Background

sentiment-logo
Technology innovations meet greatest success in business when these are entirely ‘client focussed’. Developments in the retail sector, which is consumer-led, are addressing client demand for more personalised, faster and competitive services. Artificial Intelligence, Machine Learning and Sentiment Analysis are changing the way in which these services are offered. In particular Financial Organisations are creating and leveraging such innovation in the domain of wealth management. This trend is now being taken on board by multiple innovators: academia, start-ups, technology companies and financial market participants.

AI and Machine Learning have emerged as a central aspect of analytics which is applied to multiple domains. AI and Machine Learning, Pattern classifiers and natural language processing (NLP) underpin Sentiment Analysis (SA); SA is a technology that makes rapid assessment of the sentiments expressed in news releases as well as other media sources such as Twitter and blogs. The conference addresses and explains how to extract sentiment from these multiple sources of information and showcases the advances that have taken place in the field of financial innovation.

Along with many of the highest-ranking speakers from our London events, we are inviting some new presenters; the focus is on the latest research and includes presentations by leading subject experts from all over the world, as well as a number of applications/case studies.

Event Format:

Day 1 – Full day conference tailored for the finance industry

Day 2 – Half day of Tutorials, covering three different aspects of Sentiment Analysis

Location

After the great success of the London and Singapore events, we are now bringing the conference to Hong Kong. Its reputation as one of the world’s leading international financial centres makes it an excellent setting for any event aimed at the finance sector.

Topics to be covered include:

  • Pattern classifiers, 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
  • Applications in Financial Markets

Confirmed Speakers

ashok-banerjee

Ashok Banerjee

IIM Calcutta

svetlana-borovkova

Svetlana Borovkova

Vrije Universiteit Amsterdam, Netherland

keith-chan

Keith Chan

The Hong Kong Polytechnic University

asher-curtis

Asher Curtis

University of Washington, USA

pascale-fung

Pascale Fung

Hong Kong University of Science and Technology

ravi-kashyap

Ravi Kashyap

ISH Markit

enza-messina

Enza Messina

University of Milano-Bicocca, Italy

gautam-mitra

Gautam Mitra

OptiRisk Systems

nitish-sinha

Nitish Sinha

Federal Reserve Board, USA

huyen-tran

Huyen Tran

Sentifi

xiang-yu

Xiang Yu

OptiRisk Systems, UK

Programme

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

  • 09:00 -
    gautam-mitra

    Welcome and Introduction

    Professor Gautam Mitra, OptiRisk Systems/UCL, UK

    Speakers:
    gautam-mitra

    Gautam Mitra

  • 09:15 -
    pascale-fung

    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

    Pascale Fung

  • 10:00 -

    Coffee

  • 10:30 -
    nitish-sinha

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

    Nitish Sinha, Senior Economist, Federal Reserve Board USA

    In this talk I will discuss a co-authored work with Steve Heston that uses a dataset of more than 900,000 news stories to test whether news can predict stock returns. We measure sentiment with a proprietary Thomson-Reuters neural network. We find that daily news predicts stock returns for only 1 to 2 days, confirming previous research. Weekly news, however, predicts stock returns for one quarter. Positive news stories increase stock returns quickly, but negative stories have a long-delayed reaction. Much of the delayed response to news occurs around the subsequent earnings announcement. The analysis and conclusions set forth are those of the speaker and do not indicate concurrence by other members of the research staff or the Board of Governors.

    Speakers:
    nitish-sinha

    Nitish Sinha

  • 11:15 -
    ashok-banerjee

    Predicting Corporate Default using Text

    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

    Ashok Banerjee

  • 11:45 -
    svetlana-borovkova

    Media sentiment, systemic risk and new investment factors

    Svetlana Borovkova, Associate Professor of Quantitative Finance, Vrije Universiteit Amsterdam, Netherlands

    This talk shows how we use media sentiment to measure risk in the global financial system. I introduce a new measure of systemic risk called SenSR (for Sentiment-based Systemic Risk) and demonstrate that this measure gives an early warning about financial system distress. I then discuss perceived financial networks, which we build using media attention directed to banks, and whose characteristics can also help us understand systemic risk. Finally, I address the construction of sentiment-based indicators, similar to SenSR, for other industries and their use as new factors for investment purposes.

    Speakers:
    svetlana-borovkova

    Svetlana Borovkova

  • 12:30 -

    Lunch

  • 13:30 -

    Panel

  • 14:00 -
    keith-chan

    Twitter Sentiment Analysis for Stock Prediction

    Professor Keith C. C. Chan, The Hong Kong Polytechnic University, Hong Kong

    It is believed that public sentiment, as reflected in the social media, can be used to predict stock price movement. This presentation describes the results obtained from a study in which, over a three-month period, we collected 200 million tweets that mentioned one or more of 30 companies listed in the NYSE and NASDAQ. By analyzing the ambiguous textual messages in these tweets, we produced a list of relevant key words reflecting public sentiment towards a company’s products, services and stock prices. Using the key words, a machine learning algorithm classified the tweets and determined the association between the strength of the sentiment and the stock price movement of these companies so that these could be predicted.

    Speakers:
    keith-chan

    Keith Chan

  • 14:45 -
    xiang-yu

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

    Xiang Yu, Business Development Techno Executive, OptiRisk Systems, UK

    We describe a method for generating daily trading signals to construct trade portfolios of exchange traded securities. Our model uses Second Order Stochastic Dominance (SSD) as the choice criterion for both long and short positions. We control dynamic risk of ‘draw down’ by applying money management; a technique derived in the domain of gambling. The asset choice for long and short positions are influenced by market sentiment; the market sentiments are in turn acquired from news wires and microblogs.

