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Producing good forecast is a vital aspect of a business. The accuracy of these forecasts could have a critical impact on the organization. We introduce a new, practical, and meaningful forecast performance measure called percentage forecast error (PFE). The results of comparing and evaluating this new measure to traditional forecasting performance measures under several different simulation scenarios are presented in this chapter.

This study examines the differences in corporate transparency between subsidiaries of multinational corporations (MNCs), and domestic corporations (DCs) in India.

Financial analysts' forecasts serve as a proxy for market earnings expectations, and research provides mixed evidence of the relation between financial analysts' expertise and forecast accuracy. The judgment and decision-making (J/DM) literature suggests that those with more expertise will not perform better when tasks exhibit either extremely high or extremely low complexity. Expertise is expected to contribute to superior performance for tasks between these two extremes. Using archival data, this research examines the effect of analysts' expertise on forecasting performance by taking into consideration the forecasting task's complexity. Results indicate that expertise is not an explanatory factor for forecast accuracy when the forecasting task's complexity is extremely high or low. However, when task complexity falls between these two extremes, expertise is a significant explanatory variable of forecast accuracy. Both results are consistent with our expectations.

There is ample empirical evidence in the literature for the positive effect of central bank transparency on the economy. The main channel is that transparency reduces the uncertainty regarding future monetary policy and thereby it helps agents to make better investment and saving decisions. In this chapter, we document how the degree of transparency of central banks in Central and Eastern Europe has changed during periods of financial stress, and we argue that during the recent financial crisis central banks became less transparent. We investigate also how these changes affected the uncertainty in these economies, measured by the degree of disagreement across professional forecasters over the future short- and long-term interest rates and also by their forecast accuracy.

In this paper, we follow the same logic as in Hausman (1978) to create a testing procedure that checks for the presence of outliers by comparing a regression estimator that is robust to outliers (S-estimator), with another that is more efficient but affected by them. Some simulations are presented to illustrate the good behavior of the test for both its size and its power.

The study aims at reviewing a synthesis of disclosure, transparency, and International Financial Reporting Standards (IFRS) implementation in an attempt to provide directions for future research. Prior research overwhelmingly supports that the IFRS adoption or effective implementation of IFRS will enhance high-quality financial reporting, transparency, enhance the country’s investment environment, and foreign direct investment (FDI) (Dayanandan, Donker, Ivanof, & Karahan, 2016; Gláserová, 2013; Muniandy & Ali, 2012). However, some researchers provide conflicting evidence that developing countries implementing IFRS are probably not going to encounter higher FDI inflows (Gheorghe, 2009; Lasmin, 2012). It has also been argued that the IFRS adoption decreases the management earnings in countries with high levels of financial disclosure. In general, the study indicates that the adoption of IFRS has improved the financial reporting quality. The common law countries have strong rules to protect investors, strict legal enforcement, and high levels of transparency of financial information. From the extensive structured review of literature using the Scopus database tool, the study reviewed 105 articles, and in particular, the topic-related 94 articles were analysed. All 94 articles were retrieved from a range of 59 journals. Most of the articles (77 of 94) were published 2010–2018. The top five journals based on the citations are Journal of Accounting Research (187 citations), Abacus (125 citations), European Accounting Review (107 citations), Journal of Accounting and Economics (78 citations), and Accounting and Business Research (66 citations). The most-cited authors are Daske, Hail, Leuz, and Verdi (2013); Daske and Gebhardt (2006); and Brüggemann, Hitz, and Sellhorn (2013). Surprisingly, 65 of 94 articles did not utilise the theory. In particular, four theories have been used frequently: agency theory (15), economic theory (5), signalling theory (2), and accounting theory (2). The study calls for future research on the theoretical implications and policy-related research on disclosure and transparency which may inform the local and international standard setters.

The objective of this study aims at reviewing a synthesis of the economic impact of the implementation of International Financial Reporting Standards (IFRS) in an attempt to provide directions for future research. There are significant evidences of adopting a high-quality set of harmonised accounting standards (i.e. IFRS) fosters trade and foreign direct investment (FDI), financial transparency, and comparability and reduces information asymmetries. From the extensive structured review of literature using the Scopus database tool, the study reviewed 108 articles, and in particular, the topic-related 41 articles were analysed. Seven journals contribute to 39% of the articles (The Accounting Review; European Accounting Review; International Journal of Accounting; Journal of Accounting Research; Revista Espanola de Financiacion y Contabilidad; Asian Review of Accounting; and International Journal of Economics and Management). However, most of the cited journals were Journal of Accounting Research, The Accounting Review, European Accounting Review, and International Journal of Accounting (Armstrong, Barth, Jagolinzer, & Riedl, 2010; Brüggemann, Hitz, & Sellhorn, 2013; Christensen, Lee, & Walker, 2007; Daske, Hail, Leuz, & Verdi, 2008, 2013). Most of the studies did not use any theory, and most of the articles utilised quantitative approach. The study calls for future research on the theoretical impactions on the economic impact of IFRS implementation in a country-specific study, cross-country study, and global study. Future studies should also focus on the policymaking agenda for the local and international standard setters.

In this chapter, we estimate the Expected Shortfall (ES) in conditional autoregressive expectile models by using a nonparametric multiple expectile regression via gradient tree boosting. This approach has the advantages generated by the flexibility of not having to rely on data assumptions and avoids the drawbacks and fragilities of a restrictive estimator such as Historical Simulation. We consider distinct specifications for the information sets that produce the ES estimates. The results obtained with simulated and real market data indicate that the proposed approach has good performance, with some distinctions between the specifications.

Business group affiliation seems to make a firm more opaque. The benefits of group affiliation (internal market transactions) and the negative aspects of group affiliation (agency problems of group control) both may make group firms more opaque than non-group firms. Using the opacity index developed by Anderson, Duru, and Reeb (2009), this paper reports that Korean group firms are more transparent than non-group firms after the Asian financial crisis (1997–1998) and this leads to better performance of group firms. The improved transparency results from disappeared internal market benefits and diminished agency problems. These results are robust after controlling for the size of internal markets of groups, industry diversification, the existence of group inside financial institutions, and endogeneity.

Factor investment is booming in global asset management, especially environmental, social, and governance (ESG), dividend yield, and volatility factors. In this chapter, we use data from the US securities market from 2003 to 2019 to predict dividends and volatility factors through machine learning and historical data–based methods. After that, we utilize particle swarm optimization to construct the Markowitz portfolio with limits on the number of assets and weight restrictions. The empirical results show that that the prediction ability using XGBoost is superior to the historical factor investment method. Moreover, the investment performance of our portfolio with ESG, high-yield, and low-volatility factors outperforms baseline methods, especially the S&P 500 ETF.