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This study conducts a comparative analysis of the diversification effects of China's national carbon market (CEA) and the EU ETS Phase IV (EUA) within major commodity markets.

The study employs the TVP-VAR extension of the spillover index framework to scrutinize the information spillovers among the energy, agriculture, metal, and carbon markets. Subsequently, the study explores practical applications of these findings, emphasizing how investors can harness insights from information spillovers to refine their investment strategies.

First, the CEA provide ample opportunities for portfolio diversification between the energy, agriculture, and metal markets, a desirable feature that the EUA does not possess. Second, a portfolio comprising exclusively energy and carbon assets often exhibits the highest Sharpe ratio. Nevertheless, the inclusion of agricultural and metal commodities in a carbon-oriented portfolio may potentially compromise its performance. Finally, our results underscore the pronounced advantage of minimum spillover portfolios; particularly those that designed minimize net pairwise volatility spillover, in the context of China's national carbon market.

This study addresses the previously unexplored intersection of information spillovers and portfolio diversification in major commodity markets, with an emphasis on the role of CEA.

Since stock return and volatility matters to investors, this study proposes to incorporate the textual sentiment of annual reports in stock price crash risk prediction.

Specific sentences gathered from management discussions and their subsequent analyses are tokenized and transformed into numeric vectors using textual mining techniques, and then the Naïve Bayes method is applied to score the sentiment, which is used as an input variable for crash risk prediction. The results are compared between a collection of predictive models, including linear regression (LR) and machine learning techniques.

The experimental results find that those predictive models that incorporate textual sentiment significantly outperform the baseline models with only accounting and market variables included. These conclusions hold when crash risk is proxied by either the negative skewness of the return distribution or down-to-up volatility (DUVOL).

It should be noted that the authors' study focuses on examining the predictive power of textual sentiment in crash risk prediction, while other dimensions of textual features such as readability and thematic contents are not considered. More analysis is needed to explore the predictive power of textual features from various dimensions, with the most recent sample data included in future studies.

The authors' study provides implications for the information value of textual data in financial analysis and risk management. It suggests that the soft information contained within annual reports may prove informative in crash risk prediction, and the incorporation of textual sentiment provides an incremental improvement in overall predictive performance.

The primary objective of this research is to explore the potential of utilizing Global Consciousness Project (GCP) data as a tool for understanding and predicting market sentiment. Specifically, the study aims to assess whether incorporating GCP data into econometric models can enhance the comprehension of daily market movements, providing valuable insights for traders.

This study employs econometric models to investigate the correlation between the Standard & Poor's 500 Volatility Index (VIX), a common measure of market sentiment and data from the GCP. The focus is particularly on the largest daily composite GCP data value (Max[Z]) and its significant covariation with changes in VIX. The research employs interaction terms with VIX and daily returns from global markets, including Europe and Asia, to explore the relationship further.

The results reveal a significant relationship with the GCP data, particularly Max[Z] and VIX. Interaction terms with both VIX and daily returns from global markets are highly significant, explaining about one percent of the variance in the econometric model. This finding suggests that variations in GCP data can contribute to a better understanding of market dynamics and improve forecasting accuracy.

One limitation of this study is the potential for overfitting and P-hacking. To address this concern, the models undergo rigorous testing in an out-of-sample simulation study lasting for a predefined one-year period. This limitation underscores the need for cautious interpretation and application of the findings, recognizing the complexities and uncertainties inherent in market dynamics.

The study explores the practical implications of incorporating GCP data into trading strategies. Econometric models, both with and without GCP data, are subjected to an out-of-sample simulation where an artificial trader employs S&P 500 tracking instruments based on the model's one-day-ahead forecasts. The results suggest that GCP data can enhance daily forecasts, offering practical value for traders seeking improved decision-making tools.

Utilizing data from the GCP is found to be advantageous for traders as noteworthy correlations with market sentiment are found. This unanticipated finding challenges established paradigms in both economics and consciousness research, seamlessly integrating these domains of research. Traders can leverage this innovative tool, as it can be used to refine forecasting precision.

