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

  • -
    gautam-mitra

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

    Speakers:
    gautam-mitra

    Gautam Mitra

  • -
    pascale-fung

    Keynote 1 - Towards Empathetic Human-Robot Interactions

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

    “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

  • -

    Coffee

  • -
    nitish-sinha

    Keynote 2 - What’s the Story? A New Perspective on the Value of Economic Forecasts

    Nitish Sinha, Senior Economist, Federal Reserve Board USA

    In this talk I will discuss a co-authored work with Steve Sharpe that applies tools from the emerging literature on textual analysis to evaluate a key dimension of the information conveyed in the narratives that accompany Federal Reserve's greenbook forecasts. In particular, we quantify the degree of optimism versus pessimism embedded in the text, which we call the “tonality” of the text. We find that tonality has significant and often substantive directional predictive power for three key macroeconomic variables — namely unemployment, GDP growth, and inflation. Tonality also predicts forecast errors of Blue Chip forecasts.

    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

  • -
    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

  • -

    Panel

  • -

    Lunch

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    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

  • -
    svetlana-borovkova

    Media sentiment, systemic risk and new investment factors

    Svetlana Borovkova, Associate Professor of Quantitative Finance,VrijeUniversiteit 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

  • -
    xiang-yu

    Beating Markowitz with Sentiment and Downside Risk Control

    Xiang Yu, Business Development Techno Executive,OptiRisk Systems, 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:
    xiang-yu

    Xiang Yu

  • -

    Tea

  • -
    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

  • -
    asher-curtis

    Sentiment Analysis of Social Media Conversations Around News Releases

    Asher Curtis, Herbert O. Whitten Endowed Professorship in Accounting, Foster School of Business, University of Washington, USA

    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

  • -

    Close

  • -
    enza-messina

    Deep Learning in Natural Language Processing

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

    Recently, deep learning approaches have obtained promising results across many different NLP applications.

    Deep learning has, in particular, aimed at handling efficiently huge amount of texts in an unsupervised setting by capturing, in an intuitive way, the complexity of natural language. We show how deep learning goes beyond the traditional “bag of words” representation by constructing a so-called "neural embedding" or vector space representation of each word or document. We illustrate how this representation can be exploited for sentiment analysis.

    Speakers:
    enza-messina

    Enza Messina

  • -
    huyen-tran

    Social Listening & Financial Crowd-Intelligence

    Huyen Tran, Sentifi

    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

  • -
    ravi-kashyap

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

    Ravi Kashyap, ISH Markit

    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. We provide an empirical illustration using prices, volumes and volatilities across seven countries and three different continents. Analyzing the degree to which different markets or sub groups of securities have different measures of their corresponding distributions aids investors looking for diversification or for more of the same thing.

    Speakers:
    ravi-kashyap

    Ravi Kashyap

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
  • Buy Ticket

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