Deep Autoencoders in Financial Markets
I will be talking about applications of few deep learning algorithms in finance. First, I will talk about autoencoders for dimensionality reduction in relative value strategy. I will illustrate how it is different from conventional methods such as PCA or factor models. I will also address its limitations and things to keep in mind while building autoencoders for dimensionality reduction on financial data.
I will also talk about Generative Adversarial Networks (GAN) models and how they can be used to generate synthetic stock market returns. I will talk about GANs ability to match the fat tail distributions very accurately. I will address the issue of the autocorrelation of returns and how we can use GANs to capture it.
I am cross asset quantitative analyst at Societe Generale and I specialize in machine learning. I have been building quantitative models to forecast/analyze markets from last 10 years. Prior to Societe Generale, I was working as HFT trader at Edelweiss Securities, Mumbai. I use time series analysis, machine learning (deep learning), stochastic calculus, factor analysis and other mathematical techniques to build my models.
I have bachelor’s degree in Electronics and communication from RVCE, Bangalore and master’s degree in Control and Automation from IIT Delhi. I have several hobbies. I like building robots with computer vision, IOT devices and I have used use machine learning techniques to build models to forecast rainfall, analyze genomics data etc.