Recruitment of participants The Certificate In Sentiment Analysis And Alternative Data For Finance (CSAF™)

The Certificate In Sentiment Analysis And Alternative Data For Finance (CSAF™)



The Certificate in Sentiment Analysis and Alternative Data for Finance (CSAF) programme is an instructor-led course designed for finance professionals by leading Algorithmic Traders, Sentiment Pundits, Quantitative Modelling experts and HFT thought leaders.

CSAF is comprehensive and offers unparalleled insights into the world of Algorithms and the latest thinking in financial technology. Perfect to develop your career in modern methods in finance, it covers various aspects of trading, investment decisions & application using News Analytics, Sentiment Analysis and Alternative Data.

CURRICULUM
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1Primer

• Knowledge of basic trading procedures and basics of algorithmic trading: know and understand the terminology
• Understand statistical methods and statistical measurements including autocorrelation function, partial autocorrelation function, Maximum Likelihood Estimation (MLE), Akaike Information Criterion (AIC), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE)
• Basic knowledge of time series analysis, stationarity of time series, and forecasting using ARIMA
• Fundamentals of Autoregressive and GARCH Models, and understanding volatility
• Logistic regression to predict the conditional probability of the market direction
• Different methodologies of evaluating portfolio and strategy performance (back-testing methodologies and statistical figures for evaluation including Sharpe ratio, Sortino ratio, Max drawdown)
• Basic knowledge of Asset Allocation Models
• Understand all the most practical indicators and oscillators (e.g., RSI, MA, EMA)
• Distinguish between Macroeconomic and Microeconomic news
• Basic knowledge of models for spot prices, futures prices
• General knowledge of types of multifactor models and updating a traditional factor model
• Knowledge on the basics of the financial market in general and the stock market in particular
• A clear understanding of the type of instruments and the stock markets.
• Understand the concept of the stock market index and its calculation
• Basic knowledge of machine learning, pattern recognition as well as Natural Language Processing (NLP)

2Module 1: Sentiment: What and Whose
3Module 2: Sentiment Data
4Module 3: Structure and Coverage
5Module 4: Other Sources: Alternative Data (I)
6Module 5: Models to Exploit Sentiment Analysis (I)
7Module 6: Models to Exploit Sentiment Analysis (II)
8Module 7: Opinion and Biases
9Module 8: AI, Machine Learning & Quantitative Models to predict market direction
10Module 9: Role of Alternative Data in Financial Trading: Alternative Data (II)
11CSAF Exam

CASE STUDIES​

1Grasping Behavioural Finance by Anthony Luciani

• MarketPsych has built a sentiment analytics suite on investor-relevant media from thousands of online sources with hundreds of over-arching themes and topics covering all major asset classes.
• In these sessions, we explore how the stationary processes of psychology interact with the nonstationary processes of financial markets.
• Witness two major themes of investor over- and under-reaction to the news.
• See cycles of fear and their interactions with crude oil prices.
• Find solutions to common questions within the field of sentiment analysis for markets.
• Observe how new themes become more predictive over time.

2Classifying Earnings Calls & News by Dan Joldzic
3NLP And ML Techniques in Finance: Some Examples by Dr. Matteo Campellone
4Asset Allocation Enhanced by Sentiment Data by Dr. Zryan Sadik and Prof. Christina Erlwein-Sayer
5ESG in Factors by Dr. Katharina Schwaiger
6Using traditional structured data for long-term analysis of publicly-traded equity and using alternative, unstructured data for short-term analysis of public and private equity by Dr Keith Black
7News Sentiment Analysis in Em Sovereign Debt research, Investment and Risk Management by Jacob Gelfand and Kamilla Kasymova
8The Art of (Alternative) Data Science by Ganesh Mani
9Using alternative data: from research to production by David Jessop
10ESG Data and Investment Returns by Richard Peterson
11A Quantitative Metric for Corporate Sustainability by Dan DiBartolomeo
12Creating a strategy using Machine Learning by Dr. Ernest Chan
13Discussion on Project Results

 

Done