{"id":43650,"date":"2025-12-11T22:26:22","date_gmt":"2025-12-11T21:26:22","guid":{"rendered":"https:\/\/quantpedia.com\/?p=43650"},"modified":"2025-12-11T22:42:23","modified_gmt":"2025-12-11T21:42:23","slug":"quantpedia-in-november-2025","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-in-november-2025\/","title":{"rendered":"Quantpedia in November 2025"},"content":{"rendered":"

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

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Firstly<\/strong>, based on the feedback from our clients, we extended the Correlation Analysis<\/a><\/strong> report available in the Quantpedia Pro<\/a><\/strong> subscriptions. The upgraded report now allows users to assess the correlation structure of their model portfolio directly in relation to a selected benchmark portfolio. You can examine how the average correlation between the two portfolios evolves over time, and you can evaluate point-in-time correlations across multiple horizons<\/strong> ranging from short-term 10-day and 20-day windows to medium-term 60- and 126-day periods, and further out to 1-, 3-, and 5-year windows, including since-inception statistics. This broader temporal perspective helps portfolio managers distinguish between short-lived market dynamics and persistent long-term relationships in the portfolio\u2013benchmark interaction..<\/p>\n\n\n\n

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In addition to the time-series view, the report now includes a comprehensive correlation table that displays how each constituent of the model portfolio relates to each constituent of the benchmark.<\/strong> This cross-asset matrix <\/strong>enables users to uncover hidden exposures, identify concentrations, and better understand which benchmark components are most strongly linked to their strategy. Finally, the new correlation-contribution table decomposes the total portfolio\u2013benchmark correlation into contributions from individual asset pairs<\/strong>. This decomposition clarifies which relationships are driving overall co-movement and provides an intuitive diagnostic tool for validating diversification, attribution of correlation sources, and measuring the structural alignment between a model portfolio and its benchmark.<\/p>\n\n\n\n

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Secondly<\/strong>, we would like to invite you to watch the 6th episode of our YouTube video series QuantBeats<\/a><\/strong>. Sid Ghatak from Increase Alpha<\/strong> shares his journey from government tech to quantitative finance and breaks down how decades of AI and data engineering shaped his unique approach to finding alpha. He explains why curated, trustworthy datasets outperform brute-force modelling and how quants can avoid the risks of hallucinating AI and contaminated training data. If you\u2019re curious about how we see the role of machine learning and AI in quant trading, then listen to this newest Quantbeats episode<\/strong>.<\/p>\n\n\n\n

We also sincerely invite you all to follow us on our YouTube<\/a>, Linkedin<\/a>, FB<\/a>, Twitter<\/a>, and\/or Bluesky<\/a> links.<\/p>\n\n\n\n

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Thirdly<\/strong>, we would like to announce a new cooperation between Quantpedia and Quantmatix<\/strong><\/a> – the AI investment research and trading platform. Some of you may remember the discussion with CEO Liam Boggan and Head of Product John Mulligan in our Quantbeats Episode 5<\/a><\/strong>. Quantmatix AI has productised systematic and quantitative momentum and flow trading strategies, screening 8,000 instruments to curate the best ideas and market insights. Data is provided from ICE, and all insights are rigorously back-tested. For systematic, scaled AI research, be sure to take their 7-day trial! And if you like it, you can use the promo code QUANTPEDIA<\/strong> at checkout to get a 25% discount<\/strong> on their product!<\/p>\n\n\n\n

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