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Article
Publication date: 29 October 2024

Vijaya C. and Thenmozhi M.

This study aims to examine whether tracking Smart Beta (SB) indices during bullish, bearish and stagnant market phases is a better choice for passive investors compared to…

42

Abstract

Purpose

This study aims to examine whether tracking Smart Beta (SB) indices during bullish, bearish and stagnant market phases is a better choice for passive investors compared to Cap-Weighted (CW) indices. As investors’ strategies differ with market movements, this study analyses how single-factor and multi-factor SB indices perform during different market phases, in relation to CW indices. It also attempts to determine which SB factors are more suitable for investors in these phases.

Design/methodology/approach

Using various return and risk indicators, this study analyses how SB indices perform vis-à-vis CW indices during bullish, bearish and stagnant phases. The authors also evaluate the upside and downside participation advantage of SB indices and assess their ability to capture upside returns and limit downside risk. The authors attempt to determine the cyclical or defensive nature of SB indices using Average Participation values.

Findings

This study found that SB indices outperform CW indices during the bearish and stagnant phases. Multi-factor SB indices have lower risk levels in all market phases, providing downside protection to risk-averse investors. Dividend, Low Volatility, Quality and multi-factor SB indices are defensive portfolios offering better payoffs during the down market phases, while Alpha, Beta, Equal Weight and Value SB indices provide higher payoffs during the up-market phases.

Originality/value

To the best of the authors’ knowledge, this is the first study that examines the performance of single-factor and multi-factor Indian SB indices in different market phases. It determines the suitability of various factors to passive investors during these phases and also identifies whether SB indices are cyclical or defensive.

Details

Journal of Indian Business Research, vol. 16 no. 4
Type: Research Article
ISSN: 1755-4195

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Article
Publication date: 13 May 2020

Thenmozhi M. and Aghila Sasidharan

This study aims to examine the effectiveness of governance in state-owned enterprises (SOEs) and explores if board independence enhances the firm value of SOEs in India and China…

1295

Abstract

Purpose

This study aims to examine the effectiveness of governance in state-owned enterprises (SOEs) and explores if board independence enhances the firm value of SOEs in India and China. The study further explores the moderation impact of promoter ownership in enhancing firm value.

Design/methodology/approach

The study is confined to government-owned enterprises in India and China and is based on a sample of 53 central government-owned firms listed in National Stock Exchange of India and 110 state-owned firms listed in Shanghai Stock Exchange of China for the period 2010–2017. A fixed-effect panel regression analysis has been used to examine the effect of board independence on firm value.

Findings

The study found that board independence adds value to the SOEs in India and China and the presence of independent directors (IDs) in the board of SOEs act as better monitors of performance to protect the interest of minority shareholders. Probably, they minimize agency conflict and provide resources to the firm and management. The greater the government shareholdings, the board independence further enhances value of SOEs in India and China.

Practical implications

Compliance with guidelines on IDs in SOEs serves as an effective corporate governance mechanism and the presence of IDs can signal better firm performance. The government promoters align with the IDs in better monitoring of SOE performance.

Originality/value

The study is unique and contributes to the literature by examining the impact of board independence on firm value in the context of SOEs in India and China and also provides insight on the effect of promoter ownership on the effectiveness on board independence.

Details

European Business Review, vol. 32 no. 5
Type: Research Article
ISSN: 0955-534X

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Article
Publication date: 26 September 2022

Sethiya Anuja and Thenmozhi M.

This study explores whether product market competition is a substitute for or complementary to good internal governance through promoter holdings. Specifically, it examines the…

351

Abstract

Purpose

This study explores whether product market competition is a substitute for or complementary to good internal governance through promoter holdings. Specifically, it examines the impact of product market competition on the linkage between promoter ownership and firm value and investigates whether this impact varies with the type of blockholders and level of ownership.

Design/methodology/approach

The authors used a fixed-effect panel regression method to analyze 1,136 National Stock Exchange-listed firms with 10,770 observations between the years 2005 and 2017. The authors computed product market competition using the Hirschman–Herfindahl Index and used the two-stage least squares regression model to address the issue of endogeneity.

Findings

Competition is a substitute for good corporate governance, especially in highly competitive industries, while promoters enhance firm value only in less competitive industries. This supports the theory that competition hinders a manager's “quiet life” hypothesis and creates disciplinary pressure to perform well. Additionally, the authors find that competition acts as a complement to promoters who are state-owned blockholders, while it acts as a substitute for promoters who are family-owned and private-owned blockholders.

