Optimisation of stock market prediction is regarded as a challenging task in financial time-series modelling, yet this is really important to optimise the predictions and reduce the uncertainties involved in the movement of the markets and other commodities. Predicting market price is quite difficult as they are a highly non-linear, dynamic problem. Also, the ability to predict the direction and not the exact value of the future stock prices is the most important factor in making money using financial prediction. Furthermore, investors needs to know how to buying or selling decision and the general direction of the stock, and accurate predictions can lead to higher profits.

In this project, students will apply artificial neural network algorithms on time-series data to predict stock market, cash or other commodity prices. Students will apply various ‘predictor variables, and develop forecasting models. The integration of neural network models with optimiser tools such as particle swarm optimisation (PSO) or firefly optimiser algorithm (FFA) for optimal performance of artificial intelligence models will lead to more accurate predictive models.

The purpose of this project is:
• Develop neural network models with carefully selected input variables,
• Apply various mathematical and computational tools such as multi-resolution wavelet analysis and empirical mode decomposition algorithms to optimise the performance of neural network models.
• Integrate neural network models with optimiser algorithms (such as particle swarm optimisation or firefly optimiser tools).
• Provide uncertainty assessment in stock market forecasting using various predictive models.

The outcomes of this project will lead to the enhancement of financial modelling to attain more accurate forecasting capability of models.

The project is suitable for students with background in mathematics/statistics, finance, economics and business.

Student will learn MATLAB as a data analysis and modelling tool. There is potential for advancing their research career, developing good programming related to financial modelling and publication opportunities within the Environmental Modelling and Simulation Research Group.

Professor Ravinesh Deo

• Assoc Prof Nathan Downs
• Professor Yan Li

• School of Agricultural and Environmental Sciences

• Artificial Intelligence and Image Processing
• Banking, Finance and Investment
• Econometrics

• Doctor of Philosophy (DPHD)
• Doctor of Philosophy (DPHD)
• Master of Research (MRES)