Background

Artificial Intelligence is deemed to be the main driver of the 4th Industrial Revolution. Investment in AI has grown at a phenomenal rate with companies investing $26-39bn in 2016. Adoption in 2017, however, remains low. As a result, this has spurred companies from every industry to seize the trend and innovate – from virtual assistants to cyber security to fraud detection and much more. The majority of C-level executives have identified and agree that AI will have an impact on their industry. However, only 20% of C-level executives admit they have already adopted AI technology in their businesses, according to research conducted by McKinsey. So, there is plenty of scope for change and improvement. The Finance industry is anticipated to lead the way in adoption of AI with a significant projected increase in spending over the next three years.

Until recently, practitioners have faithfully relied upon neo-classical models to measure performance, whether it’s in financial organisations or marketing corporations. AI is the new technology that offers an automated solution to these processes. It has the capability to replicate cognitive decisions made by humans and also remove behavioural bias adherent to humans.

In order to process and understand the masses of data out there, machine learning and sentiment analysis have become essential methods that open the gateway to data analytics. To keep up with the ever-expanding datasets, it is only natural that the techniques and methods with which to analyse them must also improve and update. Application of AI in the financial service industry is fundamental to the competitiveness and automation process for years to come. This event is a sophisticated conference that not only interrogates and explores the implications of AI in the financial services industry but also goes on to identify the regional business and investment opportunities.

Additionally, this conference will help you to demystify the buzz around AI and differentiate the reality from the hype. Learn about how you can benefit from the unprecedented progress in AI technologies at this conference. Participants will be presented with real insights on how they can exploit these technological advances for themselves and their companies.

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 GARP CPD credit hours. If you are a Certified Financial Risk Manager (FRM®), please record this activity in your Credit Tracker.

Topics Covered Include:

  • AI Experience and Fintech – Disrupting our world
  • Trends & Opportunities: a regional ecosystem to meet the global needs, M&A Hotspots, Where is China!
  • Wealth Management, Family office, expectations of the modern HNWIs and global institutions
  • Fundamentals and applications of machine learning and deep learning
  • Pattern classifiers, Natural Language Processing (NLP) and AI applied to data, text, and multi-media
  • Sentiment scores combined with neo-classical models of finance
  • Financial analytics underpinned by qualitative and quantitative methods
  • Predictive and normative analysis applied to finance
  • Behavioural and cognitive science
  • The future of AI and its impact on industries

Why participate?

  • Hear from leading subject experts from UK, US, Europe and India/Hong Kong
  • Programme includes the latest state-of-the-art research, practical applications and case studies
  • Expect technical and in-depth presentations and discussions; we like to stimulate your brain cells!
  • Excellent networking opportunities throughout the days with all participants, including presenters, investors and exhibitors.

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Programme

  • -

    Keynote 1 - Towards Empathetic Human-Robot Interactions

    Professor Pascale Fung, Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong

    “Sorry I didn’t hear you” may be the first empathetic utterance by a commercial machine. As people increasingly interact with voice and gesture controlled machines, they expect the machines to recognize different emotions, and understand other high-level communication features such as humor, sarcasm and intention. To make such communication possible, the machines need an empathy module - a software system that can extract emotions from human speech and behavior and accordingly decide the correct response. This talk presents our work in the areas of deep learning of emotion and sentiment recognition, as well as humor recognition, using signal processing techniques, sentiment analysis and machine learning. It gives an overview of the future direction of android development and how it can help improve people's lives.

    Speakers:

    Pascale Fung

  • -

    Daily Trade Signals using Sentiment Analysis and Stochastic Dominance for Downside Risk Control

    Xiang Yu, Business Development Techno Executive, and Gautam Mitra, CEO/Director, OptiRisk Systems/UCL, UK

    We have created an innovative and dynamic trading strategy for equities, with a particular focus on controlling downside risk. The mathematical concept behind the approach is called stochastic dominance, where investment decisions are based on distributions rather than moments. A major contribution of news sentiment is in the prediction of future distributions. Regression analysis on news sentiment and regime switching models are employed to digest market moods and account for changing market situations.

    Speakers:

    Gautam Mitra

    Xiang Yu

  • -

    Approaches to Market Forecasting with Media Sentiment Data

    Richard Peterson, CEO, MarketPsych Data, USA

    Dr. Peterson will describe the unique characteristics of media sentiment data and approaches to financial price prediction with this data. The basics of media sentiment data, various modeling approaches, and their results (including live trading results) will be described in this talk. Viewers will gain an understanding of real-world modeling tips and techniques when dealing with noisy and inconsistent data such as media sentiment streams.

    Speakers:

    Richard Peterson

  • -

    Title: TBA

    Juho Kanniainen, Professor of Financial Engineering at the Tampere University of Technology

    Speakers:

    Juho Kanniainen

  • -

    I just called to say I’m bullish – Global analyst conference calls and stock returns

    Gurvinder Brar, Macquarie Research

    Recent academic research (and our own work on US data) have found that analyst conference calls convey useful information not contained in earnings numbers and analyst forecasts. The slow reaction of markets to that type of information implies that sentiment, as expressed by analysts and management during the call, predicts returns. This effect is distinct from the well known post earnings announcement drift. We collected call transcripts for global companies from Factset going back to 2002. Using text mining techniques, we measure the tone of the management discussion and Q&A session of each call, with a goal of developing an alpha signal at low frequency.  This presentation describes the strategy and findings of our research.

    Speakers:

    Gurvinder Brar

  • -

    Extracting Embedded Alpha in Social & News Data Using Statistical Arbitrage Techniques

    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

    Speakers:

    Arun Verma

  • -

    Networks are like onions: Practical Deep Learning with TensorFlow

    Barbara Fusinska, Data Scientist

    Deep learning is the area that wins over the field of Artificial Intelligence. By using libraries like TensorFlow, it is now available to the wider audience. In this tutorial, Barbara will walk the audience through the process of creating several types of neural networks. The session will start with explaining key concepts of deep learning and introducing datasets the computation will be performed on. Along the way, attendees will have the practical opportunity to use TensorFlow to build deep networks, train them and evaluate the results. After the session, participants will become familiar with how to use TensorFlow when shaping the architecture of neural networks. By the hands-on form of the tutorial, the audience will have the chance to gain some first hand experience of how to apply deep learning to computer vision and natural language processing tasks.

    Speakers:

    Barbara Fusinska

  • -

    Why algorithmic trading in the real world is so different to academic experiments

    Humberto Brandão, Data scientist

    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.

    Speakers:

    Humberto Brandão

  • -

    Title: TBA

    Sebastien Lleo, NEOMA Business School 

    Speakers:

    Sebastien Lleo

Speakers

Humberto Brandão

Data scientist

Pascale Fung

Hong Kong University of Science and Technology

Barbara Fusinska

Data Scientist

Juho Kanniainen

Tampere University of Technology

 

Sebastien Lleo

NEOMA Business School

Gautam Mitra

OptiRisk Systems

Richard Peterson

MarketPsych, USA

Arun Verma

Quantitative Researcher, Bloomberg LP

 

Xiang Yu

OptiRisk Systems

Knowledge Partners

 

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Tickets

4 people attend for the price of 3

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End users
Price per day

  • Super Early Bird until 12 January 2018 - HK$ 2000
  • Early Bird until 9 February 2018 - HK$ 2750
  • Standard Price - HK$ 3500
  • Buy Ticket

Venue

  • The Salisbury Hong Kong 41 Salisbury Road, Tsim Sha Tsui, Kowloon Hong Kong
  • +852 58060778
  • info@unicom.co.uk
Super Early Bird End Date