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Opportunity real estate funds are an important style of real estate investing for institutional investors seeking nonlisted real estate exposure. Importantly, institutional investors have sought exposure to the China real estate market, often via opportunity real estate funds. This has been by a pure China opportunity real estate fund (100% China opportunity real estate) or by a pan-Asia opportunity real estate fund where China opportunity real estate was part of this pan-Asia opportunity real estate portfolio. Using two bespoke China opportunity real estate indices developed by the authors, this paper aims to assess the risk-adjusted performance and portfolio diversification benefits of China opportunity real estate in a mixed-asset portfolio over 2008–2020. It also highlights critical issues for institutional investors going forward to factor into their real estate investment decision-making for effective China real estate exposure.

This paper develops two bespoke China opportunity real estate fund performance indices to assess the risk-adjusted performance and portfolio diversification benefits of China opportunity real estate funds in a mixed-asset portfolio over 2008–2020. An asset allocation diagram is used to assess the role of China opportunity real estate in a mixed-asset portfolio via both the non-listed and listed real estate investment channels.

Over 2008–2020, China opportunity real estate exposure via pan-Asia opportunity real estate funds were seen to outperform pure China opportunity real estate funds. In both formats, China opportunity real estate funds were seen to have a significant role in a China mixed-asset portfolio across most of the portfolio risk spectrum; particularly compared to listed real estate exposure in China. On-going issues regarding real estate risk management in China will take on increased importance for institutional investors seeking China real estate exposure.

Opportunity real estate funds are an important style of real estate investing, often used by institutional investors to gain non-listed real estate exposure in a developing real estate market. This style of real estate investing has been popular with institutional investors seeking exposure to China real estate as part of the China economic growth dynamic. The results of this research highlight the importance of opportunity real estate investing in China, both via a pure China opportunity real estate fund and via a pan-Asia opportunity real estate fund. Based on this empirical analysis, China opportunity real estate exposure is seen to be more effective via a pan-Asia opportunity real estate fund than a 100% China opportunity real estate fund. A range of practical China real estate investment issues are also highlighted for the effective delivery of China real estate exposure for institutional investors going forward; this particularly relates to the on-going risk management for real estate investment in China.

This paper is the first empirical research analysis of the risk-adjusted performance of China opportunity real estate and its role in a mixed-asset portfolio. Using bespoke China opportunity real estate fund indices developed by the authors, this research enables empirically-validated, more informed and practical opportunity real estate investment decision-making regarding the strategic role of China opportunity real estate in an institutional investor's portfolio. It also highlights the importance of various facets of real estate risk management in China going forward.

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.

This paper investigates the relationship between the sustainable finance disclosure regulation (SFDR) and the performance of unlisted real estate funds.

While existing literature has primarily focused on the impact of voluntary sustainability disclosure, such as certifications or reporting standards, this study addresses a significant research gap by constructing and analyzing the financial J-Curve of 40 funds under the SFDR. The authors employ a panel regression analysis to examine the effects of different SFDR categories on fund performance.

The findings reveal that funds categorized under Article 8 of the SFDR do not exhibit significantly poorer performance compared to funds categorized under Article 6 during the initial phase after launch. On average, Article 8 funds even demonstrate positive returns earlier than their peers. However, the panel regression analysis suggests that Article 8 funds slightly underperform when compared to Article 6 funds over time.

While investors may not anticipate lower initial returns when opting for higher SFDR categories, they should nevertheless be aware of the limitations inherent in the existing SFDR labeling system within the unlisted real estate sector.

To the best of our knowledge, this study represents the first quantitative examination of unlisted real estate fund performance under the SFDR. By providing unique insights into the J-Curves of funds, our research contributes to the existing body of knowledge on the impact of sustainability regulations in the financial sector.

The authors test the weak-form efficiency in cryptocurrency markets using the most recent and comprehensive data as of 2021. The authors apply various technical indicators to take a long or short position on 99 cryptocurrencies and compare the 10-day returns based on the technical trading strategies to the simple buy-and-hold returns. The authors find that the trading strategies based on single indicators or the combination of two indicators do not generate higher returns than buy-and-hold returns among cryptos. These findings suggest that cryptocurrency markets are weak-form efficient in general.

The primary objective of this research is to provide evidence that there are two distinct layers of investor sentiments that can affect asset valuation models. The first is general market-wide sentiments, while the second is biased approaches toward specific assets.

