The Client Is King: Do Mutual Fund Relationships Bias Analyst Recommendations?

This paper investigates whether the business relations between mutual funds and brokerage firms influence sell‐side analyst recommendations. Using a unique data set that discloses brokerage firms’ commission income derived from each mutual fund client as well as the share holdings of these mutual funds, we find that an analyst's recommendation on a stock relative to consensus is significantly higher if the stock is held by the mutual fund clients of the analyst's brokerage firm. The optimism in analyst recommendations increases with the weight of the stock in a mutual fund client's portfolio and the commission revenue generated from the mutual fund client. However, this favorable recommendation bias toward a client's existing portfolio stocks is mitigated if the stock in question is highly visible to other mutual fund investors. Abnormal stock returns are significantly greater both for the announcement period and, in the long run, for favorable stock recommendations from analysts not subject to client pressure than for equally favorable recommendations from business‐related analysts. In addition, we find that, subsequent to announcements of bad news from the covered firms, analysts are significantly less likely to downgrade a stock held by client mutual funds. Mutual funds increase their holdings in a stock that receives a favorable recommendation but this impact is significantly reduced if the recommendation comes from analysts subject to client pressure.     

Natural disasters and analysts' earnings forecasts

The effects of natural disasters on capital markets have been investigated by limited evidence even though these calamities bring considerable damages or loss of life. To fill this gap, we investigate the impacts of natural disasters, particularly earthquakes, on security analysts' earnings forecasts for affected firms in China. We obtain three key findings. First, analysts' optimism significantly decreases for firms located in neighborhood areas. Second, earthquakes do not significantly affect firm earnings and stock returns, thereby indicating that post-earthquake analyst pessimism is not based on rational judgment. Third, media attention promotes irrational pessimism among analysts, and post-earthquake pessimism is a result of heuristics bias attributable to psychological shocks. However, analysts correct the bias after initial irrational forecasts. Taken together, our findings contribute to the broader psychology and economics literature on the effects of natural disasters on analyst forecasts.     

Relationship Incentives and the Optimistic/Pessimistic Pattern in Analysts' Forecasts

We examine whether analysts' incentives to maintain good relationships with management contribute to the optimistic/pessimistic within‐period time trend in analysts' forecasts. In our experiments, 81 experienced sell‐side analysts from two brokerage firms predict earnings based on historical information and management guidance. Analysts' forecasts exhibit an optimistic/pessimistic pattern across the two timing conditions (early and late in the quarter), and the effect is significantly stronger when the analysts have a good relationship with management than when their only incentive is to be accurate. Debriefing results indicate that analysts are aware of this pattern of forecasts, and believe that this benefits their future relationships with management and with brokerage clients. The analysts most frequently cite favored conference call participation and information access when describing benefits from maintaining good relationships with management. Our results suggest the following: The optimistic/pessimistic pattern in forecasts is in part a conscious response to relationship incentives, information access is perceived to be a major benefit of management relationships, and recent regulatory changes may have lessened but have not eliminated this conflict of interest source.     

Model-based earnings forecasts vs. financial analysts' earnings forecasts

Existing accounting-based forecasting models of earnings either do not fully consider information that is contained in stock prices or use an ad hoc specification that is not based on rigorous valuation theory. In this paper, we develop an earnings forecasting model built on the theoretical linkages between future earnings and stock prices as well as a number of accounting fundamental variables. We find that our model-based forecasts of earnings are in general less biased and more accurate than both existing model-based forecasts and analysts' consensus forecasts, at both shorter and longer horizons. We also show that the accuracy of both model-based forecasts and financial analysts' forecasts depend on firm-specific characteristics such as firm size and industry membership.     

Analysts versus time-series forecasts of quarterly earnings: A maintained hypothesis revisited

We re-examine the maintained hypothesis of analysts' quarterly earnings per share (EPS) superiority versus ARIMA time-series forecasts. While our empirical results are consistent with overall analysts' dominance, they suggest a more contextual interpretation of this important relationship. Specifically, we find that for a relatively large number of cases (approximately 40%) ARIMA time-series forecasts of quarterly EPS are equal to or more accurate than consensus analysts' forecasts. Moreover, the percentage of time-series superiority increases: (1) for longer forecast horizons, (2) as firm size decreases, and (3) for high-technology firms. Due to the data demands that ARIMA forecasting requires we also examine using a seasonal random walk (SRW) model that requires only one year of data to create quarterly forecasts. Although the ARIMA time-series model results in a significant reduction in sample size it dominates the SRW model. Our findings support the analyst dominance over time series models but suggest that ARIMA time-series models may provide useful input to researchers seeking quarterly EPS expectation models for certain types of firms.     

