MoneyLab

  This is part II of the three-part series on building analytical models using financial news data to predict stock price movement (up or down). So far, we have explored the use of LSTM to predict stock price movement based on trend and momentum indicators. We also tried the n-gram TF-IDF scheme to model financial news data for predicting stock price movement. Unfortunately, both methods failed to pick up any useful signals that would help us trade profitably. For those who missed the two earlier posts, or would like to recap the analyses, you may access them through here and here. One issue with our earlier attempt to predict stock price movement using financial news was that the language model proposed in part I of the series was not sophisticated enough to understand the meaning of words nor the sequential dependencies of words in sentences. This post introduces word embedding and Bi-directional Long Short-Term memory (Bi-LSTM) to model financial news data that could overcom...

  The inaugural post of this blog, A Random Walk down the Stock Market , sets the stage for the challenging task of predicting stock price movement. An efficient market driven by millions of investors competing against each other to uncover profitable trading patterns eliminates the influence of such patterns on future prices. This conundrum underpins the weak form of the Efficient Market Hypothesis (EMH) . But all hope is not lost. Analysing and quantifying information such as growth prospects and market conditions accurately is a costly endeavour. Analysts or predictive models that could sniff out insights from a large mess of information could potentially be rewarded with higher stock market returns. This post is the first of a three-part series that explores the feasibility of building analytical models using financial news data to predict stock price movement (up or down). We start off our series by introducing a very simple yet often effective language model to model word us...