Accounting based valuation: a simultaneous equations model for forecasting earnings to proxy for ‘other information’

This paper develops and tests a simultaneous equations model (SEM) for extending accounting based valuation models used in empirical studies. Rather than using analysts’ forecasts, we derive forecasts of operating income from the SEM to calculate the ‘other information’ variable in the Ohlson (Contemp Account Res 11:661–687, 1995) model. The SEM forecasts are based on observable data contained in the firms’ reporting, like order backlog, and other publicly available information. The SEM produces more accurate out-of-sample forecasts of operating income compared to simple benchmark models particularly in years around economic changes and instability, like the years 2001 and 2009. Integrating the SEM forecast as ‘other information’ in market value regressions significantly increases the explanatory power compared to simpler versions without or with single information proxies for ‘other information’. Finally, we find that the SEM forecast is able to explain a major portion of the information advantage of analysts relevant for explaining market values.     

Analyst Earnings Forecasts for Publicly Traded Insurance Companies

Several trends in the insurance and financial services industry, including demutualizationconsolidation, and deregulation, have attracted increasing attention from investors and financial analysts. This paper investigates the accuracy of the earnings forecasts of financial analysts for insurance companies. Our empirical results indicate that analyst forecasts outperform random walk time-series forecasts. Furthermore, we find that both disagreement over earnings forecasts among analysts and the relative forecasting error in the mean forecasts is smaller for life insurers than for property-casualty insurers, whereas the relative errors for forecasts for multiple-line insurers are in between the two. Forecasting error is a negative function of firm size and the number of analysts who are following a company, and is a positive function of the disagreement among analysts.Analyst forecasts have a timing advantage over the random walk model. Our results also suggest that the fair value reporting requirement (SFAS 115), which has been in effect since 1994, has enhanced the accuracy of analyst forecasts. The SFAS 115 has improved the superiority of analyst forecasts over the random walk forecasts for life insurers, but not for property-casualty insurers, and there is a weak improvement for multiple-line insurers. JEL Classification: G15     

The forecasting ability of correlations implied in foreign exchange options

This paper evaluates the forecasting accuracy of correlations derived from implied volatilities in dollar-mark, dollar-yen, and mark-yen options from January 1989 to May 1995. As a forecast of realized correlation between the dollar-mark and dollar-yen, implied correlation is compared against three alternative forecasts based on time series data: historical correlation, RiskMetrics' exponentially-weighted moving average correlation, and correlation estimated using a bivariate GARCH(1,1) model. At the 1-month and 3-month forecast horizons, we find that implied correlation outperforms, often significantly, these alternative forecasts. In combinations, implied correlation always incrementally improves the performance of other forecasts, but not the converse; in certain cases, historically-based forecasts contribute no incremental information to implied forecasts. The superiority of the implied correlation forecast holds even when forecast errors are weighted by realized variances, reflecting correlation's contribution to the dollar variance of a multicurrency portfolio.     

Lead indicator models and UK analysts' earnings forecasts

This study examines the predictive ability of models which adjust random walk forecasts of corporate earnings, to incorporate past changes in economic lead indicators. The results suggest that changes in the broad money supply measure M4 contain predictive ability, beyond equivalent changes in other lead indicators or an individual firm's earnings. When forecasts from the broad-money model are compared with forecasts generated by financial analysts a size effect is evident: the superiority of analysts' forecasts is apparent much earlier for large firms than for small firms. This result is consistent with studies suggesting a size-related differential in the collection and dissemination of information by market participants.     

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.     

Reputation and Performance Among Security Analysts

Members of the Institutional Investor All-American Research Team supply more accurate earnings forecasts than other analysts when forecasts are matched by the corporation followed and by the date of brokerage house issuance. This contemporaneous advantage is complemented by a timing advantage; All-Americans supply forecasts more often than other analysts. Stocks returns immediately following large upward forecast revisions suggest that All-Americans impact prices more than other analysts. However, there is virtually no difference in returns following large downward revisions. Nevertheless, the collective results suggest a positive relation between reputation and performance, and, assuming that All-Americans are better paid, pay and performance.     

Beating market expectations and the pricing of firms’ probability of default

This study explores the impact of beating analysts' forecasts on investors' perceptions about firms' default probability. The information contained in analysts’ forecasts, both earnings and revenues, provides additional information to investors in pricing CDSs. While previous research has focused on the impact of beating analysts’ earnings forecasts, this study shows that firms that beat analysts' revenue forecasts also experience, on average, a decrease in the CDS premium around the earnings announcement date. This study also documents that the effect is stronger when firms beat/miss both earnings and revenue forecasts. When firms beat (miss) earnings and miss (beat) revenues, the effect of earnings is the dominant signal. These effects are stronger for firms with high levels of default risk.     

