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This study aims to examine the investment-cash flow sensitivity (ICFS) and the impact of environmental, social and governance (ESG) on ICFS of manufacturing firms in India. Furthermore, it explores the role of group affiliation in such ESG–ICFS nexus.

The paper uses the generalized method of moments regression to analyze the data with a sample of 222 manufacturing firms from 2012 to 2022.

The paper reveals that Indian manufacturing firms mainly depend on internal cash flow for their investment decision, and ESG footprint reduces such sensitivity of investment-cash flow. Furthermore, group-affiliated firms have greater ICFS, and the impact of ESG on ICFS is more noticeable in group-affiliated firms than in standalone counterparts.

This paper provides valuable insights into current literature, with implications that extend to economies, firms, managers and investors. To the authors’ knowledge, this paper examining the impact of ESG on ICFS amidst group affiliation is first-of-its-kind.

Human development is critical for fostering economic growth and development. Given the importance of human development, this study examines the asymmetric impact of Foreign Direct Investment (FDI) on human development by decomposing total FDI into positive and negative shocks in five South Asian countries from 1990 to 2021.

The study uses the panel Non-linear Autoregressive Distributive Lag model (NARDL) to examine asymmetric long and short-run effects of FDI. Further, the direction of causality between HDI and FDI is examined using the recently developed (Joudis et al., 2021) panel granger non-causality test.

The positive and negative FDI shocks positively impact HDI, but positive shocks have a higher effect than negative shocks in the long run. The Wald Test rejects the long-run symmetric effect, confirming the asymmetric relationship between FDI and human development. More importantly, causality results reveal the FDI-led HDI and HDI-led FDI development in South Asia.

FDI should be encouraged by formulating a well-tailored policy intervention. The development policies should be interlinked with FDI policies. Absorptive capacities such as infrastructure facilities, a threshold level of human capital, and institutions should be strengthened to attract higher FDI into high-tech sectors.

Unlike the previous empirical studies, this study provides asymmetric evidence between FDI and human development in South Asia.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0380.

The purpose of this paper is to examine the impact of working capital management (WCM) on profitability under different financial conditions (constraint/unconstraint) and WCM policy (aggressive/conservative). Furthermore, the study investigates the existence of optimal working capital levels under different financial conditions and WCM policy.

Two-step system generalized method of moments and fixed effect models are used to analyze the data collected from Prowess database from 2011 to 2020 for a sample of 1,104 Indian manufacturing companies.

The study finds an inverted U-shaped relationship between working capital and profitability in all financial conditions and working capital policy. This finding advocates the existence of an optimal level of working capital that equates the costs and benefits of holding working capital to maximize the companies’ profitability. However, holding working capital beyond the optimal level negatively affects profitability. Companies under financial constraints with aggressive working capital policies have the lowest optimal cash conversion cycle (CCC). Furthermore, the relationship of working capital with profitability and the optimal CCC varies owing to firm age and industry group.

To the best of the authors’ knowledge, this is the first paper that incorporates the impact of working capital on firm’s performance from both financial constraint (unconstraint) and aggressive (conservative) working capital policy perspectives in the Indian context. Furthermore, this study also contributes in terms of reflecting the effect of firm age and industry in determining the optimum CCC of the firms.

The purpose of this study is to investigate the impact of disputed tax litigation risk on firm performance and stock return behavior using a sample of Indian listed firms.

The authors use disputed tax liability, reported as a contingent liability by the listed firms, as a proxy for the disputed tax litigation risk. To examine the impact of disputed tax litigation risk on firm performance (measured by accounting and market-based measures), the empirical approach used in this study focusses on the panel estimation technique. A portfolio-based approach using alternative asset pricing models examines the cross-sectional return variation because of the influence of disputed tax litigation risk.

The results of this study show a negative relationship between firm performance measures and disputed tax litigation risk. Cross-sectional test results reveal that higher disputed tax litigation risk is associated with higher expected returns.

This study focusses on disputed tax reported under the heading of contingent liability as a proxy for litigation risk. The study will help investors and portfolio managers to consider disputed tax litigation risk as an important parameter in the evaluation of firm performance. This study will also help regulators to get feedback on tax related policies and improve the dispute resolution process.

