portfolio optimisation
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Supervised machine learning – Linear regression
Investment decisions demand a blend of precision, speed, and foresight. Quantitative developers play a pivotal role in enabling financial firms to stay ahead by designing predictive models that extract actionable insights from vast amounts of data. Leveraging Python’s capabilities, one…
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Price modelling – Itô’s Lemma – Geometric Brownian Motion
Quantitative analysts constantly seek robust methods to model and predict asset price movements. One such powerful tool is Itô’s Lemma, a cornerstone of stochastic calculus. By leveraging this mathematical framework alongside Python, quants can enhance their analytical toolkit, making strides in…
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Approximations – Taylor series – Gordon Growth Model
In investment management, precision and efficiency are paramount when analysing how market variables impact stock prices. The Taylor series, a powerful mathematical tool, provides a robust framework for understanding price sensitivities and convexity effects. One application for the Taylor series…
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Portfolio optimisation – Linear algebra
Making investment decisions based on intuition or limited data can often lead to suboptimal outcomes. Quantitative analysis revolutionises how investors and portfolio managers approach asset allocation, enabling precise, data-backed strategies that optimise returns while controlling risks. One example of this…
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Supervised machine learning – Gaussian Naïve Bayes
In quantitative finance, having access to precise data analytics and advanced predictive models is key. With its versatile ecosystem, Python has emerged as an indispensable tool for quantitative developers seeking to optimise investment strategies and forecast market movements. One such…