The authors extend the World Bank's Logistics Performance Index (LPI) for 30 additional countries and 13 additional years. The authors develop an inexpensive method for extending survey data when frequent, universal surveys are unavailable. The authors identify groups of country characteristics that influence LPI scores.

Using data from the World Development Indicators—the broadest global dataset of country socioeconomic features—the authors test machine learning algorithms for their ability to predict the LPI. The authors examine importance scores to identify factors that influence LPI scores.

The best performing algorithm produces predictions on unseen data that account for nearly 90% of observed variation, and are accurate to within 6%. It performs twice as well as an OLS model with per capita income as the only predictor. Explanatory factors are business environment, economic structure, finance, environment, human development, and institutional quality.

Machine learning offers a simple, inexpensive way of extending the coverage of survey data. This dataset provides a richer picture of logistics performance around the world. The factors the authors identify as predicting higher LPI scores can help policymakers and practitioners target interventions.

This paper is one of the first applications of machine learning to extend coverage of an index based on an international survey. The authors use the new data to provide the most wide-ranging analysis of logistics performance across countries and over time. The output is an important resource for policymakers tracking performance, and researchers particularly in smaller and lower income countries. The authors also examine a wider range of explanatory factors for LPI scores than previous work.

The paper models the financial interconnectedness and systemic risk of shadow banks using Granger-causal network-based measures and takes the Indian shadow bank crisis of 2018–2019 as a systemic event.

The paper employs pairwise linear Granger-causality tests adjusted for heteroskedasticity and return autocorrelation on a rolling window of weekly returns data of 52 financial institutions from 2016 to 2019 to construct network-based measures and calculate network centrality. The Granger-causal network-based measure ranking of financial institutions in the pre-crisis period (explanatory variable) is rank-regressed with the ranking of financial institutions based on maximum percentage loss suffered by them during the crises period (dependent variable).

The empirical result demonstrated that the shadow bank complex network during the crisis is denser, more interconnected and more correlated than the tranquil period. The closeness, eigenvector, and PageRank centrality established the systemic risk transmitter and receiver roles of institutions. The financial institutions that are more central and hold prestigious positions due to their incoming links suffered maximum loss. The shadow bank network also showed small-world phenomena similar to social networks. Granger-causal network-based measures have out-of-sample predictive properties and can predict the systemic risk of financial institutions.

The study considers only the publicly listed financial institutions. Also, the proposed measures are susceptible to the size of the rolling window, frequency of return and significance level of Granger-causality tests.

Supervisors and financial regulators can use the proposed measures to monitor the development of systemic risk and swiftly identify and isolate contagious financial institutions in the event of a crisis. Also, it is helpful to policymakers and researchers of an emerging economy where bilateral exposures' data between financial institutions are often not present in the public domain, plus there is a gap or delay in financial reporting.

The paper is one of the first to study systemic risk of shadow banks using a financial network comprising of commercial banks and mutual funds. It is also the first one to study systemic risk of Indian shadow banks.

House price fluctuations send vital signals to many parts of the economy, and long-term predictions of house prices are of great interest to governments and property developers. Although predictive models based on economic fundamentals are widely used, the common requirement for such studies is that underlying data are stationary. This paper aims to demonstrate the usefulness of alternative filtering methods for forecasting house prices.

We specifically focus on exponential smoothing with trend adjustment and multiplicative decomposition using median house prices for Sydney from Q3 1994 to Q1 2017. The model performance is evaluated using out-of-sample forecasting techniques and a robustness check against secondary data sources.

Multiplicative decomposition outperforms exponential smoothing at forecasting accuracy. The superior decomposition model suggests that seasonal and cyclical components provide important additional information for predicting house prices. The forecasts for 2017–2028 suggest that prices will slowly increase, going past 2016 levels by 2020 in the apartment market and by 2022/2023 in the detached housing market.

