Dividend Guidance to Manage Analyst Dividend Expectations

Using a sample of dividend payers from 12 European countries, we document that managers guide analyst dividend expectations to avoid reporting dividends below the consensus forecast. Specifically, we show that dividend guidance predicts (1) a substantial reduction in analyst dividend forecast optimism over the course of the fiscal year and (2) that a firm will meet or beat the consensus dividend forecast by a small margin. Managers guide analyst dividend expectations to avoid negative price reactions when reporting negative dividend surprises. Our results, which are robust to endogeneity and self-selection concerns and control for contemporaneous earnings guidance, highlight the important role dividend guidance plays in managing analyst dividend expectations

Knowledge Spillover and Accounting Firms’ Competitive Strength in the M&A Advisory Market

Accounting firms participating in M&A advisory teams can leverage their knowledge accumulated through assurance services to give the teams a competitive advantage in advising on transactions with hard-to-value targets. Consistently, we document that bidders are more likely to select accounting firms to advise on transactions involving such targets. Knowledge spillover aids in estimating the target’s value, which translates into higher service quality offered by accounting firms as captured by higher acquirer announcement-period stock returns and lower likelihood of overpaying for the target. The effects we document are concentrated in cases when the accounting firm is the audit-specialist in the target’s industry or target’s auditor and the target has low reporting quality. Our results help explain why Thomson Reuters ranks accounting firms among top global advisors, particularly in the mid- and low-end M&A advisory market

Analyst Revenue Forecast Reporting and the Quality of Revenues and Expenses

We decompose earnings quality into revenue and expense quality and examine their associations with analyst propensity to supplement their earnings forecasts with revenue forecasts. Analysts report more revenue forecasts to I/B/E/S when expense quality is low to compensate for the low accuracy of their earnings estimates, which has a positive association with expense quality. Expense quality is unassociated with revenue forecast accuracy, thus revenue forecasts become increasingly useful for valuing firms when expense quality is low. Analysts report fewer revenue forecasts when revenue quality is low because both earnings and revenue forecast accuracy decline as revenue quality deteriorates. To control for endogeneity, we use firm-fixed effects to control for unobserved time-invariant heterogeneity across firms, instrumental variables regressions and regression in changes

Do analysts disclose cash flow forecasts with earnings estimates when earnings quality is low?

Cash flows are incrementally useful to earnings in security valuation mainly when earnings quality is low. This suggests that when earnings quality decreases, analysts will be more likely to supplement their earnings forecasts with cash flow estimates. Contrary to this prediction, we find that analysts do not disclose cash flow forecasts when the quality of earnings is low. This is because cash flow forecast accuracy depends on the accuracy of the accrual estimates and the precision of accrual forecasts decreases for firms with low quality earnings. Consequently, as earnings quality decreases, cash flow forecasts become increasingly inaccurate compared to earnings estimates. Cash flow estimates that lack reliability are not useful to investors and, consequently, unlikely to be reported by analysts. This result provides an explanation for why analysts are less likely to report cash flow estimates when earnings quality is low

Does corporate social responsibility affect the institutional ownership of firms in the hospitality and tourism industry?

As hospitality and tourism (H&T) businesses mature, they often seek institutional equity financing to support their growth. Capital intensive H&T firms, such as cruise operators, casinos and large restaurant and hotel chains, continuously rely on institutional capital to fund their operations. This study examines which corporate social responsibility dimensions affect H&T firms’ ability to attract institutional equity capital providers. We document that firms with better social and governance performance have higher institutional ownership, particularly by investors focused on long-term growth and value creation, such as dedicated institutional investors, domestic investors and blockholders. Community and environmental performance do not increase institutional holdings

The Signaling Effect of Durations between Equity and Debt Issues

This study examines whether durations between equity and debt offerings allow investors to identify firms that are more likely to time issues of overvalued securities. We show that firms with higher stock overpricing are more likely to quickly issue both seasoned equity and debt following the previous capital acquisition. Investors understand issuers’ incentives to quickly return to the capital market and react less favorably to equity and debt issues that follow shortly after the previous offering. Together, the results show that durations between equity and debt issues provide valuable signals to investors on whether the issuer is likely to be timing the market