Originality/value

This is possibly among the earliest attempts to integrate promoter ownership, product market competition, and firm value with the type of blockholder, especially in the context of the Indian market after 2005. The authors also provide evidence of situations in which both external and internal governance mechanisms either synergize or mitigate each other.

Details

Managerial Finance, vol. 49 no. 2
Type: Research Article
ISSN: 0307-4358

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Article
Publication date: 5 April 2013

Madhuri Malhotra, M. Thenmozhi and G. Arun Kumar

The purpose of this paper is to examine the short‐term and long‐term stock price volatility changes around bonus and rights issue announcements, using historical volatility…

871

Abstract

Purpose

The purpose of this paper is to examine the short‐term and long‐term stock price volatility changes around bonus and rights issue announcements, using historical volatility estimation and time varying volatility approach.

Design/methodology/approach

Changes in volatility around bonus and rights issues have been examined using the following methodologies. First, to capture historical volatility, change in standard deviation for 20 days and 100 days before and after announcement have been examined. Second, change in time varying volatility and unconditional volatility is examined using GARCH (1, 1) model.

Findings

The results indicate that the historical volatility has increased after bonus and rights issue announcement. The volatility persistence and unconditional variance have increased after the bonus and rights issue announcements. This evidence, extendable to any other type of issue announcement, is consistent with theories stating that volatility increases after the seasoned capital issue announcements.

Originality/value

This study analyses historical volatility, volatility persistence and unconditional volatility around bonus and rights issue announcements, which has not been observed in the previous literature. This study fills the gap in literature by empirically examining the change in short‐ and long‐term volatility before and after bonus and rights issue announcements. Moreover, measuring volatility using GARCH model overcomes the potential problem of heteroscedasticity associated with cross‐sectional data. The change in volatility persistence and unconditional volatility before and after the announcement are also examined. This study is useful for researchers and practitioners specialized in finance, international business and management, and professionals in the area of commercial policy development in emerging markets.

Details

International Journal of Emerging Markets, vol. 8 no. 2
Type: Research Article
ISSN: 1746-8809

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Article
Publication date: 6 August 2021

Shiba Prasad Mohanty, Santosh Gopalkrishnan and Ashish Mahendra

While traditionally it was believed that shadow banking undercuts business from traditional commercial banks, the time has now arrived to examine the various innovative practices…

287

Abstract

Purpose

While traditionally it was believed that shadow banking undercuts business from traditional commercial banks, the time has now arrived to examine the various innovative practices used by various shadow banks and non-banking finance companies (NBFCs) to explore various collaboration and competition possibilities. The parallel existence of the traditional and shadow banking systems creates a market environment where both the entities are inter-dependent for growth and development with their edge of advantages and snags. This study aims to investigate the development and growth of deposits in NBFCs and scheduled commercial banks (SCBs) and, through the adoption of innovative practices, highlights possible growth opportunities for both ahead.

Design/methodology/approach

This study uses yearly bank deposit data from 1998 to 2019. This study incorporates univariate autoregressive integrated moving average modeling to predict the future deposit growth of SCBs and NBFCs in India.

Findings

This study concludes that both the entities, i.e. NBFCs and SCBs, will experience deposit growth; however, the proportionate growth of deposits in SCBs will be higher than NBFCs.

Research limitations/implications

This study concludes that the NBFCs will exhibit higher growth in the future. Thus, a strengthened regulatory framework will boost the growth of the NBFCs, providing a safe environment to the investor. Further, as this study primarily considers only deposit-taking NBFCs and commercial banks and a single variable – “deposit” to predict its future growth, it offers a scope for future research to consider and include other kinds of NBFCs like non-deposit taking NBFCs, housing finance companies, micro-finance Institutions and infrastructure finance companies.

Originality/value

A competently regulated financial system of an emerging economy confers tremendous growth opportunities to the financial institutions functioning in the system. Deposits are a significant parameter for the performance of the financial institution; thus, by keeping it as the underlying premise, this study forecasts the future growth in deposits for both the commercial banks and NBFCs. This forecasted growth in deposits for both entities, if analyzed and acted upon appropriately, can, apart from other opportunities for investment, be used to point at directional growth of the economy and the gross domestic product, considering that credit growth is a barometer for economic growth. The scope of this study is limited to NBFCs and SCBs of India and considers only a single variable, i.e. deposit for data analysis and growth forecasting.