    Speakers:
    xiang-yu

    Xiang Yu

  • 15:30 -

    Tea

  • 16:00 -
    enza-messina

    Deep Learning and Ensemble Methods for Sentiment Analysis

    Enza Messina, Professor in Operations Research, University of Milano-Bicocca, Italy

    We show how deep learning and ensemble methods can successfully address challenging problems arising in sentiment analysis such as irony detection or domain adaptation. In particular, we propose an unsupervised framework for domain-independent irony detection built upon an existing probabilistic topic model initially introduced for sentiment analysis purposes. Moreover, in order to improve its generalization abilities, we apply Word Embeddings to obtain domain-aware ironic orientation of words. The acquisition of cross-domain high level feature representations through Word Embeddings combined with the generalization capability of ensemble methods can also be used for addressing the problem of domain adaptation also in the scenario where the testing target domain is completely unlabeled.

    Speakers:
    enza-messina

    Enza Messina

  • 16:30 -
    asher-curtis

    Sentiment Analysis of Social Media Conversations Around News Releases

    Asher Curtis, Foster School of Business, University of Washington

    Professor Curtis presents his ongoing research with co-authors relating to the analysis of social media discussions around news releases. The talk will include discussion of supervised machine learning approaches used to assess the usefulness of sentiment analysis in identifying disagreement; the role of network connections and influence; the usefulness of refinements to sentiment analysis from classifying investor discussions using investment-style-specific language; and evidence of the benefit of these approaches for market return and trading-volume prediction.

    Speakers:
    asher-curtis

    Asher Curtis

  • 17:15 -

    Close of Day 1

  • 09:00 -
    gautam-mitra

    Welcome and Introduction

    Professor Gautam Mitra, OptiRisk Systems/UCL, UK

    Speakers:
    gautam-mitra

    Gautam Mitra

  • 09:15 -
    enza-messina

    Sentiment Analysis in Microblogs

    Enza Messina, Professor in Operations Research, University of Milano-Bicocca, Italy

    In this talk we addresses the challenges of sentiment analysis of microblogs. We show how combining post contents and network structure information may lead to significant improvements in the polarity classification of the sentiment both at post and at user level. We also discuss the potential of deep learning for enhancing the classification performance through a high level feature representation.

    Speakers:
    enza-messina

    Enza Messina

  • 10:00 -

    Coffee

  • 10:30 -
    huyen-tran

    Social Listening & Financial Crowd-Intelligence

    Yuen Tran, Sentifi - Financial Crowd Intelligence, Switzerland

    In the early 90’s, the majority of financial market participants used news mainly from services like Bloomberg and Reuters to inform themselves. 20 years later, they still do. During the same period, our society went through a communication paradigm shift. Today more than 2 billion people walk around with mobile devices and communicate what they see and think on social media. These billions of voices, when structured, can generate insights which can help investors make better investment decisions. This presentation will touch on how Sentifi structures and delivers these insights, providing an information advantage for media platforms globally.

    Speakers:
    huyen-tran

    Huyen Tran

  • 11:15 -
    ravi-kashyap

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

    Ravi Kashyap, IHS Markit, Hong Kong

    Market Microstructure is the investigation of the process and protocols that govern the exchange of assets with the objective of reducing frictions that can impede the transfer. Combining distance measures and dimension reduction, provides a simple yet powerful tool that allows comparisons between any two distributions. The degree to which different markets have different measures of their corresponding distributions, indicates how different they are, aiding investors looking for diversification or more of the same thing.

    Speakers:
    ravi-kashyap

    Ravi Kashyap

  • 12:00 -
    ashok-banerjee

    Attention and Sentiment

    Ashok Banerjee, IIM Calcutta, India

    Studies at the Finance Lab of IIM Calcutta show that the effect of any news on the market depends on the attention of investors. If the attention of investors were somewhere else, even news carrying strong positive/negative sentiment would go unnoticed or would be penalized. This is particularly true in the case of major non-market attention grabbing events. In other words, attention overwhelms the effect of sentiment. Processing any attention-grabbing event requires effort. If that effort is directed towards some particular information, people are too lazy to put in the extra effort to process any other information at the same time, no matter how much sentiment that information might carry.

    Speakers:
    ashok-banerjee

    Ashok Banerjee

  • 12:45 -

    Close of Conference

Knowledge Partners

optirisk
finance-research-lab
ucl

Sponsors

sentifi
marketpsych
wisers

Media Partners

automated-traders
eurekahedge-logo
eqs

Official News Distribution Partner

pr-newswire

Academic Partner

doc

Professional Society Partner

garp
cqf

Education Partner

caia-logo

Organised by

unicom-logo
iim-calcutta

Tickets

3 people attend for the price of 2

  • Use the coupon code "HK342" when booking.
  • Buy Ticket

Early Bird Price

  • Price for Day 1 until 16 January - HK$ 2250
  • Price for Day 2 until 16 January - HK$ 1250
  • Combined Price for both days until 16 January - HK$ 2950
  • Buy Ticket

Standard Price

  • Standard Price for Day 1 - HK$ 3250
  • Standard Price for Day 2 - HK$ 1950
  • Combined Standard Price for both days - HK$ 3950
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Academic Price

  • Academic Price for Day 1 – HK$ 1000
  • Academic Price for Day 2 – HK$ 600
  • Combined price for both days – HK$ 1300
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Event venue

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