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Feature selection is an important step for data pre-processing specially in the case of high dimensional data set. Performance of the data model is reduced if the model is trained with high dimensional data set, and it results in poor classification accuracy. Therefore, before training the model an important step to apply is the feature selection on the dataset to improve the performance and classification accuracy.

A novel optimization approach that hybridizes binary particle swarm optimization (BPSO) and differential evolution (DE) for fine tuning of SVM classifier is presented. The name of the implemented classifier is given as DEPSOSVM.

This approach is evaluated using 20 UCI benchmark text data classification data set. Further, the performance of the proposed technique is also evaluated on UCI benchmark image data set of cancer images. From the results, it can be observed that the proposed DEPSOSVM techniques have significant improvement in performance over other algorithms in the literature for feature selection. The proposed technique shows better classification accuracy as well.

The proposed approach is different from the previous work, as in all the previous work DE/(rand/1) mutation strategy is used whereas in this study DE/(rand/2) is used and the mutation strategy with BPSO is updated. Another difference is on the crossover approach in our case as we have used a novel approach of comparing best particle with sigmoid function. The core contribution of this paper is to hybridize DE with BPSO combined with SVM classifier (DEPSOSVM) to handle the feature selection problems.

The purpose of this paper is to study and describe several biases in investment decision-making through the review of research articles in the area of behavioral finance. It also includes some of the analytical and foundational work and how this has progressed over the years to make behavioral finance an established and specific area of study. The study includes behavioral patterns of individual investors, institutional investors and financial advisors.

The research papers are analyzed on the basis of searching the keywords related to behavioral finance on various published journals, conference proceedings, working papers and some other published books. These papers are collected over a period of year’s right from the time when the most introductory paper was published (1979) that contributed this area a basic foundation till the most recent papers (2016). These articles are segregated into biases wise, year-wise, country-wise and author wise. All research tools that have been used by authors related to primary and secondary data have also been included into our table.

A new era of understanding of human emotions, behavior and sentiments has been started which was earlier dominated by the study of financial markets. Moreover, this area is not only attracting the, attention of academicians but also of the various corporates, financial intermediaries and entrepreneurs thus adding to its importance. The study is more inclined toward the study of individual and institutional investors and financial advisors’ investors but the behavior of intermediaries through which some of them invest should be focused upon, narrowing down population into various variables, targeting the expanding economies to reap some unexplained theories. This study has identified 17 different types of biases and also summarized in the form of tables.

The study is based on some of the most recent findings to have a quick overview of the latest work carried out in this area. So far very few extensive review papers have been published to highlight the research work in the area of behavioral finance. This study will be helpful for new researches in this field and to identify the areas where possible work can be done.

Practical implication of the research is that companies, policymakers and issuers of securities can watch out of investors’ interest before issuing securities into the market.

Under the Social Implication, investors can recognize several behavioral biases, take sound investment decisions and can also minimize their risk.

The essence of this paper is the identification of 17 types of biases and the literature related to them. The study is based on both, the literature on investment decisions and the biases in investment decision-making. Such study is less prevalent in the developing country like India. This paper does not only focus on the basic principles of behavioral finance but also explain some emerging concepts and theories of behavioral finance. Thus, the paper generates interest in the readers to find the solutions to minimize the effect of biases in decision-making.

Data envelopment analysis (DEA), a non-parametric technique is used to assess the efficiency of decision-making units which are producing identical set of outputs using identical set of inputs. The purpose of this paper is to find the technical efficiency (TE), pure technical efficiency and scale efficiency (SE) levels of Indian oil and gas sector companies and to provide benchmark targets to the inefficient companies in order to achieve efficiency level.

In the present study, a group of 22 oil and gas companies which are listed on the National Stock Exchange for which the data were available for the period 2013–2017 has been considered. DEA has been performed to compare the efficiency levels of all companies. To measure efficiency, three input variables, namely, combined materials consumed and manufacturing expenses, employee benefit expenses and capital investment and two output variables – operating revenues and profit after tax (PAT) have been considered. On the basis of performance for the financial year ending 2017, benchmark targets based on DEA–CCR (Charnes, Cooper and Rhodes) model have been provided to the inefficient companies that should be focused upon by them to attain the efficiency level. The performance of the companies for the past five years has been examined to check the fluctuations in the various efficiency scores of the companies considered in the study over the years.

