Sensex and Nifty Indices: Are They the Right Benchmarks for Mutual Funds in India?

2018 ◽  
Vol 7 (1) ◽  
pp. 1-12
Author(s):  
S. S. S. Kumar
Keyword(s):  
Stock Market ◽  
Mutual Funds ◽  
Excess Returns ◽  
Construction Companies ◽  
Index Maintenance ◽  
The Right ◽  
Stock Portfolio ◽  
Exchange Traded Fund ◽  
Stock Picking ◽  
Provident Fund

Recently two significant developments took place in the Indian capital markets: (a) SEBI’s decision making it mandatory for all mutual funds to disclose the scheme returns against a common benchmark index such as Nifty or Sensex and (b) Employee’ Provident Fund Organisation (EPFO) is permitted to invest a part of their funds into stock market through the exchange-traded fund (ETF) route, particularly SBI Sensex and SBI Nifty ETFs. Both the developments are tied by a common concept that stock market indices such as Nifty and Sensex are passive without any statistically significant alpha. In the fund management industry, alpha is a measure of the risk-adjusted excess returns from a portfolio that can be attributed to the stock-picking skills of a fund manager. In this article, an attempt is made to examine for the presence of significant alphas in the returns of both the indices. The results of the study indicate that both the indices have statistically significant excess returns, raising questions on their suitability to act as reference and/or benchmarks for evaluating performance of mutual funds in India. Further, the study examined the returns of Sensex and Nifty index ETFs and observed a statistically significant alpha. The results of the study have important implications not only for the index construction companies but also to the policymakers who are advocating investment of considerable amounts of provident fund money into stock market through ETFs linked to Sensex and Nifty. Index maintenance companies have to re-design the indices so that they remain passive and the EPFO Administration may rethink their decision to invest in the existing ETFs linked to the Sensex and Nifty indices, and should consider constructing a well-diversified stock portfolio that is truly passive so that their mandate to get exposure only to market risk is fulfilled.

Intelligent Investment Approaches for Mutual Funds

2021 ◽  
pp. 267-281
Author(s):  
Dipankar Majumdar ◽  
Arup Kumar Bhattacharjee ◽  
Soumen Mukherjee
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Stock Market ◽  
Mutual Funds ◽  
Learning Tools ◽  
Stock Trading ◽  
Strategic Investment ◽  
The Past ◽  
The Right ◽  
Selection Of

Investment in the right fund at the right time happens to be the key to success in the stock trading business. Therefore, for strategic investment, the selection of the right opportunity has to be executed crucially so as to reap the maximum returns from the market. Predicting the stock market has always been known to be very critical and needs years of experience as it involves lots of interleaving parameters and constraints. Intelligent investment in mutual funds (MF) can be done when various machine learning tools are used to predict future fund value using the past fund value. In this chapter, an elaborate discussion is presented on the different types of mutual funds and how these data can be used in prediction by machine learning in different literature. In this work, the NAV of a total of 17 different mutual funds have been extracted from the website of AMFI, and thereafter, ANFIS is used to forecast the time series of the NAV of the MF. They have been trained using ANFIS and thereafter tested for prediction with satisfactory results.

scholarly journals COVID-19 Impact On Stock Market and Economy of India

2020 ◽  
Vol 3 (11) ◽  
pp. 55-64
Author(s):  
Supratik Sarkar
Keyword(s):  
Stock Market ◽  
United Nations ◽  
Mutual Funds ◽  
Global Economy ◽  
Secondary Data ◽  
Google Scholar ◽  
Infrastructure Development ◽  
Short Term ◽  
Market Sentiment ◽  
The Right

Background: The current coronavirus (SARS-COV2/ Covid-19) pandemic has wreaked havoc on the global economy and India has been hit significantly in every sector from banking and tourism to infrastructure development and rural-urban consumption.Objectives: To snapshot a broad market view of stocks, mutual funds, FDI and the general economy of India during the current Covid-19 pandemic.Methodology: Secondary data research using google scholar, Open Athens, government and United Nations reports and online verified news outlets.Findings: The current pandemic has led the market to crash however immediate necessary fiscal implementations of major economies have ensured the markets have also seen one of the fastest recoveries and thus we are presented with a unique opportunity to make the right choices and consolidate the market and economy in such a way that this recovery is sustained on a strong foundation rather than short-term market sentiment.

scholarly journals Decision Support System for Stock Portfolio Selection Using Artificial Intelligence and Machine Learning

