Choices in the face of uncertainty: the international futures (IFs) model

The International Futures (IFs) modeling project has roots in both the world modeling projects of the 1970s (including the early Club of Rome models) and the GLOBUS modeling project of the 1980s. It has, however, added extensive capabilities for helping students and analysts think about choices in the face of uncertainty. The third edition of the International futures book and computer simulation have extended the analysis horizon and enhanced analysis potential, with particular focus in two areas: (1) the possible transition towards sustainability in the twenty-first century and (2) sociopolitical change within countries and in the global system. IFs is evolving from a global model to a ‘global modeling system’ that allows analysis of data and analytical relationships across a country-level database. This article reviews the evolution of IFs and the current model. It concludes by discussing challenging issues in the contemporary world that may be explored by students of the future using the IFs model.     

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。     

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.     

Public regulatory reform and management earnings forecasts in a low private litigation environment

We examine the impact of continuous disclosure regulatory reform on the likelihood, frequency and qualitative characteristics of management earnings forecasts issued in New Zealand’s low private litigation environment. Using a sample of 720 earnings forecasts issued by 94 firms listed on the New Zealand Exchange before and after the reform (1999–2005), we provide strong evidence of significant changes in forecasting behaviour in the post‐reform period. Specifically, firms were more likely to issue earnings forecasts to pre‐empt earnings announcements and, in contrast to findings in other legal settings, those earnings forecasts exhibited higher frequency and improved qualitative characteristics (better precision and accuracy). An important implication of our findings is that public regulatory reforms may have a greater benefit in a low private litigation environment and thus add to the global debate about the effectiveness of alternative public regulatory reforms of corporate requirements.     

百年大变局下的国际法与国际法治

当今世界正经历百年未有之大变局，对现行国际法与国际法治提出了前所未有的挑战。二战后逐渐建立和发展起来的现代国际法与国际法治从总体上讲是进步的，对战后世界和平及安全、国际合作与发展、人权尊重和保障，发挥了不可替代的作用。但现代国际法与国际法治并不完善，无论是国际法规则制度本身还是国际法治的运行，都存在深层次的问题，还有许多不公正、不合理、不符合国际格局演变大势的国际规则、国际机制需要改进。当代中国已深度融入全球治理与国际法治体系，从学习者、追随者、受益者发展成为维护者、建设者、贡献者，坚定维护以联合国为核心的国际体系、以国际法为基础的国际秩序、以联合国宪章宗旨和原则为基础的国际关系基本准则，并在理念、原则、规则、制度等方面对现代国际法与国际法治贡献了中国智慧和中国方案。面对百年未有之大变局，中国要立足中国、胸怀天下，与时俱进、守正创新，加强国际法研究和运用，善于运用国际法治思维和法治方式有效应对挑战、防范风险，坚持统筹推进国内法治和涉外法治、协调推进国内法治和国际法治，积极参与全球治理体系改革和建设，积极参与国际规则的制定，推动构建人类命运共同体。 一     

Rationality and Analysts' Forecast Bias

This paper proposes and tests a quadratic-loss utility function for modeling corporate earnings forecasting, where financial analysts trade off bias to improve management access and forecast accuracy. Optimal forecasts with minimum expected error are optimistically biased and exhibit predictable cross-sectional variation related to analyst and company characteristics. Empirical evidence from individual analyst forecasts is consistent with the model's predictions. These results suggest that positive and predictable bias may be a rational property of optimal earnings forecasts. Prior studies using classical notions of unbiasedness may have prematurely dismissed analysts' forecasts as being irrational or inaccurate.     

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.     

Viewing ‘the new economy' from diverse forecasting perspectives

Comparisons are drawn between traditional economic forecasting and more systemic, dynamic modelling of economic risks and rewards, game theory and chaos models. The paper reviews the ways these methods are used by business and financial organizations in today's globalized economy. From an ecological perspective, it shows how these different paradigms, models and statistics lead to widely-divergent forecasts about the performance of economies, central banks, securities markets, other economic actors and phenomena. It interprets such diverse forecasts by examining underlying assumptions and epistemologies in the colloquial language of a luncheon speech given to portfolio managers, venture capitalists and institutional investors.     

The role of disaggregation of earnings in stock valuation and earnings forecasting

This paper compares and contrasts two accounting information systems, the aggregate earnings system and the disaggregated cash flow/accrual system, examining their relative performance in stock valuation and in forecasting of earnings. It finds, in general, that the forecasts of earnings and predicted market values from the cash flow and accrual system have smaller forecasting errors than those from the aggregate earnings system. The adjusted R-squareds from the disaggregated system are in the main higher than those from the aggregated system when considering the explanatory power of the model-predicted values. The results also show that the cash flow and accrual system forecasts dominate the aggregate earnings system forecasts in a large majority of industries.     

