Greetings from the Editor’s desk!
As I briefly introduce the contents of Volume 35, No. 2, the June 2023 issue of IIMB Management Review (IMR), it gives me great pleasure to inform readers that 2023-24 is the Golden Jubilee year of the Institute. It has been fifty years since the inception of IIM Bangalore and the Institute has several events and projects to commemorate and celebrate the milestone throughout the academic year. IMR has scheduled the IMR Doctoral Conference (IMRDC) 2024 on 2nd and 3rd February 2024, and other content features as part of its engagement with the Golden Jubilee. We encourage readers to participate actively in IMRDC 2024. For updates, please visit https://www.iimb.ac.in/imr-doctoral-conference
I would like to remind readers that as of calendar year 2023, IMR is being published in digital form only. Readers now have free access to all articles published in the journal, to read and download, from ScienceDirect® - https://www.sciencedirect.com/journal/iimb-management-review - IMR is also a Gold Open Access journal, with the article processing charges (APC) borne by the Indian Institute of Management Bangalore (IIMB), on behalf of the authors.
In tune with the theme of sustainability being highlighted during the Golden Jubilee, is the first article “India’s low carbon value chain, green debt and global climate finance architecture”. The authors A. Damodaran and Onno van den Heuvel explain that from 1995 to 2015, India’s climate financing architecture was predominantly driven by the Government’s budgetary resources. Following the adoption of the Paris Agreement on Climate Change in 2015 and the adoption of carbon mitigation targets in the country’s Nationally Determined Contribution (NDC), India’s emphasis has shifted to newer modes of project financing thus altering the pattern of the country’s “low carbon value chain”. A low carbon value chain may be described as the trajectory of climate action by which financial resources are mobilised and invested in “low carbon” projects by a country in order to fulfil its NDC targets. The paper predominantly employs analytical methods to understand the trajectories of low carbon value chain and financing of renewable energy projects. The data utilised in the paper pertains to the yield rates of different categories of green bonds issued by Indian entities in offshore markets. The same is mapped in order to assess the patterns of volatility in yield of green bonds of Indian origin for the period 1 January 2019 to 14 December 2020.
It emerges that the vulnerable points in India’s low carbon value chain are threefold: (a) unviability of renewable energy projects on account of high capital costs and long duration of loan repayments; (b) the absence of markets for carbon and renewable energy securities and the bottlenecks in revenue realisation from sale of green power; (c) the high yield curves associated with India’s off-shore and on-shore green bonds; (d) asset-liability mismatch faced by refinancing institutions; (e) absence of Central Bank interventions in India’s green debt securities which impedes the ability to enlarge the scope of borrowings from the debt markets.
On a detailed examination of these factors, the authors observe that the resultant tensions in the low carbon value chain can be obviated if refinancing institutions finance bankable climate mitigation projects which enjoy auxiliary revenue streams from carbon and renewable energy credits generated from carbon markets. Further, supportive policy measures that enable the country’s Central Bank to conduct market support operations involving green bonds and empower lending institutions to securitise their loan assets, can enlarge the scope of debt securities in India’s climate financing plan. To sum up, revenue realisation is central to optimise the functioning of the low carbon value chain. This requires the presence of carbon markets and the strengthening of green bonds issues in the secondary markets through Central Bank market interventions. In many ways, India’s incipient climate finance architecture holds vital lessons for the global climate financial system envisaged by the Paris Agreement.
In “How much does volatility influence stock market returns? Empirical evidence from India”, Malvika Saraf and Parthajit Kayal observe that over the past two decades, the growth of the Indian stock market has been accompanied by a surge in low-risk/high-return investment strategies as well as in the volume and size of investments by both domestic and international investors. Hence, studying the volatility anomaly (VA) of the stock market and thereby the risk-adjusted expected return pattern is very important for supporting investors in making their investment decisions and monetary policy specialists for policy formulation. Furthermore, the study would be relevant for investors who may wish to invest in Asian countries. Consistent with this view, their paper showcases how the average returns on stocks within a specified period can be explained in the context of the Indian stock market using symmetric and asymmetric risk measures such as (1) variance, (2) beta, (3) relative variance, (4) relative beta, (5) downside beta, (6) downside semi-variance, and (7) value at risk. Their main goal is to understand the implication of the first four risk measures on stock returns; the other two are considered for cross verifying the results. The data for the study consists of daily stock prices for 362 companies partly constituting the NIFTY500 index for the 10-year period from 1 July 2010 to 30 June 2020.
