Forecasting the fragility of the banking and insurance sectors

Linkages between banks and insurance companies are important when forecasting the fragility of the banking and insurance sectors. We propose a novel empirical framework that allows us to estimate unobserved linkages in panel data sets that contain observed regressors. We find that taking unobserved common factors into account reduces the root mean square forecasts error of firm specific forecasts by up to 9%, of system forecasts by up to 14%, and by up to 39% for systemic forecasts of more distressed firms relative to a model based on observed variables only. Estimates of the factor loadings suggest that the correlation of financial institutions has been relatively stable over the forecast period.     

Do local analysts know more? A cross-country study of the performance of local analysts and foreign analysts

This paper examines whether analysts resident in a country make more precise earnings forecasts for firms in that country than non-resident analysts. Using a sample of 32 countries, we find an economically and statistically significant local analyst advantage even after controlling for firm and analyst characteristics. The local advantage is high in countries where earnings are smoothed more, less information is disclosed by firms, and firm idiosyncratic information explains a smaller fraction of stock returns. It is negatively related to whether a firm has foreign assets and to market participation by foreign investors and by institutions, and positively related to holdings by insiders. The extent to which U.S. investors underweight a country's stocks is positively related to that country's local analyst advantage.     

Do return prediction models add economic value?

We compare statistical and economic measures of forecasting performance across a large set of stock return prediction models with time-varying mean and volatility. We find that it is very common for models to produce higher out-of-sample mean squared forecast errors than a model assuming a constant equity premium, yet simultaneously add economic value when their forecasts are used to guide portfolio decisions. While there is generally a positive correlation between a return prediction model’s out-of-sample statistical performance and its ability to add economic value, the relation tends to be weak and only explains a small part of the cross-sectional variation in different models’ economic value.     

Product Market Competition and Shareholder Rights: International Evidence

This paper examines the interaction between product market competition and international differences in shareholder rights in relation to firm performance and corporate policies. In contrast to existing literature, we provide evidence of complementarities between product market competition and country shareholder rights protections. The benefits of shareholder rights protections for firm performance are conditional on the presence of a competitive industry environment. We find that stronger shareholder rights protections are associated with better firm performance in competitive industries. However, this relation is not significant in concentrated industries. Consistent results are obtained from the analysis of key corporate policies.     

Forecasts in IPO Prospectuses: The Effect of Corporate Governance on Earnings Management

Prior research suggests that managers may use earnings management to meet voluntary earnings forecasts. We document the extent of earnings management undertaken within Canadian Initial Public Offerings (IPOs) and study the extent to which companies with better corporate governance systems are less likely to use earnings management to achieve their earnings forecasts. In addition, we test other factors that differentiate forecasting from non‐forecasting firms, and assess the impact of forecasting and corporate governance on future cash flow prediction. We find that firms with better corporate governance are less likely to include a voluntary earnings forecast in their IPO prospectus. In addition, we find that while IPO firms use accruals management to meet forecasts; the informativeness of the discretionary accruals depends on whether or not the firm would have missed its forecast without the use of discretionary accruals.     

Determinants of analysts’ revenue forecast accuracy

This study investigates financial analysts’ revenue forecasts and identifies determinants of the forecasts’ accuracy. We find that revenue forecast accuracy is determined by forecast and analyst characteristics similar to those of earnings forecast accuracy—namely, forecast horizon, days elapsed since the last forecast, analysts’ forecasting experience, forecast frequency, forecast portfolio, reputation, earnings forecast issuance, forecast boldness, and analysts’ prior performance in forecasting revenues and earnings. We develop a model that predicts the usefulness of revenue forecasts. Thereby, our study helps to ex ante identify more accurate revenue forecasts. Furthermore, we find that analysts concern themselves with their revenue forecasting performance. Analysts with poor revenue forecasting performance are more likely to stop forecasting revenues than analysts with better performance. Their decision is reasonable because revenue forecast accuracy affects analysts’ career prospects in terms of being promoted or terminated. Our study helps investors and academic researchers to understand determinants of revenue forecasts. This understanding is also beneficial for evaluating earnings forecasts because revenue forecasts reveal whether changes in earnings forecasts are due to anticipated changes in revenues or expenses.     

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.     

Corporate Innovation Strategy,Analyst Forecasting Activities and the Economic Consequences

We examine the impact of corporate innovation strategy on analyst following and forecasting performance, as well as the associated economic consequences. Using a sample of US firms from 1992?2012, we find that firms pursuing an exploration‐oriented innovation strategy (as opposed to an exploitation‐oriented innovation strategy) are associated with lower analyst coverage, higher forecast error and dispersion. The effect is less pronounced for firms with greater disclosure of innovation activities, and for firms followed by analysts with more firm‐specific experience. We also examine how innovation strategy affects the perceived credibility of analyst forecasts and find that investors appear to be less responsive to forecast revisions issued for exploratory firms. Such firms also incur a higher level of cash holdings, greater internal financing, and lower dividend ratio. The findings of this paper advance our understanding of how a public company's choice of innovation strategy affects its performance in the capital markets as well as the associated economic consequences.     