Refining the Degree of Earnings Surprise: A Comparison of Statistical and Analysts' Forecasts

This paper compares the relative predictive ability of several statistical models with analysts' forecasts. It is one of the first attempts to forecast quarterly earnings using an autoregressive conditional heteroskedasticity (ARCH) model. ARCH and autoregressive integrated moving average models are found to be superior statistical forecasting alternatives. The most accurate forecasts overall are provided by analysts. Analysts have both a contemporaneous and timing advantage over statistical models. When the sample is screened on those firms that have the largest structural change in the earnings process, the forecast accuracy of the best statistical models is similar to analysts' predictions.     

新时期社会组织立法路径研究

在新时代背景下,社会组织正成为承接政府职能转移和公民参与社会治理的重要载体,然而立法滞后已经成为阻碍社会组织发展的桎梏。本研究通过梳理我国社会组织立法的变迁历程,剖析社会组织立法存在的问题,指出我国社会组织立法已经具备一定基础,但是立法理念、立法架构和立法内容层面还存在诸多问题。因此,在借鉴国内外不同地区社会组织立法经验的基础上,有必要转变立法理念(从管控转向培育)、立法架构(从制约转向激励)、立法内容(从程序法转向实体法)。唯有如此,才能真正发挥社会组织在社会治理中的积极作用,满足促进国家治理体系与治理能力现代化的现实需求。     

Macro to micro: Country exposures,firm fundamentals and stock returns

We outline a systematic approach to incorporate macroeconomic information into firm level forecasting from the perspective of an equity investor. Using a global sample of 198,315 firm-years over the 1998–2010 time period, we find that combining firm level exposures to countries (via geographic segment data) with forecasts of country level performance, is able to generate superior forecasts for firm fundamentals. This result is particularly evident for purely domestic firms. We further find that this forecasting benefit is associated with future excess stock returns. These relations are stronger after periods of higher dispersion in expected country level performance.     

Oil price volatility predictability based on global economic conditions

This study aims to examine the forecasting ability of five global economic activity proxies for WTI crude oil price volatility and construct a new index to improve the accuracy of WTI crude oil price volatility forecasts. We focus on the Global Economic Conditions Index (GECON) derived from 16 indicators related to real economic activity and adopt the autoregressive (AR) framework, along with three common indexes constructed by three dimensionality reduction approaches (scaled principal component analysis (sPCA), principal component analysis (PCA) and partial least squares (PLS)). The out-of-sample results show that the model incorporating the Global Economic Conditions Index (AR-GECON) has the strongest predictive power among the five global economic proxy models. More importantly, our newly constructed PLS model outperforms all the other forecasting models, including AR-GECON.     

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.     

Demographic Issues in Longevity Risk Analysis

Fundamental to the modeling of longevity risk is the specification of the assumptions used in demographic forecasting models that are designed to project past experience into future years, with or without modifications based on expert opinion about influential factors not represented in the historical data. Stochastic forecasts are required to explicitly quantify the uncertainty of forecasted cohort survival functions, including uncertainty due to process variance, parameter errors, and model misspecification errors. Current applications typically ignore the latter two sources although the potential impact of model misspecification errors is substantial. Such errors arise from a lack of understanding of the nature and causes of historical changes in longevity and the implications of these factors for the future. This article reviews the literature on the nature and causes of historical changes in longevity and recent efforts at deterministic and stochastic forecasting based on these data. The review reveals that plausible alternative sets of forecasting assumptions have been derived from the same sets of historical data, implying that further methodological development will be needed to integrate the various assumptions into a single coherent forecasting model. Illustrative calculations based on existing forecasts indicate that the ranges of uncertainty for older cohorts' survival functions will be at a manageable level. Uncertainty ranges for younger cohorts will be larger and the need for greater precision will likely motivate further model development.     

Does corporate governance transparency affect the accuracy of analyst forecasts?

