- The latest issue of the Financial Analysts Journal includes articles on a range of topics, including ESG factors, index allocation, capital market liberalization in China, hedge fund benchmarks, machine learning in fixed-income investing, and tax-loss harvesting
- One article explores the Journal’s history of addressing environmental, social, and governance (ESG) issues and how these insights remain relevant today
- Another article compares bank risk premia products to hedge fund performances, finding that risk premia within equities, rates, and credit yield positive returns
- An article by BlackRock proposes a Bayesian framework for allocating among market index, factors or smart beta, and alpha-generating funds
- Machine learning techniques are discussed in an article that applies equity momentum signals to predict returns in credit listings
- ESG rating disagreement and its relationship to stock returns are explored in a comprehensive analysis that includes seven different rating providers
- A Vanguard study demonstrates that tax-loss harvesting is not suitable for everyone and highlights the importance of individual investor characteristics
The latest issue of the Financial Analysts Journal covers a range of topics, providing insights and research on various aspects of finance. Here are the key points from each article:
1. The Journal’s history of addressing ESG issues: The first article in the issue examines the Journal’s work on environmental, social, and governance (ESG) issues since 1945. It shows how academia and investment practitioners have long been grappling with these issues and demonstrates the relevancy of their insights today.
2. Capital market liberalization in China: The second article treats the implementation of the Shanghai-Hong Kong Stock Connect in 2014 as an experiment and observes the effects on corporate investment efficiency. The research demonstrates that market liberalization improves corporate investment efficiency through better information disclosure and corporate governance.
3. Hedge fund benchmarks and bank risk premia: The third article compares bank risk premia products to hedge fund performances. The research finds that risk premia within equities, rates, and credit yield significantly positive returns and have improved explanatory power compared to traditional hedge fund models.
4. Index allocation using a Bayesian framework: The fourth article proposes a Bayesian framework for allocating among market index, factors or smart beta, and alpha-generating funds. The authors provide a step-by-step demonstration of implementing this model in your investment process.
5. Machine learning in fixed-income investing: The fifth article discusses how machine learning techniques can improve the quality of equity momentum signals used in fixed-income investing. The research shows that alpha can be doubled with the use of boosted regression trees.
6. ESG rating disagreement and stock returns: The sixth article explores the dispersion among ESG ratings and its relationship to stock returns. The research covers the differences among seven rating providers and analyzes the relationship between rating dispersions and cost of capital.
7. Individual perspective on tax-loss harvesting: The final article demonstrates that tax-loss harvesting is not suitable for every investor and highlights the importance of individual investor characteristics. The research shows substantial dispersion in outcomes based on tax rates and offsetting income.
These articles provide valuable insights into various aspects of finance, including ESG factors, index allocation, capital market liberalization, hedge fund benchmarks, machine learning, and tax-loss harvesting. Each article offers unique perspectives and research findings that can inform investment decision making.
