 26.

    We include the same control variables as in SUR specification. In untabulated analysis, we confirm that they are strong predictors of the extent of overlap itself.

  27. 27.

    See Kadan et al. (2012) for details on brokerages providing industry recommendations and other data notes.

  28. 28.

    We require the relevance score for both conference-call-holding firm and its peer to be 20. This is significantly lower than the 90 score frequently required in analysis of significant news items. We chose this lower cutoff to include a wider range of material, since our interest is any joint occurrence of firm references, rather than identification of highly targeted firm news. For example, an article about a manufacturing firm that mentions a supplier in one paragraph will likely assign a score of >90 to the former and a score significantly <90 to the latter but is highly relevant for our analysis. One effect of this lower requirement is an uncommonly large inclusion of “tabular materials,” which comprise 80% of the 1.6 million sample of joint news items. As noted above, since our focus is on any joint occurrence, we include these items. See RavenPack manual for more detail on relevance scores and news types.

  29. 29.

    Groups 1, 2, 3, and 4 represent high analyst-joint mention, high analyst-no joint mention; low analyst-joint mention, low analyst-no joint mention; high institution-joint mention, high institution-no joint mention; and low institution-joint mention, low institution-no joint mention.

  30. 30.

    Here and in section 5 below, we again focus directly on the relative information transfer associated with conference calls and earnings announcements and thus report the relevant results based on the SUR estimator. As before, we exclude peers reporting within 1 day of conference call.

  31. 31.

    In additional analysis, we examine whether the earliest reporters within an industry (“bellwethers”) exhibit information transfer intensity different from the other firms leading their peers. We find no consistent evidence that first reporters significantly differ from other peer lags firms.

  32. 32.

    Because the earnings announcement timestamps are of particular importance in this analysis, we cross-reference the I/B/E/S timestamp with the Dow Jones newswire timestamp (available from RavenPack).

  33. 33.

    We measure volatility as a coefficient of variation, a unit-free measure of variability obtained by scaling the standard deviation of prices (rather than of trade-to-trade returns) by the average price (Bushee et al. 2003, 2004).

  34. 34.

    Similar to prior research (e.g., Frankel et al. 1999), we use the total number of shares traded, rather than a metric of turnover, such as share volume scaled by the shares outstanding. Any impact of cross-sectional variation in float is minimized by the normalization of the volume against a non-event window.

  35. 35.

    The medians of some metrics are negative. A likely explanation for this is that, while we exclude from the four non-event days of each peer firm the dates on which it released its own earnings announcements, we cannot fully eliminate the effect of all information events during this “control” period.

  36. 36.

    Namely, for a conference call held at 11 a.m. on Wednesday July 27, we define the pseudo-conference-call window as starting at 11 a.m. and ending at 12 p.m. on June 29 and the pseudo-earnings-announcement window as starting at 4 p.m. on June 28 and ending at 10 a.m. on June 29. All return variables are normalized by subtracting out the return of the S&P500 ETF over the same window.

  37. 37.

    Note that, on nonevent days, the means/medians of returns are significantly lower, and the covariances are significantly higher than on event days for both conference-call and earnings-announcement windows. These results are in line with expectations, as only shared market and industry-wide information would be expected to move both conference call and peer firms’ prices on nonevent days.

  38. 38.

    To preserve sample comparability, we also deploy an indicator variable for firm quarters for which forecasts are not available.

  39. 39.

    We estimate a simple model of predicting the duration based on the number of words separately for presentation and Q&A sections using a small sample of calls made in 2014. We limit the analysis in this section to 98% of conference calls with both presentation and Q&A sections estimated to last between 6 and 46 min. Our estimated average durations are comparable to those reported by Matsumoto et al. (2011).

  40. 40.

    The similarity of findings for presentation and Q&A sections have been observed in other conference call literature, such as the work of Larcker and Zakolyukina (2012).


  1. Akhigbe, A. (2002). New product innovations, information signaling and industry competition. Applied Financial Economics, 12(5), 371–378.

    Article  Google Scholar 

  2. Ball, R., & Brown, P. (1968). An empirical evaluation of accounting income numbers. Journal of Accounting Research, 6(2), 159–178.

    Article  Google Scholar 

  3. Bhojraj, S., Lee, C. M. C., & Oler, D. K. (2003). What’s my line? A comparison of industry classification schemes for capital market research. Journal of Accounting Research, 41(5), 745–774.

