Case Study: Kyobo AXA IM achieves higher risk-adjusted returns with SESAMm's NLP alternative data

SESAMm collaborates with Kyobo AXA IM to construct & implement quant strategies

Kyobo AXA IM partnered with SESAMm to improve its quantitative investment strategies, looking for better ways to identify opportunities across asset allocation and equity trading. The firm used SESAMm’s TextReveal NLP alternative data extracted from the web to add more information beyond traditional market and financial data.

SESAMm worked with Kyobo AXA IM to build machine learning models and two trading use cases: optimal asset allocation across equities, bonds, and alternatives, and stock selection within the KOSPI 50. By combining NLP signals with market data and realistic portfolio constraints, SESAMm helped Kyobo AXA IM achieve higher risk-adjusted returns, including a substantially improved Sharpe ratio for the asset allocation strategy and double-digit outperformance on a risk-adjusted basis for the equity strategy.

Kyobo AXA IM

Frédéric Bellemin

Deputy CEO