The brain gain of CFOs in China: The case of analyst forecasts

We study the impact of CFOs with foreign experience on analysts' forecast accuracy in emerging markets. Using a unique data set from China, we find that analysts' forecast accuracy increases when firms hire CFOs with foreign experience, confirming the brain gain effect of CFOs. Our results are robust after addressing potential endogeneity by introducing the propensity score matched (PSM) procedure and Heckman two-stage method. Channel analyses show that CFOs with foreign experience are related to decreased earnings management and a greater probability of hiring high-quality auditors, indicating that the improvement in financial reporting quality and information environment brought by returnee CFOs mainly drive our results. Further cross-section tests reveal that compared to firms with more external pressure, the positive effect of returnee CFOs on analysts' forecast accuracy is more pronounced among firms with fewer analyst coverage and belonging to less competitive industries. Returnee CFOs with foreign work experience exert a more significant impact on analysts' forecast accuracy than those with foreign study experience. Overall, we provide the first evidence on the brain gain of CFOs in terms of analyst forecasts.     

Underwriting relationships,analysts' earnings forecasts and investment recommendations

We examine the effect of underwriting relationships on analysts' earnings forecasts and recommendations. Lead and co-underwriter analysts' growth forecasts and recommendations are significantly more favorable than those made by unaffiliated analysts, although their earnings forecasts are not generally greater. Investors respond similarly to lead underwriter and unaffiliated `Strong buy' and `Buy' recommendations, but three-day returns to lead underwriter `Hold' recommendations are significantly more negative than those to unaffiliated `Hold' recommendations. The findings suggest investors expect lead analysts are more likely to recommend `Hold' when `Sell' is warranted. The post-announcement returns following affiliated and unaffiliated analysts' recommendations are not significantly different.     

Effect of Analysts' Optimism on Estimates of the Expected Rate of Return Implied by Earnings Forecasts

Recent literature has used analysts' earnings forecasts, which are known to be optimistic, to estimate implied expected rates of return, yielding upwardly biased estimates. We estimate that the bias, computed as the difference between the estimates of the implied expected rate of return based on analysts' earnings forecasts and estimates based on current earnings realizations, is 2.84%. The importance of this bias is illustrated by the fact that several extant studies estimate an equity premium in the vicinity of 3%, which would be eliminated by the removal of the bias. We illustrate the point that cross‐sample differences in the bias may lead to the erroneous conclusion that cost of capital differs across these samples by showing that analysts' optimism, and hence, bias in the implied estimates of the expected rate of return, differs with firm size and with analysts' recommendation. As an important aside, we show that the bias in a value‐weighted estimate of the implied equity premium is 1.60% and that the unbiased value‐weighted estimate of this premium is 4.43%.     

Analysts' Forecasts Following Forced CEO Changes

This paper examines analysts' earnings forecasts during the period of uncertainty following a change of chief executive officer (CEO). It distinguishes between forced and non‐forced CEO changes, and examines whether analysts utilize their information advantage to reduce the heightened uncertainty of a forced change of CEO. Examining a sample of Australian companies followed by analysts between 1999 and 2009, we find that forecasting accuracy is lower and earnings forecasts are more optimistic for firms experiencing forced CEO turnover compared to firms not undergoing such a change. However, dispersion is not statistically different. The results suggest that forced CEO turnover events provide a challenge to the forecasting environment for analysts. During CEO changes, investors should be aware that forecasts are less accurate and have an optimistic bias.     

CEO happiness and forecasting

We utilize the IBM Watson Tone Analyzer to measure chief executive officers' (CEOs') levels of joy (happiness) in year-end conference calls, and empirically test how CEOs' happiness affects the properties of their own and analysts' forecasts. We find that joyful CEOs are more likely to issue forecasts, less likely to miss their forecast targets, and exhibit lower optimistic bias in their forecasts. When joyful CEOs issue earnings forecasts, analysts revise their forecasts upwards and produce forecasts that are less dispersed and more accurate. Our results demonstrate that inherent CEO happiness significantly impacts the forecast properties of both managers and analysts, thus supporting upper echelons theory.     

