Mutual Fund Performance Analysis Using Nature Inspired Optimization Techniques: A Critical Review

Afroz, Zeenat; Das, Smruti Rekha; Mishra, Debahuti; Patnaik, Srikanta

doi:10.1007/978-3-319-69096-4_104

Cited by 2 publications

(2 citation statements)

References 37 publications

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“…Markets can remain irrational for longer time. So, Performance evaluation has to be done over a longer period of time where we can evaluate performance against couple of complete bull and bear cycles (Afroz 2017). In this paper, we have considered data for a period of 15 years from 2007 to 2021 which include two major bear cycles of Subprime crisis 2007 and Covid crisis in 2020 (Abdulla et al 2021).…”

Section: Mutual Fund's Performance Metricsmentioning

confidence: 99%

Optimizing Fund Performance: A Communication Systems Approach to Data Mining in Large- Cap Equity Mutual Funds

Shikha Singla

2024

jes

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Mutual funds represent a secure and technologically driven conduit for Indian investors to engage with the dynamic Indian financial markets. These professionally managed funds strategically allocate investments across a spectrum of securities, including equities, bonds, government securities, and gold, leveraging cutting-edge information technology infrastructure for seamless operation and optimization. Within this landscape, performance emerges as a pivotal metric, driving the flow of savings into these digitally enabled funds, indicative of investors' confidence and expectations in the realm of data-driven decision-making. The core objective of this paper is to construct a robust mutual fund performance evaluation model utilizing the Statistical Package for the Social Sciences (SPSS), a sophisticated software platform renowned for its advanced analytics and predictive modeling capabilities in the information technology domain. The work focused specifically on equity mutual funds, this study meticulously examines top-rated funds within the large-cap category, drawing insights from a comprehensive validation period spanning the last fifteen years from April 2007 to March 2021. Through the systematic evaluation of various performance metrics within the context of information technology-driven analysis, this model aims to deliver actionable insights and strategic foresight, benefiting researchers, society, and financial analysts alike, while catalyzing innovation and driving positive impacts in the intersection of technology and finance.

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Section: Mutual Fund's Performance Metricsmentioning

confidence: 99%

Optimizing Fund Performance: A Communication Systems Approach to Data Mining in Large- Cap Equity Mutual Funds

Shikha Singla

2024

jes

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“…MLPs are widely used for pattern classification, recognition, prediction and approximation. This was applied in a work done by Afroz et al (2017) to predict mutual fund performance where they obtain good forecasting results in blended funds, but not for growth funds. Krauss et al (2017) suggested a hybrid intelligent system that predicts the failure of firms based on past financial performance data by combining a rough set approach with MLP.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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, 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 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.

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Mutual Fund Performance Analysis Using Nature Inspired Optimization Techniques: A Critical Review

Cited by 2 publications

References 37 publications

Optimizing Fund Performance: A Communication Systems Approach to Data Mining in Large- Cap Equity Mutual Funds

Optimizing Fund Performance: A Communication Systems Approach to Data Mining in Large- Cap Equity Mutual Funds

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

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