Using country‐level proxies for corporate governance transparency, this paper investigates how differences in transparency across 21 countries affect the average forecast accuracy of analysts for the country's firms. The association between financial transparency and analyst forecast accuracy has been well documented in previous published literature; however, the association between governance transparency and analyst forecast accuracy remains unexplored. Using the two distinct country‐level factors isolated by Bushman et al. (2004 ), governance transparency and financial transparency, we investigate whether corporate governance information impacts on the accuracy of earnings forecasts over and above financial information. We document that governance transparency is positively associated with analyst forecast accuracy after controlling for financial transparency and other variables. Furthermore, our results suggest that governance‐related disclosure plays a bigger role in improving the information environment when financial disclosures are less transparent. Our empirical evidence also suggests that the significance of governance transparency on analyst forecast accuracy is higher when legal enforcement is weak.     

The association between management earnings forecasts, earnings management, and stock market valuation: Evidence from French IPOs

This study investigates managers' motivations to engage in earnings management through purposeful interventions in the setting of discretionary accruals, in the context of initial public offerings (IPOs) in France. Firms issuing forecasts in their prospectuses are expected to differ from nonforecasters in the level of earnings management during the year following the public offering. Within the context of contracting theory, four research questions are addressed. First, are IPO firms issuing forecasts more inclined to manage earnings 1 year after an IPO compared to nonforecasting firms? Second, is a forecasting firm's level of earnings management conditioned by earnings-forecast deviation? Third, is earnings management by IPO forecasting firms affected by contractual and governance environments? Fourth, how do investors see through earnings management following IPO earnings forecasts, i.e., how do stock market participants value earnings components (i.e., nondiscretionary and discretionary accruals)? Our findings document that in the year following an IPO, the magnitude of earnings management is much higher for forecasters than for nonforecasters. Results also show that a firm's accrual behavior is affected by earnings-forecast deviation, but the relationship is moderated by contractual and governance constraints. Finally, it would appear that French investors do not adequately readjust the relationship between reported earnings and a firm's market value for the year in which earnings are subject to manipulations.     

Using Financial and Macroeconomic Indicators to Forecast Sales of Large Development and Construction Firms

Sales forecasting is a pivotal component of a corporation’s planning and control activities. Despite the panoply of approaches to sales forecasting, relatively few published studies in forecasting address firm-specific sales forecasting model development for the construction industry. While there is evidence that events in the macroeconomy significantly affect the construction market, most published studies on construction sales forecasts using S-curve models are unable to account for the economic climate. This study proposes an approach that employs financial and macroeconomic indicators to forecast sales of large development and construction corporations. First, by using data for 37 large development and construction firms listed on the construction sector of the Taiwan Stock Exchange between 1997 and 2006, hypothesis tests uncover useful relationships between firm sales and financial and macroeconomic indicators. Second, based on these relationships, a two-stage mathematical modeling procedure is used to develop firm-specific sales forecasting models for three of the sample firms. Finally, out-of-sample forecasting accuracy is evaluated using Theil’s U-statistic and mean absolute percentage error (MAPE).     

Forecasting global stock market volatilities in an uncertain world

We investigate the predictive relationship between uncertainty and global stock market volatilities from a high-frequency perspective. We show that uncertainty contains information beyond fundamentals (volatility) and strongly affects stock market volatility. Using several crucial uncertainty measures (i.e., uncertainty and implied volatility indices), we prove that the CBOE volatility index (VIX) performs best in point (density) forecasting; the financial stress index (FSI) in directional forecasting. Furthermore, VIX's predictive power improved dramatically after the COVID-19 outbreak, and the VIX-based portfolio strategy enables mean-variance investors to achieve higher returns. There are two empirical properties of VIX: (i) it helps reduce significantly forecast variance rather than bias; and (ii) its forecasts encompass other uncertainty forecasts well. Overall, we highlight the importance of considering uncertainty when exploring the expected stock market volatility.     

Forecasting hedge fund volatility: a Markov regime-switching approach

The article addresses forecasting volatility of hedge fund (HF) returns by using a non-linear Markov-Switching GARCH (MS-GARCH) framework. The in- and out-of-sample, multi-step ahead volatility forecasting performance of GARCH(1,1) and MS-GARCH(1,1) models is compared when applied to 12 global HF indices over the period of January 1990 to October 2010. The results identify different regimes with periods of high and low volatility for most HF indices. In-sample estimation results reveal a superior performance of the MS-GARCH model. The findings show that regime switching is related to structural changes in the market factor for most strategies. Out-of-sample forecasting shows that the MS-GARCH formulation provides more accurate volatility forecasts for most forecast horizons and for most HF strategies. Inclusion of MS dynamics in the GARCH specification highly improves the volatility forecasts for those strategies that are particularly sensitive to general macroeconomic conditions, such as Distressed Restructuring and Merger Arbitrage.