We demonstrate that filtering models are simple (univariate models that only require historical house prices), easy to implement (with no condition of stationarity) and widely used in financial trading, sports betting and other fields where producing accurate forecasts is more important than explaining the drivers of change. The paper puts forward a case for the inclusion of filtering models within the forecasting toolkit as a useful reference point for comparing forecasts from alternative models.

To the best of the authors’ knowledge, this paper undertakes the first systematic comparison of two filtering models for the Sydney housing market.

This study aims to contribute to the literature on migration by examining the nexus between migration-related fears and housing affordability in France, Germany, the UK and the USA using new datasets for migration-related fears.

This study adopts the feasible quasi-generalized least squares approach wherein a predictor can be isolated in the estimation process. Thus, rather than specifying a multi-predictor model that may also lead to parameter proliferation, a single-predictor model (for the predictor of interest) is formulated while also accounting for other salient features resulting from suppressing other important factors that may not be of interest to the current study. Such salient features include persistence, endogeneity and conditional heteroscedasticity issues.

Overall, the results show heterogeneous responses of housing affordability to migration fears across the four developed countries, as the latter deteriorates housing affordability in Germany and the USA and improves it in France and the UK. Similarly, the GFC makes housing less affordable in all four countries as low interest rate passes the mediation test in the nexus. The results, especially for low interest rates, are robust to different uncertainty measures.

As is often the case with economic phenomena, no single model can capture all the factors influencing an economic variable. Thus, besides examining the nexus between migration fears and housing affordability, the authors also account for the role of GDP per capita, given the influence of population and income dynamics on housing affordability. However, incorporating GDP per capita alone does not substantially enhance the model’s ability to predict housing affordability. Future research should explore additional macroeconomic and social factors, such as human capital development, to further enhance this subject.

The findings have significant implications for policymakers regarding the use of low interest rates to counteract the adverse effects of migration-related fear on housing affordability. Specifically, to mitigate the potential negative impact of migration and the associated fear on housing affordability, monetary authorities could adopt a more accommodative stance on mortgages. By allowing real estate investors to obtain loans at lower rates, this approach would help increase housing supply and reduce the housing gap exacerbated by migration influx.

The values of this study lie in its examination of housing affordability in relation to migration fears from both the demand and supply sides of the market. Furthermore, the analyses are conducted to cover out-of-sample forecast evaluation as in-sample predictability may not guarantee out-of-sample prediction.

The purpose of this study is to make property price forecasts for the Chinese housing market that has grown rapidly in the last 10 years, which is an important concern for both government and investors.

This study examines Gaussian process regressions with different kernels and basis functions for monthly pre-owned housing price index estimates for ten major Chinese cities from March 2012 to May 2020. The authors do this by using Bayesian optimizations and cross-validation.

The ten price indices from June 2019 to May 2020 are accurately predicted out-of-sample by the established models, which have relative root mean square errors ranging from 0.0458% to 0.3035% and correlation coefficients ranging from 93.9160% to 99.9653%.

The results might be applied separately or in conjunction with other forecasts to develop hypotheses regarding the patterns in the pre-owned residential real estate price index and conduct further policy research.

This study aims to investigate the determinants of Islamic stock returns listed on Jakarta Islamic Index (Indonesia) between 2008 and 2018.

This study uses a quantile bounded autoregressive distributed lag (QBARDL) model to uncover relevant relationships.

This study finds that the Dow Jones Islamic Market Index, gold returns, world oil prices and exchange rates are the determinants of the Indonesia’s Islamic stock returns. However, the relationship is time varying developing intra-/inter-quantile bounded.

Integration of the Islamic stock returns with the real economic indicators changes over time. The findings have important implications for the policymakers, the fund managers and the investors to anticipate consequences when considering the macroeconomic conditions before participating in the Indonesian Islamic stock market.

Using a QBARDL, this study finds that the Islamic stock returns have on net and “time-varying intra-/inter-quantile developing” relationship with its determinants as data quantiles progressed from 25% to 75%.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.