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The purpose of this paper is to perform a relative assessment of performance benchmarks based on alternative asset pricing models to evaluate performance of mutual funds and suggest the best approach in Indian context.

Sample of 237 open-ended Indian equity (growth) schemes from April 2003 to March 2013 is used. Both unconditional and conditional versions of eight performance models are employed, namely, Jensen (1968) measure, three-moment asset pricing model, four-moment asset pricing model, Fama and French (1993) three-factor model, Carhart (1997) four-factor model, Elton et al. (1999) five-index model, Fama and French (2015) five-factor model and firm quality five-factor model.

Conditional version of Carhart (1997) model is found to be the most appropriate performance benchmark in the Indian context. Success of conditional models over unconditional models highlights that fund managers dynamically manage their portfolios.

A significant α generated over and above the return estimated using Carhart’s (1997) model reflects true stock-picking skills of fund managers and it is, therefore, worth paying an active management fee. Stock exchanges and credit rating agencies in India should construct indices incorporating size, value and momentum factors to be used for purpose of benchmarking.

The study adds new evidence as to applicability of established asset pricing models as performance benchmarks in emerging market India. It examines role of higher order moments in explaining mutual fund returns which is an under researched area.

Banks in the South Asian region are the fulcrum of economic growth and development as they provide means to development credit and working capital, trade and infrastructure finance and are seen as custodians of the trust in the financial system. This paper aims to study the nature of banking sector linkages for the region.

The dependence structure between deposits and lending rates individually for the banks of the South Asian countries are studied using time invariant and time varying family of copula functions. The degree of connectedness is further studied by Diebold and Yilmaz methodology.

Results indicate poor levels of banking integration in the region as the dependence parameter for both deposits and lending rates was around 0 for the sample countries, thereby confirming poor banking sector integration in the region.

Policymakers of the region are interested in the co-movements of the interest rates to understand the cross-sector risk management and any systemic risk pressures for the regional economies. Corporates in these countries are scouting out for competitive borrowing rates to lower their cost of capital.

Rationale for examining the banking sector linkages is that the South Asian countries are at different stages of economic growth and development and this region in particular is the fastest growing region in the world and has largely increased its trade integration with the world albeit having lowest levels of intra-regional trade integration.

This is a first of a kind of studies to examine the banking sector linkages in South Asia.

In the present study, we investigate whether enhanced momentum strategies outperform price momentum strategies and if they show greater resilience and stability under adverse market conditions. We also examine if such strategies are explained by prominent asset pricing models or are a result of behavioral mispricing.

Data consist of the equity shares of all companies listed on National Stock Exchange over the study period. To check the efficacy of enhanced momentum over price momentum, six momentum strategies have been designed and their raw as well as risk-adjusted returns using multi-factor models have been observed. Behavioral mispricing has been examined by constructing an investor attention index. Finally, few robustness tests have been performed to confirm the results.

We find that an enhanced momentum strategy which combines relative and absolute strength momentum outperforms conventional price momentum strategy in India. We also demonstrate that rational pricing models are not able to explain momentum profits for any of the strategies. Finally, we observe that investor overreaction is the possible explanation of momentum profits in India. Thus, our results confirm the role of behavioral mispricing in explaining momentum returns.

Our research is the first major attempt to study enhanced momentum strategies in the Indian context. We experiment with several new enhanced momentum strategies which have not been explored in prior literature. The findings have strong implications for global portfolio managers who wish to design profitable trading strategies.

This study attempts to identify fundamental determinants of bond ratings for non-financial and financial firms. Further the study aims to develop a parsimonious bond rating model and compare its efficacy across statistical and range of machine learning methods in the Indian context. The study is motivated by the insufficiency of prior work in the Indian context.

The authors identify the critical determinants of non-financial and financial firms using multinomial logistic regression. Various machine learning and statistical methods are employed to identify the optimal bond rating prediction model. The data cover 8,346 bond issues from 2009 to 2019.

The authors find that industry concentration, sales, operating leverage, operating efficiency, profitability, solvency, strategic ownership, age, firm size and firm value play an important role in rating non-financial firms. Operating efficiency, profitability, strategic ownership and size are also relevant for financial firms besides additional determinants related to the capital adequacy, asset quality, management efficiency, earnings quality and liquidity (CAMEL) approach. The authors find that random forest outperforms logit and other machine learning methods with an accuracy rate of 92 and 91% for non-financial and financial firms.

The study identifies important determinants of bond ratings for both non-financial and financial firms. The study interalia finds that the random forest technique is the most appropriate method for bond ratings predictions in India.

Better bond ratings may mitigate corporate defaults.