The supply of analysts and earnings forecasts

Using a unique set of hand-collected data, this study examines whether a larger supply of prospective analysts leads to existing financial analysts' higher earnings forecast accuracy. We analyze the effect of the analyst supply proxied by the number of universities and the number of finance and economics universities located in the same city as the headquarters of brokerage firms. Our findings suggest that brokerage firms located closer to a larger supply of potential analyst candidates are associated with higher analyst forecast accuracy, as observed by a lower forecast error. We further find that the degree of employed analysts' effort acts as a mediator between the supply of prospective analysts and the accuracy of their earnings forecasts.     

The effects of social bias against female analysts on markets

I provide evidence for the effects of social bias on markets by studying female analysts. I find that the market reaction to female analysts' forecasts is weaker and slower than that to male analysts' forecasts. I also find that female analysts' forecasts are more accurate and timely than those of male analysts. Taken together, these findings imply that female analysts are underestimated even if they have better forecasting ability than male analysts. This effect decreases when female analysts are attractive, increases when the covered firm's CEO is male and is mediated by analysts' self-confidence. These results emphasize three sources of social bias against female analysts: incompetence bias, immorality bias, and credit bias. I also employ two exogenous shocks to rule out alternative explanations and conduct robustness tests to further support my results. My findings thus highlight that social bias against females remains an issue that significantly affects the capital market.     

Mandatory disclosure of comment letters and analysts' forecasts

Comment letters (CLs) have been adopted as the main supervision mechanism for information disclosure by the two main Chinese stock exchanges since 2013. Both CLs and firms' responses have been publicly disclosed since the end of 2014. Using nonfinancial listed firms from 2013 to 2019 as our sample, we investigate the impact of CLs and their mandatory disclosure on analysts' forecast quality. The results show that, in the pre-disclosure period, there is no significant relation between CLs and analysts' forecast quality. However, in the post-disclosure period, CLs are positively (negatively) correlated with analysts' forecast accuracy (optimism). The quality of analysts' forecasts is much higher when CLs contain more questions. In addition, the impact of CLs is larger for samples with a lower percentage of star analysts or samples with higher earnings volatility. CL recipients tend to disclose more information on their internal and external risks, which can offer additional information to analysts.     

Does revenue-expense matching play a differential role in analysts’ earnings and revenue forecasts?

This paper investigates whether matching has differential implications for the accuracy of analysts' earnings and revenue forecasts. We construct a novel measure of firm-level matching and document that matching improves analysts' earnings forecasts to a greater extent than their revenue forecasts. We also document matching's differential impact on analysts' earnings and sales forecasts by proposing a new count metric capturing a wedge in the accuracy of earnings and revenue forecasts. In additional tests, we report that the differential impact of matching is less (more) pronounced in a situation where the balance sheet (income statement) orientation likely dominates. We also report that matching's differential role is weaker (stronger) when firms have high intangible intensity (analysts have appropriate resources or expertise). In short window tests, matching's role in analysts' forecast revisions is more pronounced for earnings than sales forecasts. Overall, these results show how analysts benefit from better revenue-expense matching.     

Short selling and the independence of business-related analysts: Evidence from an emerging market

This paper investigates whether short-sale deregulation improves analysts' independence in an emerging market where conventional mechanisms mitigating conflicts of interest are either ineffective or absent. Short selling reduces the effectiveness of analysts' favourable opinions in creating or sustaining overvalued stock prices, thus decreasing the incentives of institutional clients of brokerages to exert pressure on related analysts to initiate coverage and issue biased opinions. Using a difference-in-difference approach, we find strong evidence that stocks that are eligible for short sales experience a greater reduction in coverage by related analysts than stocks that are ineligible for short sales. When covered firms become eligible for short sales, the quality of forecasts and recommendations issued by related analysts improves considerably. Further analyses show that shortable firms with a significant reduction in related analysts' coverage are more likely to underperform and to experience stock price crashes in the future. Altogether, our results are consistent with short selling effectively restoring related analysts' independence in emerging markets.     

