Bharath Reddy

Oct 15, 2024 | lexology.com | Bharath Reddy |Abhishek Jain

Background Global capability Centres (“GCCs”) have taken centre stage today because of their contribution towards the growth and expansion of multi-national corporations (“MNCs”) and towards boosting the economic growth of many developing countries in which they are located.[1] These centres are set up to primarily take on a service role for the global group of the MNCs. Evolving from back offices and cost-arbitrage centres, GCCS have transformed into potential alternative technological and...

Sep 26, 2024 | lexology.com | Bharath Reddy |Abhishek Jain

Companies in the twenty-first century use unique workforce retention strategies, especially long-term incentives that involve direct/indirect co-employee ownership. This post aims to discuss the regulatory framework governing share-linked and share-based employee benefits that companies offer.[1]Co-employee ownership a means for workforce retention? Currently, one of the most common strategies for workforce retention is providing employees with long-term incentives.

Sep 5, 2024 | lexology.com | Bharath Reddy |Abhishek Jain

Background Historically, companies have provided employees with share-based incentives by way of employee stock options (“ESOPs”). However, with evolving corporate incentive structures, various new models have emerged, especially driven by start-ups.

Aug 13, 2024 | lexology.com | Bharath Reddy |Abhishek Jain

This post analyses the scope of the regulatory framework governing employee benefits by equity listed companies in India and the applicability of the SEBI (Share-Based Employee Benefits and Sweat Equity) Regulations, 2021, to employee welfare trusts set up by promoters and share-linked but purely cash-based employee benefits. Applicable regulatory frameworkThe Securities and Exchange Board of India Act, 1992, and its rules and regulations govern equity listed companies in India.

Aug 7, 2024 | medium.com | Bharath Reddy

Precision is the ratio of correctly predicted positive observations to the total predicted positives. It answers the question: “Of all the positive predictions, how many were actually correct?”Example: Imagine you have a model that predicts whether an email is spam. Out of 100 emails predicted as spam, 80 are actually spam, and 20 are not (false positives). So, the precision is 0.8, or 80%. Recall is the ratio of correctly predicted positive observations to all the observations in the actual class.