This study adds to the existing literature on the relationship between litigation risk and firm performance. In the context of emerging market, this study is the first-of-its-kind study, which focusses on disputed tax as a litigation risk proxy and examines its possible impact on firm performance and stock return behavior.

The purpose of this paper is to design a cascaded Multilevel inverter with reduce number of switches for high power applications. This paper came up with an innovative three-phase multilevel inverter (MLI) topology, which is a cascaded structure based on classical three-legged voltage source inverter (VSI) bridges as an individual module. The prominent advantage of this topology is that it requires only one direct current (DC) link system. The main characteristic of it is that a higher number of voltage levels can be achieved with considerably a smaller number of semiconductor switches, which improves the reliability, power quality, cost and size of the system significantly.

The individual modules are cascaded through three-phase transformers to provide higher voltage at the output with the higher number of voltage levels. In this work, the phase-shifted pulse width modulation technique is implemented to verify the result.

The proposed topology is compared with three-phase cascaded H-bridge MLI (CHB-MLI) and a modified CHB-MLI topology and found better in many aspects. The proposed MLI can produce a higher number of voltage levels with fewer semiconductor switches and associated triggering circuitry. As the device count in the proposed MLI is less compared to other MLI discussed, it tends to have less switching and conduction loss which increases the efficiency and reliability. As the number of level increases, the voltage profile and the total harmonic distortion of the proposed MLI improves.

This is a transformer-based modular cascaded MLI, which is based on classical VSI bridges. Here in this topology, a single module provides all three phases. So, a single string of cascaded modules is enough for three-phase multilevel voltage generation.

This research paper aims to investigate the change detection filter technique with a decision tree-based event (fault type) classifier for recognizing and categorizing power system disturbances on the high-voltage DC (HVDC) transmission link.

A change detection filter is used to the average and differential current components, which detects the point of fault initiation and records a change detection point (CDP). The half-cycle differential and average currents on both sides of the CDP are sent through the signal processing unit, which produces the respective target. The extracted target indices are sent through a decision tree-based fault classifier mechanism for fault classification.

In comparison with conventional differential current protection systems, the developed framework is faster in fault detection and classification and provides great accuracy. The new technology allows for prompt identification of the fault category, allowing electrical grids to be restored as quickly as possible to minimize economic losses. This novel technology enhances efficiency in terms of reducing computing complexity.

Setting a threshold value for identification is one of the limitations. To bring the designed system into stability condition before creating faults on it is another limitation. Reducing the computational burden is one of the limitations.

Creating a practical system in laboratory is difficult as it is a HVDC transmission line. Apart from that, installing rectifier and converter section for HVDC transmission line is difficult in a laboratory setting.

The suggested scheme’s importance and accuracy have been rigorously validated for the standard HVDC transmission system, subjected to various types of DC fault, and the results show the proposed algorithm would be a feasible alternative to real-time applications.

This study aims to examine the impact of manager sentiment on the firm performance (FP) of Indian-listed nonfinancial firms. Further, it endeavors to investigate the moderating role of economic policy uncertainty (EPU) and environment, social and governance (ESG) transparency in this relationship.

A noble manager sentiment is introduced using FinBERT, a bidirectional encoder representation from a transformers (BERT)-type large language model. Using this deep learning-based natural language processing approach implemented through a Python-generated algorithm, this study constructs a manager sentiment for each firm and year based on the management discussions and analysis (MD&A) report. This research uses the system GMM to examine how manager sentiment affects FP.

The empirical results suggest that managers’ optimistic outlook in MD&A corporate disclosure sections tends to present higher performance. This positive association remains consistent after several robustness checks – using propensity score matching and instrumental variable approach to address further endogeneity, using alternative proxies of manager sentiment and FP and conducting subsample analysis based on financial constraints. Furthermore, the authors observe that the relationship is more pronounced for ESG-disclosed firms and during the low EPU.

The results demonstrate that the manager sentiment strongly predicts FP. Thus, this study may provide valuable insight for academics, practitioners, investors, corporates and policymakers.