Unlike prior literature, the study identifies determinants of bond ratings for both non-financial and financial firms. The study also experiments with modern machine learning techniques besides the traditional statistical approach for model building in case of relatively under researched market.

The main aim of the study is to identify some critical microeconomic determinants of financial distress and to design a parsimonious distress prediction model for an emerging economy like India. In doing so, the authors also attempt to compare the forecasting accuracy of alternative distress prediction techniques.

In this study, the authors use two alternatives accounting information-based definitions of financial distress to construct a measure of financial distress. The authors then use the binomial logit model and two other popular machine learning–based models, namely artificial neural network and support vector machine, to compare the distress prediction accuracy rate of these alternative techniques for the Indian corporate sector.

The study’s empirical results suggest that five financial ratios, namely return on capital employed, cash flows to total liability, asset turnover ratio, fixed assets to total assets, debt to equity ratio and a measure of firm size (log total assets), play a highly significant role in distress prediction. The study’s findings suggest that machine learning-based models, namely support vector machine (SVM) and artificial neural network (ANN), are superior in terms of their prediction accuracy compared to the simple binomial logit model. Results also suggest that one-year-ahead forecasts are relatively better than the two-year-ahead forecasts.

The findings of the study have some important practical implications for creditors, policymakers, regulators and other stakeholders. First, rather than monitoring and collecting information on a list of predictor variables, only six most important accounting ratios may be monitored to track the transition of a healthy firm into financial distress. Second, our six-factor model can be used to devise a sound early warning system for corporate financial distress. Three, machine learning–based distress prediction models have prediction accuracy superiority over the commonly used time series model in the available literature for distress prediction involving a binary dependent variable.

This study is one of the first comprehensive attempts to investigate and design a parsimonious distress prediction model for the emerging Indian economy which is currently facing high levels of corporate financial distress. Unlike the previous studies, the authors use two different accounting information-based measures of financial distress in order to identify an effective way of measuring financial distress. Some of the determinants of financial distress identified in this study are different from the popular distress prediction models used in the literature. Our distress prediction model can be useful for the other emerging markets for distress prediction.

The purpose of this paper is to analyze long-term prior return patterns in stock returns for India.

The methodology involves portfolio generation based on company characteristics and long-term prior return (24-60 months). The characteristic sorted portfolios are then regressed on risk factors using one factor (capital asset pricing model (CAPM)) and multi-factor model (Fama-French (FF) model and four factor model involving three FF factors and an additional sectoral momentum factor).

After controlling for short-term momentum (up to 12 months) as documented by Sehgal and Jain (2011), the authors observe that weak reversals emerge for the sample stocks. The risk model CAPM fails to account for these long-run prior return patterns. FF three-factor model is able to explain long-term prior return patterns in stock returns with the exception of 36-12-12 strategy. The value factor plays an important role while the size factor does not explain cross-section of average returns. Momentum patterns exist in long-term sector returns, which are stronger for long-term portfolio formation periods. Further, the authors construct sector factor and observe that prior returns patterns in stock returns are partially absorbed by this factor.

The findings are relevant for investment analysts and portfolio managers who are continuously tracking global markets, including India, in pursuit of extra normal returns.

The study contributes to the asset pricing and behavioral literature from emerging markets.

Momentum is an unresolved puzzle for the financial economists. The purpose of this paper is to dissect the sources of momentum profits and investigate the possible role played by the macro-economic variables in explaining them.

The data for 493 companies that form part of Bombay Stock Exchange 500 index in India is used for calculating 6-6 momentum profits. Profits from the strategy are regressed on Capital Asset Pricing Model (CAPM) and Fama-French (FF) model to see whether they can explain these profits. Guided by prior research, three methodologies are used to see the possible role played by macro-economic variables in explaining momentum payoffs.

The empirical results show that momentum profits are persistent in the intermediate horizon. CAPM and FF three-factor model fail to explain these returns. Price momentum seems to be explained in one of the model by lagged macro-economic variables which lend an economic foundation to the Carhart factor. The “Winner minus Loser” factor explains about 37 percent of abnormal returns on the winner portfolio that are missed by the FF model. The unexplained momentum profits seem to be an outcome of investors’ over-reaction to past information. Hence, the sources of price momentum profits seem to be partially behavioral and partially rational.

The failure of risk models in fully explaining the momentum profits may be good news for portfolio managers who are looking out for stock market arbitrage opportunities.

This paper fulfills an identified need to study the sources behind price momentum profits in Indian context.

The purpose of this paper is to evaluate if there are any momentum patterns in stock and sectoral returns and if they can be explained by the risk factors.

