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The objective of the paper is to explore the out-of-sample forecasting connections in income growth across the globe.

An autoregressive distributed lag (ARDL) framework is employed and the forecasting performance is analyzed across several horizons using different forecast combination techniques.

Results show that the foreign country's income provides superior forecasts beyond what is provided by the country's own past income movements. Superior forecasting power is particularly held by Belgium, Korea, New Zealand, the UK and the US, while these countries' income is rather difficult to predict by global counterparts. Contrary to conventional wisdom, improved forecasts of income can be obtained even for longer horizons using our approach. Results also show that the forecast combination techniques yield higher forecasting gains relative to individual model forecasts, both in magnitude and the number of countries.

The forecasting paths of income movement across the globe reveal that predictive power greatly differs across countries, regions and forecast horizons. The countries that are difficult to predict in the short run are often seen to be predictable by global income movements in the long run.

Even while it is difficult to predict the income movements at an individual country level, combining information from the income growth of several countries is likely to provide superior forecasting gains. And these gains are higher for long-horizon forecasts as compared to the short-horizon forecast.

In evaluating the forward-looking social implications of economic policy changes, the policymakers should also consider the possible global forecasting connections revealed in the study.

Employing an ARDL model to explore global income forecasting connections across several forecast horizons using different forecast combination techniques.

Forecasts of rental growth are increasingly being required by and provided to property investors by a growing number of suppliers. Reviews the uses to which such forecasts are put by a major Uk institutional investor and, from a relatively unique vantage point, critically reviews the forecasting services available in the marketplace. In doing so, it identifies the main forecasting approaches adopted, highlights some of the clear inconsistencies between forecasters in terms of what they are forecasting, how they are forecasting and the different data sources they are using. Explains some of the causes for substantial variations observed in the forecasts provided and, finally, explores the potential for systematic forecasting errors. Concludes by emphasizing the need to switch attention from technical methods to improved “view formation”.

This study examines the role of intellectual capital disclosure (ICD) on earnings forecasting by analysts in the pharmaceutical industry in emerging countries, particularly in Indonesia, Malaysia and Thailand. This study specifically examines the role of each component of the ICD on analysts' forecasts, which consists of errors of forecasted earnings, the standard deviation of forecasted earnings and analyst recommendations.

Panel data analysis is conducted using a sample of 17 companies from pharmaceuticals industries in Indonesia, Malaysia, Thailand – Growth Triangle (IMT-GT), which are listed in the Indonesia Stock Exchange (IDX), Malaysia Stock Exchange (MYX) and Stock Exchange of Thailand (SET) from 2010 to 2017. Secondary data is obtained from Bloomberg and Annual report, where they are being analyzed to measure the ICD and gather the control variables.

The results indicate that the three components of ICD, namely human capital disclosure (HCD), structural capital disclosure (SCD) and relational capital disclosure (RCD), insignificantly influence average analysts' consensus recommendation and analysts' earnings forecast dispersion. However, the findings show a significant negative influence of relational capital disclosure (RCD) on analysts' earnings forecast error. In contrast, HCD and SCD have an insignificant impact.

Transparency in disclosing activities related to external parties is essential for the pharmaceutical industry. It is found that relational capital disclosure is the only ICD indicator that can strengthen analysts' profit predictions. Transparency about company activities in maintaining customer satisfaction and activities related to strategic alliances with other organizations are two critical things that can accommodate the accuracy of earnings forecasting from analysts in pharmaceutical companies.

This study contributes to ICD-related research by discussing the financial analyst's response to this voluntary disclosure in the pharmaceutical industry, particularly in Indonesia, Malaysia and Thailand. The selected observation period is seven years, starting one year after the global financial crisis. The results showed that the disclosure of IC is not an exciting thing for financial analysts. In forecasting current earnings, financial analysts are more interested in errors than the previous year's estimates.

The purpose of this paper is to examine the effects of institutional factors and the European Union (EU) accounting harmonization on the value‐relevance and comparability of dirty surplus accounting flows (DSFs) in the member countries throughout the period 1993 to 2002.

The returns‐earnings models and fixed‐effect operating income growth models are used to examine the differences in the incremental and relative relevance of DSFs between countries which have a piecemeal system of regulation with significant input from the profession and/or market participants, and the code law countries with the government being the most important institution with regard to accounting regulation. Moreover, the relevance of DSFs in the three sub‐periods is compared, each reflecting quite distinct attitudes in the EU towards international accounting harmonization.

DSFs are incrementally relevant in Denmark, Finland, Ireland, Sweden and the UK, where equity market plays an important role in the country's financing system; and in comparison to comprehensive income, reported income is a dominant measure of performance in most European countries, with the exception of the five afore‐mentioned countries. There is also evidence that cross country differences in the value‐relevance and predictability of DSFs decrease in the later years of this sample period.

Future research focusing upon the specific accounting changes made by companies in the EU is needed for a better understanding of the relative importance of stock market integration and standard setting changes in explaining the characteristics of DSFs.

The results indicate that the convergence in the reporting of DSFs over time is driven by global capital market integration, and more importantly, the accounting harmonization activities carried out via self‐regulation with significant input from the profession and/or market participants at national level.

The paper seeks to explore, firstly, the extent to which differences in the reporting of DSFs across the EU may be explained by institutional differences. Secondly, it explores whether or not differences across the countries have decreased in three phases of the EU harmonization process.

