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Using data spanning 145 years for Sweden, the authors investigate the benefits of holding multi-family properties for investors who aim to hedge wage growth.

The authors assess the risk-adjusted excess return that results from adding multi-family properties to a mixed-asset portfolio that aims to track wage growth. The authors also analyse the macroeconomic determinants of asset returns. Finally, the authors test whether a causal relationship exists between the growth rate of real wages and that of real net operating income.

The benefits from holding multi-family properties are the greatest for low-risk allocation approaches. For more risky strategies, the role of real estate is more muted, and it varies greatly over time. Holding real estate was most beneficial during the first two decades of the 21st century. Multi-family properties are found to be the only asset class to be positively related to wage growth. The authors show that the net operating income acts as the transmission channel between wages and property returns.

The paper assesses whether the growing interest of pension funds for multi-family properties is warranted in the context of a portfolio that aims to track wage growth.

Using long term data makes it possible to use a rolling windows approach and hence to consider multiple outcomes for an allocation strategy over a typical investment horizon. This permits to assess the dispersion of performance across several periods rather than just one as is commonly done in the literature. The results show that the conclusions that would be drawn from looking at the past two or three decades of data differ substantially from those for earlier time periods.

The purpose of this paper is to unveil the main changes in the UAE’s policy towards Iran since its foundation in 1971. The UAE favored strategic hedging, extending its commercial and diplomatic relations with Iran, in addition to developing its military capabilities and maintaining military/security alliances with Saudi Arabia and the USA. However, the UAE started to reorient its policy towards Iran by adopting some sort of balancing strategy in the aftermath of the Arab Spring of 2011. This paper examines how and why the UAE had to change course and explores whether it would revert back to strategic hedging with Iran.

The study will be carried out based on a theoretical framework drawn from strategic hedging theory, a new structural theory in international relations, to examine the shifts in UAE policy towards Iran. Previous literature suggests that small states prefer hedging over balancing or bandwagoning. The authors also undertake a descriptive analysis and deploy a longitudinal within-case method to investigate changes in UAE policy towards Iran and identify the causal mechanisms behind these changes. That method allows investigating the impact of a particular event on a case by comparing the same case before and after that event occurred.

The main finding of this study is that the UAE hedging strategy towards Iran allowed maximizing the political and economic returns from the cooperation with Iran and mitigating the long-range national security risks without breaking up the consistent and beneficial ties with other regional and global powers. Hedging achieved the desired outcome, which is preventing direct military confrontation with Iran. Hard balancing, adopted by Abu Dhabi after the 2011 Arab Spring, has proved to have some negative effects, most importantly provoking Tehran. Some recent indicators suggest, though that the UAE may revert back to its long-established hedging policy towards Iran.

Strategic hedging is a new structural theory in international relation, although hedging behavior in states’ foreign policies is far from new. It is new enough, thus, not have been researched sufficiently, strategic hedging still needs theorizing and comparison. This paper highlights the importance of strategic hedging as the most appropriate strategy for small states. It provides an important contribution to the application of the theory to the case of UAE policy towards Iran. The paper also assesses the conventional wisdom that small states prefer hedging over balancing in the light of the changes in the UAE foreign policy since 2011.

This study aims to provide a comparative insight into the level of informational efficiency and irregularities of Shariah-compliant stocks and conventional stocks in three emerging markets, namely, China, Malaysia and Pakistan. The empirical evidence is provided for pre-crisis and crisis periods caused by the Covid-19 pandemic.

Informational efficiency is measured using the variance ratio (VR) Test developed by Kim (2006). The Approximate Entropy (ApEn) Metrics is used to investigate the level of irregularities in stock prices caused by the pandemic.

All the three emerging markets in the sample are not immune to the crisis caused by Covid-19 pandemic. The level of informational efficiency of both the Shariah-compliant and conventional stock is affected by the crisis. However, the former exhibits relatively high level of informational efficiency and stability in returns as compared to more volatility of conventional stocks.

This study provides market agents and policy makers with a robust assessment of the impact of the Covid-19 pandemic on informational efficiency of Shariah-compliant and conventional stocks. Relatively high informational efficiency of Shariah-compliant stocks indicates that they are more transparent and that investors can trust the Shariah-compliant stocks more. This higher level of transparency and trust leads to more steady returns and lower levels of risk even during turbulent time like Covid-19. Investors can gain superior returns by conducting fundamental analysis and investing in index funds.

To the best of the authors’ knowledge, this is the first study that highlights the difference in informational efficiency of conventional stocks and Shariah-compliant stocks in the crisis period caused by Covid-19. Unlike previous studies, this study uses firm level data which enables firm-wise assessment of informational efficiency.

Political risk is an important determinant of portfolio returns. The basic purpose of this study is to revisit the importance of political risk in a constrained portfolio, namely, a Shariah-compliant equity portfolio (SCEP). Furthermore, the performance of such a constrained portfolio is also compared with a conventional portfolio that invests in all stocks.

The portfolios are constructed from stock-level data and invested in 61 international markets. The set of Shariah-compliant stocks is obtained with screening guidelines of Dow Jones Islamic Market indices. The weights of each constituent in both Shariah-compliant and conventional portfolios are driven by its relative exposure to political risk for the period 1996–2018.

Results show that, compared to conventional investors, Shariah-compliant investors gain substantial benefits when the allocation decision is based on political risk. A Shariah-compliant portfolio outperforms its conventional counterpart by 7.98% annually when tilted toward politically stable countries. The economic benefits further increase to 804 basis points when the portfolio allocates more funds to politically unstable countries. The tilted SCEP successfully reduces the downside risk, resulting in improved financial performance stability.