2021 ◽  
Vol 26 (1) ◽  
pp. 87-93
Author(s):  
Sandeep Patalay ◽  
Madhusudhan Rao Bandlamudi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Support ◽  
Stock Market ◽  
Decision Support System ◽  
Portfolio Selection ◽  
Support System ◽  
Financial Analysis ◽  
Traditional Approach ◽  
Stock Portfolio

Investing in stock market requires in-depth knowledge of finance and stock market dynamics. Stock Portfolio Selection and management involve complex financial analysis and decision making policies. An Individual investor seeking to invest in stock portfolio is need of a support system which can guide him to create a portfolio of stocks based on sound financial analysis. In this paper the authors designed a Financial Decision Support System (DSS) for creating and managing a portfolio of stock which is based on Artificial Intelligence (AI) and Machine learning (ML) and combining the traditional approach of mathematical models. We believe this a unique approach to perform stock portfolio, the results of this study are quite encouraging as the stock portfolios created by the DSS are based on strong financial health indices which in turn are giving Return on Investment (ROI) in the range of more than 11% in the short term and more than 61% in the long term, therefore beating the market index by a factor of 15%. This system has the potential to help millions of Individual Investors who can make their financial decisions on stocks and may eventually contribute to a more efficient financial system.

scholarly journals MANAGERIAL ABILITIES AND FACTOR INVESTMENT STYLE PERFORMANCES OF MALAYSIAN MUTUAL FUND MANAGERS

2021 ◽  
Vol 6 (1) ◽  
pp. 118-135
Author(s):  
Pick-Soon Ling ◽  
Ruzita Abdul-Rahim
Keyword(s):  
Mutual Funds ◽  
Portfolio Management ◽  
Mutual Fund ◽  
Market Timing ◽  
Stock Selection ◽  
Investment Style ◽  
Fund Managers ◽  
Equity Mutual Funds ◽  
Timing Ability ◽  
Stock Picking

Background and Purpose: Studies focusing on mutual fund managerial abilities and investment style strategies are still scarce in the literature. Thus, this study aims to provide new evidence and insights into the managerial abilities and investment style performances of Malaysian fund managers.   Methodology: A total of 444 Malaysian equity mutual funds (EMFs) were evaluated using Carhart’s model incorporated with Treynor-Mazuy (T-M) and Henriksson-Merton (H-M) market timing models for the study period, from January 1995 to December 2017.   Findings: Fund managers displayed superior stock selection skills with 32 percent and 43 percent of funds for T-M and H-M respectively, with perverse market timing ability which accounted for 39 percent and 42 percent of funds for T-M and H-M respectively. Perverse timing ability had reduced the superior stock-picking skills of fund managers. This suggests that the EMFs performance could further improve if respective fund managers perform better in market timing ability. The finding also indicates that size effect (SMB) and value effect (HML) play significant roles in investment style strategies, while results of momentum factor (WML) propose that Malaysian fund managers have followed the contrarian strategy.   Contributions: This study contributes in several ways especially in the literature of portfolio management as the evidence is obtained from the largest mutual funds sample size and the longest study period. Moreover, this study also used the highest frequency data to study the effects of market timing which were overlooked in previous studies.   Keywords: Adjusted carhart, Malaysian market, market timing, mutual fund, stock selection.   Cite as: Ling, P-S., & Abdul-Rahim, R. (2021). Managerial abilities and factor investment style performances of Malaysian mutual funds.  Journal of Nusantara Studies, 6(1), 118-135. http://dx.doi.org/10.24200/jonus.vol6iss1pp118-135

scholarly journals A Predictive Meta Model for Forecasting Stock Price using Time Series Data

2021 ◽  
Vol 10 (1) ◽  
pp. 345-356
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Price ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Meta Model ◽  
Price Range ◽  
Stock Traders ◽  
The Right

Stock market prediction through time series is a challenging as well as an interesting research areafor the finance domain, through which stock traders and investors can find the right time to buy/sell stocks. However, various algorithms have been developed based on the statistical approach to forecast the time series for stock data, but due to the volatile nature and different price ranges of the stock price one particular algorithm is not enough to visualize the prediction. This study aims to propose a model that will choose the preeminent algorithm for that particular company’s stock that can forecastthe time series with minimal error. This model can assist a trader/investor with or without expertise in the stock market to achieve profitable investments. We have used the Stock data from Stock Exchange Bangladesh, which covers 300+ companies to train and test our system. We have classified those companies based on the stock price range and then applied our model to identify which algorithm suites most for a particular range of stock price. Comparative forecasting results of all algorithms in diverse price ranges have been presented to show the usefulness of this Predictive Meta Model

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