Rohit Bansal

Drivers of Green Economy: An Indian Perspective

Sustainable economic development is much needed to ensure human well-being and reduce social inequality. In recent times, global leaders have come closer to arrive at consensus to transform current unsustainable economic developments into sustainable green economic growth. Viability of green economy depends on several factors such as government policy, investment climate, and concerns for environment. This chapter discusses drivers of green economy and the role of renewables to drive green growth in India.

Vector Auto-regressive Analysis of Determinants of IPO Underpricing: Empirical Evidence from Bombay Stock Exchange

Underpricing of IPOs has been contemplated as a prevalent phenomenon across the world. The principal objective of the article is to investigate the difference in the firm and market specific factors that significantly affect the level of underpricing of IPOs. The sample for the study consists of 320 IPOs, listed at Bombay Stock Exchange (BSE). All the market and firm specific variables are regressed against the level of underpricing. Cross-sectional multiple regressions are used to reveal which variables are relevant in affecting the level of underpricing. A two-way ANOVA is done to see whether there is a statistically significant difference in the level of underpricing between book build issues and fixed price issues. Based on the multiple regression results, we found the values of R square and adjusted R square to be 43 per cent and 40 per cent, respectively. In this study vector autoregressive (VAR) analysis is also the reason that it can be helpful in the understanding of interrelationships among worldly variables and in the formulation of a more structured economic model. The study revealed that there is a negative relationship between issue sizes—firm’s age, firms issuing IPO for the first time, book built pricing mechanism, and the number of underwriters (dummy variable) with the level of underpricing. However, there is positive relationship between subscription rate, market capitalization, and number of shares offered at the level of underpricing. On the other hand, there is no consequential difference between a level of underpricing and timing of the offer. Consequently, the study provides useful insight into which market and firm specific variables are significant in determining the extent of underpricing of IPOs. The study has important implications for investors who subscribe to different IPOs for listing day gain as this study would help them in understanding which type of firms are more likely to be underpriced.

CA-DE: Hybrid Algorithm Based on Cultural Algorithm and DE

Optimization problems can be articulated by numerous practical problems. These glitches stance a test for the academics in the proposal of proficient procedures skilled in digging out the preeminent elucidation with the slightest computing cost. In this study, we worked on differential evolution and cultural algorithm, conglomerates the features of both the algorithms, and proposes a new evolutionary algorithm. This jointure monitors the complex collaboration amalgam of two evolutionary algorithms, where both are carried out in analogous. The novel procedure termed as CA-DE accomplishes an inclusive inhabitant that is pooled among both metaheuristics algorithms concurrently. The aspect of the recycled approval action in credence space is to update the information of the finest individuals with the present information. This collective collaboration arises among both the algorithms and is presented to mend the eminence of resolutions, ahead of the individual performance of both the algorithms. We have applied the newly proposed algorithm on a set of six standard benchmark optimization problems to evaluate the performance. The comparative results presented demonstrate that CA-DE has an encouraging accomplishment and expandable conducts while equated with new contemporary advanced algorithms.

A Novel Approach of Optic Disk Detection for Diagnosis of Diabetic Retinopathy

Diabetic retinopathy (DR) is emerging technology in the field medical imaging. Because of diabetic symptom in body, loss of vision can happen and it is mainly due to Diabetic Macular Edema (EMD) and Retinopathy Proliferative Diabetic (RPD). Recent studies claims the new treatment methods and prevention. There are very effective treatments and they are optimal when the DR is detected in early stages, even when the patient has no symptoms. This paper deals with development of a computational oriented system to help in the pre-diagnosis of diabetic retinopathy, a progressive disease that develops in patients with diabetes mellitus and is characterized by the appearance of vascular lesions in the retina as increase in permeability and intra-retinal haemorrhages, intra-retinal accumulation of fluids and fluid, closure of blood vessels, capillaries and retinal arterioles and growth of new vessels blood inside and on the retinal surface. Some of these injuries are visible in fundus images, which are basically the images of the retina illuminated with white light and taken with a camera. The main objective of this paper is to develop a recognition algorithm of images that can automatically detect the optic disk. It is first proposed to correct distortions of luminosity produced by nature intrinsic optics of the image acquisition method. Then it is proposed to perform the detection of the optical disk.