Revenue Forecasting Practices: Differences across Countries and Consequences for Forecasting Performance*

This paper reviews the practice and performance of revenue forecasting in selected OECD countries. It turns out that the cross‐country differences in the performance of revenue forecasting are first of all associated with uncertainty about the macroeconomic fundamentals. To some extent, they are also driven by country characteristics such as the importance of corporate and (personal) income taxes. Also, differences in the timing of the forecasts prove important. However, controlling for these differences, we find that the independence of revenue forecasting from possible government manipulation exerts a robust, significantly positive effect on the accuracy of revenue forecasts.     

ARE FIRM‐ AND COUNTRY‐SPECIFIC GOVERNANCE SUBSTITUTES? EVIDENCE FROM FINANCIAL CONTRACTS IN EMERGING MARKETS

We investigate how borrowers’ corporate governance influences bank loan contracting terms in emerging markets and how this relation varies across countries with different country‐level governance. We find that borrowers with stronger corporate governance obtain favorable contracting terms with respect to loan amount, maturity, collateral requirements, and spread. Firm‐level and country‐level corporate governance are substitutes in writing and enforcing financial contracts. We also find that the distinctiveness of borrowers’ characteristics affect the relation between firm‐level corporate governance and loan contracting terms. Our findings are robust, irrespective of types of regression methods and specifications.     

VOLATILITY FORECASTS,TRADING VOLUME,AND THE ARCH VERSUS OPTION‐IMPLIED VOLATILITY TRADE‐OFF

We investigate empirically the role of trading volume (1) in predicting the relative informativeness of volatility forecasts produced by autoregressive conditional heteroskedasticity (ARCH) models versus the volatility forecasts derived from option prices, and (2) in improving volatility forecasts produced by ARCH and option models and combinations of models. Daily and monthly data are explored. We find that if trading volume was low during period t?1 relative to the recent past, ARCH is at least as important as options for forecasting future stock market volatility. Conversely, if volume was high during period t?1 relative to the recent past, option‐implied volatility is much more important than ARCH for forecasting future volatility. Considering relative trading volume as a proxy for changes in the set of information available to investors, our findings reveal an important switching role for trading volume between a volatility forecast that reflects relatively stale information (the historical ARCH estimate) and the option‐implied forward‐looking estimate.     

Why Do Managers Explain Their Earnings Forecasts?

Managers often explain their earnings forecasts by linking forecasted performance to their internal actions and the actions of parties external to the firm. These attributions potentially aid investors in the interpretation of management forecasts by confirming known relationships between attributions and profitability or by identifying additional causes that investors should consider when forecasting earnings. We investigate why managers choose to provide attributions with their forecasts and whether the attributions are related to security price reactions to management earnings forecasts. Using a sample of 951 management earnings forecasts issued from 1993 to 1996, we find that attributions are more likely for larger firms, less likely for firms in regulated industries, less likely for forecasts issued over longer horizons, more likely for bad news forecasts, and more likely for forecasts that are maximum type. Furthermore, attributions are associated with greater absolute price reactions to management forecasts, more negative price reactions to management forecasts (forecast news held constant), and a greater price reaction per dollar of unexpected earnings. Our findings hold after control for the aforementioned determinants of attributions and after control for other firm‐ and forecast‐specific variables that are often associated with security prices.     

Why do countries matter so much for corporate governance?

This paper develops and tests a model of how country characteristics, such as legal protections for minority investors and the level of economic and financial development, influence firms’ costs and benefits in implementing measures to improve their own governance and transparency. We find that country characteristics explain much more of the variance in governance ratings (ranging from 39% to 73%) than observable firm characteristics (ranging from 4% to 22%). Further, we show that firm characteristics explain almost none of the variation in governance ratings in less-developed countries and that access to global capital markets sharpens firms’ incentives for better governance.     

Equity market information,bank holding company risk,and market discipline

For market discipline to be effective, market factors such as changes in firm equity and debt values and returns, must influence firm decision making. In banking, this can occur directly via bank management or indirectly though supervisory examinations and oversight influencing bank management. In this study, we investigate whether equity market variables can provide timely information and add value to accounting models that predict changes in bank holding company (BOPEC) risk ratings over the 1988–2000 period. Using a variety of equity market indicators, the findings suggest that one-quarter lagged market data adds forecast value to lagged financial statement data and prior supervisory information in the logistic regressions. Furthermore, using extensive out-of-sample testing for the years 2001–2003, we find: (1) that multiple models estimated over different phases of the business and banking cycles are superior to a single model for forecasting BOPEC rating changes; (2) that equity data adds economically significant power in forecasting BOPEC rating upgrades and performs well for identifying no changes; (3) that for downgrades, the accounting model forecasts the best; (4) that modeling the three possible risk ratings categories simultaneously (downgrade, no change and upgrade) minimizes both Type I and Type II classification errors; and (5) that using multiple models to forecast risk ratings enhances the overall percentage of correct classifications.     