To the best of the authors’ knowledge, this is the first effort of its nature to highlight the importance of political risk in the context of SCEPs.

We propose a risk factor for idiosyncratic entropy and explore the relationship between this factor and expected stock returns.

We estimate a cross-sectional model of expected entropy that uses several common risk factors to predict idiosyncratic entropy.

We find a negative relationship between expected idiosyncratic entropy and returns. Specifically, the Carhart alpha of a low expected entropy portfolio exceeds the alpha of a high expected entropy portfolio by −2.37% per month. We also find a negative and significant price of expected idiosyncratic entropy risk using the Fama-MacBeth cross-sectional regressions. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

We propose a risk factor of idiosyncratic entropy and explore the relationship between this factor and expected stock returns. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

Considering the frequency of extreme events, enhancing the global financial system's stability has become crucial. This study aims to investigate the contagion effects of extreme risk events in the international commodity market on China's financial industry. It highlights the significance of comprehending the origins, severity and potential impacts of extreme risks within China's financial market.

This study uses the tail-event driven network risk (TENET) model to construct a tail risk spillover network between China's financial market and the international commodity market. Combining with the characteristics of the network, this study employs an autoregressive distributed lag (ARDL) model to examine the factors influencing systemic risks in China's financial market and to explore the early identification of indicators for systemic risks in China's financial market.

The research reveals a strong tail risk contagion effect between China's financial market and the international commodity market, with a more pronounced impact from the latter to the former. Industrial raw materials, food, metals, oils, livestock and textiles notably influence China's currency market. The systemic risk in China's financial market is driven by systemic risks in the international commodity market and network centrality and can be accurately predicted with the ARDL-error correction model (ECM) model. Based on these, Chinese regulatory authorities can establish a monitoring and early warning mechanism to promptly identify contagion signs, issue timely warnings and adjust regulatory measures.

This study provides new insights into predicting systemic risk in China's financial market by revealing the tail risk spillover network structure between China's financial and international commodity markets.

Factor investment is booming in global asset management, especially environmental, social, and governance (ESG), dividend yield, and volatility factors. In this chapter, we use data from the US securities market from 2003 to 2019 to predict dividends and volatility factors through machine learning and historical data–based methods. After that, we utilize particle swarm optimization to construct the Markowitz portfolio with limits on the number of assets and weight restrictions. The empirical results show that that the prediction ability using XGBoost is superior to the historical factor investment method. Moreover, the investment performance of our portfolio with ESG, high-yield, and low-volatility factors outperforms baseline methods, especially the S&P 500 ETF.

On completion of the case study, students will understand the following learning objectives: the characteristics of quantitative easing (QE) and when it may be appropriate to implement QE; how QE differs from a conventional bond purchasing programme; the impact of direct financing of the fiscus by the central bank on its independence; how the macro-economic and political environments affect and influence national economic policy; the difference between traditional and unconventional monetary policies and potential implications for an economy like South Africa. The learnings from this case study can be used in other global economic environments, particularly in emerging markets. This case study provides valuable insights into decision-making, institutional independence, policy coordination, deficit financing, causes and consequences of price inflation, risks relating to monetary instability and the correct application of monetary policy.

After the announcement of the COVID-19-related lockdown in March 2020 and the subsequent slow-down of economic activity in South Africa, the South African Reserve Bank (SARB) had to consider appropriate macro-economic tools to ensure both price and financial stability in South Africa. The macro-economic policy tools had to be considered in light of the South African economic context, which included acknowledgement of South Africa’s debt crisis and slow economic growth. The central bank responded by introducing the following measures: reducing interest rates to a record low of 3.5% to give consumers financial relief and to promote spending in the economy; purchasing government bonds in the secondary markets to stabilise financial markets; facilitating the loan guarantee scheme that was aimed at providing financial relief to small- and medium-sized enterprises; relaxing the capital and liquidity adequacy requirements that commercial banks are required to meet; and ensuring availability of liquidity to banks through facilities such as the weekly repo auctions. However, despite introducing these interventions, the SARB faced calls from politicians, analysts and academics to do more. Various commentators argued that the SARB could introduce QE and directly finance government spending by purchasing government bonds. Some commentators argued that the reluctance of the SARB to pursue these suggestions was a result of the close alignment and relationship between the SARB and National Treasury. The dilemma faced by Governor Lesetja Kganyago of the SARB was threefold, namely, whether it was appropriate for the central bank to pursue the initiatives and, if so, whether the bank could pursue them without compromising its independence, and if the introduction of those initiatives would not adversely affect the ability of the central bank to fulfil its mandate of price stability and financial stability. In this regard, the governor and his executive team were required to consider the long-term implications of introducing the initiatives on consumer price inflation, independence of the SARB and the appropriate use of monetary policy tools to fulfil the central bank’s mandate. But the question was: What policies should the governor favour?

This case study is based on various macro-economic theories. Therefore, it would be useful to teach this case study in macro-economic courses in the following programmes: master’s in business administration, bachelor of commerce, bachelor of economic sciences and business science studies, as well as on executive education programmes, which consider macro-economic policy. In general, students who undertake economics, business and general management, finance, legal, commerce and banking studies could learn from this case study.

Teaching notes are available for educators only.

CSS 3: Entrepreneurship.

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.