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.
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.
We understand that successful projects are written up as “White Papers”. Please share these with us. But projects that did not achieve their targets – “Black Papers” – are of interest to us too. They can be a very important topics of discussion / panels that you can present. Talk to us about both, we welcome your input.
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.
Professor Gautam Mitra, CEO, OptiRisk Systems/Visiting Professor, UCL
Arun Verma, Quantitative Researcher, Bloomberg LP
♦ Extracting actionable information in the high volume, time-sensitive environment of news and social media stories
♦ Using machine learning to address the unstructured nature of textual information
♦ Techniques for identifying relevant news stories and tweets for individual stock tickers and assigning them sentiment scores
♦ Demonstrating that using sentiment scores in your trading strategy ultimately helps in achieving higher risk-adjusted returns
Gautam Mitra, CEO, OptiRisk Systems/Visiting Professor, UCL, and Xiang Yu, OptiRisk Systems
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.
Johnson Poh, Head, Data Science, DBS Bank / Adjunct Faculty, Singapore Management University
Much information is embedded within the large volumes of unstructured data that we so often neglect in the implementation of business analytics. How do we seamlessly classify and extract key ideas with automation? In this presentation, we explore the open source tools, algorithms and services that relevant for the design of a reference architecture to surface underlying insights from texture descriptions.
Mohammad Yousuf Hussain, Senior Technology 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 session, the presenter will provide an overview of the GANs framework and highlight their explain 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.
Satoshi Shizume, Deputy Managing Director, Financial Technology Research Institute
In this presentation, I am going to introduce the Japanese language news analyzer ‘News Dolphin’, an innovative tool that classifies Nikkei news for hedge funds and investment managers. It analyzes sentiments (positive or negative) by using both rule base and machine learning. News Dolphin generates various classification outputs such as referred companies, keywords, and events for each article. I will present this unique news analyzer including details of its engine, back test of its sentiments and its use for investors.
Lei Chen, Professor of Computer Science & Engineering, Hong Kong University of Science and Technology
Big Data has made great impact in many application fields, together with advanced AI technology, it will change our world dramatically. In this talk, I will first start with several cases to motivate the importance of Big Data analysis in Finance Industry. Then, I will discuss the challenges in applying Big Data analysis. Finally, I will highlight some possible solutions to address those challenges.
Wendy Cheong, Moody’s Investors Service, Head of Hong Kong
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.
Juho Kanniainen, Professor of Financial Engineering, Tampere University of Technology, Laboratory of Industrial and Information Management
Nowadays, available datasets are so large and complex that such "Big Data" is becoming difficult to process with the current data management tools and methods. This data could provide valuable information to design trading algorithms, manage risks, and supervise markets. At the same time, financial research has been quite slow to embrace the data revolution. This talk elaborates the opportunities and challenges of using data science methods and large data sets in finance-related industries and research.
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.
Richard Peterson, CEO, 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.