The predictability of aggregate returns on commodity futures

This paper provides evidence that aggregate returns on commodity futures (without the returns on collateral) are predictable, both in-sample and out-of-sample, by various lagged variables from the stock market, bond market, macroeconomics, and the commodity market. Out of the 32 candidate predictors we consider, we find that investor sentiment is the best in-sample predictor of short-horizon returns, whereas the level and slope of the yield curve have much in-sample predictive power for long-horizon returns. We find that it is possible to forecast aggregate returns on commodity futures out-of-sample through several combination forecasts (the out-of-sample return forecasting R² is up to 1.65% at the monthly frequency).     

Stock price synchronicity and analyst coverage in emerging markets

This paper examines the relation between the stock price synchronicity and analyst activity in emerging markets. Contrary to the conventional wisdom that security analysts specialize in the production of firm-specific information, we find that securities which are covered by more analysts incorporate greater (lesser) market-wide (firm-specific) information. Using the R² statistics of the market model as a measure of synchronicity of stock price movement, we find that greater analyst coverage increases stock price synchronicity. Furthermore, after controlling for the influence of firm size on the lead–lag relation, we find that the returns of high analyst-following portfolio lead returns of low analyst-following portfolio more than vice versa. We also find that the aggregate change in the earnings forecasts in a high analyst-following portfolio affects the aggregate returns of the portfolio itself as well as those of the low analyst-following portfolio, whereas the aggregate change in the earnings forecasts of the low analyst-following portfolio have no predictive ability. Finally, when the forecast dispersion is high, the effect of analyst coverage on stock price synchronicity is reduced.     

Dynamic forecasting behavior by analysts: Theory and evidence

We develop a multi-period learning model to examine the relation between analysts’ forecasting behavior and their performance. In a competitive market for banking services, the surplus and the analyst's payoff, which is determined through bargaining, are convex in her reputation. The convexity of her payoff structure and the presence of employment risk lead to a U-shaped relation between the analyst's forecast boldness and prior performance and a positive relation between forecast boldness and experience. We find support for these predictions in our empirical analysis. Significant underperformers (outperformers) face higher (lower) employment risk and are more likely to issue bolder forecasts.     

Forecasting the macroeconomy with contemporaneous financial market information: Europe and the United States

Using Economic Tracking Portfolios (ETP), I find that it is possible to forecast future values of inflation and changes in industrial production in the United States and three core euro countries - Italy, France and Germany - utilizing only current and past financial market information. The longer the forecasting horizon, the better the forecasts based solely on financial market information compared to results from other methods. Of the countries I analyze, the overall forecasting performance of the tracking portfolios is the best for the U.S., and my method outperforms the forecasting performance of a VAR approach.     

Do Managers Always Know Better? The Relative Accuracy of Management and Analyst Forecasts

We examine the relative accuracy of management and analyst forecasts of annual EPS. We predict and find that analysts’ information advantage resides at the macroeconomic level. They provide more accurate earnings forecasts than management when a firm's fortunes move in concert with macroeconomic factors such as Gross Domestic Product and energy costs. In contrast, we predict and find that management's information advantage resides at the firm level. Their forecasts are more accurate than analysts’ when management's actions, which affect reported earnings, are difficult to anticipate by outsiders, such as when the firm's inventories are abnormally high or the firm has excess capacity or is experiencing a loss. Although analysts are commonly viewed as industry specialists, we fail to find evidence that analysts have an information advantage over managers at the industry level. The two have comparable abilities to forecast earnings for firms with revenues or earnings that are more synchronous with their industries.     

The Value of Crowdsourced Earnings Forecasts

Crowdsourcing—when a task normally performed by employees is outsourced to a large network of people via an open call—is making inroads into the investment research industry. We shed light on this new phenomenon by examining the value of crowdsourced earnings forecasts. Our sample includes 51,012 forecasts provided by Estimize, an open platform that solicits and reports forecasts from over 3,000 contributors. We find that Estimize forecasts are incrementally useful in forecasting earnings and measuring the market's expectations of earnings. Our results are stronger when the number of Estimize contributors is larger, consistent with the benefits of crowdsourcing increasing with the size of the crowd. Finally, Estimize consensus revisions generate significant two‐day size‐adjusted returns. The combined evidence suggests that crowdsourced forecasts are a useful supplementary source of information in capital markets.