From the results obtained, it is observed that 59 percent, i.e. 13 out of 22 companies are technically efficient. By considering DEA BCC (Banker, Charnes and Cooper) model, 16 companies are observed to be pure technically efficient. In terms of SE, there are 14 such companies. The inefficient units need to improve in terms of input and output variables and for this motive, specified targets are assigned to them. Some of these companies need to upgrade significantly and the managers must take the concern earnestly. The study has also thrown light on the performance of the companies over last five years which shows Oil India Ltd, Gujarat State Petronet Ltd, Petronet LNG Ltd, IGL Ltd, Mahanagar Gas, Chennai Petroleum Corporation Ltd and BPCL Ltd as consistently efficient companies.

The present study has made an attempt to evaluate the efficiency of Indian oil and gas sector. The results of the study have significant inferences for the policy makers and managers of the companies operating in the sector. The results of the study provide benchmark target level to the companies of Oil and Gas sector which can help the managers of the relatively less efficient companies to focus on the ways to improve efficiency. The improvement in efficiency of a company would not only benefit the shareholders, but also the investors and other stakeholders of the company.

In the context of Indian economy, very limited number of studies have focused to measure the efficiency of oil and gas sector in the context of Indian economy. The present study aims to provide the latest insight to the efficiency of the companies especially operating in the Indian oil and gas sector. Further, as per our knowledge, this study is distinctive in terms of analyzing the efficiency of Indian oil and gas sector for a period of five years. The longitudinal study of the sector efficiency provides a bird eye view of the average efficiency level and changes in the efficiency levels of the companies over the years.

The purpose of this paper is to study the disposition effect that is exhibited by the investors through the review of research articles in the area of behavioral finance. When the investors are hesitant to realize the losses and quick to realize the gains, this phenomenon is known as the disposition effect. This paper explains various theories, which have been evolved over the years that has explained the phenomenon of disposition effect. It includes the behavior of individual investors, institutional investors and mutual fund managers.

The authors have used the existing literatures from the various authors, who have studied the disposition effect in either real market or the experimental market. This paper includes literature over a period of 40 years, that is, Dyl, 1977, in the form of tax loss selling, to the most recent paper, Surya et al. (2017). Some authors have used the PGR-PLR ratio for calculating the disposition effect in their study. However, some authors have used t-test, ANNOVA, Correlation coefficient, Standard deviation, Regression, etc., as a tool to find the presence of disposition effect.

The effect of disposition can be changed for different types of individual investors, institutional investors and mutual funds. The individual investors are largely prone to the disposition effect and the demographic variables like age, gender, experience, investor sophistication also impact the occurrence of the disposition effect. On the other side, the institutional investors and mutual funds managers may or may not be affected by the disposition effect.

The skilled understanding of the disposition effect will help the investors, financial institutions and policy-makers to reduce the adverse effect of this bias in the stock market. This paper contributes a detailed explanation of disposition effect and its impacts on the investors. The study of disposition effect has been found to be insufficient in the context of Indian capital market.

The investors and society at large can gains insights about causes and influences of disposition effect which will be helpful to create sound investment decisions.

This paper has complied the 11 causes for the occurrence of disposition effect that are found by the different authors. The paper also highlights the impact of the disposition effect in the decision-making of various investors.

The purpose of this paper is to quantify several measures to examine the determinants of profitability for the listed Indian banks. The authors include both public sector (PSUs) and private sector’s banks in the study. The authors have taken all the banks that are registered on the Bombay stock exchange (BSE) in the sample. This paper also intends to identify the association between the net profit margin (PM) and return on assets (ROA) with the several other independent variables of the Indian banking sector including private banks and public banks over the past six years starting from April 1, 2012 to March 31, 2017. Therefore, a sample of 39 listed banking companies and total 195 balanced observations are selected for the analysis purpose.

