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.
Read more…
Professor Gautam Mitra, CEO, OptiRisk Systems/Visiting Professor, UCL
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.
Sam Ho, CEO, ThinkCol.AI
It is undeniable that A.I. is becoming the pioneer to the technological advancement of the coming decades. Whether it is for predicting market direction using social media mentions or building Chatbots for customers service in the financial industry. While much attention has been paid to improving A.I.’s accuracy, this has come at the cost of ignoring the importance of creating annotated data which trains the algorithm. In this talk, Sam will share his experience on designing an effective data annotation strategy for a Fortune 500 company.
Richard Peterson, CEO, MarketPsych
Kyle Wong, COO, Artificial Intelligence Hong Kong
Sam Ho, CEO, ThinkCol.AI
Katherine Liu, Of Counsel, Stephenson Harwood
Mohammad Yousuf Hussain, Data Scientist, Jasmine 22
♦ Overview of techniques explored for intelligent forecasting
♦ Lessons learnt - mistakes that can be avoided when using ML for forecasting
♦ Extracting signals from alternative data and integrating them into trading strategies
Mohammad Yousuf Hussain, Data Scientist, Jasmine22
Kevin Soong, Partner, Nearby Limited
Marco Chung, Regional Head of Legal, Morgan Stanley
Jennifer Shen May, Investment Promotion, Malta Enterprise
This presentation explains how Malta has set up its financial infrastructure so as to enable Blockchain to flourish, amongst other key sectors such as technology, digital media and IT.
Xiang Yu, Chief Business Development Officer, 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.
Kevin Kwan, Greater China Lead Financial Model Developer, Bloomberg
The asset management business has been increasingly difficult despite the easy money boom that began in 2012, marked by low interest rates and economic recovery. The business faces multi-front challenges coming from regulators and a change in consumer behavior that limits the growth of profit margin. This decline in profitability has accelerated the transformation for technology adoption to induce comparative edge over their competitors. Kevin will share possible paths that traditional asset managers follow to adopt technologies in their research and investment processes, the challenges they face, and the target states for the transformation.
Yifeng Hou, Quantitative Trading Lead, FinFabrik
Kevin Kwan, Greater China Lead Financial Model Developer, Bloomberg
Carolina Hoffmann-Becking, Senior Consultant, Ernst & Young
Yifeng Hou, Quantitative Trading Lead, FinFabrik
Reinforcement learning has been successfully applied to many areas such as robotics, Go, and video games. This presentation gives a quick introduction of how reinforcement learning can be applied to quantitative finance. It discusses the advantages and caveats of the application, and compares reinforcement learning with classical methods in quantitative finance.
Antoine Freches, Senior VP – FICC Trading, Haitong International Securities
♦ What are the challenges linked to using machine learning techniques to design a systematic investment strategy linked to commodity futures?
♦ What data is relevant and of practical use to attempt to forecast the behavior of a forward curve?
♦ Can sequence models (RNNs, LSTMs) apply to the noisy data of commodity markets?
♦ How complex can the overall model be for optimal performance and interpretability?
Silver Sponsor
