Quantitative methods are the vanguard of the investment management industry. This book shows how to enhance trading strategies and investments in financial markets using deep learning algorithms.
This book is an excellent reference to understand how deep learning models can be leveraged to capture insights from financial data. You will implement deep learning models using Python libraries such as TensorFlow and Keras. You will learn various deep learning algorithms to build models for understanding financial market dynamics and exploiting them in a systematic manner. This book takes a pragmatic approach to address various aspects of asset management. The information content in non-structured data like news flow is crystalized using BLSTM. Autoencoders for efficient index replication is discussed in detail. You will use CNN to develop a trading signal with simple technical indicators, and improvements offered by more complex techniques such as CapsNets. Volatility is given due emphasis by demonstrating the superiority of forecasts employing LSTM, and Monte Carlo simulations using GAN for value at risk computations. These are then brought together by implementing deep reinforcement learning for automated trading.
This book will serve as a continuing reference for implementing deep learning models to build investment strategies.
Luigi Troiano, Ph.D., is an Associate Professor of Artificial Intelligence, Data Science, and Machine Learning at the University of Salerno (Italy), Dept. of Management and Innovation Systems. He is a coordinator of Computational and Intelligent System Engineering Lab at the University of Sannio and an NVIDIA Deep Learning Institute University Ambassador. He is also the chairman of the ISO/JTC 1/SC 42, AI and Big Data, Italian section.
Arjun Bhandari is Chief Investment Officer of a family office. His previous positions have been Head of Quantitative Strategies at ADIA ( largest sovereign wealth fund in the middle east ) and APG Investments ( largest pension plan in Europe ). He has been deploying quantitative techniques for multi-asset class investments for over 20 years, bringing this experience to bear on his most recent focus on machine learning applied to fund management.
Elena Mejuto Villa
Elena Mejuto Villa, Ph.D., is a data scientist in the Advanced Analytics team for Technology Services Consulting in a multinational firm in Milan. She completed her Master's Degree in Telecommunication Engineering at the University of Oviedo (Spain), and she received her Ph.D. in Information Technologies for Engineering from the University of Sannio (Italy). During her Ph.D., she researched the application of machine learning and signal processing techniques to time-varying signals/data in the fields of finance and gravitational wave data analysis.
- Deep learning for finance 101
- Designing neural network architectures
- Construction, testing and validation of financial models
- Index replication by auto-encoders
- Volatility forecasting by LSTM
- Trading rule identification by CNN
- Asset allocation by LSTM over CNN
- Digesting news by NLP with BLSTM
- Risk Measurement Using GAN
- Chart visual analysis by transfer learning
- Better chart analysis using CapsNet
- Training trader robots by deep reinforcement learning
- What’s next ?