ACCT 665 Fall 2006 Midterm Exam Answer any two of the following (50 points each): 1. Accounting researchers assume efficient markets in the semi-strong form. What are efficient markets? Why is the semi-strong form important for accounting research? Are stock markets really efficient? What evidence does Ball & Brown (1968) and Sloan (1996) present to suggest efficient market anomalies? Shiller (2003) suggests several reasons for inefficiency based on behavioral finance, including price-to-price feedback theory, biased self-attribution, and prospect theory. Explain what these reasons mean and how they relate to market efficiency. Markets are efficient when information is impounded into price immediately and in an unbiased fashion. In the semi-strong form, the focus is on publicly available information. Accounting is interested in the semi-strong form, because of the importance of publicly available financial information, especially earnings. Most market analysis suggests relatively efficient markets. However, in Ball & Brown abnormal returns continued in the same direction (up for “good news”) considerably beyond the announcement date (post-announcement drift), contrary to market efficiency expectations. Sloan found that earnings were persistent relative to cash flows, but not accruals. Stock prices “fixated” on earnings. Therefore, a trading strategy based on going long on the lowest accrual decile portfolio and short on the highest “beat” the market, again contrary to efficiency expectations. Shiller incorporated behavioral finance to explain anomalies. Price-to-price feedback theory suggests that speculative success increases investor demand, feeding into a speculative bubble. Individuals use biased self-attribution to confirm the validity of their action to their own ability and attribute “bad events” to bad luck or other factors beyond their control. These also can facilitate speculative bubbles and other irrational behavior. Prospect theory suggests that investors are more upset by losses than pleased by gains, resulting in various irrational behaviors. They may avoid selling losers for example. 2. Swanson (2004) talks about q-r theory to describe the academic review process in business. Kachelmeier (2004) claims that the review process evolves toward preoccupation with r-quality. What is ―q-r theory‖ and what are the implications of Kachelmeier’s point?
Bauer (1992) discusses revolutionary shifts in theory (―paradigm shifts,‖ also related to the ―unknown unknown‖). How do paradigm shifts relate to q-r theory? Q-r theory is used to predict the differences in quality norms across disciples and over time. Q-quality is the inherent importance and interest in the major ideas of a paper. R-quality refers to tangential issues such as robustness checks and discussion to related literature. The point of Kachelmeier reinforces Swanson’s point that over time the shift of reviewers is toward r-quality (e.g., getting bogged down in the minutia) rather than considering the major contributions generated from papers. Bauer’s discussion of revolutionary shifts in theory requires high q-quality to generate “paradigm shifts,” less likely when r-quality is stressed. 3. Management forecast precision was analyzed by Baginski & Hassell (1997), Bamber & Cheon (1998), and Choi et al. (2006). In Baginski & Hassell (1997) precision is significantly related to days, number of analysts, and size; while Bamber & Cheon (1998) find blockshare, concentration, horizon, venue, and year significant. What do these results mean? Choi et al. (2006) are interested in the effect of ―bad news‖ on precision. What does this mean and how is it tested? Precisions is defined as a categorical variable ranging from imprecise (e.g., =0) to a point estimated (e.g., =3). This is the dependent variable in the articles using logit models. The sign and significance of the independent variables indicate the impact on forecast precision. The results of Baginski & Hassel indicate: (1) the negative sign for days (forecast horizon, number of calendar days from forecast to period-end)greater earnings uncertainty and lower precision, (2) that more analysts require higher precision (private information), and (3) larger firm size decreases precision (public information). Bamber & Cheon’s findings indicate: (1) that both legal liability (block ownership, % of shares held in blocks greater than 5%) and proprietary information (product market concentration ratio) decreases precision, (2) horizon (same at B&H days) and size also decrease precision, while (3) venue (source, 1=press release, 3=analysts or reporters) and year (smaller values for earlier years) increase precision. “Bad news” earnings forecasts (i.e., based on the sign of the news, typically MF
Xt-1 (where Xt is EPS for one year) then earnings are higher than expected and that firm-year observation is placed in the ―good news‖ portfolio. If Xt < Xt-1 that observation is part of the ―bad news‖ portfolio. It is predicted (and demonstrated) that return as measured by the API from market model residuals is positive for the good news portfolio and visa versa. In summary both direction and magnitude are important components of Ball & Brown’s method. Beaver (1968) develops a ―U‖ statistic from the market model where U it = eit2 / st2. By squaring the residuals all ―U‖ observations are positive and the focus is on magnitude, not direction. Consequently, no expectation model is needed to determine direction. Instead, the prediction is that the U statistic is significantly larger when new information in the form of annual earnings is publicly announced. 3. Leftwich (1981) used a two-stage method for analyzing market reaction. How does this method work? Why is this paper an analysis of agency theory?
Leftwich (1981) uses the market model to analyze 21 events associated with the passage of APB 16 & 17, related to business combinations. Stage 1: The market model is used to measure stock market reaction around an 11 day (or more) window for each event date. Significant reaction (based on the residuals from the market model) were noted for 9 events (8 in the expected negative direction) based on cumulative prediction errors. Stage 2: The prediction errors become the dependent variables in additional regression runs where the independent variables are associated with specific agency factors of debt and size. The PEs generally were significantly related to private debt, call provisions, and firm size. These are important agency theory factors to explain firm performance based on management incentives.