    Article  Google Scholar 

  4. Bischof, J., Daske, H., & Sextroh, C. (2013). Analysts’ demand for fair value-related information: Evidence from conference calls of international banks. Working paper.

  5. Bowen, R. M., Davis, A. K., & Matsumoto, D. A. (2002). Do conference calls affect analysts’ forecasts? The Accounting Review, 77(2), 285–316.

    Article  Google Scholar 

  6. Bozanic, Z., Roulstone, D. T., & Van Buskirk, A. (2018). Management earnings forecasts and other forward-looking statements. Journal of Accounting and Economics, 65(1), 1–20.

    Article  Google Scholar 

  7. Brochet, F., Naranjo, P., & Yu, G. (2016). The capital market consequences of language barriers in the conference calls of non-U.S. firms. The Accounting Review, 91(4), 1023–1049.

    Article  Google Scholar 

  8. Bushee, B. J., Matsumoto, D. A., & Miller, G. S. (2003). Open versus closed conference calls: The determinants and effects of broadening access to disclosure. Journal of Accounting and Economics, 34(1–3), 149–180.

    Article  Google Scholar 

  9. Bushee, B. J., Matsumoto, D. A., & Miller, G. S. (2004). Managerial and investor responses to disclosure regulation: The case of Reg FD and conference calls. The Accounting Review, 79(3), 617–643.

    Article  Google Scholar 

  10. Bushee, B. J., Core, J. E., Guay, W., & Hamm, S. J. W. (2010). The role of the business press as an information intermediary. Journal of Accounting Research, 48(1), 1–19.

    Article  Google Scholar 

  11. Call, A. C., Sharp, N. Y., & Shohfi, T. (2017). Implications of buy-side analysts’ participation in public earnings conference calls. Working paper.

  12. Cohen, L., & Frazzini, A. (2008). Economic links and predictable returns. The Journal of Finance, 63(4), 1977–2011.

    Article  Google Scholar 

  13. Davis, A. K., Ge, W., Matsumoto, D., & Zhang, J. L. (2015). The effect of manager-specific optimism on the tone of earnings conference calls. Review of Accounting Studies, 20(2), 639–673.

    Article  Google Scholar 

  14. De Franco, G., Hope, O., & Larocque, S. (2015). Analysts’ choice of peer companies. Review of Accounting Studies, 20(1), 82–109.

    Article  Google Scholar 

  15. Doran, J. S., Peterson, D. R., & Price, S. M. (2012). Earnings conference call content and stock price: The case of REITs. The Journal of Real Estate Finance and Economics, 45(2), 402–434.

    Article  Google Scholar 

  16. Drake, M. S., Jennings, J., Roulstone, D. T., & Thornock, J. R. (2016). The co-movement of investor attention. Management Science, 63(9), 2847–2867.

    Article  Google Scholar 

  17. Fama, E., & French, K. (1997). Industry costs of equity. Journal of Financial Economics, 43(2), 153–193.

    Article  Google Scholar 

  18. Firth, M. (1976). The impact of earnings announcements on the share price behavior of similar type firms. The Economic Journal, 86(342), 296–306.

    Article  Google Scholar 

  19. Foster, G. (1981). Intra-industry information transfers associated with earnings releases. Journal of Accounting and Economics, 3(3), 201–232.

    Article  Google Scholar 

  20. Frankel, R., Johnson, M., & Skinner, D. J. (1999). An empirical examination of conference calls as a voluntary disclosure medium. Journal of Accounting Research, 37(1), 133–150.

    Article  Google Scholar 

  21. Freeman, R., & Tse, S. (1992). An earnings prediction approach to examining intercompany information transfers. Journal of Accounting and Economics, 15(4), 509–523.

    Article  Google Scholar 

  22. Frost, C. A. (1995). Intraindustry information transfer: An analysis of research methods and additional evidence. Review of Quantitative Finance and Accounting, 5(2), 111–126.

    Article  Google Scholar 

  23. Guenther, D. A., & Rosman, A. J. (1994). Differences between Compustat and CRSP SIC codes and related effects on research. Journal of Accounting and Economics, 18(1), 115–128.

    Article  Google Scholar 

  24. Han, J. C. Y., & Wild, J. J. (1997). Timeliness of reporting and earnings information transfers. Journal of Business Finance and Accounting, 24(3), 527–540.

    Article  Google Scholar 

  25. Heinrichs, A., Park, J., & Soltes, E. F. (2015). Who consumes firm disclosures? Evidence from Earnings Conference Calls. Working paper.

