By Michael Newman, Wynne Chin, George Gamble and Michael Murray

1. An Investigation of the Impact of Publicly Available Accounting Data, Other Publicly Available Information and Management Guidance on Buy-Side Analysts’ Forecasts By Michael Newman, Wynne Chin, George Gamble and Michael Murray

2. Impetus for Study • This was an exploratory study • There is a lot of information in the academic literature about sell-side analysts but little about buy-side analysts • Question arose: what sources of information do buy-side analysts use to arrive at their stock recommendations and to what degree do they use it?

3. Academic Literature • Givoly and Lakonishok (1979), Lys and Sohn (1990), Francis and Soffer (1997); and Barth and Hutton (2000) find that: • Financial (sell-side) analysts’ recommendations are superior to other forecasts, such as those by time-series models, and • Financial (sell-side) analysts help improve market efficiency since the information they provide on accruals and cash flows to their clients are quickly incorporated into stock prices.

4. Financial Analysts • Financial analysts collect information about publicly traded companies from both public and private sources. • They then develop financial (earnings) forecasts based on their evaluation of the current performance of firms that they follow, and make a stock recommendation of buy, hold or sell the stock. • Source: Kothari (2001) and Fogarty & Rogers (2005)

5. Financial Analysts • Sell-side Analysts produce research reports and/or perform analysis for customers of and are employed by securities brokerage and investment banking firms. • Buy-side Analysts conduct analysis for their own firm that will be used for internal investment decisions, usually for institutional investors. • Most academic studies have focused on sell-side analysts due to the availability of a large data set of individual analyst's actual decisions. • Little has been written about buy-side analysts because they are very secretive. • Source: Kothari (2001) and Fogarty & Rogers (2005)

6. Limited Academic Literature on Buy-side Analysts • Schipper (1991) finds that buy-side analysts’ stock recommendations are influenced by the recommendations made by sell-side analysts. • Fogarty and Rogers (2005, p. 332) states that buy-side and sell-side analysts “have differing motivations and different information use motivations”. • Question: what information is used and which sources of information are used the most by buy-side analysts?

7. Academic Literature • To answer this question, we searched the literature to see what information sell-side analysts use in making their recommendations and to see if sell-side analysts base their stock recommendations on recommendations of other sell-side analysts. • This information could then be used to form questions to ask of buy-side analysts.

8. Key Findings

9. Key Findings

10. Research Questions • What sources of information do buy-side analysts use to make their stock recommendation? Do buy-side analysts use: • publicly available accounting data in making their stock recommendation? • other publicly available information in making their stock recommendation? • information received from corporate managers in the form of management guidance in making their stock recommendation? • information received from other analysts’ in making their stock recommendation?

11. Research Questions • Do buy-side analysts find the sources of information they use to make their stock recommendation to be accurate and unbiased?Do buy-side analysts find: • publicly available accounting data to be representationally faithful? • other publicly available information to be representationally faithful? • information received from corporate managers to be representationally faithful? • information received from other analysts’ to be representationally faithful?

12. Research Questions • Is a buy-side analyst’s attitude towards a company affected by the sources of information he or she uses? Is a buy-side analyst’s attitude towards a firm: • affected by publicly available accounting data? • affected by other publicly available information? • affected by information received from corporate managers in the form of management guidance? • heavily influenced by the opinions and recommendations of analysts they respect?

13. Research Questions • Does the degree to which buy-side analysts’ trust management of a firm affect their attitude or their stock purchase recommendation? • Are buy-side analysts’ attitude towards a company affected by how much they trust the management of a firm? • Are buy-side analysts’ stock purchase decisions affected by how much they trust the management of a firm?

14. Research Questions • Does a buy-side analyst’s attitude toward a firm influence his/her stock purchase recommendation for that firm? • Are buy-side analysts’ stock purchase decisions affected by their attitude towards the firm?

15. Development of the Models • Three main models were then developed from the existing literature using the SEM (structural equation modeling) based method: • The Stock Purchase Recommendation Model • The Attitude Model • The Full (Combined) Model • The SEM (structural equation modeling) based method was used.

