Hedge fund Renaissance Technologies is looked upon by Wall Street with awe and envy in equal measure. Particularly, Medallion Fund, an employees only fund it runs. Bloomberg last year wrote the fund has returned more than $55 billion, making it more profitable than funds run by feted veterans such as George Soros. The Renaissance flagship fund, which will turn 30 next year, has returned more than 25% profits in most of its years of investing. Money doubles in a little more than three years at that rate.
Within the silos of incumbent financial services, so-called fintech companies are good at picking off one thing only and doing it well. This approach is also taken within data science, where a lot of the properly intelligent work is about understanding the domain (problem) and how best to use the information/data for the problem you have. In doing so, a fintech approach – collaboration, open-sourcing code – is helping to gradually change the culture of finance, even in some hitherto heavily guarded domains. Newsweek is hosting an AI and Data Science in Capital Markets conference on December 6-7 in New York. Dr Tristan Fletcher, research director, Thought Machine, said: "Without this specialisation and domain knowledge, it's very hard to rise above the noise.
Deep learning has been very successful in social sciences and specially areas where there is a lot of data. Trading is another field that can be viewed as social science with a lot of data. With the advent of Deep Learning and Big Data technologies for efficient computation, we are finally able to use the same methods in investment management as we would in face recognition or in making chat-bots. In his session at 20th Cloud Expo, Gaurav Chakravorty, co-founder and Head of Strategy Development at qplum, will discuss the transformational impact of Artificial Intelligence and Deep Learning in making trading a scientific process. This focus on learning a hierarchical set of concepts is truly making investing a scientific process, a utility.
Note: This post is part of a broader work for predicting stock prices. The outcome (identified anomaly) is a feature (input) in a LSTM model (within a GAN architecture)- link to the post. Options valuation is a very difficult task. To begin with, it entails using a lot of data points (some are listed below) and some of them are quite subjective (such as the implied volatility -- see below) and difficult to calculate precisely. As an example let us check the calculation for the call's Theta -- θ: Another example of how difficult options pricing is, is the Black-Scholes formula which is used for calculating the options prices themselves.