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3899 items in total found

Working Papers | 2022

Performance of quality factor in Indian Equity Market

Joshy Jacob, Pradeep K.P., and Jayanth R.Varma

We study the characteristics of Quality factor (QMJ) in India, which is the second largest emerging market. Dimensions of quality factor are impacted by the weaker enforcement of corporate governance norms in emerging markets. Diversion of revenues by promoters would result in poor profitability, while tunneling of profits would result in lower payout and lower growth. Therefore, investors are likely to attach greater significance to the quality dimensions in stock pricing. Consistent with this hypothesis, the Quality factor is even more important for asset pricing in India than in developed markets. The QMJ factor earns a four factor alpha of 0.92% per month, significantly outperforming the other widely employed factors, market, size, value and momentum factors. A long-only Quality factor earns an alpha of 0.69% per month. The alpha of quality factors is highly significant, judged by the thresholds recommended by Harvey, Liu, and Zhu (2016). The key drivers of the alpha are profitability and payout, which are both consistent with the tunnelling hypothesis. Besides the alpha, the low portfolio churn, lower risk, shorter drawdowns, and viability of long-only strategies restricted to large capitalization stocks suggest that portfolios tilted towards high-quality stocks are highly attractive to institutional and retail investors.

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Popular Press | 2022

Mindfulness, the myth of multitasking and winning Our inner game

Vishal Gupta

People Matters

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Popular Press | 2022

Chief Marketing Officers: Do organisations really need them?, Brand Equity

Sourav Borah

Economic Times

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Popular Press | 2022

Cargo drones and the future of logistics in India?(with Ajay Antony, Avi Dutt and Dr. Debjit Roy)

Sandip Chakrabarti

Times of India

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Popular Press | 2022

Numbers and Beyond: Gender Equity in Corporate India at Board Level, (with Moksh Garg)

Promila Agarwal

Financial Express

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Popular Press | 2022

Cargo drones and the future of logistics in India?(with Ajay Antony, Avi Dutt and Dr. Sandip Chakrabarti)

Debjit Roy

Times of India

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Popular Press | 2022

How Different MSMEs Are Planning To Respond To Covid-19 Crisis?

Chitra Singla

BusinessWorld

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Popular Press | 2022

The real victims of nativist labour laws? Low-income migrant workers

Chinmay Tumbe

Indian Express

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Journal Articles | 2022

First-generation and continuing-generation college graduates’ application, acceptance, and matriculation to US medical schools: A national cohort study.

Hyacinth RC Mason, Ashar Ata, Mytien Nguyen, Sunny Nakae, Devasmita Chakraverty, Branden Eggan, Sarah Martinez, and Donna B. Jeffe

Medical Education Online

Many U.S. medical schools conduct holistic review of applicants to enhance the socioeconomic and experiential diversity of the physician workforce. The authors examined the role of first-generation college-graduate status on U.S. medical school application, acceptance, and matriculation, hypothesizing that first-generation (vs. continuing-generation) college graduates would be less likely to apply and gain acceptance to medical school.Secondary analysis of de-identified data from a retrospective national-cohort study was conducted for individuals who completed the 2001–2006 Association of American Medical Colleges (AAMC) Pre-Medical College Admission Test Questionnaire (PMQ) and the Medical College Admissions Test (MCAT). AAMC provided medical school application, acceptance, and matriculation data through 06/09/2013. Multivariable logistic regression models identified demographic, academic, and experiential variables independently associated with each outcome and differences between first-generation and continuing-generation students. Of 262,813 PMQ respondents, 211,216 (80.4%) MCAT examinees had complete data for analysis and 24.8% self-identified as first-generation college graduates. Of these, 142,847 (67.6%) applied to U.S. MD-degree-granting medical schools, of whom 86,486 (60.5%) were accepted, including 14,708 (17.0%) first-generation graduates; 84,844 (98.1%) acceptees matriculated. Adjusting for all variables, first-generation (vs. continuing-generation) college graduates were less likely to apply (odds ratio [aOR] 0.84; 95% confidence interval [CI], 0.82–0.86) and be accepted (aOR 0.86; 95% CI, 0.83–0.88) to medical school; accepted first-generation college graduates were as likely as their continuing-generation peers to matriculate. Students with (vs. without) paid work experience outside hospitals/labs/clinics were less likely to apply, be accepted, and matriculate into medical school. Increased efforts to mitigate structural socioeconomic vulnerabilities that may prevent first-generation college students from applying to medical school are needed. Expanded use of holistic review admissions practices may help decision makers value the strengths first-generation college graduates and other underrepresented applicants bring to medical educationand the physician workforce.

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Journal Articles | 2022

SEntFiN 1.0: Entity-aware sentiment analysis for financial news

Ankur Sinha, Satishwar Kedas, Rishu Kumar, and Pekka Malo

Journal of the Association for Information Science and Technology

Fine-grained financial sentiment analysis on news headlines is a challenging task requiring human-annotated datasets to achieve high performance. Limited studies have tried to address the sentiment extraction task in a setting where multiple entities are present in a news headline. In an effort to further research in this area, we make publicly available SEntFiN 1.0, a human-annotated dataset of 10,753 news headlines with entity-sentiment annotations, of which 2,847 headlines contain multiple entities, often with conflicting sentiments. We augment our dataset with a database of over 1,000 financial entities and their various representations in news media amounting to over 5,000 phrases. We propose a framework that enables the extraction of entity-relevant sentiments using a feature-based approach rather than an expression-based approach. For sentiment extraction, we utilize 12 different learning schemes utilizing lexicon-based and pretrained sentence representations and five classification approaches. Our experiments indicate that lexicon-based N-gram ensembles are above par with pretrained word embedding schemes such as GloVe. Overall, RoBERTa and finBERT (domain-specific BERT) achieve the highest average accuracy of 94.29% and F1-score of 93.27%. Further, using over 210,000 entity-sentiment predictions, we validate the economic effect of sentiments on aggregate market movements over a long duration.

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