16. The Stock Purchase Recommendation Model (Model #1) • The Stock Purchase Recommendation Model is developed based upon the findings in the academic literature that suggest that financial analysts use: • publicly available accounting data, • other publicly available information, • management guidance, • the opinions of other financial analysts and • their trust of management as a basis for the recommendation (buy, hold or sell) they give on a stock purchase decision.

17. The Stock Purchase Recommendation Model

18. The Stock Purchase Recommendation Model Hypotheses The Hypotheses we formulated for The Stock Purchase Recommendation Model are: • H1: Buy-side analysts use publicly available accounting data in making their stock recommendation. • H2: Buy-side analysts use other publicly available information in making their stock recommendation. • H3: Buy-side analysts use information received from corporate managers in the form of “management guidance” in making their stock recommendation. • H4: Buy-side analysts use information received from other analysts in making their stock recommendation. • H5: Buy-side analysts’ stock recommendation for a firm is affected by how much they trust management of a firm.

19. The Stock Purchase Recommendation Model with Interaction Effects (Model #2) • The Stock Purchase Recommendation Model is then modified to test if representational faithfulness and usefulness/degree of use of these sources is important in this process.

20. The Stock Purchase Recommendation Model with Interaction Effects

21. The Stock Purchase Recommendation Model with Interaction Effects Hypotheses The additional hypotheses we formulated for The Stock Purchase Recommendation Model with Interaction Effects are: • H6: Buy-side analysts find publicly available accounting data to be representationally faithful. • H7: Buy-side analysts find other publicly available information to be representationally faithful. • H8: Buy-side analysts find information received from corporate managers in the form of management guidance to be representationally faithful.

22. The Stock Purchase Recommendation Model with Interaction Effects Hypotheses The additional hypotheses we formulated : • H9: Buy-side analysts use publicly available accounting data heavily in determining their forecast and recommendation for a firm. • H10: Buy-side analysts use other publicly available information in determining their forecast and recommendation for a firm. • H11: Buy-side analysts predominantly use information received from corporate managers in the form of management guidance in determining their forecast and recommendation for a firm.

23. The Attitude Model (Model #3) • The Attitude Model is developed based upon the findings in the academic literature that suggests that financial analysts’ attitudes may influence their stock purchase recommendation. • This model tests the effect that publicly available accounting data, other publicly available information, management guidance, the opinions of other financial analysts and their trust of management has on their attitude towards the organization they are making a stock purchase recommendation about.

24. The Attitude Model

25. The Attitude Model Hypotheses The relationships of the buy-side analysts’ attitude towards the firm they are making a stock purchase recommendation for and their stock purchase recommendation are captured in the following hypotheses: • H12: Buy-side analysts’ attitude towards a firm is affected by publicly available accounting data.. • H13: Buy-side analysts’ attitude towards a firm is affected by other publicly available information. • H14: Buy-side analysts’ attitude towards a firm is affected by information received from corporate managers in the form of management guidance. • H15: Buy-side analysts’ attitude towards a company is heavily influenced by the opinions and recommendations of analysts they respect. • H16: Buy-side analysts’ attitude towards a company is affected by how much they trust management of a firm.

26. The Attitude Model with Interaction Effects (Model #4) • The Attitude Model is then modified to test if representational faithfulness and usefulness/degree of use of these sources is important in this process. • By examining both models, we hope to see if the information buy-side analysts receive from these sources (publicly available accounting data, other publicly available information, management guidance, the opinions of other financial analysts and the analyst’s trust of management) has similar or different influences on their stock purchase recommendation or their attitude.

27. The Attitude Model with Interaction Effects

28. The Full (Combined) Model • The Full (Combined) Model, tests to see if attitude has an effect on an analyst’s stock purchase recommendation. • It is tested in three ways. • First, we add attitude as a construct to the Stock Purchase Recommendation Model (the Combined Model). • Next we test it by adding attitude as a construct to the Stock Purchase Recommendation Model with Interaction Effects (the Combined Model with Interaction Effects). • Finally, we take the Stock Purchase Recommendation Model and add the Attitude Model as a construct to see the extent of the effect that the constructs the academic literature identified have on the stock purchase recommendation decision (the Full (Combined) Model).

