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This paper aims to examine potential diversification benefits between Eurozone (i.e. EURO STOXX 50) and key Asia markets: HSI (Hong Kong), KOSPI (South Korea), NIKKEI 225 (Japan) and TSEC (Taiwan). The sample covers the period from 04-01-2008 to 19-10-2023 in daily frequency.

The empirical investigation is based on the wavelet coherence analysis, which is a localized correlation coefficient in the time and frequency domain.

The results provide evidence that long-term diversification benefits exist between EURO STOXX and NIKKEI, EURO STOXX and KOSPI (after 2015) and there are signs for the pair and EURO STOXX-TSEC (after 2014). During the short term, there are signs of diversification benefits during the sample period. However, during the medium term, the diversification benefits seem to diminish.

These results have crucial implications for investors regarding the benefits of international portfolio diversification.

The purpose of this paper is to examine the evolution of interconnectedness of European insurers among themselves, as well as with other non-financial firms, for the period 2000–2021 and to analyze the stock return movements around the costliest catastrophic events (hurricanes) in the past two decades.

This paper follows the “simple” approach of Patro et al.(2013) and examines the daily stock return correlations of the largest 30 insurers and the largest 30 non-financial firms headquartered in Europe. In addition, the study uses event study methodology to examine stock return movements around the costliest hurricanes.

We find that the European insurance sector has become highly interconnected during the past two decades; however, its increasing connectedness with non-financial firms is limited to a few firms. In addition, we find weak evidence of the destabilizing effects of catastrophic events on European insurers and non-financial firms; however, the potential for cat risk contagion effects exists as the insurance industry becomes heavily interconnected.

The extant literature is largely concerned with the contribution of the insurance sector to the systemic risk of the financial sector. We focus on a specific region (Europe) and analyze the evolution of interconnectedness of the largest insurers within the insurance sector as well as with the largest non-financial firms encapsulating important crisis periods. In addition, we relate to the literature that examines the market reactions around catastrophic events to test the relevance of traditional insurance activities in instigating potential contagion shocks.

The purpose of this study is to explore the intricate relationship between oil prices, house prices in the UK and Norway, and the mediating role of gold and stock prices in both the short- and long-term, unraveling these complex linkages by employing an empirical approach.

This study benefits from a comprehensive set of econometric tools, including a multiequation vector autoregressive (VAR) system, Granger causality test, impulse response function, variance decomposition and a single-equation autoregressive distributed lag (ARDL) system. This rigorous approach enables to identify both short- and long-run dynamics to unravel the intricate linkages between Brent oil prices, housing prices, gold prices and stock prices in the UK and Norway over the period from 2005:Q1 to 2022:Q2.

The findings indicate that rising oil prices negatively impact house prices, whereas the positive influence of stock market performance on housing is more pronounced. A two-way causal relationship exists between stock market indices and house prices, whereas a one-way causal relationship exists from crude oil prices to house prices in both countries. The VAR model reveals that past housing prices, stock market indices in each country and Brent oil prices are the primary determinants of current housing prices. The single-equation ARDL results for housing prices demonstrate the existence of a long-run cointegrating relationship between real estate and stock prices. The variance decomposition analysis indicates that oil prices have a more pronounced impact on housing prices compared with stock prices. The findings reveal that shocks in stock markets have a greater influence on housing market prices than those in oil or gold prices. Consequently, house prices exhibit a stronger reaction to general financial market indicators than to commodity prices.

This study may have several limitations. First, the model does not include all relevant macroeconomic variables, such as interest rates, unemployment rates and gross domestic product growth. This omission may affect the accuracy of the model’s predictions and lead to inefficiencies in the real estate market. Second, this study does not consider alternative explanations for market inefficiencies, such as behavioral finance factors, information asymmetry or market microstructure effects. Third, the models have limitations in revealing how predictors react to positive and negative shocks. Therefore, the results of this study should be interpreted with caution.

These findings hold significant implications for formulating dynamic policies aimed at stabilizing the housing markets of these two oil-producing nations. The practical implications of this study extend to academics, investors and policymakers, particularly in light of the volatility characterizing both housing and commodity markets. The findings reveal that shocks in stock markets have a more profound impact on housing market prices compared with those in oil or gold prices. Consequently, house prices exhibit a stronger reaction to general financial market indicators than to commodity prices.

These findings could also serve as valuable insights for future research endeavors aimed at constructing models that link real estate market dynamics to macroeconomic indicators.

Using a variety of econometric approaches, this paper presents an innovative empirical analysis of the intricate relationship between euro property prices, stock prices, gold prices and oil prices in the UK and Norway from 2005:Q1 to 2022:Q2. Expanding upon the existing literature on housing market price determinants, this study delves into the role of gold and oil prices, considering their impact on industrial production and overall economic growth. This paper provides valuable policy insights for effectively managing the impact of oil price shocks on the housing market.

We explore how sustainable management education (SME) can help prepare future leaders to manage crises effectively. Precisely, the intricacies of articulating moral and economic imperatives for businesses in a manner that engages students in sustainable behavior are a serious challenge for SME. We study how to integrate reminders of moral and economic imperatives in a socially responsible investment (SRI) stock-picking simulation created for SME.

Adopting an experimental design, we analyzed how the reminders affected the average environment social governance (ESG) integration in the portfolios of 127 graduate students in finance over a twelve-week period.

Our results show how essential it is to balance the two imperatives. The highest level of sustainable investment is attained when utilizing both reminders.

Our findings have practical implications for implementing and organizing SME in business schools to educate responsible leaders who are able to effectively manage crises. Learning responsible management is most effective when students are exposed to the inherent tension between moral and economic imperatives. Hence, our findings corroborate the win-win conception of SME.

No management decision study has experimentally measured the effects of SME practices on students' actual behavior. Our research fills this gap by complementing previous studies on the effectiveness of teaching practices, first by drawing on behavioral sciences and measuring changes in students' actual sustainability behavior and second by introducing moral and economic imperatives into an innovative teaching resource (TR) dedicated to SME.

Machine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a systematic literature review of deep learning applications for portfolio management. The findings are likely to be valuable for industry practitioners and researchers alike, experimenting with novel portfolio management approaches and furthering investment management practice.

This review follows the guidance and methodology of Linnenluecke et al. (2020), Massaro et al. (2016) and Fisch and Block (2018) to first identify relevant literature based on an appropriately developed search phrase, filter the resultant set of publications and present descriptive and analytical findings of the research itself and its metadata.

The authors find a strong dominance of reinforcement learning algorithms applied to the field, given their through-time portfolio management capabilities. Other well-known deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN) and its derivatives, have shown to be well-suited for time-series forecasting. Most recently, the number of papers published in the field has been increasing, potentially driven by computational advances, hardware accessibility and data availability. The review shows several promising applications and identifies future research opportunities, including better balance on the risk-reward spectrum, novel ways to reduce data dimensionality and pre-process the inputs, stronger focus on direct weights generation, novel deep learning architectures and consistent data choices.

Several systematic reviews have been conducted with a broader focus of ML applications in finance. However, to the best of the authors’ knowledge, this is the first review to focus on deep learning architectures and their applications in the investment portfolio management problem. The review also presents a novel universal taxonomy of models used.