To the best of the authors’ knowledge, this is the first study to predict FP by using FinBERT-based managerial sentiment, particularly in an emerging market context.

The purpose of this paper is to use investor sentiment (IS) as a conditioning information variable for the cross-sectional return predictability tests of alternative asset pricing models (APMs).

Cross-sectional tests of alternative APMs in the linear beta representation and stochastic discount factor specifications, Fama and Macbeth and generalized method of moments techniques have been used.

Results reveal that IS as a conditioning information variable contains significant information for making the discount factors time varying. Model comparison test statistics suggests that among the alternative APMs, the conditional five-factor model (FFM) performs better.

Empirical analysis does not extend to the inclusion of the business-cycle conditioning information variables for the test of APMs.

The potential benefit of the conditional FFM can be leveraged upon for cost of capital determination, and mutual fund manager’s portfolio performance evaluation when the portfolio is heavily weighted with sentiment-sensitive hard to value and difficult to arbitrage stocks. During volatile and boom periods in stock markets the IS scaled conditional APMs may be useful for the fundamental value determination of sentiment-sensitive stocks.

This study extends available literature in the context of both developed and emerging equity markets by exploring the cross-sectional tests of conditional APMs using IS as the conditioning information variable. To the author’s knowledge, this is perhaps the first study that examines IS as conditioning information for the cross-sectional tests of alternative APMs.

This paper aims to investigate the moderating role of government policy interventions amid the early spread of novel coronavirus (COVID-19) (January–May 2020) on the investor sentiment and stock returns relationship.

This paper uses panel data from a sample of 53 countries to examine the impact of investor sentiment, measured by the financial and economic attitudes revealed by the search (FEARS) index (Da et al., 2015) on the stock return.

The moderating role of government policy response indices with the FEARS index on the global stock returns is further explored. This paper finds that government policy responses have a moderating role in the sentiment and stock returns relationship. The effect holds true even when countries are split based on five classifications, i.e. cultural distance, health standard, government effectiveness, social well-being and financial development. The results are robust to an alternative measure of pandemic search intensity, quantile regression and two measures of stock market activity, i.e. conditional volatility and exchange traded fund returns.

The sample period of this study encompasses the early spread phase (January–May 2020) of the novel COVID-19 spread.

This paper provides some early evidence on whether the government policy interventions are helpful to mitigate the impact of investor sentiment on the stock market. The paper also helps to shed better insights on the role of different country characteristics for the sentiment and stock return relationship.

This paper aims to investigate whether the use of conditional and unconditional Fama and French (1993) three-factor and Carhart (1997) four-factor asset pricing models (APMs) captures the role of asset pricing anomalies in the context of emerging stock market like India.

The first step time series regression approach has been used to drive the risk-adjusted returns of individual securities. For examining the predictability of firm characteristics or asset pricing anomalies on the risk-adjusted returns of individual securities, the panel data estimation technique has been used.

Fama and French (1993) three-factor and Carhart (1997) four-factor model in their unconditional specifications capture the impact of book-to-market price and liquidity effects completely. When alternative APMs in their conditional specifications are tested, the importance of medium- and long-term momentum effects has been captured to a greater extent. The size, market leverage and short-term momentum effects still persist even in the case of alternative unconditional and conditional APMs.

The empirical analysis does not extend for different market scenarios like high and low volatile market or good and bad macroeconomic environment. Because of the constraint of data availability, the authors could not include certain important anomalies like net operating assets, change in gross profit margin, external equity and debt financing and idiosyncratic risk.

Although the active investment approach in stock market shares a common ground of semi-strong form of market efficiency hypothesis which also supports the presence of asset pricing anomalies, less empirical evidence has been explored in this regard to support or repute such belief of practitioners. Our empirical findings make an attempt in this regard to suggest certain anomaly-based trading strategy that can be followed for active portfolio management.

From an emerging market perspective, this paper provides out-of-sample empirical evidence toward the use of conditional Fama and French three-factor and Carhart four-factor APMs for the complete explanation of market anomalies. This approach retains its importance with respect to the comprehensiveness of analysis considering alternative APMs for capturing unique effects of market anomalies.