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LSTM Networks in Details

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Today I’d like to dig deeper into the details of LSTM algorithm. Below is a flow chart of LSTM networks. LSTM were designed specifically to overcome the long-term dependency problem faced by recurrent neural networks RNNs (due to the vanishing gradient problem). LSTMs have feed back  connections which make them different to more traditional feed forward  neural networks. This property enables LSTMs to process entire sequences of data (e.g. time series) without treating each point in the sequence independently, but rather, retaining useful information about previous data in the sequence to help with the processing of new data points. As a result, LSTMs are particularly good at processing sequences of data such as text, speech and general time-series. LSTMs use a series of ‘gates’ to control how the information in a sequence of data comes into, is stored in, and leaves the network. There are three gates in a typical LSTM:  forget gate,  input gate, and  outp...
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An Introduction of Recurrent Neural Networks

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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...
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A Peek on How Statistical Analysis and Machine Learning Work in Constructing Quantitative Strategy

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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...
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Are Machine Learning And AI the Future of Investing?

Nowadays, we are hearing machine learning and artificial intelligence almost everywhere, I remember watching my partner playing with an astrology application that supposed to tell one’s fortune this month, in 6 months or even the whole year according to the "big data" and "artificial intelligence" embedded behind it. In recent years. these technologies have developed so fast and been broadly marketed across all different industries. The proliferation of the concept has people already react: “what’s the big fuss?" when talking about AI. But do we really understand it and how it applies? I'd say NO with a high level of certainty. That's why I feel passionate to study them in both academic and empirical manner and share my humble opinions on how these technologies have affected and transformed the investment industry, where I work as an insider, over the past few decades.    Brief History of Machine Learning and Artificial Intelligence in Financial Industr...
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