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.
Day 1 – Full day conference tailored for the finance industry
Day 2 – Half day of Tutorials, covering three different aspects of Sentiment Analysis
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.