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The purpose of this paper is to examine the risk-adjusted performance of US-based global real estate mutual funds (GREMFs) with emphasis on their ability to manage their domestic and foreign portfolios exposures.

The paper applies common econometric measures of portfolio performance and implements a non-traditional methodology called attribution returns to measure forecasting ability. In this setting the paper compares the actual monthly fund return to what would have been earned by the set of indices that best reflects the fund's investment strategy during the previous month. Performance and forecasting ability is examined during two different time periods: 2001-2005 and 2006-2010.

It is found that global real estate fund managers outperform the market and show good forecasting ability during the 2001-2005 time period. Good forecasting ability translates to positive risk-adjusted performance, as attribution returns are positively correlated with α.

Despite the significant growth in the number of US-based GREMFs and the ample coverage these funds receive in the popular press, few studies are solely devoted to the examination of these funds. In this study the paper empirically examines the ability of fund managers to successfully forecast country/regional political and economic conditions as well as fluctuations in currency exchanges rates brought about by the changes they made to their portfolios’ domestic and foreign exposures.

The paper aims to empirically examine the forecasting ability of US-based world mutual funds during the 2001-2007 time period.

World mutual funds are treated as portfolios composed of two sets of securities, i.e. domestic and foreign and two methodologies are used to measure forecasting ability: domestic differential exposure and assertion rates. Domestic differential exposure is based on the difference between each fund exposure to the domestic market when it is the outperforming market and the portfolio exposure to the domestic market when the foreign market is outperforming. Similar to the differential exposure, assertion rates measure the ability of fund managers to pick, on a monthly basis, an outperforming market.

Although changing economic conditions in both domestic and foreign markets provided plenty of opportunities to outperform market benchmarks, the results of two empirical tests reveal that fund managers fail to effectively manage their exposure to both markets. Some evidence of good forecasting ability is found when funds are examined on a yearly basis.

This study provides the first implementation of both methodologies: domestic differential exposure and assertion rates, to examine global funds.

This paper verifies whether popular Internet information from Internet forum and search engine exhibit useful content for forecasting the volatility in Chinese stock market.

First, the authors’ study commences with several HAR-RV-type models, then the study amplifies them respectively with the posting volume and search frequency to construct HAR-IF-type and HAR-BD-type models. Second, from in-sample and out-of-sample analysis, the authors empirically investigate the interpretive ability, forecasting performance (statistic and economic). Third, various robustness checks are utilized to reconfirm the authors’ findings, including alternative forecast window, alternative evaluation method and alternative stock market. Finally, the authors further discuss the forecasting performance in different forecast horizons (h = 5, 10 and 20) and asymmetric effect of information from Internet forum.

From in-sample perspective, the authors discover that posting volume exhibits better analytical ability for Chinese stock volatility than search frequency. Out-of-sample results indicate that forecasting models with posting volume could achieve a superior forecasting performance and increased economic value than competing models.

These findings can help investors and decision-makers obtain higher forecasting accuracy and economic gains.

This study enriches the existing research findings about the volatility forecasting of stock market from two dimensions. First, the authors thoroughly investigate whether the Internet information could enhance the efficiency and accuracy of the volatility forecasting concerning with the Chinese stock market. Second, the authors find a novel evidence that the information from Internet forum is more superior to search frequency in volatility forecasting of stock market. Third, they find that this study not only compares the predictability of the posting volume and search frequency simply, but it also divides the posting volume into “good” and “bad” segments to clarify its asymmetric effect respectively.

This study aims to verify whether posting volume and search frequency contain predictive content for estimating the volatility in Chinese stock market.

The forecasting model with posting volume can achieve a superior forecasting performance and increases economic value than competing models.

The results are robust in alternative forecast window, alternative evaluation method and alternative market index.

The posting volume still can help to forecast future volatility for mid- and long-term forecast horizons. Additionally, the role of posting volume in forecasting Chinese stock volatility is asymmetric.

Recently, it has been suggested that macroeconomic forecasts from estimated dynamic stochastic general equilibrium (DSGE) models tend to be more accurate out-of-sample than random walk forecasts or Bayesian vector autoregression (VAR) forecasts. Del Negro and Schorfheide (2013) in particular suggest that the DSGE model forecast should become the benchmark for forecasting horse-races. We compare the real-time forecasting accuracy of the Smets and Wouters (2007) DSGE model with that of several reduced-form time series models. We first demonstrate that none of the forecasting models is efficient. Our second finding is that there is no single best forecasting method. For example, typically simple AR models are most accurate at short horizons and DSGE models are most accurate at long horizons when forecasting output growth, while for inflation forecasts the results are reversed. Moreover, the relative accuracy of all models tends to evolve over time. Third, we show that there is no support to the common practice of using large-scale Bayesian VAR models as the forecast benchmark when evaluating DSGE models. Indeed, low-dimensional unrestricted AR and VAR forecasts may forecast more accurately.

