‘Smart’copycat mutual funds:on the performance of partial imitation strategies

Using a novel measure of stock-level trade imitation,we uncover‘smart’copycats:fund managers that use their own information when beneficial,and otherwise imitate other managers’better trades.Contrary to previous research,we find that these partial imitation strategies lead to outperformance.Our‘Copycat Score’combines the propensity to imitate and to lead trades.Funds at the high and low ends of the score outperform all others.The Copycat Score is persistent in time,related to other measures of skill,and a good predictor of fund performance.We conclude that smart copycatting is another skill of successful fund managers.

How mutual funds respond to asymmetric feedback tradingin China’s stock market

Previous studies show that individual investors play a dominant role in China's stock market.Their behavior of chasing-rise being stronger than killing-fall leads to asymmetry of feedback trading.Our article investigates how mutual funds react to this market force.Using China's stock and fund data from 2003 to 2019,we find that mutual funds tend to hold fewer shares when asymmetric feedback trading of the relevant stock gets more intense.This negative relationship is robust after controlling past returns,turnover rates,and firm risk factors,moreover,it attenuates when the market sentiment is bullish or when stocks are allowed short-selling.Further results show that mutual funds'selling towards asymmetric feedback trading does not make excess return but leads to significant risk reduction.Our findings may be related to uncertainty associated with asymmetric feedback trading,and thus support the limit market participation theory from the second largest stock market.

Information asymmetry, mutual funds and earnings management: Evidence from China

This paper investigates how information asymmetry and mutual fund ownership affect listed companies' earnings management. We show that(1) reducing information asymmetry improves firms' earnings management behavior;(2)relative to short-term mutual funds, long-term mutual funds promote earnings quality by adopting a monitoring role; and(3) by dividing firms into high/low information asymmetry groups, we find that the information environment significantly increases the effect of long-term mutual funds on firms' earnings management. In this paper, we provide new evidence for the role that institutional investors play in a typical emerging capital market. Our results have clear policy implications: to increase earnings quality, it is essential to improve information transparency and develop long-term institutional investors.

Modeling the Spatial-temporal Characteristics of Mutual Funds’Herd Behavior

Herd behavior in financial markets often leads to unjustified macroscopic phenomena.However,despite existing studies on modeling herd behavior,how it varies across individual agents and over time remains unclear.We show that herd behavior in mutual fund companies can be understood from the functional networks representing interactions inferred from investment similarities.Specifically,in this paper,the spatial characteristics of herd behavior stand for the topology relationships of observations in networks.We analyze the collective dynamics of mutual fund investment from 2003 to 2019 in China using the language of network science and show that herding behavior accompanies this industry's development but dwindles after the 2015 Chinese market crash.By integrating community detection analysis,we found an increased degree of coherence in the collective herding behavior of the system,even though the localization of herding behavior decreases for clusters of mutual fund companies when the systemic risk builds up.Further analysis showed that herding behavior impacts the payoff of individual fund companies differently across years.The spatial-temporal changes of herding behavior between mutual funds presented in this paper shed light on the debate of individual versus systemic risk and,thus,could interest regulators and investors.

Investigating the Islamic and Conventional Mutual Fund Performance: Evidence From Malaysia Equity Market

The phenomena of financial crisis (2007-2008) shows a significant breakdown of US$16 trillion in conventional financial industry. This provides an opportunity for Islamic capital market to drive the global asset growth investments to investors. Previous studies signal mixture results on Islamic mutual fund (IMF) and conventional mutual fund (CMF) performance. This study aims to analyze the performance of 200 IMFs and CMFs from 2007 to 2015. The sub-sample period of 2007 to 2015 will be compared to the era of financial crisis from 2007-2008. Findings show all types of mutual funds are performed throughout 2007 until 2015. The results provide information that would benefit the investors and market players in asset funds selection.

Empirical study on mutual fund objective classification

Mutual funds are usually classified on the basis of their objectives. If the activities of mutual funds are consistent with their stated objectives, investors may look at the latter as signals of their risks and incomes. This work analyzes mutual fund objective classification in China by statistical methods of distance analysis and discriminant analysis; and examines whether the stated investment objectives of mutual funds adequately represent their attributes to investors. That is, if mutual funds adhere to their stated objectives, attributes must be heterogeneous between investment objective groups and homogeneous within them. Our conclusion is to some degree, the group of optimized exponential funds is heterogeneous to other groups. As a whole, there exist no significant differences between different objective groups; and 50% of mutual funds are not consistent with their objective groups.

