Investment Analytics Project (BUSA90573)

In this capstone subject, students will work in groups to build an investment portfolio using one or more of the investment business analytics algorithms below used in the finance industry:

1. Fundamental analysis algorithms to analyse financial (e.g. earning forecast) and economic data to evaluate the underlying value of a security.
2. Technical analysis algorithms to analyse market data, such as price and volume, to identify trends and patterns that may indicate future price movements.
3. Quantitative analysis algorithms including statistical models and machine learning techniques to identify patterns and predict market movements.
4. Arbitrage algorithms using price discrepancies between different markets or securities to generate profits.
5. Trend following algorithms using historical price data to identify trends and generate trading signals based on the direction of the trend.
6. Mean reversion algorithms to look for securities that are trading outside their historical price range and generate trading signals based on the assumption that the security will revert to its historical mean.
7. High-frequency trading algorithms to execute trades at high speeds to take advantage of small price discrepancies in the market.
8. Sentiment analysis algorithms using natural language processing and machine learning techniques to analyse news and social media data to gauge investor sentiment and predict market movements.
9. Portfolio optimisation algorithms use mathematical models to optimise the allocation of assets in a portfolio based on risk and return objectives.