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Title: Machine learning applications in finance : some case studies
Author: Wei, Zhen
ISNI:       0000 0004 8504 8467
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2019
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The first part of this thesis discusses the application of artificial intelligence to stock price prediction and the building of another artificial intelligence model for stock price prediction during mergers and acquisitions. It is demonstrated that a combination of Long Short-Term Memory/Recurrent Neural Network + Convolutional Neural Network + Attention (LSTM/RNN+CNN+Attention) trained on daily and minute stock price and volume data are able to predict future stock prices and stock price changes three times better than traditional non-deep learning methods. Further research has been done on stock price prediction during Mergers and Acquisitions for technology unicorns, finding that when more information on M&A has been added into the input dataset, the accuracy can be improved by about 2%. It is the first time that a deep learning algorithm has been applied to modelling volume and price/price change, and to M&A circumstances. In the second part of this thesis, a Support Vector Machine (SVM) method is proposed, which enables adverting agencies to assess the effectiveness of their advertisement rapidly, quantitatively and without bias. This is crucial work for business/equity trading because when the product/service of a company sells well, then the corresponding company stock prices rise. The success of an advertising agency in their ability to produce compelling advertisement is therefore related to stock price prediction. This research explores models that outperform existing research; even the basic machine learning model improves the accuracy of a prediction to purchase from 70% up to 80%. The last part of this thesis incorporates a deep learning method to encrypt the data going between input nodes and the rest of the network. This method has the advantage of simultaneously preventing sensitive data being intercepted during the transmission from the network edge over insecure transports, and also anonymize incoming data in a way that could help satisfy privacy laws such as General Data Protection Regulation (GDPR). This research has provided a new data privacy and security structure that is more cost efficient than existed deep learning model done solely locally or remotely.
Supervisor: Guo, Yi-ke Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral