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This study attempts to conduct a comparative analysis between dynamic and static asset allocation to achieve the long-term target return on asset liability management (ALM). This study conducts asset allocation using the ex ante expected rate of return through the outlook of future economic indicators because past economic indicators or realized rate of returns which are used as input data for expected rate of returns in the “building block” method, most adopted by domestic pension funds, does not fully reflect the future economic situation. Vector autoregression is used to estimate and forecast long-term interest rates. Furthermore, it is applied to gross domestic product and consumer price index estimation because it is widely used in financial time series data. Based on asset allocation simulations, this study derived the following insights: first, economic indicator filtering and upper-lower bound computation is needed to reduce the expected return volatility. Second, to reach the ALM goal, more stocks should be allocated than low-yielding assets. Finally, dynamic asset allocation which has been mirroring economic changes actively has a higher annual yield and risk-adjusted return than static asset allocation.

The purpose of this paper is to present a review of the student managed investment fund at the School of Business, University of Connecticut.

The authors trace the history and growth of the fund and identify the special features and dimensions that have contributed to its success.

The operation of the fund is a constantly evolving program and the authors discuss the important changes and improvements made in the program since its inception in the early 2000s in response to growth in the number of finance majors, new career opportunities in the field of investments and most importantly, the strength of capital markets and the development of new instruments in the capital markets. The authors also discuss the common features of over 300 student funds in the USA. The authors close with a discussion of the limitations and constraints the fund advisors at, and possibly, at other schools, face in the management and administration of the fund, and also what developments and adjustments the authors expect to see in these funds in the future.

The authors combine extensive analyses of fund history and performance. The authors also provide some suggestions for the future direction and priorities for student funds.

This paper aims to replicate 148 anomalies and to examine whether the performance of the Korean market anomalies is statistically and economically significant. First, the authors observe that only 37.8% anomalies in the universe of the KOSPI and the KOSDAQ and value-weighted portfolios have t-statistics that exceed 1.96. When the authors impose a higher threshold (an absolute value of t-statistics of 2.78), only 27.7% of the 148 anomalies survive. Second, microcaps have large impacts. The results vary significantly depending on whether the sample included stocks in the KOSDAQ and whether value-weighted or equal-weighted portfolios are used. The results suggest that data mining explains large portion of abnormal returns. Any tactical asset allocation strategies based on market anomalies should be applied very cautiously.

As of March 2021, the National Pension Service (NPS) is the world’s 3rd largest pension fund with 872.5tn won (KRW) in management. Recently, the NPS proposed a policy to gradually reduce the proportion of domestic stocks in the portfolio in the future. This change in the asset allocation strategy is related to the NPS’s exit strategy for domestic stocks. This study aims to examine the market impact cost asymmetry between buys and sells of the NPS and suggest a trading strategy for mitigating the market impact cost. The results are as follows. First, there is an asymmetry between buys and sells in the market impact cost of the NPS. The market impact cost of the NPS is gradually increasing over time. In particular, the market impact cost from selling has increased significantly in recent years. Second, past returns, volatility, liquidity and trading intensity can be found as external factors affecting the asymmetric market impact cost of the NPS. Although there is no difference between the buying and selling ratios of the NPS, the market impact cost from sells is relatively higher than that from buys. Third, after controlling for the order execution size of the NPS, the longer the trade execution period, the lower the market impact cost. This result implies that the strategy of splitting orders as a way to reduce the market impact cost is effective. The trading behavior of the NPS directly or indirectly affects other investors. If the sell of the NPS incurs excessive market impact cost, the negative impact on the stock price will be further exacerbated. Therefore, it is necessary for the NPS to reduce the market impact cost through split trading in executing orders in the domestic stock market. Findings of this study provide implications for countermeasures and long-term management strategies that can minimize the market impact cost of the NPS in the process of reducing the proportion of domestic stocks in the future.

The aim of the study was to analyze the performance of Black-Litterman (BL) portfolios using a views estimation procedure that simulates investor forecasts based on technical analysis.

Ibovespa, S&P500, Bitcoin and interbank deposit rate (IDR) indexes were respectively considered proxies for the national, international, cryptocurrency and fixed income stock markets. Forecasts were made out of the sample aiming at incorporating them in the BL model, using several portfolio weighting methods from June 13, 2013 to August 30, 2022.

The Sharpe, Treynor and Omega ratios point out that the proposed model, considering only variable return assets, generates portfolios with performances superior to their traditionally calculated counterparts, with emphasis on the risk parity portfolio. Nonetheless, the inclusion of the IDR leads to performance losses, especially in scenarios with lower risk tolerance. And finally, given the impact of turnover, the naive portfolio was also detected as a viable alternative.

The results obtained can contribute to improve investors practices, specifically by validating both the performance improvement – when including foreign assets and cryptocurrencies –, and the application of the BL model for asset pricing.

The main contributions of the study are: performance analysis incorporating cryptocurrencies and international assets in an uncertain recent period; the use of a methodology to compute the views simulating the behavior of managers using technical analysis; and comparing the performance of portfolio management strategies based on the BL model, taking into account different levels of risk and uncertainty.

This study aims to conduct an empirical investigation to assess the hedge, diversifier and safe-haven properties of different environmental, social and governance (ESG) assets (i.e. green bonds and ESG equity index) vis-à-vis conventional investments (namely, equity index, gold and commodities).

The authors examine the sample period 2007–2021 using the bivariate cross-quantilogram (CQG) analysis and a dynamic conditional correlation (DCC) multivariate generalized autoregressive conditional heteroskedasticity (GARCH) experiment with several extensions.