The methodology involves portfolio generation based on company characteristics and short‐term prior return (six to 12 months). The characteristic‐sorted portfolios are then regressed on risk factors using one‐factor (CAPM) and multi‐factor model (Fama French model and four‐factor model involving three Fama French factors and an additional sectoral momentum factor).

The authors find momentum profits in Indian context for prior return portfolios which are stronger for 6‐6 compared to 12‐12 strategies. These momentum profits are larger for some characteristic‐sorted portfolios. Risk models such as CAPM and Fama French model fail to capture momentum profits. In fact, winner portfolios generally comprise large firm and high P/B stocks, thus defying the risk story. Some zero investment momentum‐based trading strategies do provide significant payoffs. The authors also observe momentum profits in sectoral returns. A part of stock momentum profits is captured by sectoral factor, thus implying that it may mainly be an outcome of sectoral momentum.

The findings are pertinent for portfolio managers and investment analysts who are continuously in pursuit of trading strategies that provide extra normal returns. From an academic point of view, the authors suggest that sectoral factor should be used in the multi‐factor framework for explaining asset returns.

The study contributes to the asset pricing and behavioral literature from emerging markets.

This paper aims to examine the destabilization effect in the case of India’s agricultural commodity market for the sample period of 01 January 2009 to 31 May 2013.

The daily data of eight agricultural commodities traded on the National Commodity & Derivatives Exchange, viz., barley, castor seed, chana (chickpea), chilli, potato, pepper, refined soya and soybean, have been used in this study. At the first stage of the empirical analysis, the study estimates the time-varying spot market volatility by using the exponential generalized autoregressive conditional heteroscedasticity model and applies three different high and band-pass filters, viz., the two-sided linear band-pass filter by Hodrick and Prescott (1997), the fixed-length symmetric band-pass filter by Baxter and King (1999) and the asymmetric band-pass filter by Christiano and Fitzgerald (2003), to calculate the unexpected liquidity of sample commodities. At the second stage of the empirical analysis, the study applies linear Granger causality and recently developed non-linear causality given by Diks and Panchenko (2006) to examine the cause and effect between time-varying volatility of spot market and futures market liquidity of sample commodities.

The linear and non-linear causality results suggest the destabilizing effect of commodity futures on the underlying spot market for chana, chilli and pepper. The empirical findings are in contrast with the recommendations of Abhijit Sen’s committee and provide important direction for further policy research.

The study has a limitation in that it is based on the daily data. The use of intra-day data would have been more suitable for such type of analysis.

The study has strong policy implications from a financial policy perspective, as there is already disagreement among researchers and policy makers with regard to the functioning of commodity derivatives markets in India. There have been many occasions when commodity market regulators have to undertake decisions of suspension of trading of many commodities. The study also provides new directions of policy research with regards to the restructuring of the commodity derivatives market in India.

The findings of this study may further help the regulators and policy makers to undertake decisions about how to provide an alternative platform for farmers to sell their agricultural produce more efficiently. This will certainly have some impact on the socioeconomic set-up of the country, as India is primarily an agriculture-dominated country.

So far not many studies have investigated the destabilization hypothesis in the case of emerging markets. This study is a novel attempt to fill the gap. In the case of emerging markets and especially in the case of India’s commodity derivatives market, this is the first study that examines the destabilization hypothesis in the case of India by applying new methods of high and band-pass filters and non-linear causality.

The purpose of this paper is to evaluate the profitability of investment strategies based on past price changes and trading volumes.

Data are employed from January 1998 to December 2011 for select emerging markets. Portfolios are formed on the basis of past information on prices and/or volumes. Unrestricted and risk adjusted returns for sample portfolios are analyzed. The risk models employed in study are Capital Asset Pricing Model (CAPM), Fama-French (F-F) Model and Fama-French augmented models.

Price momentum patterns are observed for Brazil, India, South Africa and South Korea, while there are reversals in Indonesia and China. Low-volume stocks outperform high-volume stocks for all sample countries except China. Further, volume and price based bivariate strategies do a better job than univariate strategies in case of India, South Africa and South Korea. The past price and volume patterns in stock returns are not fully explained by CAPM as well as the F-F Model. Price and volume momentum factors do play a role in explaining some of these return patterns. Finally, the unexplained returns seem to be an outcome of investor under or overreaction to past information. The sources of price and volume momentum seem to be partly risk based and partly behavioral.

The study analyzes combined role of price and volume in portfolio formation with post holding analysis. The work is useful for global portfolio managers, policy makers, market regulators and the academic community. The study contributes to asset pricing and behavioral finance literature for emerging markets.