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Quantitative finance is the use of frontier mathematical and statistical models with extremely large datasets to analyze financial markets and securities. Common examples include (1) the pricing of derivative securities such as options, and (2) risk management, especially as it relates to portfolio management applications.

Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning techniques can be practical investment tools. Although machine learning algorithms can uncover subtle, contextual, and nonlinear relationships, overfitting poses a major challenge when one is trying to extract signals from noisy historical data.

There are very few things which we know, which are not capable of being reduced to a Mathematical Reasoning, and when they cannot, its a sign our Knowledge of them is very small and confused.

Where a mathematical reasoning can be had, it’s as great folly to make use of any other, as to grope for a thing in the dark when you have a Candle standing by you.

Portfolio optimization is not the only possible way of building a portfolio. Several modern financial technologies like Machine Intelligence, Data Science and Advanced Analytics are available as a new kind of math to work with the level of uncertainty.

Adding assets with low correlations to a portfolio can decrease the total risk without any loss in potential returns. When I increase the diversity within investments, it results in a higher Sharpe Ratio.

At Global Accountancy Institute, We now apply mathematical and computational methods to develop and exploit financial opportunities for return enhancement and risk control in the Global Financial Markets.