  26. Heston, S. L., Korajczyk, R. A., & Sadka, R. (2010). Intraday patterns in the cross-section of stock returns. Journal of Finance, 65(4), 1369–1407.

    Article  Google Scholar 

  27. Hilary, G., & Shen, R. (2013). The role of analysts in intra-industry information transfer. The Accounting Review, 88(4), 1265–1287.

    Article  Google Scholar 

  28. Hollander, S., Pronk, M., & Roelofsen, E. (2010). Does silence speak? An empirical analysis of disclosure choices during conference calls. Journal of Accounting Research, 48(3), 531–563.

    Article  Google Scholar 

  29. Holthausen, R. W., & Verrecchia, R. E. (1988). The effect of sequential information releases on the variance of price changes in an intertemporal multi-asset market. Journal of Accounting Research, 26(1), 82–106.

    Article  Google Scholar 

  30. Holthausen, R. W., & Verrecchia, R. E. (1990). The effect of informedness and consensus on price and volume behavior. The Accounting Review, 65(1), 191–208.

    Google Scholar 

  31. Huang, A., Lehavy, R., Zang, A., & Zheng, R. (2017). Analyst information discover and interpretation roles: A topic modeling approach. Management Science.

  32. Jain, P. C., & Joh, G. (1988). The dependence between hourly prices and trading volume. Journal of Financial and Quantitative Analysis, 23(3), 269–283.

    Article  Google Scholar 

  33. Jung, M. J. (2013). Investor overlap and diffusion of disclosure practices. Review of Accounting Studies, 18(1), 167–206.

    Article  Google Scholar 

  34. Jung, M. J., Wong, M. H. F., & Zhang, X. F. (2015). Analyst interest as an early Indicator of firm fundamental changes and stock returns. The Accounting Review, 90(3), 1049–1078.

    Article  Google Scholar 

  35. Jung, M. J., Wong, M. H. F., & Zhang, X. F. (2017). Buy-side analysts and earnings conference calls. Journal of Accounting Research.

  36. Kadan, O., Madureira, L., Wang, R., & Zach, T. (2012). Analysts’ industry expertise. Journal of Accounting and Economics, 54(2–3), 95–120.

    Article  Google Scholar 

  37. Karpoff, J. M. (1986). A theory of trading volume. The Journal of Finance, 41(5), 1069–1087.

    Article  Google Scholar 

  38. Kaustia, M., & Rantala, V. (2013). Common analyst-based method for defining peer firms. Working paper.

  39. Kelly, B., & Ljungqvist, A. (2012). Testing asymmetric-information asset pricing models. Review of Financial Studies, 25(5), 1366–1413.

    Article  Google Scholar 

  40. Lang, L., & Stulz, R. (1992). Contagion and competitive intra-industry effects of bankruptcy announcements. Journal of Financial Economics, 32(1), 45–60.

    Article  Google Scholar 

  41. Lansford, B., Lee, J., & Tucker, J. W. (2009). Disclosure of management guidance in conference calls: Materiality, determinants, and consequences. Working paper.

  42. Larcker, D. F., & Zakolyukina, A. A. (2012). Detecting deceptive discussion in conference calls. Journal of Accounting Research, 50(2), 495–540.

    Article  Google Scholar 

  43. Lee, C. M. C., Ready, M. J., & Seguin, P. J. (1994). Volume, volatility, and New York stock exchange trading halts. The Journal of Finance, 49(1), 183–214.

    Article  Google Scholar 

  44. Li, F., Lundholm, R., & Minnis, M. (2013). A measure of competition based on 10-K filings. Journal of Accounting Research, 51(2), 399–436.

    Article  Google Scholar 

  45. Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. The Journal of Finance, 66(1), 35-65.

  46. Matsumoto, D., Pronk, M., & Roelofsen, E. (2011). What makes conference calls useful? The information content of managers’ presentations and analysts’ discussion sessions. The Accounting Review, 86(4), 1383–1414.

    Article  Google Scholar 

  47. Mayew, W. J. (2008). Evidence of management discrimination among analysts during earnings conference calls. Journal of Accounting Research, 46(3), 627–659.

    Article  Google Scholar 

  48. Mayew, W. J., Sharp, N. Y., & Venkatachalam, M. (2013). Using earnings conference calls to identify analysts with superior private information. Review of Accounting Studies, 18(2), 386–413.