For the 10-year period, the results confirm the existence of the VA. The results show that low-risk stocks (i.e., with low beta, variance, relative beta, and relative variance) yield higher expected returns than high-risk stocks. To cross-verify these findings for smaller time intervals, the procedure was repeated for a period of 7 years, 5 years, and 3 years on a rolling basis. Results showed that this anomaly exists for cases of 7 and 5 years on a rolling basis. However, for the short period of 3 years and the ultra-short period of 6 months (January to June 2020) during the COVID lockdown period, this anomaly could not be confirmed. Hence, they conclude that the VA is a medium- to long-term phenomenon but not necessarily a short-term one. A satisfactory explanation is that the market typically requires three years or more for risk adjustment and volatility smoothing in order for higher returns to accrue.
In “Do debt payments beget debt? Evidence from an emerging market”, Vishnu K. Ramesh and Aravind Sampath test the interaction between financing and investment decisions in an idiosyncratic market with financial frictions—India. Due to the structural limitations (institutional voids), Indian firms predominantly depend on private debt (bank borrowing) when it comes to borrowing but this also comes with its idiosyncrasies. In contrast to developed markets characterised by a mature external financing environment, Indian firms heavily rely on internal cash flows for various business activities. To investigate how firms utilise these funds, they analyse how Indian firms allocate their incremental internal cash flows to competing uses such as capital expenditures (CAPEX), increasing cash reserve, the retirement of debt, share repurchase, and dividend payment. They also investigate the long-term pattern of cash flow allocations. They source their data from the Prowess DX database, their sample covering listed and permitted companies in India’s two major stock exchanges, the BSE and the NSE, from 2000 to 2019.
The study documents that cash flows are highly sensitive to borrowing, and firms majorly use internal cash flow to repay debt. Faced with a positive cash flow, firms allocate about 40% of the funds to repay borrowed funds. Regardless of the firms' characteristics in terms of size, leverage, and group affiliation, firms, on average, use contemporaneous cash flow to repay the debt. This higher debt-cash flow sensitivity facilitates the firms in maintaining investment in the future. Debt repayment enables firms to borrow funds to increase their CAPEX in subsequent periods. Apart from facilitating investments, borrowing also helps firms in managing negative cash flows. The results imply that firms respond symmetrically through borrowing to manage their cash flow shocks, which contradicts firms' responses in developed economies. Overall, the findings suggest that debt is a significant source of finance in countries like India, where private debt in the form of borrowing from banks is cheaper than the cost of raising funds via equity issues, and also yields tax-shield benefits.
In “New moon day anomalies of Amavasya and Muhurat trading: Gestalting the role of culture and institutions”, Avinash Ghalke, Satish Kumar, Ram Kumar Kakani, and Kameshwar Rao V.S. Modekurti explore whether local religious beliefs influence the decisions of investors in a globally integrated financial world. They attempt to answer this question using India’s context, where the new moon day (Amavasya) is generally considered an inauspicious day for any economic activity. Their study tests the abstinence hypothesis to examine the new moon day effect, and empirically verify the psychological phenomenon of duality of minds based on one of India’s biggest festivals, Diwali, which is considered an excellent and auspicious day to make investments and commence any wealth-generating activity (Muhurat trading).
Using the GARCH (1,1) framework, they test the impact of the new moon day on the daily returns of both the Bombay Stock Exchange (BSE) index (Sensex) and National Stock Exchange (NSE) index (Nifty 50), taking into consideration the daily values of BSE Sensex and NSE Nifty 50 for an extended period of 34 years (1985–2019). The study finds that Friday’s new moon day hurts the returns, even after adjusting for the known calendar day effects and the trading volume; the markets generate a significant positive return on the Muhurat trading day. A comparison of the results with four other markets driven by varied socio-cultural systems and faiths finds that the negative impact of Friday’s new moon is distinctive to India. Based on these findings, they develop a trading strategy which the investors could employ to earn excess returns over the passive buy-and-hold (BH) strategy, and which exploits the impact of the new moon day.
In “Forecasting the direction of daily changes in the India VIX index using deep learning”, Akhilesh Prasad, Priti Bakhshi, and Debashis Guha begin by underlining the importance of the VIX index in indicating and managing risk in the stock market, and attempt to test the ability of several commonly used deep neural network (DNN) architectures to forecast the direction of the day-to-day change in the VIX index for Indian stocks. Daily data of India VIX and Nifty 50 were taken from the NSE of India, and daily data of the Chicago Board of Options Exchange (CBOE) VIX and S&P 500 were downloaded from Yahoo Finance using the yfinance Python module, from March 2009 to April 2021.
