MoneyLab

The AI revolution has arrived. ChatGPT, an AI language model developed by OpenAI and launched in Nov 2022, has taken the world by storm. Its powerful language processing capabilities has revolutionised the way people interact with technology and transformed the way we work and live. From writing emails, handling enquiries, brainstorming ideas to making court decisions , the use of ChatGPT is making our job easier and boost our productivity at work. ChatGPT could also potentially be a valuable tool for investors looking to construct an investment portfolio according to an investor's preferences and risk profile. In this blog post, I will delve into how ChatGPT can be utilised for this purpose. To begin, I will inform ChatGPT of my investment preferences and risk tolerance, and specify the evaluation criteria for the portfolio. Specifically, my goal is to create a dividend-generating portfolio entirely composed of ETFs. Additionally, I would like a performance analysis of the recomme...

  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...

  For the first post of this blog, I think it is apt to start with a random walk down the stock market. People in the market should be of no stranger to the Random Walk Hypothesis (RWH) . The concept of random walk could be traced back to Jules Regnault, a French broker in the 1800s. The term was later popularised by the book, A Random Walk Down Wall Street , written by Burton Malkiel. It is a financial theory that suggests stock prices evolve randomly and thus cannot be predicted by past trend. In this post, I put the RWH to test by constructing a Long Short-Term Memory (LSTM) model to predict stock price movement (up or down) one day ahead. The data used in this analysis was obtained from Kaggle . It contains stock market information of securities listed on the two US stock exchanges, NASDAQ and NYSE. The Date, Open, Low, High, Close, Adjusted Close and Volume of each trading day was provided for each stock. For this ...