Details

International Journal of Innovation Science, vol. 14 no. 3/4
Type: Research Article
ISSN: 1757-2223

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Article
Publication date: 21 March 2023

Jasleen Kaur and Khushdeep Dharni

The stock market generates massive databases of various financial companies that are highly volatile and complex. To forecast daily stock values of these companies, investors…

617

Abstract

Purpose

The stock market generates massive databases of various financial companies that are highly volatile and complex. To forecast daily stock values of these companies, investors frequently use technical analysis or fundamental analysis. Data mining techniques coupled with fundamental and technical analysis types have the potential to give satisfactory results for stock market prediction. In the current paper, an effort is made to investigate the accuracy of stock market predictions by using the combined approach of variables from technical and fundamental analysis for the creation of a data mining predictive model.

Design/methodology/approach

We chose 381 companies from the National Stock Exchange of India's CNX 500 index and conducted a two-stage data analysis. The first stage is identifying key fundamental variables and constructing a portfolio based on that study. Artificial neural network (ANN), support vector machines (SVM) and decision tree J48 were used to build the models. The second stage entails applying technical analysis to forecast price movements in the companies included in the portfolios. ANN and SVM techniques were used to create predictive models for all companies in the portfolios. We also estimated returns using trading decisions based on the model's output and then compared them to buy-and-hold returns and the return of the NIFTY 50 index, which served as a benchmark.

Findings

The results show that the returns of both the portfolios are higher than the benchmark buy-and-hold strategy return. It can be concluded that data mining techniques give better results, irrespective of the type of stock, and have the ability to make up for poor stocks. The comparison of returns of portfolios with the return of NIFTY as a benchmark also indicates that both the portfolios are generating higher returns as compared to the return generated by NIFTY.

Originality/value

As stock prices are influenced by both technical and fundamental indicators, the current paper explored the combined effect of technical analysis and fundamental analysis variables for Indian stock market prediction. Further, the results obtained by individual analysis have also been compared. The proposed method under study can also be utilized to determine whether to hold stocks for the long or short term using trend-based research.

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Article
Publication date: 25 September 2023

Xiao Yao, Dongxiao Wu, Zhiyong Li and Haoxiang Xu

Since stock return and volatility matters to investors, this study proposes to incorporate the textual sentiment of annual reports in stock price crash risk prediction.

474

Abstract

Purpose

Since stock return and volatility matters to investors, this study proposes to incorporate the textual sentiment of annual reports in stock price crash risk prediction.

Design/methodology/approach

Specific sentences gathered from management discussions and their subsequent analyses are tokenized and transformed into numeric vectors using textual mining techniques, and then the Naïve Bayes method is applied to score the sentiment, which is used as an input variable for crash risk prediction. The results are compared between a collection of predictive models, including linear regression (LR) and machine learning techniques.

Findings

The experimental results find that those predictive models that incorporate textual sentiment significantly outperform the baseline models with only accounting and market variables included. These conclusions hold when crash risk is proxied by either the negative skewness of the return distribution or down-to-up volatility (DUVOL).

Research limitations/implications

It should be noted that the authors' study focuses on examining the predictive power of textual sentiment in crash risk prediction, while other dimensions of textual features such as readability and thematic contents are not considered. More analysis is needed to explore the predictive power of textual features from various dimensions, with the most recent sample data included in future studies.

Originality/value

The authors' study provides implications for the information value of textual data in financial analysis and risk management. It suggests that the soft information contained within annual reports may prove informative in crash risk prediction, and the incorporation of textual sentiment provides an incremental improvement in overall predictive performance.

Details

China Finance Review International, vol. 14 no. 2
Type: Research Article
ISSN: 2044-1398

Keywords

Available. Open Access. Open Access
Article
Publication date: 25 January 2023

Mikko Ranta and Mika Ylinen

This study aims to examine the association between board gender diversity (BGD) and workplace diversity and the relative importance of various board and firm characteristics in…

6857

Abstract

Purpose

This study aims to examine the association between board gender diversity (BGD) and workplace diversity and the relative importance of various board and firm characteristics in predicting diversity.

Design/methodology/approach

With a novel machine learning (ML) approach, this study models the association between three workplace diversity variables and BGD using a social media data set of approximately 250,000 employee reviews. Using the tools of explainable artificial intelligence, the authors interpret the results of the ML model.

Findings

The results show that BGD has a strong positive association with the gender equality and inclusiveness dimensions of corporate diversity culture. However, BGD is found to have a weak negative association with age diversity in a company. Furthermore, the authors find that workplace diversity is an important predictor of firm value, indicating a possible channel on how BGD affects firm performance.

