What Do Mutual Fund Investors Really Care About?

Ben‐David, Itzhak; Li, Jiacui; Rossi, Andrea; Song, Yang

doi:10.1093/rfs/hhab081

Cited by 214 publications

(31 citation statements)

References 90 publications

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“…Machine learning simply refers to learning about computers and making predictions from data. Linear regression does this, as more data improves its predictive ability [17][18][19][20][21][22]. Although other methods like SVM and neural networks are more generally thought of as machine learning, these algorithms actually boil down to minimizing a cost function from a model, as linear regression does.…”

Section: Simple Regressionmentioning

confidence: 99%

ML-Based Interconnected Affecting Factors with Supporting Matrices for Assessment of Risk in Stock Market

Singh

Henge

Sharma

et al. 2022

Wireless Communications and Mobile Computing

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In today’s world, people study and evaluate trading stocks to make informed decisions, based on available financial data and market information. Previous researchers relied on trend identification before making any decision to buy or sell stocks but fail to make accurate decisions due to complex systems. Some studies showed analysis to apply to stop loss on every stock transaction that got wrong levels due to limited features scaling that relied on single indicators without checking the performance metrics such as mean, standard deviation, and value at risk. Some existing models are based on theoretical implementation and they possess inaccurate success in real-time stock market transactions. Earlier risk management techniques were based on fundamental statistics of the company performance based on specific quarters that propose the future expects in the positive direction that is not every true which results in huge financial loss. Previous researchers failed to consider dynamic risk management parameters to ensure minimum loss for decision-making in fast-moving stock variations. Machine learning simply refers to learning about computers and making predictions from data. Identifying and analyzing the risk factors in the stock market are the major and crucial stage for predicting the company stock values at the national and international levels. In existing research, all risk management-related factors are analyzed based on fundamental statistics of the company performance which are measured as quarterly results, which will not give long-term true predictions and will not provide positive directions to invest in further stocks. This research majorly focused on risk management for national stock companies using the machine learning methodology and algorithms. The objective is to determine if stock market indicators are suitable decision-aid tools within the context of intraday risk management. The review of the literature revealed that while there are many studies looking to foresee changes in the stock market, there are few studies looking to improve stock market risk management methods using machine learning algorithms. The goal of this study was to fill this gap by utilizing the body of existing research on stock index forecasting combined with machine learning techniques for both short- and long-term risk managements. It has described the association between machine learning models and implicated the data with respect to discrete models based on supportive, dependable, nondependable parameters along with the name and type of the stock. This research has integrated a few crucial dependable parameters such as oil prices, on-hand projects, and future projects. It has integrated with the simple, multiple linear regression models to generate a signal for SPY growth. The proposed ML-based model has been evaluated by comparing two states of training and testing and achieved 96.3% of accuracy. The parameters used for evaluation are closing price, price differences, and daily return. The performance range of the proposed multiple regression model lies along the maximum drawn down which is 0.04411 for test cases and 1.2533 for training cases. Compare the performance of the proposed approach with that existing models with respect to the number of keys and methods associated with training and testing the data.

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Section: Simple Regressionmentioning

confidence: 99%

ML-Based Interconnected Affecting Factors with Supporting Matrices for Assessment of Risk in Stock Market

Singh

Henge

Sharma

et al. 2022

Wireless Communications and Mobile Computing

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“…This paper is also related to the literature on mutual fund flows. Some studies suggest that higher net alpha leads to higher inflows (Sirri and Tufano (1998); Lynch and Musto (2003); Ben-David et al (2021); Evans and Sun (2021)), and fund firms obtain substantial flows through spillover effects of star funds (Warner and Wu (2011); Sialm and Tham (2016)), some papers find that brokers and marketing teams attract investors (Bergstresser et al (2009); Christoffersen et al (2013); Roussanov et al (2020)), and Cohen and Schmidt (2009)show that fund firms attract flows themselves by being named trustee to secure sizable inflows.…”

Section: Introductionmentioning

confidence: 99%

Reallocation of Mutual Fund Managers and Capital Raising Ability

2022

SSRN Journal

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“…Within each of these fund categories, through a fund's realized returns and volatility Morningstar then ranks and gives an aggregate rating in the form of "Morningstar Stars." 2 These Morningstar Star summaries of mutual funds have been shown throughout the literature to have a strong and significant impact on investor flow from both retail and institutional investors (Nanda, Wang, and Zheng (2004), Del Guercio and Tkac (2008), Evans and Sun (2018), Reuter and Zitzewitz (2015), Ben-David et al (2019)). 3 In addition, the data releases provided by Morningstar are used ubiquitously throughout the industry.…”

Section: Introductionmentioning

confidence: 99%

Don't Take Their Word for It: The Misclassification of Bond Mutual Funds

CHEN¹,

Cohen²,

Gurun

2021

The Journal of Finance

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We provide evidence that bond fund managers misclassify their holdings, and that these misclassifications have a real and significant impact on investor capital flows. The problem is widespread, resulting in up to 31.4% of funds being misclassified with safer profiles, compared to their true, publicly reported holdings. “Misclassified funds”—those that hold risky bonds but claim to hold safer bonds—appear to on‐average outperform lower risk funds in their peer groups. Within category groups, misclassified funds receive more Morningstar stars and higher investor flows. However, when we correctly classify them based on actual risk, these funds are mediocre performers.

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What Do Mutual Fund Investors Really Care About?

Cited by 214 publications

References 90 publications

ML-Based Interconnected Affecting Factors with Supporting Matrices for Assessment of Risk in Stock Market

ML-Based Interconnected Affecting Factors with Supporting Matrices for Assessment of Risk in Stock Market

Reallocation of Mutual Fund Managers and Capital Raising Ability

Don't Take Their Word for It: The Misclassification of Bond Mutual Funds

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