Evaluation of the robusticity of mutual fund performance in Ghana using Enhanced Resilient Backpropagation Neural Network(ERBPNN)and Fast Adaptive Neural Network Classifier(FANNC)

Mutual fund investment continues to play a very important role in the world financial markets especially in developing economies where the capital market is not very matured and tolerant of small scale investors.The total mutual fund asset globally as at the end of 2016 was in excess of$40.4 trillion.Despite its success there are uncertainties as to whether mutual funds in Ghana obtain optimal performance relative to their counterparts in United States,Luxembourg,Ireland,France,Australia,United Kingdom,Japan,China and Brazil.We contribute to the extant literature on mutual fund performance evaluation using a collection of more sophisticated econometric models.We selected six continuous historical years that is 2010-2011,2012-2013 and 2014-2015 to construct a mutual fund performance evaluation model utilizing the fast adaptive neural network classifier(FANNC),and to compare our results with those from an enhanced resilient back propagation neural networks(ERBPNN)model.Our FANNC model outperformed the existing models in terms of processing time and error rate.This makes it ideal for financial application that involves large volume of data and routine updates.

Corporate managers,price noise and the investment factor

This study investigates the impact of flows between bond and equity funds on investment factors over the period 1984–2015.It determines contemporaneous mispricing effects and a statistical reversal relation between these flows and both legs of the investment factor.The statistical reversal relationship between previous flows and the investment factor is economically significant.A one-standard-deviation shock to flows causes a 0.29%decrease in investment factor returns,which are reversed within 5 months.A trading strategy based on signals from past flows and the investment factor outperforms the market by 0.68%in the months following positive flows and produces significant alphas after accounting for well-known equity risk factors.The findings are interpreted as evidence in favor of a behavioral explanation,in which sentiment influences actual managerial decisions.When retail investors and managers are swept up in market euphoria,retail investors shift their holdings from bond to equity mutual funds,and high-investment firms invest more aggressively.Market-level euphoria has a different impact on high-and low-investment firms,and thus the investment factor can be influenced.Hence,the mispricing occurs during these periods,and the reversal relationship is especially pronounced for a high-investment portfolio versus a low-investment portfolio.As a result,during the months following periods of positive flows,the investment factor outperforms the market factor.Interestingly,this study’s measure of flows,which serves as a proxy for market-level euphoria,outperforms other measures of investor sentiment.

Development and Performance Trends of Malaysian Unit Trusts

Unit trust is a collective investment scheme for investors, who seek to meet their long-term investment objectives for better returns with diversified portfolios. Unit trust is one of the largest sources of funding in Malaysia and plays a significant role in financial development and economic growth. The objective of this paper is to provide an overview of the development and the performance trends of unit trust investment schemes in Malaysia. There are several types of unit trust products available in Malaysia： both conventional as well as Islamic unit trust schemes （UTSs）. The rapid growth of investment management industry over the last 10 years was largely driven by the unit trust industry, in which the net asset value （NAV） grew annually by 18.0% from RM43.3 billion in 2000 to RM226.8 billion in 2010. The penetration rate of the unit trust industry rose from 10.0% in 2000 to 17.8% in 2010.

How Product Importance Perception, Need for Cognition and Enduring Involvement Influence Investors＇ Knowledge of Investment Products？

The lack of proper knowledge about investment products can have a large negative influence on the financial well being of investors. Inspire of that, there is a dearth of studies conducted, specifically to investigate the factors influencing the knowledge of investors about investment products. This paper propose a model and investigates the direct and indirect （mediated through investor＇s enduring involvement with product） influence of investor＇s product importance perception （PIP） and need for cognition （NFC） on investor＇s knowledge of investment products. The hypothesized relationship is empirically validated in the context of mutual fund schemes （MFs）. Survey conducted on the sample of 268 MF investors suggests that there is an insignificant direct influence of investor＇s PIP and NFC on investor＇s knowledge of investment product （KIP）. However, when mediated through enduring involvement with product （EIP）, the influence was found to be significant. The results suggest that EIP perfectly mediates the influence of PIP and NFC on investor＇s KIP. The relevance of the results to the policy makers is also discussed.

