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We offer five practice-based graduate programmes focusing on information technology (IT) and data science.
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Financial markets have been known to be driven by both fundamental and behavioural factors. With the advent of textual analysis and natural language processing, this has entered into the mainstream analytics of many financial institutions. Asset managements, banks and regulators have used textual analysis to better understand the markets. In this talk, we have invited key industry players from the financial sector who will present on the industry initiatives in this area. This includes the use of textual and sentiment analysis in asset and portfolio management. Some of the uses include financial compliance and regulatory needs.
Practitioners working in financial institutions interested in knowing more how textual analysis and sentiment analysis are applied in finance. Quants, risk managers, traders and regulators.
Predictability of Forex/ Stock Market with Deep Learning
The financial market is very hard to predict as the market is dependent on real-time and real-world events happening all over the world. As such, it is hard to model the market linearly with its variables. Deep Learning technology provides a non-linear way of modelling the market for predictions. With much hope given by the progress of Deep Learning in fields such as machine vision and text analytics, an attempt has been made to predict price movements using Long Short-Term Memory (LSTM) with past price data and sentiment data. Insights gained in this attempt will be shared..
by Mr.Eugene Chian
Refreshment & Networking
Machine Readable News in Finance News flow and sentiment are important sources of signals in quantitative stock selection and systematic trading – for example, quants using price momentum and reversal as building blocks for their models have measured how news can soften the reversal effect and magnify momentum. However news and social media data is unstructured and there is a lot of it. It is difficult to understand whether news is perceived by the market as positive or negative, whether the story is relevant to a company, and whether the story is new or recycled. Radha will present Refinitiv machine readable news which are used by financial institutions worldwide for asset and risk management. by Mr. Radha Pendyala, Enterprise Data Scientist, Refinitiv
Investment insights from News and Social Media Analysis
MarketPsych has over ten years of history in developing and using sentiment analysis in finance, and currently supports clients in more than 20 countries. Its pillars of strength stem from the variety of media sources and assets covered, as well as the ability to extract nuances of sentiment, such as price expectations and emotions like fear and joy. In this talk, Changjie Liu will present some interesting examples and insights gained from analyzing such a unique dataset, on how various sentiments can predict bubbles or under-reaction to news.
Programme may be subjected to changes.
Eugene is a deep-learning researcher and a mid-career switcher. He has successfully switched his career to become a data scientist from a non-computing background by obtaining a Master of Technology from ISS, NUS. He is passionate about deep-learning and has performed deep-learning architectures such as CNN and LSTM on medical, financial, defects and construction datasets. Now a full-time data science researcher, he is committed to bringing AI technology into industries.
Enterprise Data Scientist, Refinitiv View Biography
Radha works as an Enterprise Data Scientist at Refinitiv. His work involves applying machine learning and quantitative financial modeling techniques to large datasets in order to solve specific problems in the financial sector. Prior to Reuters, he has worked as a portfolio manager at Goldman Sachs Asset Management. He has more than a decade of experience in building financial and statistical models. Radha has obtained his masters in financial engineering degree from City University of New York, a post graduate degree in management from Indian Institute of Management, Indore and a B.Tech in Civil Engineering from Indian Institute of Technology, Madras.
Head of Research, MarketPsych View Biography
Quantitative researcher, investigating sentiment data in multiple asset classes to create trading strategies around behavioral anomalies. Created research infrastructure and repository to quickly investigate and review various investment strategies. Created web infrastructure to assist clients in exploring data. Created database infrastructure for trading and research. Guides clients , partners and interns on the processes when dealing with sentiment data research.