Enhancing portfolio return based on market-sentiment linked topics

While time-series analysis techniques are commonly used in financial forecasting, a key source of market volatility is omitted from these models. Financial news is known to be making persuasive impact to the markets. Without considering these additional signals, only sub-optimal predictions can be m...

Full description

Saved in:
Bibliographic Details
Main Authors: Chu, Victor W., Wong, Raymond K., Fang Chen, Ho, Ivan, Lee, Joe
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
Australia
Conferences
Forecasting
Historic
Human performance
Learning
Market-sentiment
Markets
Mathematical models
News
Numerical models
Portfolios
Predictive models
Resource management
Sentiment analysis
Time-series analysis
Topic modeling
Volatility
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:While time-series analysis techniques are commonly used in financial forecasting, a key source of market volatility is omitted from these models. Financial news is known to be making persuasive impact to the markets. Without considering these additional signals, only sub-optimal predictions can be made. This paper proposes a supervised topic learning approach to improve portfolio return. It is achieved by considering market-sentiment linked topics retrieved from financial news. Using this approach, we successfully improve the prediction accuracy of a proprietary trade recommendation platform. Different from traditional sentiment analysis and unsupervised topic modeling methods, topics specific to different sentiment levels are identified by our proposed model to quantify market conditions. The topics are learned from historical market performances and commentaries instead of using subjective interpretation of sentiments from human expressions. By capturing the knowledge specific to respective industries and markets, an impressive double-digit improvement in portfolio return is obtained as shown in our experiments.
ISSN:2375-9356
DOI:10.1109/BIGCOMP.2016.7425805