Originality/value

The effects of BGD on workplace diversity below management levels are mainly omitted in the current corporate governance literature. Furthermore, existing research has not considered different dimensions of this diversity and has mainly focused on its gender aspects. In this study, the authors address this research problem and examine how BGD affects different dimensions of diversity at the overall company level. This study reveals important associations and identifies key variables that should be included as a part of theoretical causal models in future research.

Details

Corporate Governance: The International Journal of Business in Society, vol. 23 no. 5
Type: Research Article
ISSN: 1472-0701

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Book part
Publication date: 22 July 2021

Hsiu-Chen Fan Chiang, Pei-Xuan Jiang and Chia-Chien Chang

We empirically investigate the forecasting ability of USD-INR exchange rate volatility models by considering Google Trends data. Within a multiple regression framework, we use…

Abstract

We empirically investigate the forecasting ability of USD-INR exchange rate volatility models by considering Google Trends data. Within a multiple regression framework, we use historical volatility and liquidity measures to build our benchmark volatility model (Chandra & Thenmozhi, 2014). Moreover, we extend Bulut (2018) to incorporate indexes for 15 keywords (price-related, income-related, and liquidity-related) from Google Trends data into our benchmark volatility model to evaluate the forecasting ability of the models. Our results indicate that Google Trends data can improve volatility prediction and that among the groups of keywords that we consider, the price-related keywords have the best forecasting ability. Incorporating data on searches for “prices” into the model produces the highest reduction in the forecasting error: a 22.75% decrease compared to the level in the benchmark model. Hence, these empirical findings indicate that Google Trends data contain information that influences exchange rate movements.

Details

Advances in Pacific Basin Business, Economics and Finance
Type: Book
ISBN: 978-1-80043-870-5

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Article
Publication date: 17 April 2020

Houda Chakiri, Mohammed El Mohajir and Nasser Assem

Most local governance assessment tools are entirely or partially based on stakeholders’ surveys, focus groups and benchmarks of different local governments in the world. These…

373

Abstract

Purpose

Most local governance assessment tools are entirely or partially based on stakeholders’ surveys, focus groups and benchmarks of different local governments in the world. These tools remain a subjective way of local governance evaluation. To measure the performance of local good-governance using an unbiased assessment technique, the authors have developed a framework to help automate the design process of a data warehouse (DW), which provides local and central decision-makers with factual, measurable and accurate local government data to help assess the performance of local government. The purpose of this paper is to propose the extraction of the DW schema based on a mixed approach that adopts both i* framework for requirements-based representation and domain ontologies for data source representation, to extract the multi-dimensional (MD) elements. The data was collected from various sources and information systems (ISs) deployed in different municipalities.

Design/methodology/approach

The authors present a framework for the design and implementation of a DW for local good-governance assessment. The extraction of facts and dimensions of the DW’s MD schema is done using a hybrid approach, where the extraction of requirement-based DW schema and source-based DW schema are done in parallel followed by the reconciliation of the obtained schemas to obtain the good-governance assessment DW final design.

Findings

The authors developed a novel framework to design and implement a DW for local good-governance assessment. The framework enables the extraction of the DW MD schema by using domain ontologies to help capture semantic artifacts and minimize misconceptions and misunderstandings between different stakeholders. The introduction and use of domain ontologies during the design process serves the generalization and automation purpose of the framework.

Research limitations/implications

The presently conducted research faced two main limitations as follows: the first is the full automation of the design process of the DW and the second, and most important, is access to local government data as it remains limited because of the lack of digitally stored data in municipalities, especially in developing countries in addition to the difficulty of accessing the data because of regulatory aspects and bureaucracy.

Practical implications

The local government environment is among the public administrations most subject to change-adverse cultures and where the authors can face high levels of resistance and significant difficulties during the implementation of decision support systems, despite the commitment/engagement of decision-makers. Access to data sources stored by different ISs might be challenging. While approaching the municipalities for data access, it was done in the framework of a research project within one of the most notorious universities in the country, which gave more credibility and trust to the research team. There is also a need for further testing of the framework to reveal its scalability and performance characteristics.

Originality/value

Compared to other local government assessment ad hoc tools that are partially or entirely based on subjectively collected data, the framework provides a basis for automated design of a comprehensive local government DW using e-government domain ontologies for data source representation coupled with the goal, rationale and business process diagrams for user requirements representations, thus enabling the extraction of the final DW MD schema.

Details

Transforming Government: People, Process and Policy, vol. 14 no. 2
Type: Research Article
ISSN: 1750-6166

Keywords

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