Hong Kong Productivity Council Academy

Social Sentiments and Stock Price Correlations

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Course Information

Schedules
  • 28 Sep 2021 (Tue) - 30 Sep 2021 (Thu) 9:30 AM - 12:30 PM
Registration period
20 May 2021 (Thu) - 27 Sep 2021 (Mon)
Price
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
Duration
6 Hour(s)
Language
Cantonese
Location
HKPC Building 78 Tat Chee Avenue Kowloon
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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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