Gopu Jayaraman’s research while affiliated with National University of Science and Technology, Oman and other places

The growing intricacy of international financial markets requires sophisticated approaches to managing investments and minimizing losses. This paper evaluates the use of Structural Equation Modeling (SEM) to improve forecast accuracy by integrating multiple technical indicators within the Bank Nifty Index. The study employs SEM to estimate the effect of key technical indicators such as the Simple Moving Average (SMA), Relative Strength Index (RSI), Volume Weighted Average Price (VWAP), and Moving Average Convergence Divergence (MACD) on trading volumes and closing values. The model considers both direct and indirect relationships among these indicators to determine their overall impact. The study highlights the significance of certain technical indicators in predicting market trends. It demonstrates SEM’s effectiveness in estimating interrelationships among these indicators and formulating predictive models. This study underscores SEM’s effectiveness in financial forecasting by showing that incorporating multiple technical indicators enhances prediction accuracy and improves decision-making in financial markets. Investors and traders can use these findings to develop better trading strategies, improve market stability, and maximize returns. This analysis supports the case for a multi-indicator approach in forecasting models.

Explore how the organization can formulate and implement digital strategy as a success factors to resilience, stability and achieve competitive advantage through innovation in the era of digital transformation (DT) and turbulent times. Based on the cautious adoption and exhaustive evaluation of literature, we present research findings concerning challenges and strategies that businesses use to find its way to gain success and capture value creation by its unique DT industry mode. The study includes the in-depth perception of managers involved in DT across different industry and business levels. The articles presented various effects of DT that provide greater influence on the organization outcomes affecting performance, efficiency, costs and competitiveness. The paper highlight essential strategies of DT for innovation and success based on organizations environment in which the businesses operate. This article synthesizes the full picture on the challenges and strategic application that are significant, relevant, connecting its theoretical background with practical research. The study can be further explored with quantitative data based research providing new and applied implication on DT. The article presents crucial factors needed to empower business DT in this complex and competitive era. The businesses from various sectors faces dissimilar challenges based on their environmental factors while implementing DT. This unique study provides a valuable outlook to establish DT pathways towards a sustainable and robust organizational future to execute digital changes in businesses.

In particular, an option is a contract that confers the right, but not the commitment to buy a volume of the underlying asset in case of a call option and sell in case of the put option at a specified price on or before the expiry date. The practice of trading options dates back to ancient times, and the earliest documentation of option exchange can be traced back to historical texts such as the Bible. The beauty of the option is that an investor can make more profit from a small investment. Investments in options are made utilizing leverage, they are regularly used to enhance the profit of the portfolio greatly. This study is divided into two main sections: the initial part delves into the historical evolution of options and the models used to price them. In contrast, the subsequent section focuses on practical applications of options.

The valuation of Goodwill (GW) has remained one of the several critical issues in financial analysis. This aspect is particularly important for mergers and acquisitions due to the significance of intangible assets. This study delves into the capitalization method of super profit (CMSP), a prominent technique for GW valuation, enhanced by the integration of statistical tools. Assessing a company’s excess profits over its average return on tangible assets is part of the CMSP. Finding the variables that have a significant impact on GW valuation, such as average profit, capital employed, and rate of return, is the main goal of this research. These issues are thoroughly investigated through statistical analysis to give stakeholders useful information for well-informed decision-making. Additionally, the study seeks to identify the external elements influencing this process as well as the internal aspects influencing GW valuation. Regression analysis, correlation matrices, response analysis and ANOVA are used to improve GW assessment and comprehension of the complex relationships between different factors.

The Black Scholes Model (BSM), a well-known approach for determining the fair value of options, has a long history. The price of the underlying asset, the strike price, volatility, time period, interest rates, and dividend yields are several of the input variables that have an impact on option pricing. This study intends to investigate the effects of these variables on option values. Taguchi’s methodology of design of the experiment is used for the experimental setup and to optimize the factors for option values. Taguchi method L27 design of experiment (DOE), analysis of variance (ANOVA), regression analysis, and analysis of means (ANOM) were used to investigate the effects of these input parameters on option values. MINITAB 18 software is used to carry out the analysis. The factors, in order of their significance from highest to lowest, were identified as: strike price, time period, interest rate, underlying asset price, volatility and dividend concerning to call option value for the given dataset. This technique can be used by traders to determine how various factors impact option values. The proportion contribution of these factors to the call values is also quantified using ANOVA.