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The purpose of this study is to introduce working capital efficiency multiplier (WCEM) as a direct profitability measure of working capital management. The existing accounting measures in the literature establish an indirect approach to study the relationship between working capital efficiency and profitability of the firms.

Using the help of a set of companies from CMIE Prowess database, the study introduces WCEM as a direct profitability measure of working capital efficiency.

In this study, a new direct measure of working capital efficiency is introduced which is multiplicative in nature. WCEM is a product of three components, namely, WACC, ratio of the sum of trade receivables and inventories to trade payables and ratio of net working capital (NWC) to net sales.

The importance of direct measure like WCEM could be enormous in performance evaluation of a firm. It can be used as an indicator for choosing a suitable investment opportunity by an investor. This is due to the fact that the firm that is highly efficient in managing working capital is less exposed to liquidity risk. At the same time, the firm is less dependent on external financing. Therefore, such firms eventually create more value for their shareholders. Another indication that WCEM provides is to gauge the bargaining power of the firm and its competitive position in the market. Lower WCEM indicates higher bargaining power of a firm across the value chain, and its superior position relative to its competitors.

Most of the studies on WCM are of the empirical type and there is a complete dearth on theoretical framework. Researchers hereafter can consider WCEM as one of the financial performance variables in place of the existing measures such as return on asset (ROA), return on invested capital (ROIC), return on equity (ROE), gross operating income (GOI) and net operating income (NOI) and thereby can contribute new empirical insights through their research outcomes.

This study aims to implement a novel approach of using the Realized generalized autoregressive conditional heteroskedasticity (GARCH) model within the conditional extreme value theory (EVT) framework to generate quantile forecasts. The Realized GARCH-EVT models are estimated with different realized volatility measures. The forecasting ability of the Realized GARCH-EVT models is compared with that of the standard GARCH-EVT models.

One-step-ahead forecasts of Value-at-Risk (VaR) and expected shortfall (ES) for five European stock indices, using different two-stage GARCH-EVT models, are generated. The forecasting ability of the standard GARCH-EVT model and the asymmetric exponential GARCH (EGARCH)-EVT model is compared with that of the Realized GARCH-EVT model. Additionally, five realized volatility measures are used to test whether the choice of realized volatility measure affects the forecasting performance of the Realized GARCH-EVT model.

In terms of the out-of-sample comparisons, the Realized GARCH-EVT models generally outperform the standard GARCH-EVT and EGARCH-EVT models. However, the choice of the realized estimator does not affect the forecasting ability of the Realized GARCH-EVT model.

It is one of the earliest implementations of the two-stage Realized GARCH-EVT model for generating quantile forecasts. To the best of the authors’ knowledge, this is the first study that compares the performance of different realized estimators within Realized GARCH-EVT framework. In the context of high-frequency data-based forecasting studies, a sample period of around 11 years is reasonably large. More importantly, the data set has a cross-sectional dimension with multiple European stock indices, whereas most of the earlier studies are based on the US market.

This study aims to forecast daily value-at-risk (VaR) for international stock indices by using the conditional extreme value theory (EVT) with the Realized GARCH (RGARCH) model. The predictive ability of this Realized GARCH-EVT (RG-EVT) model is compared with those of the standalone GARCH models and the conditional EVT specifications with standard GARCH models.

The authors use daily data on returns and realized volatilities for 13 international stock indices for the period from 1 January 2003 to 8 October 2014. One-step-ahead VaR forecasts are generated using six forecasting models: GARCH, EGARCH, RGARCH, GARCH-EVT, EGARCH-EVT and RG-EVT. The EVT models are implemented using the two-stage conditional EVT framework of McNeil and Frey (2000). The forecasting performance is evaluated using multiple statistical tests to ensure the robustness of the results.

The authors find that regardless of the choice of the GARCH model, the two-stage conditional EVT approach provides significantly better out-of-sample performance than the standalone GARCH model. The standalone RGARCH model does not perform better than the GARCH and EGARCH models. However, using the RGARCH model in the first stage of the conditional EVT approach leads to a significant improvement in the VaR forecasting performance. Overall, among the six forecasting models, the RG-EVT model provides the best forecasts of daily VaR.

To the best of the authors’ knowledge, this is the earliest implementation of the RGARCH model within the conditional EVT framework. Additionally, the authors use a data set with a reasonably long sample period (around 11 years) in the context of high-frequency data-based forecasting studies. More significantly, the data set has a cross-sectional dimension that is rarely considered in the existing VaR forecasting literature. Therefore, the findings are likely to be widely applicable and are robust to the data snooping bias.

