Background

AI, Machine Learning and Sentiment Analysis Applied to Finance Hong Kong

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.

Machine learning and sentiment analysis are specific techniques that are applied in AI. These techniques are maturing and rapidly proving their value within businesses. 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.

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.

Topics Covered Include:

  • 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

  • -

    Predicting Corporate Default using Text of Corporate Filings

    Ashok Banerjee, Departmental Head of Finance and Control, Indian Institute of Management, Calcutta (IIMC), India

    Banks and financial institutions in emerging markets are saddled with a huge proportion of bad loans. Banking regulations require lenders to provide for troubled debt which adversely affects the profitability of banks. The capital market also reacts negatively to such write-offs of big ticket debts. Banks are, therefore, putting significant resources into developing early warning signals to arrest eventual default. The financial institutions use a wide range of default prediction models to estimate the loan loss. These models use data from financial statements and the market. The present study shows that such models fail to provide effective early warning signals. We use annual reports of companies to develop a default model which is predictive and hence has the capability of providing early warning signals. Using information from Directors' Reports, Audit Reports and notes to accounts, our model successfully discriminates the 'good' firms from the 'bad' ones.

    Speakers:

    Ashok Banerjee

  • -

    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

Speakers

Ashok Banerjee

IIM Calcutta

Pascale Fung

Hong Kong University of Science and Technology

Gautam Mitra

OptiRisk Systems

Richard Peterson

MarketPsych, USA

 

Xiang Yu

OptiRisk Systems

Previous Programme

  • Programme Chair: Professor Gautam Mitra, OptiRisk Systems/UCL, UK -

  • 09:00 -
    gautam-mitra

    Welcome and Introduction

  • 09:15 -
    pascale-fung

    Keynote 1 - Towards Empathetic Human-Robot Interactions

  • 10:00 -

    Coffee

  • 10:30 -
    nitish-sinha

    Keynote 2 - News versus Sentiment: Predicting Stock Returns from News Stories (forthcoming Financial Analyst Journal)

  • 11:15 -
    ashok-banerjee

    Predicting Corporate Default using Text

  • 11:45 -
    svetlana-borovkova

    Media sentiment, systemic risk and new investment factors

  • 12:30 -

    Lunch

  • 13:30 -

    Panel

  • 14:00 -
    keith-chan

    Twitter Sentiment Analysis for Stock Prediction

  • 14:45 -
    xiang-yu

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

  • 15:30 -

    Tea

  • 16:00 -
    enza-messina

    Deep Learning and Ensemble Methods for Sentiment Analysis

  • 16:30 -
    asher-curtis

    Sentiment Analysis of Social Media Conversations Around News Releases

  • 17:15 -

    Close of Day 1

  • 09:00 -
    gautam-mitra

    Welcome and Introduction

  • 09:15 -
    enza-messina

    Sentiment Analysis in Microblogs

  • 10:00 -

    Coffee

  • 10:30 -
    huyen-tran

    Social Listening & Financial Crowd-Intelligence

  • 11:15 -
    ravi-kashyap

    Microstructure under the Microscope: Tools to Survive and Thrive in The Age of (Too Much) Information ​

  • 12:00 -
    ashok-banerjee

    Attention and Sentiment

  • 12:45 -

    Close of Conference

Previous Speakers

Ashok Banerjee

IIM Calcutta

Svetlana Borovkova

Vrije Universiteit Amsterdam, Netherland

Keith Chan

The Hong Kong Polytechnic University

Asher Curtis

University of Washington, USA

Pascale Fung

Hong Kong University of Science and Technology

Ravi Kashyap

ISH Markit

Enza Messina

University of Milano-Bicocca, Italy

Gautam Mitra

OptiRisk Systems

Nitish Sinha

Federal Reserve Board, USA

Huyen Tran

Sentifi

Xiang Yu

OptiRisk Systems, UK

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