The authors have used profitability as a dependent variable represented by net PM, ROA and several financial ratios as independent variables. Financial statement and income statement of all listed banks were obtained from BSE and particular company’s website. Panel data regression has been analyzed with both the descriptive research techniques, i.e., fixed effects and random effects. The authors also verified both panel techniques with Hausman’s specification test, which is a widely used procedure for selecting a panel effect. The authors applied PP – Fisher χ², PP – Choi Z-statistics and Hadri to testing whether the data set is free from unit root problem and data set is a stationary series.

Results imply that interest expended interest earned (IEIE) and credit deposit ratio (CRDR) reduced the profitability of private banks in India. IEIE, CRDR and quick ratio (QR) reduced the profitability of public banks in India, while cash deposit ratio (CDR) and Advances to Loan Funds (ALF) increased the effectiveness of public banks. Under the total banks IEIE, CRDR reduced the profitability, on the other side, CDR, ALF and Total Debt to Owners Fund (TDOF) increased the profitability of total banks in India. Under the dependency of ROA, CRDR and TDOF reduced the return of private banks in India, while CDR, ALF and QR enhanced the profitability of private banks.

No variables found significant under public banks while taking ROA as a dependent variable. Under the overall banking data, CRDR reduced the profitability. On the other side, capital adequacy ratio and ALF increased the profitability of total banks in India. The findings of this study will support policy creators, financial executives and investors in constructing investment decisions.

The purpose of this paper is to use Twitter analytics for analyzing the startup ecosystem of India.

The paper uses descriptive analysis and content analytics techniques of social media analytics to examine 53,115 tweets from 15 Indian startups across different industries. The study also employs techniques such as Naïve Bayes Algorithm for sentiment analysis and Latent Dirichlet allocation algorithm for topic modeling of Twitter feeds to generate insights for the startup ecosystem in India.

The Indian startup ecosystem is inclined toward digital technologies, concerned with people, planet and profit, with resource availability and information as the key to success. The study categorizes the emotions of tweets as positive, neutral and negative. It was found that the Indian startup ecosystem has more positive sentiments than negative sentiments. Topic modeling enables the categorization of the identified keywords into clusters. Also, the study concludes on the note that the future of the Indian startup ecosystem is Digital India.

The analysis provides a methodology that future researchers can use to extract relevant information from Twitter to investigate any issue.

Any attempt to analyze the startup ecosystem of India through social media analysis is limited. This research aims to bridge such a gap and tries to analyze the startup ecosystem of India from the lens of social media platforms like Twitter.

The purpose of the study is two-fold. First, to find out the different barriers that prevent the implementation of sustainable manufacturing practices in the case of Micro, Small and Medium Enterprises (MSMEs). Second, quantifying the relative intensity of these barriers to understand the importance and dependence power of the observed variables.

Primary data were collected via structured questionnaires from 150 Indian MSMEs working in the automobile industry. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) was performed. Finally, barriers are ranked using graph theory and matrix approach (GTMA), considering the inheritance and interaction among the observed variables.

There are a total of 22 barriers to “sustainable manufacturing”. EFA and CFA models confirmed four different categories of barriers. In order of importance, they are ranked as – production and operations related; organization related; collaboration related; and government rules and regulations.

The findings show that there are multiple barriers to sustainable manufacturing. The barriers are categorized into four major categories, and their ranking provides an opportunity for prioritization from both policy and managerial perspectives. The barriers can be overcome through both policy interventions as well as through best practice sharing, benchmarking and organizational-wide initiatives.

The study is among the first studies that provide valuables insights on what are the 22 different barriers to sustainable manufacturing and its relevance to the MSMEs working in the automobile industry. The study would provide further opportunities for similar studies in different sectors.