Emerging market, like India, is characterised by poor institutional structure, weaker regulations and higher information asymmetry which may lead to stock price manipulation. This study shed some light on such manipulation by investigating front-running behaviour around the bulk deals of stocks traded at the National Stock Exchange (NSE) from 2010 to 2019.

The authors employ an event study methodology to identify front-running in pre-event period of bulk deals. The bulk deals are classified into Only Buy, Only Sell, Partial Buy and Partial Sell trades. They are further subsampled based on the category of investors. Through cross-sectional regression, the authors also identify factors explaining such front-running.

The results show that the front-runners can achieve 5%–7% returns within a week around the event day. Abnormal Returns (AR) before the deals are higher for “Buy” deals than “Sell” deals. The authors also examine the role of volume and delivery in explaining the AR and cumulative abnormal returns (CAR). Lagged CAR, change in volume and change in delivery explain the AR. The results are robust after controlling for Bullish and Bearish Periods.

To the best of authors’ knowledge, this is the first study that explores the front-running in “Partial Buy” and “Partial Sell” bulk deals. Further, it investigates whether the category of investors has any role in front running. It empirically tests the asymmetric market reaction between “Buy” and “Sell” trades. Finally, it examines the role of volume and delivery in front-running.

The purpose of this paper is to utilize a constrained random portfolio-based framework for measuring the skill of a cross-section of Indian mutual fund managers. Specifically, the authors test whether the observed performance implies superior investment skill on the part of mutual fund managers. Additionally, the authors investigate the suitability of mutual fund investments under diverse investor expectations.

The authors use a new skill measurement methodology based on a cross-section of constrained random portfolios (Burns, 2007).

The authors find no evidence of superior investment skill in the sample of Indian equity mutual funds. Using a series of statistical tests, the authors conclude that the mutual funds fail to outperform the random portfolios. Furthermore, mutual funds show no persistence in their performance over time. These results are robust to choice of performance measure and the investment horizon. However, mutual funds provide lower downside risks and may be suitable for investors with high degree of risk aversion.

The authors extend Burns’ (2007) methodology in several aspects, especially by using a much wider range of performance and downside risk measures to address diverse investor expectations. To the best of the authors’ knowledge, this is first study to apply the constrained random portfolios-based skill tests in an emerging market.

Angel investments are increasingly getting specialized. In recent years, start-ups are raising pre-seed funding before seed-stage funding. Investors in pre-seed and seed-stage companies commonly are angel investors. The purpose of this paper is to understand the differences between these two groups of angel investors.

Data for this study obtained from angel funding deals from the sources such as Venture Intelligence, VCCEdge, Keiretsu Forum, Dealcurry and The Chennai Angels. A total of 732 angel investments made by 405 investors during 2014–18 were used in the analysis. Non-parametric tests and regression estimations were used to identify the differences between angel investors investing in pre-seed and seed-stage ventures. An index was developed to measure the extent of syndication in angel investments and used as an independent variable in the regression.

There are significant differences between angel investors investing in pre-seed and seed-stage ventures. The results show that angels with more industry-specific experience make a higher proportion of investment in seed-stage ventures. Seed-stage ventures attract investors from Tier-1 cities, whereas the pre-seed stage has higher investors from smaller cities. Though the investment size is smaller, the extent of syndication is greater in pre-seed stage investments.

To differentiate the angel investments between pre-seed and seed-stage funding, this study uses data from Indian start-ups. Further, this study develops a composite syndication index to measure the extent of syndication in angel investments and assesses its impact on an angel investor’s choice of pre-seed stage investments.

Digital transformation is the way forward for all businesses. The technology is advancing at a rapid pace and the companies need to adapt to the change, not just to take advantage of the enormous opportunities it provides but even to stay relevant in this volatility, uncertainty, complexity, and ambiguity world. This study aims to define the concept of digital transformation and what it means in today’s business scenario. It helps to understand the different stages of digital maturity, identify the barriers in adopting different technologies and provide solutions to overcome those challenges.

This is a qualitative study in which opinions of the digital transformation experts were collected using a qualitative questionnaire. Natural language processing (NLP) and text mining techniques were applied along with a thorough analysis of the text to generate the results.

The study was able to uncover – what it means to be digitally transformed, different challenges an organization faces during the digital transformation journey and their potential solutions.

The existing literature on the topic is scattered and does not provide a roadmap for a company to adopt digital transformation. This study aims to fill up the gap and cover various aspects of the whole transformation process. The uniqueness of the study lies in the use of NLP techniques to perform text analytics on the data.