Brokerage trading volume and analysts’ earnings forecasts: a conflict of interest?

Using unique new data, we examine whether brokerage trading volume creates a conflict of interest for analysts. We find that earnings forecast optimism is associated with higher brokerage volume, even controlling for forecast and analyst quality, recommendations, and target prices. However, forecast accuracy is also significantly associated with higher volume. When analysts change brokerage houses, they bring trading volume with them, influencing trading volume at the new brokerage. This indicates that analysts drive the volume effects we observe. Consistent with a reward for generating volume, brokerage houses are less likely to demote analysts who generate more volume. Finally, analysts strategically adjust forecast optimism based on expected volume impact. Analysts become more (less) optimistic if their optimistic forecasts in the prior year were more (less) successful at generating volume. However, consistent with higher costs to increasing accuracy, analysts do not update accuracy based on expected volume impact. Overall, our results are consistent with a brokerage trading volume conflict of interest moving analysts towards more optimistic earnings forecasts, despite the volume reward for accuracy.

Out-of-sample earnings forecasting for OLS and Theil–Sen models relative to a na.ı.ve no-change model

PurposeThe purpose of this paper is to enlarge the exposure of the Theil–Sen (TS) methodology to the academic, analyst and practitioner communities using an earnings forecast setting. The study includes an appendix that describes the TS model in very basic terms and SAS code to assist readers in the implementation of the TS model. The study also presents an alternative approach to deflating or scaling variables.Design/methodology/approachArchival in nature using a combination of regression analysis and binomial tests.FindingsThe binomial test results support the hypothesis that the forecasting performance of the naïve no-change model is at least equal to or better than the ordinary least squares (OLS) model when earnings volatility is low. However, the results do not support the same hypothesis for the TS model nor do the results support the hypothesis that the OLS and TS models will outperform the naïve no-change model when cash flow volatility is high. Nevertheless, the study makes notable contributions to the literature, as the results indicate that the performance of the naïve model is at least as good as the OLS and TS models across 18 of the 20 binomial tests. Moreover, the results indicate that the performance of the TS model is always superior to the OLS model.Research limitations/implicationsThe results are generalizable to US firms and may not extend to non-US firms.Practical implicationsThe TS methodology is advantageous to OLS in that the results are robust to outlier observations, and there is no heteroscedasticity. Researchers will find this study to be useful given the use of a model (i.e. TS) which has to date received little attention, and the provision of the details for the mechanics of the model. A bonus for researchers is that the study includes SAS code for implementing the procedure.Social implicationsAwareness of alternative forecast methodologies could lead to improved forecasting results in certain contexts. The study also helps the financial community in general, as improved forecasting abilities are important for all capital market participants as they improve market efficiency.Originality/valueAlthough a healthy literature exists for examining out-of-sample forecasts for earnings, the literature lacks an answer for a simple question before pursuing additional analyses: Are the results any better than those from a naive no-change forecast? The current study emphasizes the idea that the naïve no-change forecast is the most elementary model possible, and the researcher must first establish the superiority of a more complex model before conducting further analyses.

Individual investors' responses to mutual fund fire sales and sell-side analysts' price-correcting revisions

PurposeThis paper investigates individual investors' responses to stock underpricing and how their trading decisions are affected by analysts' forecasts and recommendations.Design/methodology/approachThis empirical study uses mutual fund fire sales as an exogenous source that causes stock underpricing and analysts' forecasts and recommendations as price-correcting information. The study further uses regression analysis to examine individual investors' responses to fire sales and how their responses vary with price-correcting information.FindingsThe authors first show that individual investors respond to mutual fund fire sales by significantly decreasing net buys, and this effect appears to be prolonged. Next, the authors find that the decrease of net buys diminishes following analysts' price-correcting earnings forecast revisions and stock recommendation changes. Hence, the authors suggest that individual investors are not “wise” enough to recognize flow-driven underpricing; however, this response is weakened by analysts' price-correcting information.Originality/valueThere is an ongoing debate in the literature about whether individual investors should be portrayed as unsophisticated traders or informed traders who can predict future returns. The authors study a unique information event and provide new evidence related to both perspectives. Overall, our evidence suggests that the “unsophisticated traders” perspective is predominant, whereas a better information environment significantly reduces individual investors' information disadvantage. This finding could be of interest to both academic researchers and regulators.

Types of politically connected firms and analysts' earnings forecast

PurposeThis study examined the effect of different types of politically connected (PCON) Malaysian firms on analysts' forecast accuracy and dispersion.Design/methodology/approachThe study identified different types of PCON firms according to Wong and Hooy's (2018) classification, which divided political connections into government-linked companies (GLCs), boards of directors, business owners and family members of government leaders. The sample covered the period 2007–2016, for which earnings forecast data were obtained from the Institutional Brokers' Estimate System (IBES) database and financial data were extracted from Thomson Reuters Fundamentals. We deleted any market consensus estimates made by less than three analysts and/or firms with less than three years of analyst forecast information to control for the impact of individual analysts' personal attributes.FindingsThe study found that PCON firms were associated with lower analyst forecast accuracy and higher forecast dispersion. The effect was more salient in GLCs than in other PCON firms, either through families, business ties or boards of directors. Further analyses showed that PCON firms—in particular GLCs—were associated with more aggressive reporting of earnings and poorer quality of accruals, hence providing inadequate information for analysts to produce accurate and less dispersed earnings forecasts. The results were robust even after addressing endogeneity issues.Research limitations/implicationsThis study found new evidence of the impact of different types of PCON firms in exacerbating information asymmetry, which was not addressed in prior studies.Practical implicationsThis study has a significant practical implication for investors that they should be mindful of high information asymmetry in politically connected firms, particularly government-linked companies.Originality/valueThis is the first study to provide evidence of the impact of different types of PCON firms on analysts' earnings forecasts.

Institutional Dual-Holders and Managers' Earnings Disclosure

We investigate how institutional (non-commercial bank) investors that simultaneously invest in a firm's debt and equity (dual-holders) influence the firm's voluntary disclosure. Because institutional dual-holders trade on private information gleaned through lending relationships, we predict and find that borrowers increase earnings forecast disclosure to reduce these investors' information advantage following the origination of loans with their participation. We also show that the increase in disclosure is stronger when the access to a borrower's private information endows dual-holders with a greater information advantage and when the consequences of this access are likely to be more pronounced. We further find that institutional dual-holders earn excess returns when trading equity of non-guider firms following loan origination, but not when firms issue guidance, confirming that earnings disclosure helps level the playing field among investors. Our findings highlight that firms actively use disclosure to mitigate the adverse effect of dual-holders on their information environment.