Artificial Intelligence and Machine Learning (AI & ML) are said to “predict the future through analysing the past” – the Holy Grail of the finance sector. They can replicate cognitive decisions made by humans yet avoid the behavioural biases inherent in humans.
Processing news data and social media data and classifying (market) sentiment and how it impacts Financial Markets is a growing area of research. The field has recently progressed further with many new “alternative” data sources, such as email receipts, credit/debit card transactions, weather, geo-location, satellite data, Twitter, Micro-blogs and search engine results. AI & ML are gaining adoption in the financial services industry especially in the context of compliance, investment decisions and risk management.
This is a sophisticated conference that not only interrogates and explores the implications of AI & ML in the financial services industry but also goes on to identify the investment opportunities of sharing knowledge and exploiting IP in the finance domain.
Attend this event and earn GARP/CPD credit hours.
UNICOM has registered this program with GARP for Continuing Professional Development (CPD) credits. Attending this program qualifies for 7 credit hours. If you are a Certified ERP® or FRM®, please record this activity in your Credit Tracker.
We are inviting speakers – thought leaders, subject experts and start up entrepreneurs – to share their knowledge and enthusiasm about their work and their vision in the field of AI, Machine Learning, Sentiment Analysis.
Please complete the speaker’s response form and submit a proposal to present at this event.
UNICOM’s Code of Conduct & Views on Diversity
Our approach is that our events are dedicated to providing a harassment-free experience for everyone, regardless of gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity or religion. We do not tolerate intimidation, stalking, harassing photography or recording, sustained disruption of sessions or events, and unwelcome physical contact or sexual attention. We do not tolerate harassment of conference participants in any form. Sexual language and imagery is not appropriate for any conference venue, including talks, workshops, Twitter and other online media. Event participants violating these rules may be sanctioned or expelled from the event without a refund at the discretion of the conference organisers. Please bring your concerns to the immediate attention of the event staff.
Diversity: In our endeavour to be the provider of knowledge to the business community, we understand that this depends on hearing from and listening to a variety of perspectives that come from people of all races, ethnicities, genders, ages, abilities, religions, sexual orientation, and military service. We welcome diverse speakers for all our events, we do not always fully achieve this goal, but it is an ongoing process.
Utpal Chakraborty, Head of Artificial Intelligence, Yes Bank
♦ Importance of data management and data processing: Impacting the variables
♦ Lack of data or missing data for machine learning models: Sourcing internally or externally
♦ Identifying where financial institutions have the data sets to conduct deep learning
♦ Practicalities of using machine learning to create the data required: Quality consistency issues and more
Humberto Brandão, Head of R&D Lab, Federal University of Alfenas
It is not difficult to find academic papers showing how to make money easily using algorithmic trading, which includes graphs, statistical tests, etc. However, in real markets, the majority of them cannot be replicated. In this presentation, I will discuss some reasons for this problem and try to explain how to improve validation processes before applying an algotrader in real stock exchanges.
Gautam Mitra, CEO, OptiRisk System & Visiting Professor, UCL and Xiang Yu, Chief Business Development Officer, OptiRisk
We compute daily trade schedules using a time series of historical equity price data and applying the powerful mathematical concept of Stochastic Dominance. In contrast to classical mean-variance method this approach improves the tail risk as well as the upside of the return. In our recent research we have introduced and combined market sentiment indicators and technical indicators to construct enhanced RSI and momentum filters. These filters restrict the choice of asset universe for trading. Consistent performance improvement achieved in back-testing vindicates our approach.
Mohammad Yousuf Hussain, Senior Tech and Innovation Specialist, HSBC
As digital innovation and cognitive solutions gain more traction, there is a need to create greater awareness and familiarity with the latest technology trends amongst ourselves. Generative Adversarial Networks (GANs) seems to be advancing well through their hype cycle and are entering the phase of widespread deployment. In this presentation, the speaker will provide an overview of the GANs framework and highlight their ability through the concepts of game theory, enabling the discussion to move towards the application of GANs in algorithmic trading. The main use case would be about independent behaviour modelling of the market participants, construction of objective functions and suitable optimisation techniques.
Richard Peterson, Managing Director, MarketPsych
In this talk Dr. Richard Peterson describes how media analytics are providing new insights into the origins and topping process of asset price bubbles. Examples from price bubbles including the China Composite, cryptocurrencies, housing, and many others will be explored. Recent mathematical models of bubble price action will be augmented with sentiment analysis. Attendees will leave with new models for identifying and taking advantage of speculative manias and panics.
Romain Deborne, Deputy Head of Global Investment Solutions Department and Quantitative Research, Nomura Asset Management Taiwan Ltd
Kevin Kwan, Greater China Lead Financial Model Developer/Consultant, Bloomberg LP
Wendy Cheong, SVP of Strategy and Business Management – Asia Pacific, Moody’s Investors Service
Moody’s is an acknowledged global leader in assessing credit risk, with more than 100 years of experience in the credit rating business. Our work is the product of our very diversified workforce and its resulting analyses and research. As technology has introduced both opportunity and disruption in many areas, we have willingly acknowledged and embraced technological innovation, seeing it as essential to reinforcing our relevance and importance to the credit landscape. In this presentation, we will provide two case studies, showing how we leverage on two different technologies to resolve inefficiencies and create capacity for higher valued-added work. The two technologies are Robotic Process Automation or “bots”, and Natural Language Processing and Generation.
Ashish Kishore Bindal, CTO, Deeption SA
Alternative data sources and machine learning technologies can be used to understand the relationship between companies. In this study, we show how NLP and network analysis can help to discover the subtle signals to anticipate the movement in price of related securities.
Dr. Nishant Chandra, Senior Director of Data Products, VISA
Deep learning has created a revolution in the natural language processing domain and corporations are leveraging it in various ways. The technology barrier is significantly reduced with open source technologies that are easy to configure and use. Several generic open source tools are available in machine learning, including deep learning, which can be customized for natural language processing. This presentation will help the audience to go beyond generic NLP problem solving by leveraging deep learning, customizing it for their industry. Specifically, they’ll learn that:
♦ Sentiment doesn’t have to be positive, negative or neutral but it can be extracted from the conversation
♦ Summarization doesn’t have to be entire document but only certain context
♦ Text classification doesn’t have to be exactly text/phrase/spelling based but can also include variation of acronym and synonym
♦ NLP can be applied broadly, and complex use cases can be built through intelligent iteration on simple examples.