Analyst Dividend Forecasts and Their Usefulness to Investors

In contrast to the disappearing dividends view prevalent in the literature, we document extensive dividend payments by firms and significant variability within firms and across 16 countries during 2000–2013. We predict that within-firm variability in dividends increases investor demand for forward-looking dividend information, and analysts respond by producing informative dividend forecasts. We find that analyst dividend forecasts are available for most dividend-paying firms and are more prevalent for firms with higher variability of dividends. Analyst dividend forecasts are more accurate than alternative proxies based on extrapolations of past dividends. Finally, dividend forecasts (i) are incrementally useful to investors beyond information in other fundamentals such as earnings and cash flow forecasts, (ii) help investors interpret earnings quality, and (iii) are associated with investors’ portfolio allocation decisions

Enforcing Disclosure Compliance in Mergers and Acquisitions: Evidence from China

This study examines how enforcement of acquisition disclosure regulation affects investors’ assessment of the transactions’ quality at merger and acquisition (M&A) announcements. Using a novel sample of comment letters on acquisition filings by public companies in China, we document that regulatory requests for disclosure enhancement and clarifications are more common on lower quality transactions obfuscated by weaker disclosure as evidenced by (i) a lower likelihood of the deal closing, and if the deal does close, lower post-deal firm profitability; and, (ii) a greater likelihood of subsequent goodwill impairment. Using entropy balancing matching, we document that transactions that receive comment letters associate with significant negative bidder announcement returns suggesting that regulatory actions reveal new information that aids investors to identify lower quality deals. The negative price effect is greater when comment letters have more acquisition-specific comments, compared to letters with more comments on general accounting and governance issues. Our results showcase that enforcing disclosure compliance in M&A filings aids investors in assessing the quality of M&A transactions at the time when the filings are made public

Pattern Spectra from Different Component Trees for Estimating Soil Size Distribution

We study the pattern spectra in context of soil structure analysis. Good soil structure is vital for sustainable crop growth. Accurate and fast measuring methods can contribute greatly to soil management decisions. However, the current in-field approaches contain a degree of subjectivity, while obtaining quantifiable results through laboratory techniques typically involves sieving the soil which is labour- and time-intensive. We aim to replace this physical sieving process through image analysis, and investigate the effectiveness of pattern spectra to capture the size distribution of the soil aggregates. We calculate the pattern spectra from partitioning hierarchies in addition to the traditional max-tree. The study is posed as an image retrieval problem, and confirms the ability of pattern spectra and suitability of different partitioning trees to re-identify soil samples in different arrangements and scales

HRTF Magnitude Synthesis via Sparse Representation of Anthropometric Features

International audienceWe propose a method for the synthesis of the magnitudes of Head-related Transfer Functions (HRTFs) using a sparse representation of anthropometric features.Our approach treats the HRTF synthesis problem as finding a sparse representation of the subject's anthropometric features w.r.t. the anthropometric features in the training set.The fundamental assumption is that the magnitudes of a given HRTF set can be described by the same sparse combination as the anthropometric data.Thus, we learn a sparse vector that represents the subject's anthropometric features as a linear superposition of the anthropometric features of a small subset of subjects from the training data.Then, we apply the same sparse vector directly on the HRTF tensor data.For evaluation purpose we use a new dataset, containing both anthropometric features and HRTFs.We compare the proposed sparse representation based approach with ridge regression and with the data of a manikin (which was designed based on average anthropometric data), and we simulate the best and the worst possible classifiers to select one of the HRTFs from the dataset.For instrumental evaluation we use log-spectral distortion.Experiments show that our sparse representation outperforms all other evaluated techniques, and that the synthesized HRTFs are almost as good as the best possible HRTF classifier