Do investors exhibit behavioral biases in investment decision making? A systematic review

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.

Hybrid Nature-Inspired Algorithms: Methodologies, Architecture, and Reviews

Evolutionary computation has turned into a significant problem-solving approach among several researchers. As compared to other existing techniques of global optimization, the population-based combined learning procedure, robustness, and self-adaptation are some of the vital topographies of evolutionary algorithms. In spite of evolutionary algorithms has been broadly acknowledged for resolving numerous significant real applications in various areas; however in practice, occasionally they carry only fringe performance. There is slight motivation to assume that one can discover an unvaryingly finest optimization algorithm for resolving all optimization problems. Evolutionary algorithm depiction is resolute by the manipulation and survey liaison retained during the course. All this evidently elucidates the necessity for fusion of evolutionary methodologies, and the aim is to enhance the performance of direct evolutionary approach. Fusion of evolutionary algorithms in recent times is gaining popularity owing to their proficiencies to resolve numerous legitimate problems such as, boisterous environment, fuzziness, vagueness, complexity, and uncertainty. In this paper, first we highlight the necessity for fusion of evolutionary algorithms and then we explain the several potentials of an evolutionary algorithm hybridization and also discuss the general architecture of evolutionary algorithm’s fusion that has progressed all through the recent years.

Multilevel Image Thresholding Established on Fuzzy Entropy Using Differential Evolution

This study establishes a new methodology based on fuzzy partition of the image histogram and entropy for multi-level image thresholding. We propose a new methodology which is implemented in framework of the multi-step segmentation of image format. Further framework is improved to attain improved threshold value. A meta-heuristic Differential Evolution (DE) algorithm is cast-off in a direction to resolve the optimization problem, that results in a more rapid and accurate conjunction headed for the ideal situation. The accomplishment of DE can also be measured in reference to more or less widely held universal optimization procedures like Genetic Algorithms and Particle Swarm Optimization. Simulation results are equated with other entropy technique like Shannon entropy for the purpose of establishing the distinguishable difference in image.

Proposed method to construct Boolean functions with maximum possible annihilator immunity

Nonlinearity and Algebraic(annihilator) immunity are two core properties of a Boolean function because optimum values of Annihilator Immunity and nonlinearity are required to resist fast algebraic attack and differential cryptanalysis respectively. For a secure cypher system, Boolean function(S-Boxes) should resist maximum number of attacks. It is possible if a Boolean function has optimal trade-off among its properties. Before constructing Boolean functions, we fixed the criteria of our constructions based on its properties. In present work, our construction is based on annihilator immunity and nonlinearity. While keeping above facts in mind,, we have developed a multi-objective evolutionary approach based on NSGA-II and got the optimum value of annihilator immunity with good bound of nonlinearity. We have constructed balanced Boolean functions having the best trade-off among balancedness, Annihilator immunity and nonlinearity for 5, 6 and 7 variables by the proposed method.

Relation between Political Events, Union Budgets and Index Return:Empirical Evidence from the Indian Stock Market

This paper examines political events, and union budget effects associated with index NIFTY surrounding its announcement and effective days. The objective of this paper is to test the relationship between political events, and union budget effects with NIFTY index’s return. The study adopted event study methodology and analyzed secondary data collected from 2014-2016 for political events, and from 2011-2016 for analyzed the impact of union budget on Index. All the results of central and state elections (post Modi government) and central union budgets have been taken in this study. Stock market data were obtained from NSE websites, prowess, and money control and yahoo finance, union budget data were obtained from ministry of finance’ website, and results of political election were taken from Election commission of India. Necessary information derived from these sites were summarized and used to compute the relationship with Index return. Empirical results have given unpredictable results. The study found that market response to elections, and union budget are extremely negative or positive depending on the volatility of the election environment. The study, thus recommends that Election result, and union budget are also one of the factors of an investment judgment which has evidenced in this study by the way of directing an event study.