ATTRIBUTES AND PERFORMANCE OF FUND MANAGEMENT COMPANIES: EVIDENCE FROM THE LARGEST SHARIAH-COMPLIANT FUND MARKETS

Background and Purpose: The study examines the effect of fund management companies’ (FMCs) attributes on FMC performance in the four countries with the largest number of Islamic funds from 2007 to 2018. Methodology: The study uses pooled regression analysis on 70 FMCs, comprising Saudi Arabia (25), Malaysia (20), Indonesia (14) and Pakistan (11). The sample is further divided into FMC with Islamic funds focused (IFFMC) and conventional funds focused (CFFMC). Findings: Only past flows are insignificantly related to performance. Both proxies for size positively relate to returns, but only in the case of Saudi Arabia. In Pakistan, performance improves with assets under management (AUM), while in Malaysia and Indonesia, an increasing number of funds negatively relate to performance. A relatively high number of better performing funds positively affect FMC and vice versa. Additionally, there are significant differences in the factors determining IFFMC and CFFMC performance, with the number of funds and AUM positively affecting the performance of IFFMC but not CFFMC. Poorly performing funds adversely affect CFFMC but not IFFMC. Contributions: This study provides useful information for investors using a top-down approach to FMC then fund selection, and for managers in evaluating the impact of factors like FMC scale and scope on performance. The impact of these attributes differs between CFFMCs and IFFMCs which lies in the performance differences commonly observed, at the FMC and fund level. Keywords: Islamic funds management industries, Islamic mutual fund, fund performance, Islamic finance. Cite as: Marzuki, A., Bani Atta, A. A., & Worthington, A. (2022). Attributes and performance of fund management companies: Evidence from the largest Shariah-compliant fund markets. Journal of Nusantara Studies, 7(1), 114-141. http://dx.doi.org/10.24200/jonus.vol7iss1pp114-141

You Learn When It Hurts: Evidence in the Mutual Fund Industry

This paper aims to contribute to the lack of research on the learning process of mutual fund markets. The empirical design is focused on the ability of the Spanish equity mutual fund industry to learn from its important errors. The choice of this industry is justified by both its relevance in the European mutual fund markets and some specific characteristics, such as the concentration and the banking control of the industry, which may affect the learning process. Our main objectives are to identify important trading errors in mutual fund management by applying three independent filters based on the relative importance of each decision, and then testing the evolution of these errors both at the industry level and at the fund family level. We apply the dynamic model of generalized method of moments (GMM), and we find an overall significant decrease in the percentage of important trading errors over time, thereby providing evidence of the global learning process of the industry. In addition, we find that a large number of fund families drive this evidence. Finally, we obtain that the family size and its dependence on financial groups do not seem to play significant roles in explaining the learning process. Therefore, we conclude that fund managers have incentives to learn from their important trading errors, in order to avoid them in future decisions, due to their serious negative consequences on fund performance, regardless of the characteristics of the families to which they belong.

Social interactions and mutual fund portfolios: the role of alumni networks in China

PurposeThis paper aims to investigate the influence of social interactions on mutual fund portfolios from the perspective of alumni network in China.Design/methodology/approachBased on a data set that consists of 162 actively managed equity funds in China during the time period of 2003–2014, this study employs multiple linear regression model to control for organization- and location-based interpersonal connections as well as other confounding factors and clarify the causality relationship between alumni networks of mutual fund managers and their portfolios.FindingsAfter controlling for organization- and location-based interpersonal connections, we find that mutual fund managers who graduated from the same college/university have more similar stock holdings and are more likely to buy or sell the same stocks contemporaneously. As a result, alumni managers exhibit a higher correlation of fund returns. Moreover, the effect of alumni relationship on mutual fund investments becomes weaker when more managers are connected within the network. We also find that valuable information is shared among alumni managers: (1) the average returns for the alumni common holdings portfolios is significantly higher than those for non-alumni holdings portfolios and (2) a long-short strategy composed of stocks purchased minus sold by alumni managers yields positive and significant risk-adjusted returns.Practical implicationsThe findings suggest that information dissemination among connected fund managers could be one of the driving forces for mutual fund herding behavior, and that a portfolio of funds whose managers are educationally connected could be highly exposed to certain stocks and risks.Originality/valueThis paper contributes to the growing finance literature addressing the influence of personal connections on information dissemination that specifically contributes to price formation. It corresponds more closely to Cohen et al. (2008), who investigate college alumni connections between fund managers and corporate board members. Since the authors simultaneously examine three potentially overlapped social networks, which are based on education, locality and fund family, the authors are able to disentangle their effects on fund managers' investment decisions. Moreover, the findings suggest that institutional investors make investment decisions based on share private information, and therefore, it also contributes to the literature on fund herding behaviors (Grinblatt et al., 1995; Wermers, 1999).

INVESTIGATING MEDIA AND COMMUNICATION CHANNEL SEARCH BEHAVIOUR OF RETAIL INVESTORS FOR INVESTMENT DECISION IN EMERGING FINANCIAL MARKETS

The purpose of this study aims at investigating the major sources of information which drives the investor’s behaviour in investment decisions in Indian Financial Markets. Diverse sources are classified into three categories, that is, financial advice, word-of-mouth communication and specialised press to investigate their effects on the investment behaviour of investors. A total of 258 investors filled a survey on a questionnaire in the National Capital Region of India by using the purposive sampling method. For analysis of data, PLS-SEM was applied on the software version 3.2.9. The key outcome of the study revealed that financial advice was considered as first choice (β = 0.265, p<0.000) to build their investment decision primarily on weekly basis followed by word-of-mouth communication (β = 0.154, p<0.05). Lastly, the mutual fund investors prefer financial newspapers and financial reports published by mutual fund regulatory body and their companies in India to do mutual funds investment. This study proposed a conceptual model in the literature of information search behaviour for mutual funds and contributes significantly to the mutual fund companies and investment agencies to market financial products in an effective manner for investors.

Cluster-Based Mutual Fund Classification and Price Prediction Using Machine Learning for Robo-Advisors

The rise of FinTech has been meteoric in China. Investing in mutual funds through robo-advisor has become a new innovation in the wealth management industry. In recent years, machine learning, especially deep learning, has been widely used in the financial industry to solve financial problems. This paper aims to improve the accuracy and timeliness of fund classification through the use of machine learning algorithms, that is, Gaussian hybrid clustering algorithm. At the same time, a deep learning-based prediction model is implemented to predict the price movement of fund classes based on the classification results. Fund classification carried out using 3,625 Chinese mutual funds shows both accurate and efficient results. The cluster-based spatiotemporal ensemble deep learning module shows better prediction accuracy than baseline models with only access to limited data samples. The main contribution of this paper is to provide a new approach to fund classification and price movement prediction to support the decision-making of the next generation robo-advisor assisted by artificial intelligence.