To achieve the goal, the authors conducted a multi-step analysis of stock returns and constructed complex sentiment indices that reflect the optimism or pessimism of stock market participants. The authors used panel regression with fixed effects and a sample of the US stock market to improve the explanatory power of the three-factor models.

The analysis showed that both market-level and stock-level sentiments have significant contributions, although they are not equal. The impact of stock-level sentiments is more profound than market-level sentiments, suggesting that neglecting the stock-level sentiment proxies in asset valuation models may lead to severe deficiencies.

In contrast to previous studies, the authors propose that investor sentiments should be measured using a multi-level factor approach rather than a single-factor approach. The authors identified two distinct levels of investor sentiment: general market-wide sentiments and individual stock-specific sentiments.

This study aims to test mutual fund superiority, comparing the performance of 646 Islamic mutual funds with 475 ethical funds and conventional proxies.

This study uses statistical methods including paired t-statistics of independent samples, one-way Bonferroni test–analysis of variance–F-statistic for testing means equality, the chi-squared test for median equality and regression models corrected for heteroscedasticity. These methods are used to identify superiority of mutual funds and to validate the significance of the results.

The findings confirm the superiority of conventional funds over ethical funds and ethical funds over Islamic funds. Both ethical and Islamic funds, however, outperform conventional proxies during some recessionary periods. Moreover, stronger performance is recorded for Islamic funds in Europe and North America regions and across age and asset allocation categories, but limited support for reversal fund size, composition focus and reversed price effect.

These findings should assist investors when deciding to invest and motivate Islamic and ethical funds to improve their portfolio formation and asset allocation strategies set by their professional managers.

The originality of this study is in its comprehensive approach in that it compares the performance of funds after accounting for such characteristics as fund objectives, size, age, asset allocation, geographical investment focus, fund composition focus, share price levels and the effect of global crises. This study approach is not only original and productive in documenting Islamic funds’ performance for the past three decades (1990–2022) but can also update the literature on these characteristics collectively and individually.

Australian Securities Exchange (ASX) initial public offerings (IPOs) are an important source of early-stage capital and have also driven a substantial increase in main-board listed companies post-millennium. By contrast, Australian venture capital (VC) funding has remained largely dormant. The opposite has occurred in the US: IPOs have fallen by half, and VC funding has surged. The authors examine the reason for this divergence between ASX IPO and US VC systems that, with their supporting ecosystems, have many features in common and function similarly. The authors explore the potential factors that could explain the US VC surge vis-à-vis Australia's VC stagnation.

The authors’ analysis is predominantly qualitative. The authors describe the Australian listing process and its similar features and functions as for the prototypical VC. The authors also describe the developments in US VC driving its recent exceptional surge and highlight that such developments have not yet materialised on the Australian scene, where early-stage IPOs have served as a substitute.

The ASX's structure and ecosystem have been critical to its success in fostering early-stage main-board listings. While the US has succeeded in alternatively growing VC, there is an increasing concern that the latter has occurred partially because valuations are stretched, tax concessions for carried-interest capital gains are too high and corporate control benefits are becoming increasingly diluted. These developments could have important implications for Australia, where VC structures are currently being reviewed.

To the best of the authors’ knowledge, no prior study has attempted to bridge the broad differences in IPO and VC funding trends for early-stage companies in Australia and the USA.

This paper aims to investigate the dynamic relationship between the investor sentiment and the return of various sectors in the Chinese stock market.

The wavelet coherence and wavelet phase angle approaches are used to study the lead–lag associations between sentiment index and stock returns in a time–frequency way. The multiscale linear and nonlinear Granger causality tests are performed to explore whether there is a causality between them.

The empirical results show that during normal period, investor sentiment index has a stronger relationship with stock returns of industrials, consumer discretionary, health care, utilities, real estate and financial sectors. In crisis period, investor sentiment has a significant positive relationship with all industry sectors. In the short term, there is bidirectional causality between investor sentiment and stock returns of all sectors. In the medium and long run, almost all sector stock returns Granger-cause the investors' sentiment index but investor sentiment does not Granger-cause all sectors, which is in contrast to the developed markets.

The interindustry impact of investment sentiment on the stock market can help construct arbitrage portfolio by investors who are interested in Chinese stock market.

This paper focuses on the industry sector differences of investor sentiment impact on the Chinese stock market. As far as the authors know, this is the first paper to explore the time–frequency relationship between sentiment index and industry stock returns in China using the time–frequency method based on wavelet coherence, which considers the heterogeneity of different types of investors' responses to various economic and financial events.