Hedge fund allocation: Evaluating parametric and nonparametric forecasts using alternative portfolio construction techniques

We propose a model for constructing Asian funds of hedge funds. We compare the accuracy of forecasts of hedge fund returns using an ordinary least squares (OLS) regression model, a nonparametric regression model, and a nonlinear nonparametric model. We backtest to assess these forecasts using three different portfolio construction processes: an “optimized” portfolio, an equally-weighted portfolio, and the Kelly criterion-based portfolio. We find that the Kelly criterion is a reasonable method for constructing a fund of hedge funds, producing better results than a basic optimization or an equally-weighted portfolio construction method. Our backtests also indicate that the nonparametric forecasts and the OLS forecasts produce similar performance at the hedge fund index level. At the individual fund level, our analysis indicates that the OLS forecasts produce higher directional accuracy than the nonparametric methods but the nonparametric methods produce more accurate forecasts than OLS. In backtests, the highest information ratio to predict hedge fund returns is obtained from a combination of the OLS regression with the Fung–Hsieh eight-factor variables as predictors using the Kelly criterion portfolio construction method. Similarly, the highest information ratio using forecasts generated from a combination of the nonparametric regression using the Fung–Hsieh eight-factor model variables is achieved using the Kelly criterion portfolio construction method. Simulations using risk-adjusted total returns indicate that the nonparametric regression model generates superior information ratios than the analogous backtest results using the OLS. However, the benefits of diversification plateau with portfolios of more than 20 hedge funds. These results generally hold with portfolio implementation lags up to 12 months.     

The use of management forecasts to dampen analysts' expectations by Chinese listed firms

This paper studies the use of management earnings forecasts (MEF) to dampen analysts' expectations, i.e. expectation management, by Chinese listed companies. We reveal several important findings: Firstly, information asymmetry is positively associated with the use of MEF to dampen analysts' expectations. State control has been found to moderate this relationship. Secondly, dampening analysts' expectations using MEF leads to negative stock return reactions and downward analysts' forecast revisions. Thirdly, the effectiveness of “pre-empting bad news through MEF” appears mixed and dependent on the information content of MEF and measures of actual earnings surprises. Finally, firms that disclose MEF are found to engage in more earnings management to meet the forecasts than firms that do not.     

Analysts and anomalies

Analysts' price targets and recommendations contradict stock return anomaly variables. Using an index based on 125 anomalies, we find that analysts' annual stock return forecasts are 11% higher for anomaly-shorts than for anomaly-longs. Anomaly-shorts’ return forecasts are excessively optimistic, exceeding realized returns by 34%. Recommendations also tend to be more favorable for anomaly-shorts, although this result varies across anomaly types. Consistent with analysts' slowly incorporating anomaly information, anomalies forecast revisions in both price targets and recommendations. Our findings imply that investors who follow analysts' actionable information contribute to mispricing.     

Blurred stars: Mutual fund ratings in the shadow of conflicts of interest

Using unique data on trading commission payments to mutual fund rating companies (MFRCs) by mutual funds in China, this paper investigates whether the conflicts of interest arising from trading commission payments bias MFRCs’ mutual fund star ratings and hence affect their informativeness. We find the rating of a mutual fund is more optimistic when the MFRC either (i) receives trading commission fees from the mutual fund or (ii) can potentially receive fees in the future. The paper further shows that the usefulness of ratings in terms of predicting a fund’s future performance is negatively impacted by conflicts of interest. There is also evidence that investors can see through the problem, responding less enthusiastically (in terms of fund flows) to the ratings of conflicted MFRCs. We further find that the introduction of a rating qualification system that aims to improve mutual fund rating quality exacerbates the rating bias.     

The impact of meeting or beating analysts' operating cash flow forecasts on a firm's cost of debt

Extant literature provides conflicting results with respect to the usefulness and accuracy of analysts' operating cash flow forecasts. Our study empirically examines the importance and influence of meeting or beating analysts' operating cash flow forecasts on a firm's cost of debt. Results indicate that firms meeting/beating analysts' cash flow forecasts have higher initial bond ratings as well as lower initial bond yields. Additionally, based upon an analysis of rating changes, firms meeting or beating cash flow forecasts have a higher probability of receiving a debt rating upgrade and a lower probability of a ratings downgrade compared to firms missing cash flow forecasts. A direct comparison of the importance of meeting/beating cash flow versus earnings benchmarks indicates that debt market participants appear to incrementally value both types of forecasts, and contrary to selected equity market findings, neither forecast subsumes the other for debt market participants.