Sanjay Sehgal’s research while affiliated with University of Delhi and other places

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

In this study, we examine the relationship between tail measures and stock return for China and India using data from 2000 to 2021. For both the markets, left and right tails exhibit a negative relationship with stock returns and confirm the tail risk puzzle and tail preference, respectively. Tail-based anomalies seem to be a small stock phenomenon, except for the right tail effect in case of India. We find that the two-tail strategy outperforms one-tail strategies for both markets. We confirm that the tail anomaly is primarily a behavioral phenomenon driven by both retail and institutional investors. Market and firm-level sentiments tend to play a more critical role in India than in China. A negative relationship exists between the change in the tail and expected returns for both markets. Contrary to recent research, the change in the tail factor does not explain tail-based returns. Chinese and Indian investors prefer tail risk irrespective of the initial capital state, contrasting with the prospect theory. Investor attention impacts expected returns in both markets, but Indian investors exhibit limited attention bias for both left and right-tail-based information, which is not the case in China. The findings are pertinent for policymakers and global investors. JEL Codes G11, G12, G23, G40.

This article investigates monetary policy and stock market interaction across 41 developed and developing economies using GMM-Panel VAR model. The analysis is undertaken in two sub-periods—before and after the crisis of 2008 to make a comparative assessment of whether the relationship between monetary policy and stock prices altered in the aftermath of the crisis. We verify the existence of different channels of monetary transmission to stock prices. Our results point to the prevalence of discount rate channel of monetary policy in affecting stock prices after the crisis of 2008. Further, our results indicate an important role of excess liquidity in pushing stock prices upward in developed economies in the post-crisis period. In developing economies, term premia channel is the dominant channel of transmission to stock prices. Also, we find evidence of monetary authorities of developed economies responding directly to stock price movements to ensure financial stability in the post-crisis period. Central banks react primarily to inflationary pressures by tightening monetary policy both before and after the crisis in developed and developing economies. JEL Codes: C32, E44, E52, F42

Purpose 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. Design/methodology/approach 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. Findings 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. Practical implications 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. Social implications Better bond ratings may mitigate corporate defaults. Originality/value 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.

We examine prominent market anomalies and evaluate the efficacy of alternative asset pricing models under different financial integration settings. A financial integration index is developed for classifying 25 sample markets into high-, medium- and low integration groups. Size is found to be the strongest anomaly in world markets, followed by value and liquidity. Value and profitability effects are larger for low-integrated markets. Highly integrated markets experience short-term momentum while many low-integrated markets exhibit mild reversals. Fama and French five-factor model outperforms capita l asset pricing model (CAPM) and Fama and French three-factor model in explaining returns. International factors augment the role of local factors for more integrated markets. Our study has implications for global investors to design anomaly based investment strategies.

This study examines the betting against beta (BAB) anomaly and its drivers for five major Asian markets, using data from January 1999 to January 2020. We find positive raw returns-based BAB premiums for India, China, and South Korea, while they are negative in Japan and Indonesia. Cross-sectional differences in BAB premiums seem to be positively associated with the level of information uncertainty and financial market development. Decomposition of the BAB phenomenon shows that BAB premiums are driven by betting against correlation (BAC) premiums in India while betting against volatility (BAV) premiums drive it in China and S.Korea. Funding liquidity risk and margin constraints drive positive BAC in India and Indonesia. Positive BAV in China is driven by MAX (lottery behavior), while idiosyncratic volatility (IVOL) and MAX together drive them in S.Korea. Negative BAB in Japan and Indonesia is mainly due to negative BAV premiums caused by MAX in Japan and both IVOL and MAX in Indonesia. CAPM-based risk-adjusted BAB premiums are not significant for S.Korea, Japan, and Indonesia, while in India and China, they are significant but get explained by the profitability factor. We conclude that the BAB strategy is not universally applicable, and its drivers vary across sample markets.

The aim of this paper is to explore the macroeconomic determinants of corporate financial distress using a data from the Indian corporate sector. We also consider a set of quasi-macro variables to develop a hybrid model of financial distress. For this purpose, we use accounting information-based three-factor criteria to construct a series for probability of financial distress of firms. We initially use a systematic variable selection approach to develop alternative models of financial distress and then apply the bounds test to establish a long-run equilibrium relationship between financial distress of firms and its determinants. We find that macroeconomic factors play crucial role in determining financial distress. Results suggest that aggregate output, flow of international funds, international demand, and corporate profitability are negatively associated with probability of financial distress. However, periods of high inflation may not be beneficial as the results suggest that a high inflation leads to high financial distress. The findings are important in the sense that movement in these macroeconomic and quasi-macro factors can be useful in monitoring the buildup of risk or financial distress in the balance sheet of firms over time. The set of indicators identified in the study can be used to develop an efficient distress prediction models.

Using data for 1 848 companies, we find that quality increases, not quality, drive stock returns in India. Profitability and safety seem to be relevant attributes for measuring quality. Our cross-sectional tests show that the role of quality in predicting returns is partially subsumed by momentum in short holding periods. Rational sources are not able to explain quality premiums. We find that quality premiums result from investor overreaction. At the same time, momentum profits are an outcome of investor underreaction, suggesting that investors pay more attention to fundamentals than past price trends. High investments by institutional investing may account for such behaviour.

We examine round-the-clock international price discovery of gold among the major gold markets—New York, London and Shanghai during news-intensive and no-news time zones using one-minute data. Using GMM based parallel price discovery measure, we find global leadership of the US as New York gold futures lead across five time zones with 56% information share. New York/London (Nylon) timezone (51%) is the most informative trading session in sequential price discovery for all markets in 24-h. Our aggregate and disaggregate news analysis reveals that the US news surprises have a substantial and positive impact on its price discovery leadership while Eurozone news surprises have a negative impact and Chinese news have negligible impact. Using least absolute shrinkage and selection operator (LASSO) regression, we find scheduled news with a large surprise index has a significant yet asymmetric impact as negative news triggers a strong reaction. The impact of news surprise is state-dependent and display sign-reversals during extreme uncertainty, adverse macroeconomic conditions and abnormal investor behaviour.