How Data Science Has Transformed Investment World

Last time I have explained what is statistical arbitrage strategy that is applied by quantitative hedge funds. I mentioned the machine learning algorithms such as Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM), which are used as foundation to predict action of stock price. This time I will dig deeper and explore what is RNN (mainly) and LSTM, and how they work in practice. Normally when learning about machine learning, we start with linear and multi-linear regression. These are fundamental concepts for building a Feed-Forward Neural Networks, which allows information to flow only in the forward direction, from input layer, through hidden layers, and to the output layer. There are no cycles or loops in the network. Below is the flow chart of a feed-forward neural network: The problem with FFN is that decisions are based on the current input. It does not memorize the past data, so there is no future scope. That’s why it is used in general regression and classific...

I remember talking about statistical arbitrage strategy with a peer years back. The concept was simple, but I would not know the specific steps to implement this strategy on financial data as my knowledge on programming and machine learning models were limited. In this blog, I will brief introduce what is statistical arbitrage strategy, and applications of data analysis and machine leaning techniques in a practical case study. Statistical arbitrage , also referred as stat arb, is a type of investing strategies that involves investing thousands of stocks in a short period of time by utilizing mean reversion analysis on stock prices. This strategy aims to reduce beta exposure as much as possible. Beta is a measure of a stock’s volatility in relation to the overall market such as S&P500. For example, if the price of SPY (S&P500 ETF) went up 1%, a stock price with beta of 2 would go up 2%. It is same for the opposite case that if SPY went down 1%, this stock price would fall 2...