To achieve the stated goal, six deep learning architectures are applied, and their performance is compared. They are Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), bidirectional LSTM, bidirectional GRU, simple Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). Five of them are recurrent networks and one is feedforward. The study focused more on recurrent architectures as VIX forecasting, like stock market forecasting, is a time-series forecasting task in which the variable of interest demonstrates temporal dependence that is likely to be nonlinear and the range of the dependence structure is likely to be long.
All six architectures performed well and achieved a higher level of accuracy than in previous studies. This study found that although the recurrent structures LSTM and GRU do perform slightly better, feedforward structure Conv1D is preferred in forecasting the India VIX because its performance is comparable, and it takes less time to train and can even run on limited hardware. The findings of the study are of relevance for assessing short term risk as well as long term strategies for hedgers, risk averse investors, volatility traders, investors, and financial researchers. Specifically, the findings would be important for investors and traders who are interested in investing in India’s stock markets.
Introducing their paper “Pandemics and Cryptocoins”, Afees A. Salisu, Ahamuefula E. Ogbonna, and Tirimisiyu F. Oloko observe that when the economic atmosphere is characterised by uncertainties, such as during pandemics, investors seek alternative ways to invest, as also a better platform to hedge their funds against risk/uncertainty associated with other assets. While cryptocoins have been seen as new investment opportunities, the hedge and safe haven advantage of crypotocoins during periods of uncertainty have been keenly contested in the literature. The main objective of their study is to examine the effect of pandemic-induced uncertainty on cryptocoins (specifically, Bitcoin, Ethereum and Ripple) over the period August 7, 2015 to June 27, 2020, for which they constructed a predictive model.
The study utilises two new datasets on uncertainty due to pandemics; Equity Market Volatility in Infectious Disease Index (EMV-IDI) developed by Baker, Bloom, Davis, and Terry (2020), which captures all the pandemics including COVID-19, and the Global Fear Index (GFI) developed by Salisu and Akanni (2020), which is a complementary dataset on COVID-19 that is developed using a different approach. The analysis is partitioned into the full sample, pre-COVID-19 period, and post-COVID-19 period. The study employs the Westerlund and Narayan (2012, 2015) predictive model, which simultaneously incorporates salient data features (persistence, endogeneity and conditional heteroscedasticity) within a single model framework, to examine the predictability of pandemic-induced uncertainty for returns on cryptocoins; the authors assess their predictive model’s forecast performance in comparison with a benchmark historical average model.
Their results indicate that cryptocoins act as a hedge against uncertainty due to pandemics, although with a reduced degree of safe haven potential in the COVID-19 period. Accounting for asymmetry was found to improve the predictability and forecast performance of the model, which indicates that failure to account for asymmetry in modelling the effect of uncertainty due to the pandemic on cryptocoin may lead to incorrect conclusions. The results are found to be sensitive to the choice of measure of uncertainty due to pandemics.
In “Do IPL teams escalate commitment for costly players? When do player status and reputation matter?” Sandeep Yadav and Deepak Dhayanithy examine how individual-level (target) factors, namely sunk cost, status, and reputation influence the organisational escalation of commitment (EOC) decision. They develop a theoretical model based on self-justification theory and institutional perspective and test it in the professional sports (Indian Premier League -- IPL) context. They use the Cox proportional hazard model on IPL players' data from 2008 to 2019 to test the proposed hypotheses.
They argue that the decision of player selection and retention in the IPL teams is a crucial mechanism through which franchises look to reduce institutional pressure and gain institutional legitimacy. Players' status and reputation are important mechanisms for gaining legitimacy in the sports setting; they influence IPL franchises’ EOC. The teams extend their commitment with high status and reputation players (resources) to gain internal (internal justification) and external (organisational justification) legitimacy. Specifically, sunk cost, reputation, and status of the individual players directly influence EOC, and player reputation and status moderate the sunk cost – EOC relationship.
Findings support player-level sunk cost's direct effect on IPL teams' EOC. IPL franchises exhibit higher EOC for players with high status and reputation. The study also found positive moderating effect of player status and reputation on the relationship between player sunk cost and team escalation of the commitment to the player. It shows that IPL teams escalate the commitment for costly players, and EOC for the costly player (a high sunk cost) depends on player reputation and status. High value player deals place self-justification pressures on franchise management when players carry high institutional legitimacy in the form of player status and reputation. This study contributes to the research on the positive impact of sunk cost on the EOC decisions as also the EOC research in sports management literature.
With best wishes,
Jishnu Hazra
Editor-in-Chief
IIMB Management Review
E-mail address: eic@iimb.ac.in