An evaluation of volatility forecasting techniques

The existing literature contains conflicting evidence regarding the relative quality of stock market volatility forecasts. Evidence can be found supporting the superiority of relatively complex models (including ARCH class models), while there is also evidence supporting the superiority of more simple alternatives. These inconsistencies are of particular concern because of the use of, and reliance on, volatility forecasts in key economic decision-making and analysis, and in asset/option pricing. This paper employs daily Australian data to examine this issue. The results suggest that the ARCH class of models and a simple regression model provide superior forecasts of volatility. However, the various model rankings are shown to be sensitive to the error statistic used to assess the accuracy of the forecasts. Nevertheless, a clear message is that volatility forecasting is a notoriously difficult task.     

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.     

Analyst information production and the timing of annual earnings forecasts

We investigate whether the reputation-herding theory or the tradeoff theory explains variation in the timing of individual analysts’ forecasts. Using forecast accuracy improvements, forecast boldness, and the price impact of forecasts as measures of forecast quality, we find that in the information discovery phase that precedes an earnings announcement, earlier forecasts have higher quality than later forecasts. We also find a similar pattern in the information analysis phase that begins with the earnings announcement date. Our findings suggest that consistent with the herding theory, analysts who are more capable participate early in discovering and analyzing information, and therefore earlier forecasts in the information discovery and analysis phases are of higher quality than later forecasts in that phase.     

Combining conditional volatility forecasts using neural networks: an application to the EMS exchange rates

The present paper examines the out-of-sample forecasting performance of four conditional volatility models applied to the European Monetary System (EMS) exchange rates. In order to provide improved volatility forecasts, the four models’ forecasts are combined through simple averaging, an ordinary least squares model, and an artificial neural network. The results support the EGARCH specification especially after the foreign exchange crisis of August 1993. The superiority of the EGARCH model is consistent with the nature of the EMS as a managed float regime. The ANN model performed better during the August 1993 crisis especially in terms of root mean absolute prediction error.     

The information content of implied volatilities and model-free volatility expectations: Evidence from options written on individual stocks

We measure the volatility information content of stock options for individual firms using option prices for 149 US firms and the S&P 100 index. We use ARCH and regression models to compare volatility forecasts defined by historical stock returns, at-the-money implied volatilities and model-free volatility expectations for every firm. For 1-day-ahead estimation, a historical ARCH model outperforms both of the volatility estimates extracted from option prices for 36% of the firms, but the option forecasts are nearly always more informative for those firms that have the more actively traded options. When the prediction horizon extends until the expiry date of the options, the option forecasts are more informative than the historical volatility for 85% of the firms. However, at-the-money implied volatilities generally outperform the model-free volatility expectations.     

Forecasting Commodity Market Returns Volatility: A Hybrid Ensemble Learning GARCH-LSTM based Approach

This study investigates the advantage of combining the forecasting abilities of multiple generalized autoregressive conditional heteroscedasticity (GARCH)-type models, such as the standard GARCH (GARCH), exponential GARCH (eGARCH), and threshold GARCH (tGARCH) models with advanced deep learning methods to predict the volatility of five important metals (nickel, copper, tin, lead, and gold) in the Indian commodity market. This paper proposes integrating the forecasts of one to three GARCH-type models into an ensemble learning-based hybrid long short-term memory (LSTM) model to forecast commodity price volatility. We further evaluate the forecasting performance of these models for standalone LSTM and GARCH-type models using the root mean squared error, mean absolute error, and mean fundamental percentage error. The results highlight that combining the information from the forecasts of multiple GARCH types into a hybrid LSTM model leads to superior volatility forecasting capability. The SET-LSTM, which represents the model that combines forecasts of the GARCH, eGARCH, and tGARCH into the LSTM hybrid, has shown the best overall results for all metals, barring a few exceptions. Moreover, the equivalence of forecasting accuracy is tested using the Diebold–Mariano and Wilcoxon signed-rank tests.     