After completing this case, students should be able to: identify and compute relevant cash flows in relation to a real estate project and compute the net present value (NPV). Determine the target return or cost of capital (by looking at historical economic indicators). Design or formulate a sensitivity analysis to determine the drivers of the project value. Evaluate real estate and other investments taking qualitative and quantitative factors into consideration. Demonstrate the computation of a break-even rate to determine the minimum or maximum revenue or cost required for a project to be viable.

This case study is about the Golden Beak Securities Pension Fund that wanted to invest in a Hostel Project in one of the universities in Ghana. Most universities in Ghana faced an acute shortage of on-campus accommodation. Also, the Government of Ghana, in 2017, implemented a programme to make Senior High School in Ghana free. This was expected to increase the number of students who will enter the existing universities. The project was therefore seen as strategic, as it would help ease the pressure of on-campus accommodation while providing diversification for the pension fund. As part of the investment committee’s (IC) quest to improve the skill set available to it, especially in relation to real estate investments, Esi Abebrese was appointed as one of the members of the IC of GSB. Her main task was to collect information on key macroeconomic variables, as well as granular information on project costs and revenues and conduct investment appraisal. Esi was scheduled to make a presentation to the IC on the 15th of October 2019 following which the Committee will debate and make a decision. The project had an estimated cost of GH¢52m with a total number of 3,424 student beds and ancillary facilities. Undertaking the project required moving funds from investments in money market securities with one of the banks in Ghana. The investments in the money market securities were currently yielding about 16% a year. The determination of the cost of capital was critical and Esi and Nana eventually settled on a long-term weighted average cost of capital of 14%. This was after considering the trend of inflation, monetary policy rates, treasury rates, stock market returns and a report on returns on commercial real estate properties in Ghana. An exit capitalisation rate of 20% was also estimated for the purposes of determining the value of the property at the end of the investment horizon. Esi also obtained estimates of cost and revenue for the project and proceeded to carry out a feasibility analysis on the project. This consisted of an NPV analysis and sensitivity analysis on various factors to determine the drivers of the project value. The IC had to take several factors (both quantitative and qualitative) into consideration before making a decision. Esi believed that these factors included the diversification of the fund’s assets, the return on investment, potential oversupply of hostel accommodation, the social responsibility of providing student accommodation and the impact of any prolonged shutdown of the university.

Masters/advanced undergraduate.

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CSS 1: Accounting and Finance.

In the backdrop of emerging market setting of India, this study aims to attempt to identify how Institutional investors use sell side analyst outputs for their decision-making processes in light of inherent biases in their forecasts and recommendations. The study also conceptualizes the role of internal buy side teams in the process and try to figure out the key attributes and services provided by sell side analysts, which provide maximum value to the investors.

The study is centered upon in-depth semi-structured interviews of ten institutional investors from top Indian asset management companies covering a wide range of topics tied back to theoretical explanations. The data collected was transcribed, coded and analyzed using content analysis to ensure a systematic synthesis of point of view.

The findings show that internal analyst teams of institutional investors play a dominant role in terms of validation of sell side analysts’ outputs (given the inherent biases in sell side analyst forecasts). Further, the engagement of sell side analysts by the investors are determined not only through profitable recommendations but also on the basis of soundness of the investment rationale along with other services provided. Finally, this study puts into perspective, the critical role of analyst industry knowledge and access to company management (as opposed to analyst pedigree and forecast accuracy) for institutional investors decision-making.

The findings of the paper have profound implications for various stakeholders such as companies, sell side analysts, policy makers, researchers and students of finance in terms of detailed understanding of investment processes of institutional investors in the context of emerging markets like India, which have a different legal and regulatory set-up compared to developed markets. The authors also provide a critical perspective through an intriguing paradox that exists between finance theory and its relevance for actual practitioners.

To the best of the authors’ knowledge, this is the first study in India which look inside the “black box” of institutional investors and their decision-making process, especially with respect to how they use sell side outputs.

The causal relationship between money and income (output) has been an important topic and has been extensively studied. However, those empirical studies are almost entirely on Granger-causality in the conditional mean. Compared to conditional mean, conditional quantiles give a broader picture of an economy in various scenarios. In this paper, we explore whether forecasting conditional quantiles of output growth can be improved using money growth information. We compare the check loss values of quantile forecasts of output growth with and without using past information on money growth, and assess the statistical significance of the loss-differentials. Using U.S. monthly series of real personal income or industrial production for income and output, and M1 or M2 for money, we find that out-of-sample quantile forecasting for output growth is significantly improved by accounting for past money growth information, particularly in tails of the output growth conditional distribution. On the other hand, money–income Granger-causality in the conditional mean is quite weak and unstable. These empirical findings in this paper have not been observed in the money–income literature. The new results of this paper have an important implication on monetary policy, because they imply that the effectiveness of monetary policy has been under-estimated by merely testing Granger-causality in conditional mean. Money does Granger-cause income more strongly than it has been known and therefore information on money growth can (and should) be more utilized in implementing monetary policy.

State and federal revenues fell well short of projections in 2002. While revenues normally turn down in a recession, those revenue shortfalls were much greater than would have been expected given how mild the 2001 recession turned out to be. This paper examines some of the reasons for the large forecast variances observed in recent years using specific examples from forecasts made for the state of Minnesota. Key factors identified include inaccurate forecast for U.S. economic growth; inadequate, untimely and inaccurate data; imperfect models; and unrecognized changes in the structure of the economy. These factors came together and reinforced each other, ultimately producing a larger reduction in state revenues than could have been anticipated in advance.