This course covers two of the seven trading strategies that work in emerging markets. The seven include strategies based on momentum, momentum crashes, price reversal, persistence of earnings, quality of earnings, underlying business growth, behavioral biases and textual analysis of business reports about the company.
In the first part of the course, you will learn how to read an academic paper. What parts to pay attention to and what parts to skim through will be discussed here. For every strategy, first you will be introduced to the original research and then how to implement the strategy.
The first strategy, Piotroski F -score will be discussed in detail. You will be taught how to calculate the F - Score and how to use this score in a strategy. This is followed by the next strategy, Post earnings announcement drift (PEAD).
Overview
Syllabus
- Module 1 - Introduction to Trading Strategies and Benchmarks
- After completing this module you will be able to understand what market efficiency means. You will be able to list different types of market efficiencies.
- How to read an academic paper
- After completing this module you will be able to read and understand an academic paper. You will know what are the important parts of a paper and how to build a trading strategy based on them.
- Module 3 - Trading Strategy 1 - F Score
- After completing this module you will understand the Piotroski F Score Strategy and the economic intuition behind it. You also be able to implement the trading strategy.
- Module 4 - Trading Strategy 2 - PEAD
- In this module you will learn a strategy based on Post earnings announcement drift and will be able to implement it.
Taught by
Prasanna Tantri
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Reviews
2.0 rating, based on 1 Class Central review
4.5 rating at Coursera based on 1079 ratings
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I liked the content that was presented. There was some really good advice, like the importance of being disciplined in sticking to the algorithm you have designed. Unfortunately there was no detail at all about how to backtest, where to get data for backtesting, where to find research papers upon which to base an algorithm, etc. In other words, everything was kept at a very abstract level, and not enough detail was provided to actually do anything.