29. The Full (Combined) Model (Model #5) • The Full (Combined) Model integrates “Attitude” into the Stock Recommendation Model based on the findings in the literature that analysts' attitudes towards companies play a role in their stock purchase recommendations (Lewellen, Lease & Schlarbaum,1977 and 1979, Francis & Soffer, 1997), and Fogarty & Rogers, 2005)

30. The Full (Combined) Model These relationship between Attitude and a buy-side analyst’s stock purchase recommendation is captured in the final hypothesis: • H17: Buy-side analysts’ attitude towards a company influences their forecast and stock purchase recommendation for a firm

31. The Combined Model • The Combined Model is developed by taking the Stock Purchase Recommendation Model and adding Attitude as a construct

32. The Combined Model

33. The Combined Model with Interaction Effects (Model #6) • Next we take the Stock Purchase Recommendation Model with Interaction Effects and add Attitude. • We refer to this model as the Combined Model with Interaction Effects

34. The Combined Model with Interaction Effects

35. The Full (Combined) Model (Model #7) • The Full (Combined) Model is a combination of the Stock Purchase Recommendation Model and the Attitude Model. • Attitude is added as a construct to see the extent of the effect that the constructs the academic literature identified have on the stock purchase recommendation decision and on the buy-side analyst’s attitude.

36. The Full (Combined) Model

37. Selection of Subjects • The selection of the subjects for this study (i.e., buy-side analysts currently employed by institutional investors) was guided by the academic literature. • The literature finds that institutional investors (e.g., employers of buy-side analysts) prefer to own stock in large firms with high visibility. • O’Brien and Bhushan (1990), Hessel and Norman (1992), Del Guercio (1996), Falkenstein (1996), Gompers and Metrick (2001), and Bushee (2001) find that institutional investors prefer to invest in larger public firms. • Bushee and Noe (2000), and Bradshaw, Bushee and Miller (2004) find that disclosure quality and visibility also makes a difference in how they choose to invest their funds.

38. Selection of Subjects • Based on this, a list of 703 buy-side analysts was compiled from a number of sources that met this criteria, primarily from analyst meetings sponsored by public companies and from referrals (from Investor Relations Departments and other buy-side analysts). • The list was then culled to eliminate bad email addresses and each buy-side analyst was then contacted to verify if they were still buy-side analysts and asked if they would be willing to participate in a survey. • We had responses from 227 analysts. • One hundred forty analysts participated in the survey. • Of these, one hundred seventeen identified themselves as “buy-side” and gave complete sets of data.

39. Selection of Subjects • The sample size needed for this experiment, calculated to be 60, was determined based on the amount of power needed given the relationships that were described earlier in this section. • The actual sample of 117 “buy-side” analysts surpassed this minimum requirement.

40. Method of Analysis • The partial least squares (PLS) approach is used in this investigation because it can be used to model more complex situations (compared to covariance methods). • It is also consistent with Multiple Regression Analysis in that it gives Beta and R2 statistics. • And allows the researcher to put greater reliance on theory in analyzing data when it is strong (Chin, 1998).

41. Accounting Literature using PLS • Ittner, Larcker and Rajan (1997, p. 243-245) used PLS to estimate their latent variables (the underlying theoretical constructs) to minimize the impact of their observable (manifest) indicators in their paper which examines the factors influencing the relative weights placed on financial and non-financial performance measures in CEO bonus contracts. • Ittner and Larcker (1998, p. 5) used PLS to construct their customer-satisfaction index, stating “the index is constructed using Partial Least Squares (PLS) to weight the three items such that the resulting index has the maximum correlation with expected economic consequences (customers' self-reports of recommendations, repurchase intentions, and price tolerance).”

42. Analysis of Data • The analysis of data was completed by first assessing the measurement model and then assessing the structural model. • In this case, significant tests were assessed using boot strap analysis with 1000 re-samples. • No assumptions of normality were made in accordance with the partial least squares (PLS) approach used in this study. • Individual item loadings, internal consistency and discriminant validity were assessed for the measurement model using PLS. • The structural model and the 17 hypotheses were tested by examining the path coefficients and their respective statistical significance. • The predictive power of the model was based on the explained variance in the dependent constructs.