    Article  Google Scholar 

  49. National Investor Relations Institute. (2014a). ]NIRI Earnings Call Practices Survey, 2014 Report. NIRI Analytics.

  50. National Investor Relations Institute. (2014b) (updated (2016)). Standards of Practice for Investor Relation: Disclosure. NIRI Analytics.

  51. Patell, J. M., & Wolfson, M. A. (1984). The intraday speed of adjustment of stock prices to earnings and dividend announcements. Journal of Financial Economics, 13(2), 223–252.

    Article  Google Scholar 

  52. Patton, A. J., & Verardo, M. (2012). Does Beta move with news? Firm-specific information flows and learning about profitability. The Review of Financial Studies, 25(9), 2789–2839.

    Article  Google Scholar 

  53. Price, S. M., Salas, J. M., & Sirmans, C. F. (2015). Governance, conference calls and CEO compensation. The Journal of Real Estate Finance and Economics, 50(2), 181–206.

    Article  Google Scholar 

  54. Ramnath, S. (2002). Investor and analyst reactions to earnings announcements of related firms: An empirical analysis. Journal of Accounting Research, 40(5), 1351–1376.

    Article  Google Scholar 

  55. Schipper, K. (1990). Information transfers. Accounting Horizons, 4(4), 97–107.

    Google Scholar 

  56. Steuer, R., Simala, J., & Roberti, J. (2010). Trends in antitrust law: Avoid the traps in investor and analyst calls. New York Law Journal, 243(43), 9.

    Google Scholar 

  57. Thomas, J., & Zhang, F. (2008). Overreaction to intra-industry information transfers? Journal of Accounting Research, 46(4), 909–940.

    Article  Google Scholar 

  58. Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data. Cambridge, MA: MIT Press.

    Google Scholar 

Download references


We appreciate the helpful comments of Robert Battalio, Pietro Bonetti (discussant), Theodore Christensen, Paul Fischer, Peter Joos, Michael Jung, Marina Niessner, Richard Sloan (editor), Jacob Thomas, Benjamin Whipple (discussant), two anonymous reviewers, and workshop participants at Bentley University, Bocconi University, Florida Atlantic University, George Washington University, North Carolina State University, McGill University, the 2017 Review of Accounting Studies conference, Southern Methodist University, Tel Aviv University, Temple University, Texas A&M University, the 2015 UNC-Duke Fall Camp, University of Alberta, University of Connecticut, University of Nebraska, the 2015 Yale SOM Summer Research Conference, and the 2015 American Accounting Association Annual Meeting. We appreciate the excellent research assistance of Alex Garland, Jake Hall, Thao Phuong Nguyen, and Portia Schultz. We thank RavenPack for providing media data. All errors are ours.

Author information



Corresponding author

Correspondence to Alina Lerman.


Appendix 1: Variable Definitions

ᅟ Capital Market Variables
ᅟ Mechanism and Control Variables

Appendix 2: Sample Construction

ᅟ ᅟ

Appendix 3: Conference Call Transcript Examples

ᅟ ᅟ

Appendix 4: Macroeconomic Dictionary

  • Step 1: Retrieval of Economic Reports

We search Thomson One for English language analyst reports with Report Type equal to “Geographic” or “Investing/Economic,” Geography equal to “United States,” period 2002–2010, and the term “economy” in the title. We manually review the list of identified reports and download all (approximately 300) that were English language and pertaining to the United States. Additionally, we search for reports containing the word “economic” in the title retaining the same type, geography, and date filters. We manually review the resulting list of more than 2000 reports to download an additional sample of about 300 reports reflecting a variety of sources and time periods, with length between four and 30 pages. (The average length of such a report is seven pages). The most frequent contributors are Barclays, Barrington, Citi, HSBC, JP Morgan, Morgan Stanley, RBS, and UBS, although nearly 30 other outfits are represented. From the resulting sample of roughly 600 reports, we extract the body of the text (excluding graphs, charts, tables, and legal disclosures). This translates to a textual file of about half a million words. We conduct an unweighted frequency count and identify over 10,000 distinct words with frequency count of two or more in the full corpus.

  • Step 2: Amendment with Corporate Disclosure

We augment our word list with the 5000 most common words used in 10-K filings, obtained from Bill McDonald’s website (see Loughran and McDonald 2011).

