The Journal of Index Investing

10.3905/jii ◽  
2019 ◽  
Keyword(s):  
Author(s):  
Jonathan Brogaard ◽  
Matthew C. Ringgenberg ◽  
David Sovich

2003 ◽  
Vol 12 (1) ◽  
pp. 81-91 ◽  
Author(s):  
Scott D. Below
Keyword(s):  

Author(s):  
C. Alteen ◽  
Veit Wohlgemuth

Actuality of the study: Mutual funds are a favourite investment product among many investors. They provide a simple means of diversification, especially for those with smaller amounts of capital, and the popularity of mutual funds has increased with the success of the marketing efforts behind them.Purpose: This study evaluates the performance of actively managed and index mutual funds within the Canadian equities market.Findings: As index investing has increased in popularity, and other markets have become more connected and open, there is a need for research on equity mutual funds in countries outside the US.Originality / Value: The majority of previous research on index funds and actively managed mutual funds is focused on the US market and related indexes such as the S&P 500.Practical implications: This study suggests that, on average, active funds in Canada fail to beat their benchmarks net (but not gross) of the common fee or management expense ratio. Surprisingly, this research finds no positive relationship between higher fees and better gross performance. Actively managed funds also have poorer performance over the long term. This study finds that investors would be better off purchasing low cost index funds as they provide a more secure return.Future research: This study endorses research on other markets with inclusion of additional variables in order to explain gross performance and secure returns.


Author(s):  
Georgy Chabakauri ◽  
Oleg Rytchkov

2003 ◽  
Vol 29 (2) ◽  
pp. 113-123 ◽  
Author(s):  
Jimmy Liew
Keyword(s):  

2013 ◽  
Vol 303-306 ◽  
pp. 1595-1598
Author(s):  
Li Na Ni ◽  
Jin Quan Zhang

Index investing is an important issue for researchers and practitioners. This paper proposes an index portfolio optimization model for index investing via employing CSI 300 as underlying index. Firstly, a self-organizing neural network clustering model is constructed to complete the stock clustering based on stock trend which regards stock price as input. The index portfolio optimization model is proposed to determine the optimal investment proportion of each cluster sampling and achieve the minimum tracking error. The constraint BP algorithm is improved to benefit the optimization calculation of stock weights. Empirical results show that our approach achieves smaller tracking error and better index tracking effect than the random sampling.


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