Rise of the Machines in Active and ESG Investing | #𝗦𝗔𝗟𝗧𝗡𝗬 | Summary and Q&A

TL;DR

The adoption of AI and machine learning in the investment industry has become popular due to the drop in computing costs, availability of algorithms, and the presence of big data.

Key Insights

• 😮 The rise of AI and machine learning in the investment industry is driven by the decreasing cost of computing and the availability of algorithms.
• 🔤 ESG analysis can enhance investment strategies and generate alpha.
• 🎰 Machine learning can help identify risks and prevent greenwashing in ESG investing.

Transcript

foreign thank you very much for joining us today we're going to be talking about the rise of the Machines in active and ESG investing systematic strategy is a Hot Topic at the moment dominating increasing parts of alternative investment Quant strategies have been outperforming we saw that during the volatility early in the pandemic and again throug... Read More

Questions & Answers

Q: Why has AI and machine learning become popular in the investment industry?

The drop in computing costs, availability of algorithms, and the presence of big data have driven the adoption of AI and machine learning in the investment industry.

Q: Can machine learning data help offset the risks associated with ESG investing?

Machine learning can help identify risks and prevent greenwashing by looking at unstructured and niche data sets to enhance ESG analysis.

Q: How can AI and machine learning be a risk in ESG investing?

AI and machine learning require a significant amount of computing power, which can have environmental implications. Additionally, biases in the data can lead to biased predictions and ethical concerns regarding the use of private information.

Q: Is ESG investing just about aligning with values or can it generate alpha?

ESG analysis has been shown to generate alpha, even when portfolios look the same as benchmarks in terms of sectors, industries, and factors. There appears to be a separate ESG factor driving performance.

Summary & Key Takeaways

• The cost of computing has decreased, leading to the popularity of AI and machine learning in the investment industry.

• Open source packages and robust algorithms have made it easier for investors to adopt AI and ML techniques.

• Big data has become essential for quant investors, allowing for non-linear models and predictions.