Financial forecasting and portfolio optimisation in the 21st Century

Low cost and infinitely scalable storage and computing costs, advanced algorithms enabled by new ‘cloud computing paradigms’ are now being ‘democratised’.

Financial forecasting and portfolio optimisation in the

Low cost and infinitely scalable storage and computing costs, advanced algorithms enabled by new ‘cloud computing paradigms’ are now being ‘democratised’.
fintech By FINTECH Books Contributor, Jeremy Sosabowski Follow:  The ongoing disruptions within the asset/wealth management space including in the areas of Robo advisory and optimised portfolio construction are already visible on numerous fronts. What still isn’t visible and probably even more urgently required are changes in existing market risk and portfolio construction methodologies. Mathematical models and calibration from past events are the foundation for most models used in today’s financial services industry including portfolio diversification (Markovitz), option pricing (Black–Scholes), interest rate evolution (Black–Karasinski) and tail risk impact probabilities (VaR and others). Global financial markets are becoming even more interdependent by the day, making diversification and financial forecasting increasingly challenging. Past assumptions about correlations and the frequency of extreme events (US Elections, Brexit) are making this even more complicated. Needless to say that occasionally things can and do go very wrong in global financial markets. The irony is not lost on the AlgoDynamix team that most financial models stop working when most required. AlgoDynamix is redressing these fundamental flaws using its analytic engine based on deep data from primary data sources (the world’s global financial exchanges) and its proprietary unsupervised machine learning algorithms. Financial forecasting methodologies not only improve risk adjusted returns but also construct ‘fundamentally’ solid portfolios with characteristics very carefully tailored to exact (individual) user requirements. This is enabled by phenomenal progress in the following areas: very low cost and infinitely scalable storage and computing costs, even more advanced algorithms enabled by new ‘cloud computing paradigms’ and advances in certain types of (unsupervised!) machine learning. These advanced solutions are now being ‘democratised’, readily available to even more organisations within the asset/wealth management space including family offices, hedge funds and even the ‘traditional’ long only longer term asset managers. If you would like to learn more about WealthTech please look at our video courses at FINTECHCircleInstitute.com