ARIMA models have been extensively examined in the context of the real estate market. The purpose of this paper is to examine issues relating to their application in a forecasting context. Specifically, the paper seeks to examine whether in‐sample measures of best‐fit and also past forecasting accuracy bear any relation to future forecasting performance.

The forecasting performance of alternative ARIMA specifications are compared over rolling estimation and forecasting windows. The forecasting accuracy of the alternative specifications is compared with specific attention placed on the accuracy of the respective specification that in‐sample provides the best fitting model.

The results highlight the limitations in using the conventional approach to identifying the best‐specified ARIMA model in sample, when the purpose of the analysis is to provide forecasts. The results show that while ARIMA models can be useful in anticipating broad market trends, there are substantial differences in the forecasts obtained using alternative specifications. The use of conventional measures of best‐fit provide little indication as to future forecasting ability, nor does the forecasting performance of a specification in previous periods.

ARIMA modelling has frequently been highlighted as a useful forecasting approach. This paper illustrates that care needs to be paid in their use in a forecasting context and full appreciation of the strengths and limitations of the ARIMA approach.

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.

When facing a clouded global economy, many countries would increase their gold reserves. On the other hand, oil supply and demand depends on the political and economic situations of oil producing countries and their production technologies. Both oil and gold reserve play important roles in the economic development of a country. The paper aims to discuss this issue.

This paper uses the historical data of oil and gold prices as research data, and uses the historical price tendency charts of oil and gold, as well as cluster analysis, to discuss the correlation between the historical data of oil and gold prices. By referring to the technical index equation of stocks, the technical indices of oil and gold prices are calculated as the independent variable and the closing price as the dependent variable of the forecasting model.

The findings indicate that there is no obvious correlation between the price tendencies of oil and gold. According to five evaluating indicators, the MFOAGRNN forecast model has better forecast ability than the other three forecasting models.

This paper explored the correlation between oil and gold prices, and built oil and gold prices forecasting models. In addition, this paper proposes a modified FOA (MFOA), where an escape parameter Δ is added to Si. The findings showed that the forecasting model that combines MFOA and GRNN has the best ability to forecast the closing price of oil and gold.

This article surveys recent developments in the evaluation of point and density forecasts in the context of forecasts made by vector autoregressions. Specific emphasis is placed on highlighting those parts of the existing literature that are applicable to direct multistep forecasts and those parts that are applicable to iterated multistep forecasts. This literature includes advancements in the evaluation of forecasts in population (based on true, unknown model coefficients) and the evaluation of forecasts in the finite sample (based on estimated model coefficients). The article then examines in Monte Carlo experiments the finite-sample properties of some tests of equal forecast accuracy, focusing on the comparison of VAR forecasts to AR forecasts. These experiments show the tests to behave as should be expected given the theory. For example, using critical values obtained by bootstrap methods, tests of equal accuracy in population have empirical size about equal to nominal size.

This paper aims to examine whether corporate social responsibility (CSR) is related to management sales forecast accuracy.

Use KLD measures of corporate responsibility combined with forecast accuracy regression model, including controls for management skills and expertise.

Socially responsible firms commit forecast errors of lower magnitude and sales forecast accuracy is positively related to the level of CSR.

A strong motive for research on the field of CSR topic under the scope of reporting quality. Future research could focus on alternative measures of CSR; such as announcements included into the financial statements or separately disclosed expenses. Examine the magnitude of confirmed relation, among different economies worldwide.

CSR effect on manager sales forecasting activity, highlight the impact of brand awareness and customer loyalty, as created by implementing CSR strategies, on firm growth and sales expansion.

The research enhances the era towards more socially responsible firms, presenting evidence of such an adoption on corporate fundamentals.

To the knowledge there is no prior research examining the implications of CSR on sales forecast accuracy.

This paper seeks to examine the predictive ability of disaggregated earnings in forecasting future profitability conditional on keiretsu affiliation in the context of Japan.

Industry‐adjusted future profitability measure is regressed on current profitability measures and on interaction terms of keiretsu affiliation and profitability measures. Out‐of‐sample forecasting test is conducted by regressing future profitability on current profitability measures for keiretsu‐affiliated and independent firms.

Disaggregated earnings improve out‐of‐sampling forecasting for firms not affiliated with keiretsu networks. For the keiretsu‐affiliated firms industry adjustments actually diminish the out‐of‐sample forecasting accuracy.

The boundary between keiretsu‐affiliated and independent firms is not so obvious.

Examining industry competitiveness in Japan using accounting information is a significant contribution to the literature.