  • Step 3: Manual Review

We manually examine the full list of over 15,000 words to identify those likely to pertain to the discussion of macroeconomic or industry-wide conditions but unlikely to be related to a firm-specific discussion. For example, we do not consider the terms “spending” and “growth” appropriate, even though they are in the top 100 most frequent terms in the macroeconomic reports, since, in the context of corporate conference calls, they likely refer to firm-specific discussion.

  • Step 4: Geographic Areas

We supplement the list with geographic area names presenting significant economic areas.

Descriptive Statistics of Macroeconomic Discussion Variables Used in Analysis:

Variable N Mean Median Minimum Maximum Std. Dev
PCT_MACRO_EA 20,378 0.0397 0.0378 0.0129 0.0843 0.0155
PCT_MACRO_CC 20,667 0.0917 0.0867 0.0371 0.1981 0.0331

where PCT_MACRO_EA (PCT_MACRO_CC) is the number of macro-related words used in the earnings announcement (during the conference call) divided by the total number of nonstop words in the earnings announcement (conference call transcript). Stop words are the most common words in the English language such as “and,” “the,” and “that.”

Final List: * indicates root.

abroad, administration*, affordability, africa*, agencies, agency, agriculture*, america*, asia*, atlantic, austerity, australia*, authorities, authority, bailout*, bank*, bearish, bears, bernanke, bill, bills, bloomberg, bls, border*, brazil*, britain, british, bubble*, budget*, builders, bull, bullish, bureau, bush, bust*, buyer*, canada, canadian, capitol, cbo, cds, census, china, chinese, climate, climatic, clinton, coast, cold, commerce, commercial, commodit*, commonwealth, communities, community, compete, competes, competi*, compression, condition*, congress*, conservatives, constituent*, consum*, contagion, contraction, contractionary, council, countries, country*, cpi, crash, credit, crises, crisis, crude, currenc*, curve, customer*, cycl*, defence, defense, deficit*, deflation*, democrat*, deregulation, diesel, disaster*, discretionary, disinflation*, disposable, dodd, domestic*, dow, downturn*, durable*, east, ecb, economi*, economy, elected, election, elections, electoral, electricity, emerging, employment, energy, england, environment*, ethanol, euro*, exchange, export*, exposure*, factory, fannie, farm, fdic, fed, federal*, feds, fire, firms, fluctuat*, foreclos*, foreign*, forex, franc, france, freddie, french, fuel*, fx, gas, gasoline, gdp, geithner, geograph*, geopolitical, german*, global*, globe, gold, government*, governor*, greenspan, gulf, headline*, healthcare, holiday*, home*, house, household, housing, hurricane*, imf, immigr*, import, imported, importer, imports, index, indexes, india, indian, indicators, indices, industrial*, industries, industry, inflation*, infrastructure, input, institut*, interest, international*, investors, italy, japan*, jobless*, jobs, katrina, korea*, labor, labour, law*, layoff*, legal*, legislat*, lender*, lending, libor, local*, macro*, mandate*, manufacturers, manufacturing, market*, materials, medicaid, medicare, meltdown, mexic*, microeconomic, military, monetary, money, mortgage*, multifamily, multinational*, municipal*, nation*, nfib, nominal, norms, north*, obama, officials, oil, opec, overseas, oversight, pacific, peer*, petroleum, pipeline*, policies, policy*, politic*, population*, postwar, ppi, president*, price*, pricing, procyclical*, protectionis*, public, purchas*, qe, recession*, recovery, reflation*, reform*, regime*, region*, regulat*, repeal, repo, repos, republican*, reregulation, residential, resources, risk, risks, rules, russia*, season*, secretary, sector*, security, senat*, shiller, shock*, shop*, slowdown, south*, sovereign, spending, spillover*, spreads, stagflation, standards, staples, states, stimulus, storm*, subprime, subsidies, subsidized, subsidy, suppli*, supply, surplus, survey, talf, tarp, taxpayer*, telecom, territory, terror*, trade, transatlantic, treasuries, treasury, trillion*, unemployed, unemployment, union*, utilities, vendor, vix, volatility, volcker, voters, wage*, war, washington, wealth, weather, west*, workers, world*, yellen, yen, yields, yuan, zone.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Brochet, F., Kolev, K. & Lerman, A. Information transfer and conference calls. Rev Account Stud 23, 907–957 (2018).

Download citation


  • Conference calls
  • Information transfer
  • Intra-day
  • TAQ
  • Information intermediaries

JEL codes

  • D83
  • M41