{"id":14486,"date":"2025-08-15T11:00:50","date_gmt":"2025-08-15T15:00:50","guid":{"rendered":"http:\/\/dev.laplaceinsights.com\/?p=14486"},"modified":"2025-08-20T11:28:26","modified_gmt":"2025-08-20T15:28:26","slug":"machine-learning-and-market-data-complexity-bp","status":"publish","type":"post","link":"https:\/\/dev.laplaceinsights.com\/fr\/machine-learning-and-market-data-complexity-bp\/","title":{"rendered":"Machine Learning and Market Data Complexity\u200b"},"content":{"rendered":"
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Machine Learning and Market Data Complexity<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Financial metrics are different than physical systems<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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All key metrics in finance – risk, return, correlation – are conditional, multi-layered, and cyclical. It is the nature of the data that there are cycles within cycles. This makes analysis tremendously complicated.<\/p>

Working with financial data is very different from working with physical systems. The way atoms move can be expressed with fundamental laws that remain stable over time. There are exceptions, of course, but physical systems usually behave in the same way, especially in comparison to financial markets. Newton\u2019s equations for gravity and motion were the foundation that enabled space travel, in spite of the inherent approximations.<\/p>

Data for financial markets does not have equivalent stable patterns. For example,\u00a0the negative correlation between stocks and bonds is well established. However, that correlation shifts and becomes positive when there is inflation. The original two-dimensional relationship is made more complex by adding the third dimension of inflation. It only gets more complex from there. What is that relationship for specific sectors or industries?\u00a0 How does foreign exchange, and in particular the US dollar, affect this relationship?\u00a0What about different regions in the world? How are commodities affecting the relationship?\u00a0 The behavior very quickly becomes overwhelmingly complex.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Financial markets are continuously shifting<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Embracing the difference between financial and physical systems is key to recognizing the value that machine learning brings for portfolio management. Financial markets continuously adapt and shift, creating distinct regimes where relationships between variables remain stable for a period of time before changing again, sometimes very abruptly.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Machine Learning provides a powerful perspective<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Machine Learning (ML) is built to handle dynamic market environments. It adapts to changing market dynamics to uncover new relationships as they emerge, thanks to capabilities that have only recently become practical with modern computing power.<\/p>

Traditional statistical methods do give useful insights, and a lot of Machine Learning techniques are based on these methods. However, Machine Learning has two key advantages:<\/p>