Forecasting the past: the case of US interest rate forecasts

This study evaluates 10-year US government bond yield forecasts and three-month US Treasury bill rate forecasts for the period between October 1989 and December 2004. In total, 136 forecast time series with approximately 13,800 forecast data were scrutinized, making this the most extensive analysis of interest rate forecasts to date. Not one of the forecast time series proved to be unbiased. In the majority of cases, information from the past was not efficiently integrated into the forecasts. The sign accuracy is significantly better than random walk forecasts in only a very few of the forecast time series. The modified Diebold–Mariano test for forecast encompassing reveals that the information content of most of the forecast time series is lower than that of the naïve forecasts, the simple ARIMA models, the implicit forward rates, or average interest rate expectations. The forecasting process is dominated by the present and past market situation.     

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.     

An Empirical Analysis of Qualitative Management Earnings Forecasts

Despite efforts by the Securities and Exchange Commission (SEC) to encourage corporate disclosure of quantitative management earnings projections, only a small fraction of firms voluntarily do so. Instead of quantitative estimates, a large number of firms choose to disclose qualitative (verbal) assessments of their earnings prospects. This paper is a study of the information characteristics and the usefulness of this alternative form of forecast disclosure to investors. The study examines a sample of qualitative forecast statements from the 1979–1985 period and finds associations between these forecasts and percentage changes in realized earnings per share, the direction of financial analysts' forecast revisions following the disclosure of these forecasts, and abnormal stock returns on the date of their disclosure. These associations are, however, shown to be more significant for negative (bad news) than for positive (good news) forecasts.     

Hedge fund return predictability; To combine forecasts or combine information?

While the majority of the predictability literature has been devoted to the predictability of traditional asset classes, the literature on the predictability of hedge fund returns is quite scanty. We focus on assessing the out-of-sample predictability of hedge fund strategies by employing an extensive list of predictors. Aiming at reducing uncertainty risk associated with a single predictor model, we first engage into combining the individual forecasts. We consider various combining methods ranging from simple averaging schemes to more sophisticated ones, such as discounting forecast errors, cluster combining and principal components combining. Our second approach combines information of the predictors and applies kitchen sink, bootstrap aggregating (bagging), lasso, ridge and elastic net specifications. Our statistical and economic evaluation findings point to the superiority of simple combination methods. We also provide evidence on the use of hedge fund return forecasts for hedge fund risk measurement and portfolio allocation. Dynamically constructing portfolios based on the combination forecasts of hedge funds returns leads to considerably improved portfolio performance.     

Economic Value Added,Future Accounting Earnings,and Financial Analysts' Earnings Per Share Forecasts

Empirical research to date on the relative effectiveness of Economic Value Added (EVA) and earnings per share (EPS) as measures of firm performance for stock valuation has been mixed. In contrast to prior research, which primarily focuses on the correspondence of these measures with shareholder value and changes therein, we examine their relative effectiveness in predicting future earnings and their role in enhancing the accuracy of analysts' forecasts. Our results indicate that EVA contains information that is incremental to EPS in predicting future earnings. In addition, we find that despite this potential for EVA to add incremental value to analysts' forecasts of future earnings, analysts do not use the information in reported EVA appropriately, but appear rather to overweigh it.     

A re-examination of analysts’ superiority over time-series forecasts of annual earnings

We re-examine the widely held belief that analysts?? earnings per share (EPS) forecasts are superior to random walk (RW) time-series forecasts. We investigate whether analysts?? annual EPS forecasts are superior, and if so, under what conditions. Simple RW EPS forecasts are more accurate than analysts?? forecasts over longer horizons, for smaller or younger firms, and when analysts forecast negative or large changes in EPS. We also compare the accuracy of a third forecast of longer-term earnings based on a na?ve extrapolation of analysts?? 1-year-ahead forecasts. Surprisingly, this na?ve extrapolation provides the most accurate estimate of long-term (2- and 3-year-ahead) earnings. These findings recharacterize prior generalizations about the superiority of analysts?? forecasts and suggest that they are incomplete, misleading, or both. Moreover, in certain settings, researchers can rely on forecasts other than these explicit forecasts.     

预测中国宏观经济变量:专家与模型的组合预测

本文使用组合预测方法,探究以“朗润预测”为代表的专家预测以及计量模型对于中国宏观经济变量的预测效果,并研究对不同预测进行组合预测是否有助于改进预测效果。本文发现,对我国CPI和GDP的增长率,专家预测效果总体上优于模型预测。从原因看,一方面,专家在预测时已经考虑了计量模型的预测信息;另一方面,在经济出现“拐点”的时期,专家通过对实际经济环境和政策的把握,得出更准确的经济预测。组合预测有助于提升预测精度,对专家预测进行组合得到的预测效果优于大多数的专家预测,“模型—专家”组合预测的效果也优于所有的模型和大部分专家预测。