Bluetooth wireless technology is a low power, low cost and short‐range RF technology that permits communication between bluetooth enabled devices, and focuses on replacement of cables between electronic devices. Communication between Bluetooth devices follows a strict master‐slave scheme. Each master device can have up to 7 active slaves and forms a so called piconet. In Bluetooth employing conventional scheduling policies such as Round Robin (RR), POLL or NULL packet is sent when the Master or Slave node does not have any data to send which causes a significant waste of resources. The DRR (Deficit Round Robin) scheduling algorithm can avoid the waste of time and slot of the RR scheduling at the sacrifice of fairness. In this paper we propose an improved DRR (IDRR) scheduling algorithm which effectively combines the DRR and bin packing algorithm. Computer simulation reveals that slot utilization is increased up to about 60% while the total number of used slots is decreased up to about 100%. The proposed IDRR scheduling is thus effective for not only basic data transmission but also real‐time multimedia data transmission.

The spirit of entrepreneurship among nationals of a country leads and contributes significantly to the growth process of a country. In this drive, there cannot be gender biases among men and women. The government in India has taken several initiatives to increase the participation of women entrepreneurs and, thus, making a partner to the growth and development process of the country. The initiatives in this direction were taken up through five-year development policy plans commencing from the fifth five-year plan (1974-1978). The focus of five-year plans since then has been reoriented toward women welfare stressing more specifically on women empowerment through women entrepreneurship programs. The purpose of this paper is to appraise the series of development plans and programs initiated for the empowerment of women entrepreneurship in India.

The paper is developed based on the concepts, policies and practices being implemented across the regions to promote women entrepreneurs. The methodology adopted to assess and analyze the relevant components very much matches the existing practices. The paper relates the concepts, programs and practices. This study has revealed certain issues based on the available data and information on women entrepreneurship to establish a view and rational as of why research is needed in the area of women entrepreneurship. To support this concept, the study has considered the experiences of two important states of India, namely, Uttar Pradesh and Uttarakhand.

The study reveals that Government of India has launched several policies and development programs to infuse the spirit of entrepreneurship among the women groups and thereby not only making them financially self-sustained but also to contribute to the growth process of the economy. However, there remain many gaps in achieving the goals due to the variety of factors. This becomes more significant as the level of women entrepreneurship in India is very low as compared to other economies and also between the men and women entrepreneurs within the country. The study brings out the need and importance for the future research in the area of women entrepreneurship and thereby exploring the areas of concern and bringing out needed improvements to strengthen the women entrepreneurship programs and policies.

This research provides a future direction to the academicians, researchers and policymakers to provide a framework to reorient the programs and policies for the cause of women entrepreneurship growth in India. The paper very much emphasis that the research findings will have influence on government policies and serve as an effective tool for implementation of state programs meant for woman entrepreneurs more effectively and efficiently. The ultimate impact of research findings will be an economic change in the quality of life of woman enterprise in the society.

Base-of-the-pyramid (BoP) markets are frequently characterized by institutional voids. However, it remains unclear how institutional voids impact corporate and supply chain risk and performance. This intersection will be analyzed in this paper.

This paper presents a systematic literature review of 94 BoP papers published between 2004 and 2019 in peer-reviewed, English-language journals available on Scopus. Drawing upon established frameworks for examining institutional voids, supply chain risks and BoP performance, frequency, and contingency analyses are conducted. Contingencies are established to provide insights into the associations between different constructs from the selected frameworks.

Supply chain risks are pervasive in the BoP discourse, especially when BoP markets are characterized by institutional voids. The frequency analysis of the constructs suggests that the key supply chain risks discussed in the BoP literature include social risk, credit risk, product market and operating uncertainties, knowledge and skill biases and decision-maker risks due to bounded rationality. The contingency analysis suggests that institutional voids are associated with supply chain risks that affect performance.

A theoretical framework aligning three research streams in the context of BoP calls for future studies to test the causality of highlighted constructs that are significantly associated. The analysis is confined to the constructs that are taken into account based on specific conceptual frameworks.

The study provides practitioners with a framework to manage supply chain risks in BoP-related firms to enhance firm performance. Managers can use key dimensions of supply chain risk, such as the product market, the input market and operating uncertainties, to evaluate performance in the BoP context.

Specifically, this research has strengthened the inquiry of supply chain risks in the presence of institutional voids that may have an impact on firm performance