The usage of machine learning techniques for the prediction of ﬁnancial time series has been used investigated for a (very) long time. Both in the academic and fund management words, generative methods such as Switching Autoregressive Hidden Markov and changepoint models are generally found to be unsuccessful at predicting daily (and higher frequency) prices from a wide range of asset classes. However, committees of discriminative techniques (such as Support Vector Machines or Relevance Vector Machines and Neural Networks) are found to give some interesting results. It however remains that there is no such thing as a magic recipe and that using machine learning model for financial markets faces enormous challenge due to the very nature of financial markets, i.e. an ecology of learning agents continuously creating information and having different
Joel Guglietta has been working as a quantitative strategist & portfolio manager for hedge funds and investment banks (Brevan Howard, BTIM, Graticule, HSBC) for more than 12 years. His expertise is in quantitative models for asset allocation, portfolio construction & management using a wide range of technics of which machine learning techniques and genetic algorithms.