Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153308
Title: Applications of machine learning in portfolio management
Authors: Numair Fazili
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Numair Fazili (2021). Applications of machine learning in portfolio management. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/153308
Project: SCSE20-0798
Abstract: Advances in predicting market returns using machine learning have seen tremendous growth which can be attributed to veritable data mining and computational capabilities. However, a large portion of this literature focuses on short-term effects by evaluating models solely on their prediction error rates. Long-term consequences of these predictions across a portfolio are not assessed and as such the compounding effects remain unexplored. We propose a novel framework towards implementing the Constant Proportion Portfolio Insurance (CPPI) strategy aided by predictive returns. This work is inspired by the advances in machine learning frameworks for financial markets in conjunction with gaps within the existing CPPI system. Technical analysis was used to augment the standard asset price/volume data by introducing 14 additional features associated with momentum, trend, volume, and volatility measures. These features were used in training Support Vector Regression (SVR), Facebook Prophet (FBP), and Long Short Term Memory (LSTM) models. The evaluations were conducted for six years from 2015 to 2020 using RMSE, MAE, and MAPE as error metrics. The performance analysis indicates that LSTM network with a 7-day walk forward validation serves as the ideal architecture for attaining the lowest error rate. Predictive returns generated from machine learning algorithms were subsequently used inside a modified CPPI framework. Our empirical analysis shows that this approach outperforms the existing CPPI towards achieving consistently high portfolio valuations. We observe that all three machine learning architectures outperform the standard CPPI with the LSTM model delivering optimum portfolio performance attributed to a 13.79% increase in Sharpe Ratio. Keywords: CPPI, Machine Learning, Predictive returns, Technical Analysis, Sharpe Ratio.
URI: https://hdl.handle.net/10356/153308
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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