43. Survey Design • As discussed earlier, the structural model was developed after an extensive review of the existing academic literature. • Key research areas were identified, constructs were established, and key questions were developed based on the key facets of each construct. • The study includes the appropriate use of negation in some cases, and asks questions several ways to better measure each construct. • The items were measured using mostly seven-point Likert-type scales (with anchors such as “strongly disagree” to “strongly agree” and measurements ranging from “-3” to “+3” where “0” was neutral) and some eleven-point Likert-type scales (with anchors such as “pessimistic” to “optimistic” with a range of “0” to “+10”). • A pilot study involving subject matter experts (including buy-side analysts) was conducted to clarify questions and to measure consistency and validate these items. • Some changes in wording were made and some questions were eliminated as a result of the pilot study in order to refine the survey instrument.

44. Data Collection • The methodological approach used to test the relationships involved a survey that was accessed on the Web. • A number of papers have been written about the use of Web based surveys. • Recommendations made in this academic literature were incorporated into the survey design, especially at it pertained to increasing survey response and the development of the survey tool. • Kaplowitz, Hadlock and Levine (2004) states “For special populations that regularly use the Internet, the Web has been found to be a useful means of conducting research.” • Selection of the relationships that were tested was appropriate since each was based on evidence of such relationships in the academic literature among financial analysts. • In addition, the survey questionnaire was administered to buy-side analysts, a group whose use of various sources of information (the constructs in this study) is not public information.

45. Development of the Survey Instrument • A facet based approach was used to develop the questions to be asked in the survey. • As key areas for research were identified: • Definitions were developed, • The definition was decomposed into component parts (the key facets of the question) in order to understand the underlying meaning, and • Several questions were developed in order to select those that had the most congruence/meaning. • We began with the main concept (e.g., the final outcome) -- how buy-side analysts determine their stock recommendation…..

46. Stock Purchase Recommendation • The literature confirms that analysts make financial forecasts, provide investment advice and make stock purchase recommendations. • We next defined the variable and developed the questions for the main variable as well as the reflective and formative variables

47. Stock Purchase Recommendation DEFINITION: Extent or degree to which an analyst would make a recommendation involving the purchase or sale of a company's common stock. OVERALL STOCK RECOMMENDATION Overall, all things considered, my feelings towards this firm's stock is (please circle one rating for each descriptor): Neutral Bad -3 -2 -1 0 1 2 3 Good Unsound -3 -2 -1 0 1 2 3 Sound Below Standard -3 -2 -1 0 1 2 3 Above Standard Weak -3 -2 -1 0 1 2 3 Strong Negative -3 -2 -1 0 1 2 3 Positive Pessimistic -3 -2 -1 0 1 2 3 Optimistic

48. Stock Purchase Recommendation OVERALL STOCK RECOMMENDATION My recommendation for this firm's stock is very positive. -3 -2 -1 0 1 2 3 Strongly Disagree Somewhat Disagree Somewhat Agree Strongly Agree I base my stock investment recommendations on my financial forecasts for this firm. -3 -2 -1 0 1 2 3 Strongly Disagree Somewhat Disagree Somewhat Agree Strongly Agree Overall, I really like this firm's stock and recommend its purchase. -3 -2 -1 0 1 2 3 Strongly Disagree Somewhat Disagree Somewhat Agree Strongly Agree

49. Attitude • The literature next led to another potential direct relationship based on the question of whether or not the buy-side analyst formulates his or her stock recommendation based on these variables or if the relationship is one between the buy-side analyst and his/her attitude towards the firm he/she is analyzing. • DEFINITION: Affective evaluation an analyst has towards this firm’s financial performance.

50. Sources of Information • Next we determined the sources of information analysts use to make their decisions. • The literature identifies several sources of information that sell-side analysts use to arrive at their stock recommendation: • Publicly Available Accounting Data (Previts, Bricker, Robinson and Young, 1994), • Other Publicly Available Information (Pankoff and Virgil, 1970), • Management Guidance (Hongren, 1978; and Fogarty & Rogers, 2005), • Opinions of Other Analysts (Schipper, 1991), and • Trust of Management (Tan, Libby and Hutton, 2002)