AbstractThis thesis proposes an Artiﬁcial Neural Network (ANN) enhanced decision support system for ﬁnancial risk management. The decision support system allows hedgers to maximise their expected return while practising the hedge against ﬁnancial risks.
The importance of the research stems from the fact that it can be used to reduce the risk associated with adverse price movements in the stock market.
The literature review reveals that there are a large number of studies trying to forecast movements in the stockmarket, but there is a lack of literature trying to improve stock market risk management strategies with machine learning techniques.
This thesis addresses this gap by applying the existing body of literature in stock index forecasting with machine learning techniques to the domain of portfolio risk management. Inparticular,itanalyseswhetherstrategiesusedtopredictmovementsinthestock index can also be used to derive hedging strategies and improve the overall risk-return trade off an investor faces.
A new market timing model based on ANNs is developed which forms the heart of the proposed decision support system. The system analyses stockmarket and futures data and makes a prediction about expected stock market conditions one month ahead. The proposed ANN based hedging strategy uses stock index futures to protect the portfolio against downturns in the share market.
Overall, this thesis concludes that the proposed model achieves a signiﬁcant improvement in the risk-return tradeoff compared to the benchmark hedging strategies in the Australian stockmarket.
|Date of Award||11 Feb 2012|
|Supervisor||Bruce Vanstone (Supervisor) & Gavin Finnie (Supervisor)|
Risk Management in the Australian Stockmarket using Artificial Neural Networks
Krollner, B. (Author). 11 Feb 2012
Student thesis: Doctoral Thesis