The evidence shows that the analyzed ESG investments exhibit mainly diversifying features depending on the asset class taken as a reference, with some potential hedging/safe-haven qualities (for the green bond) in peculiar timespans. Therefore, the results suggest that investors might consider sustainable investing as a new measure of risk reduction, which has interesting implications for both portfolio allocation and policy design.

To the best of the authors’ knowledge, this study is the first that empirically investigates at once the dependence between different ESG investments (i.e. equity and green bond) with different conventional investments such as gold, equity and commodity market indices over a large sample period (2007–2021). Well-suited methodologies like the bivariate CQG and the DCC multivariate GARCH are used to capture the spillover effect and the hedging/diversifying nature, even in temporary contexts. Finally, a global perspective is used.

Recently, socially and responsible investments (SRI) have constantly grown becoming a highly discussed issue. Therefore, the main purpose of this paper is to better understand if environmental social governance (ESG) criteria integration in investment strategies can support the transition of finance toward a more sustainable growth.

An explorative analysis based on a multiple case study has been conducted and addressed by a content analysis on the Key Investors Information Documents (KIIDs) that the sample companies published for 2020.

The achieved results demonstrated that the case companies differently integrated ESG into their SRI; thus, if some of them are quite near to a full integration, the others demonstrated less than a full commitment with ESG. This seems to be mainly due to the different approach that asset management companies (AMCs) and/or managers have adopted for integrating ESG criteria.

Even though the achieved results offered some interesting insights for asset managers, the explorative and qualitative nature of this study and the small sample investigated somewhat limits it.

AMCs, consultants and managers in developing and implementing their SRI strategy could be much more focused on the importance of ESG integration for the transition toward a more responsible and sustainable finance (micro-level) as well as a more sustainable development (macro-level).

The paper provides new insights into the essence of SRI strategies and their potential to contribute to sustainable development. Thus, it tries to shed new lights on the role that ESG can have to stimulate and support investment decisions and, in so doing, contributing to make finance grow more sustainable.

This paper aims to identify if there is an impact of the rating announcements issued by the agencies on the returns of the stocks of Brazilian companies listed on Brasil Bolsa Balcão, from August 2002 to August 2018, identifying which types of announcement (upgrade, downgrade or the same initial classification) cause variations in prices around the date of disclosure of the rating.

The event study methodology was applied to verify the market reaction around the announcement dates in a 21-day event window (−10, +10). The market model was used to calculate the abnormal returns (ARs), and subsequently, the accumulated ARs.

The hypotheses tests allowed to verify that the accumulated ARs are different, before and after the three types of rating announcements (upgrades, downgrades and the same classification); in upgrades, the mean of accumulated ARs increases in the days before the event, while in downgrades, this increase occurs after the event. This paper concluded that the rating announcements have an impact on the return of stock of the Brazilian market and that the market reaction occurs most of the time before the event happens, which indicates that the market can anticipate the information contained in the changes in credit ratings.

The results have considerable implications for portfolio managers, institutional investors and traders. It facilitates investment decision-making in the face of rating classification announcements. Market participants can pay more attention to their investment strategies and asset allocation during periods of risk rating announcements. Additionally, traders can understand the form of investment strategy for superior earnings.

The importance of the study is related to the fact that the results may explain the causes of specific movements in the Brazilian financial market related to a source of information that may or may not be able to influence the decisions of the financial agents that operate in this market. The justification is centred on the idea that, for investors who somehow react to the announcements, it is relevant to understand the impact of rating classifications on companies, as access to such information allows for more conscious decision-making.

This paper aims to examine the performance of Islamic and conventional stocks listed at the Pakistan Stock Exchange by using both parametric and non-parametric approaches. The motivation is to do risk-return analysis of Islamic stock prices and conventional stock prices.

It uses various measures of performance, e.g. Sharpe ratio, Treynor ratio, Jensen's alpha, beta, generalized auto-regressive conditional heteroskedasticity and stochastic dominance. Using the Karachi Meezan Index-30 (KMI-30) and the Karachi Stock Exchange Index-30 (KSE-30) as proxies for Islamic and conventional stock prices, respectively, it examines the performance of Islamic and conventional stocks. The daily data of KMI-30 and KSE-30, covering period from June 9, 2009 to June 20, 2020 are used.

The results show that the overall KMI-30 outperforms the KSE-30. The returns of the KMI-30 are greater than the KSE-30. However, the risk and volatility of the KMI-30 and KSE-30 are similar. Further, the KMI-30 has higher excess returns per unit of total risk than the KSE-30. But both indexes have similar excess returns per unit of systematic risk. Moreover, the KMI-30 returns have stochastically dominance over the KSE-30 returns. These results reveal that the Islamic index performs better than the conventional index.

The findings provide several practical implications in financial and investment decisions making by investors, managers and policymakers such as strategies for asset allocation and investment. Further, in risk management, it provides guidance for allocating portfolios and managing risk. The investment in Islamic stocks may mitigate potential risk within asset portfolios.

This research is unique in its approach to the analysis of the performance comparison of conventional and Islamic stock by using comprehensive parametric and non-parametric estimation techniques. Such research has not been undertaken in the Pakistan's equity market since.

This study analyzes small-sized asset owners’ optimal choice problems in selecting an outsourced chief investment officer (OCIO). While large-sized asset owners can select OCIOs through procurement auctions, it is difficult for small-sized asset owners to use this method. Instead, they access OCIO services by participating in an investment pool or utilizing OCIO funds. In this study, the authors compare the two OCIO selection methods. The authors construct an agent-based model for OCIO selection to reflect the heterogeneity in production efficiency and preferences. The results of this study imply that when the market has enough investment pools, the utility of all small-sized asset owners increases. To enhance the growth in the OCIO market, the investment pool should represent the preferences of small-sized asset owners and enable individual owners to find an appropriate OCIO.