Artificial Intelligence and Machine Learning (AI & ML) and Sentiment Analysis 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.

  • Learn how you can benefit from the unprecedented progress in technological advances for yourself and your company
  • Find out about the impact of Quantum Computing and Alternative Data
  • Benefit from the experience of world class presenters from the UK, US, Europe and India/Hong Kong
  • Gain exclusive insights into pioneering projects in AI, Machine Learning & Sentiment Analysis in Finance
  • Programme includes the latest state-of-the-art research, practical applications and case studies
  • Enjoy excellent networking opportunities throughout the days with all participants, including presenters, investors and exhibitors.

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Call for Participation

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.

Code of Conduct & Diversity

UNICOM’s Code of Conduct & Views on Diversity

We at UNICOM strive to be a leading provider of knowledge to the business community and to engage the global business community as a specialised provider of knowledge. We strive to do this maintaining a culture of co-operation, commitment and trust. We want every UNICOM conference and training day to be a safe and productive environment for everyone – a place to share research and innovation and to build professional networks. To that end, we will enforce a code of conduct throughout all our events. We expect cooperation from all participants to help ensure a safe environment for everybody.

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…

Programme March 2020 Under Development

Previous Programme March 2019

08:30 -Coffee & Registration

Morning Chair: Professor Gautam Mitra, CEO, OptiRisk Systems/Visiting Professor, UCL -

09:00 -Welcome and Introduction

Professor Gautam Mitra, CEO, OptiRisk Systems/Visiting Professor, UCL

09:10 -Blowing Bubbles: Quantifying How News, Social Media and Contagion Effects Drive Speculative Manias - Read More

Richard Peterson, CEO, MarketPsych

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.

09:45 -Closing the Data Gap in the Artificial Intelligence Age with Semi-Supervised Learning - Read More

Sam Ho, CEO, ThinkCol.AI

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.

10:15 - Panel 1: Alternative Data

Moderator: Gautam Mitra, CEO, OptiRisk Systems


Richard Peterson, CEO, MarketPsych
Kyle Wong, COO, Artificial Intelligence Hong Kong
Sam Ho, CEO, ThinkCol.AI

10:45 -Coffee


Katherine Liu, Of Counsel, Stephenson Harwood

Katherine Liu, Of Counsel, Stephenson Harwood

11:30 -Application of Generative Adversarial Networks (GANs) in Algorithmic Trading - Read More

Mohammad Yousuf Hussain, Data Scientist, Jasmine 22

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

12:00 - Panel 2: Protecting New Technologies in Finance

Moderator: Jonathan Chu, Partner, Stephenson Harwood


Mohammad Yousuf Hussain, Data Scientist, Jasmine22
Kevin Soong, Partner, Nearby Limited
Marco Chung, Regional Head of Legal, Morgan Stanley

12:30 - Lunch

Afternoon Chair: Xiang Yu, Chief Business Development Officer, OptiRisk Systems -

13:30 -Doing Business in Malta - Read More

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.

13:45 -Equity Trading Strategy using Sentiment and Technical Indicators - Read More

Xiang Yu, Chief Business Development Officer, OptiRisk Systems

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.

14:15 -Technology Innovation for Asset Managers: the journey from traditional to systematic and AI investments - Read More

Kevin Kwan, Greater China Lead Financial Model Developer, Bloomberg

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.

14:45 - Panel 3: Impact of AI in Finance

Moderator: Antoine Freches, Senior VP – FICC Trading, Haitong International Securities


Yifeng Hou, Quantitative Trading Lead, FinFabrik
Kevin Kwan, Greater China Lead Financial Model Developer, Bloomberg

15:15 - Tea

15:45 -Business Applications for AI in Finance - Read More

Carolina Hoffmann-Becking, Senior Consultant, Ernst & Young

Carolina Hoffmann-Becking, Senior Consultant, Ernst & Young

16:15 -Reinforcement Learning and Quantitative Finance - Read More

Yifeng Hou, Quantitative Trading Lead, FinFabrik

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.

16:15 -Using Machine Learning Techniques for Quantitative Investment Strategies: A Commodity Case Study - Read More

Antoine Freches, Senior VP – FICC Trading, Haitong International Securities

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?

17:15 -Close of conference; Networking Drinks


Jonathan Chu

Partner, Stephenson Harwood

Marco Chung

Regional Head of Legal, Morgan Stanley

Antoine Freches

Senior VP – FICC Trading, Haitong International Securities

Sam Ho

CEO and co-founder of ThinkCol.AI

Carolina Hoffmann-Becking

Senior Consultant, Ernst & Young

Yifeng Hou

Quantitative Trading Lead, FinFabrik

Mohammad Yousuf Hussain

Senior Tech and Innovation Specialist, Jasmine22

Kevin Kwan

Greater China Lead Financial Model Developer, Bloomberg

Katherine Liu

Of Counsel, Stephenson Harwood

Gautam Mitra

CEO, OptiRisk System & Visiting Professor, UCL

Richard Peterson

Managing Director, MarketPsych

Jennifer Shen May

Investment Promotion, Malta Enterprise

Kevin Soong

Partner, Nearby Limited

Kyle Wong

Artificial Intelligence Hong Kong Limited

Xiang Yu

Chief Business Development Officer, OptiRisk Systems
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