Investigain:A Productive Asset Management Web Application

The Investigain is a progressive web application to make mutual funds investments through a Systematic Investment Plan.The application utilizes the web’s modern capabilities,such as Asynchronous JavaScript and XML(AJAX),JavaScript,and Hypertext Marker Language(HTML5).The application also uses a powerful relational database management system,such as MySQL,to display asset management information.The application has two portals,one for investors and one for a particular asset manager or asset management company.Each investor has an account in the investor portal.The investor can view his/her profile,current balance,balance history,dividends,the units of mutual funds bought,unit price,the value of each mutual fund,and can pay installments using an embedded online payment gateway.Asset managers can monitor all investments,manage user accounts,and reimburse dividends using the admin portal.This paper also presents the experimental results of using the Investigain application,compares them with existing systems,and details the application’s prospect to improve its socio-economic conditions.The system’s frontend is designed with Bootstrap and jQuery frameworks.The backend is designed using Hypertext Preprocessor(PHP)server-side scripting language.The system demonstrated increased satisfaction from its clients.

The relationship between political connections and the mutual fund performance:Evidence from the US

This paper examines the effect of political connections between mutual fund managers and politicians on the fund performance.Using the publicly available data of individual political donations,we regard the mutual funds as politically connected when their managers make financial contributions to politicians.First,we show that aggregated stock holding changes of politically connected funds predict subsequent abnormal stock returns around the earnings announcement day,implying that the political connection promotes managers’stock picking abilities.Further evidence derived from the sample of entry buys and exit sells shows that politically connected funds outperform politically non-connected funds by 82 basis points annually.Second,we provide evidence that earnings forecast accuracy for the firms whose stocks are held by politically connected funds is improved significantly when time is approaching the announcement date,highlighting the importance of political channels through which information flows between firms and the market are facilitated.

How to Construct a Lower Risk FOF Based on Correlation Network? The Method of Principal Component Risk Parity Asset Allocation

In order to build a low-risk Fund of Funds(FOF), from the perspective of correlation, the principal component factor is used to improve the traditional risk parity model. Principal component analysis is used to decompose the underlying assets and generate unrelated principal component factors,and then the authors can construct a principal component risk parity portfolio. The proposed empirical results based on China’s mutual fund market show that the performance of principal component risk parity portfolio(PCRPP) is better than that of equal weight portfolio(EWP) and traditional risk parity portfolio(RPP). That is to say, not only the PCRPP in this paper has much lower risk than EWP and RPP, but also slightly better than EWP and RPP in terms of average return. Moreover, the study of dividing the underlying assets shows that the PCRPP in this paper is not sensitive to the underlying assets. The PCRPP in this paper is better than EWP and RPP for both the better performing funds and the worse performing funds. In addition, the empirical results on dynamic portfolio adjustments show that it is not appropriate to adjust asset allocation too frequently when the expected rate of return is calculated using the arithmetic mean.

Partial portfolio disclosure,investors’attention,and window dressing

I show that the disclosure of mutual funds’holdings significantly affects investors’investment decisions.As most mutual fund websites,advertisements,and fund-trading platforms only disclose a fund’s 10 largest holdings(top-10),this study finds that investors disproportionately focus on these stocks.However,this bias does not lead to additional profit because relative to their peers,funds with good top-10 performance tend to generate poor long-term returns.I design a clean and innovative discontinuity test between the performance of the 10th and 11th portfolio holdings to examine such window dressing behavior.I find that relative to their peers,funds that are small,new,and highly active are more likely to window dress and incur greater costs if they suffer from severe capital outflows.My findings suggest that partial disclosure misleads investors and allows effective window dressing.

Modeling correlated samples via sparse matrix Gaussian graphical models

A new procedure of learning in Gaussian graphical models is proposed under the assumption that samples are possibly dependent.This assumption,which is pragmatically applied in various areas of multivariate analysis ranging from bioinformatics to finance,makes standard Gaussian graphical models(GGMs) unsuitable.We demonstrate that the advantage of modeling dependence among samples is that the true discovery rate and positive predictive value are improved substantially than if standard GGMs are applied and the dependence among samples is ignored.The new method,called matrix-variate Gaussian graphical models(MGGMs),involves simultaneously modeling variable and sample dependencies with the matrix-normal distribution.The computation is carried out using a Markov chain Monte Carlo(MCMC) sampling scheme for graphical model determination and parameter estimation.Simulation studies and two real-world examples in biology and finance further illustrate the benefits of the new models.