Hong Kong Productivity Council Academy

Social Sentiments and Stock Price Correlations

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Course Information

  • 28 Sep 2021 (Tue) - 30 Sep 2021 (Thu) 9:30 AM - 12:30 PM
Registration period
20 May 2021 (Thu) - 27 Sep 2021 (Mon)
HKD 6,000
(HK$6,000 (May apply up to $4,000* subsidy)

* This course is an approved Reindustrialisation and Technology Training Programme (RTTP ), which offers up to 2/3 course fee reimbursement upon successful applications.)
Course Level
Study Mode
6 Hour(s)
HKPC Building 78 Tat Chee Avenue Kowloon

Course Overview

This programme aims at enabling banking and finance practitioners to apply Sentiment Analysis on investment and trading which includes portfolio construction, re-balancing and trading strategies.

In addition, participants will learn how to apply Deep Learning and NLP (Natural Language Processing) techniques on textual sentiment processing and understanding.

Target Audiences

  • Financial practitioners interested in applying sentiments on investment and trading
  • Professionals who would like to sharpen their skills with the latest AI and Machine Learning technologies
  • SME / start-up entrepreneurs who wish to get inspiration from incorporating sentiment analysis into their businesses

What You’ll Learn

Day 1 (3 hours): Sentiment Visualisation and Trading Applications

  • Raw data collection, pre-processing and visualisation
  • Calculate sentiment scores with aggregation, weighting, volume counting and moving average in different timeframes
  • Industry applications: Sentiment index for long/short trading strategies in different asset classes: Equities, Bonds, Forex and Commodities, Smart Beta for portfolio construction and re-balance by pair-algorithm
  • Case analysis: Apply rule-based sentiment APIs to extract emotions, social media sentiments and stock price correlation

Day 2 (3 hours): Natural Language Processing and Deep Learning Techniques

  • Building blocks of Natural Language Processing
  • News relevance and classifications by topic modelling
  • Word2Vec embedding to understand language semantics
  • Case analysis: Understand news content and semantics by training a Word2Vec
  • embedding, develop a sentiment analyser by Deep Learning Sequence model (LSTM) with pre-trained language model

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