{"id":40129,"date":"2025-05-12T15:02:01","date_gmt":"2025-05-12T13:02:01","guid":{"rendered":"https:\/\/quantpedia.com\/?p=40129"},"modified":"2025-06-04T14:24:49","modified_gmt":"2025-06-04T12:24:49","slug":"quantpedia-in-april-2025","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-in-april-2025\/","title":{"rendered":"Quantpedia in April 2025"},"content":{"rendered":"

Hello all,<\/p>\n\n\n\n

In the previous month, we hinted that our front-end migration would help us to integrate some of the new features. So, let’s not waste time and start with the first one – our brand new Black-Litterman Portfolio Optimization report<\/a><\/strong>.<\/p>\n\n\n\n

The Black-Litterman model<\/strong> is an advanced portfolio optimization framework designed to blend investors’ subjective market views<\/strong> with the market equilibrium implied by asset prices<\/strong>. Developed by Fischer Black and Robert Litterman, the model addresses the sensitivity and instability often seen in classical mean-variance optimization. By incorporating investor opinions explicitly through Bayesian adjustments to equilibrium returns, the Black-Litterman method provides more stable, intuitive, and practical asset allocation decisions<\/strong>, resulting in portfolios that better reflect both market conditions and investor expectations.<\/p>\n\n\n\n

How will it work for the Quantpedia Pro<\/a><\/strong> clients? At the beginning, users must define a benchmark portfolio weights (usually a global market portfolio). They can use any combination of their uploaded equity curves and\/or equity curves that are available in the Portfolio Manager (mix of selected ETFs and Quantpedia strategies).<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Then, the Black-Litterman model calculates market-implied returns (from the benchmark weights defined by users) and compares them to the realized asset returns using an equilibrium chart and table.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Users have the possibility to express their own subjective views about the relative expected returns<\/strong> (outperforms\/underperforms) for specific assets (or strategies) in the report’s left widget.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

The adjusted expected returns are then fed into a mean-variance optimization, producing a portfolio allocation that reflects both the market consensus and investor-specific insights.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n
\n\n\n\n

Secondly<\/strong>, some of you, our readers, are probably interested to know the progress of the Quantpedia Awards 2025<\/a><\/strong> competition. The deadline for paper submission is behind us, and Quantpedia\u2019s team has processed papers. The final 10 have been sent to our committee<\/a> for the next stage, ranking. We will list the whole top 10 and who will get a share of the $25.000 prize pool <\/strong>in the second half of May. Stay tuned, and we will announce more soon \u2026.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n
\n\n\n\n

At last, let\u2019s also quickly recapitulate Quantpedia Premium<\/strong><\/a> development:<\/p>\n\n\n\n