Difference between revisions of "Computational Investing"
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Latest revision as of 16:38, 4 August 2015
Contents
- 1 Lesson 1: So you want to be a hedge fund manager?
- 2 Lesson 2: Market mechanics
- 3 Lesson 3: What is a company worth?
- 4 Lesson 4: The Capital Assets Pricing Model (CAPM)
- 5 Lesson 5: How hedge funds use the CAPM
- 6 Lesson 6: Technical Analysis
- 7 Lesson 7: Dealing with data
- 8 Lesson 8: Efficient Markets Hypothesis (short)
- 9 Lesson 9: The Fundamental Law of active portfolio management
- 10 Lesson 10: Portfolio optimization and the efficient frontier
- 11 Projects
- 12 Exam
Lesson 1: So you want to be a hedge fund manager?
- Introduce investing from a hedge fund manager's point of view
Reading: "What Hedge Funds really do", Chapter 2: So you want to be a hedge fund manager?
Lesson 2: Market mechanics
- How exchanges operate
- How orders arrive, are executed
- Buy, sell, market/limit
- Crashes
Reading: "What Hedge Funds really do", Chapter 4: Market - making mechanics
Lesson 3: What is a company worth?
- Why does it matter?
- Prop: Magic money-making machine
- Mismatch between what a company is worth and how they are valued in the market
- Take into account dollars in the future
Reading: "What Hedge Funds really do", Chapter 5: Introduction to company valuation
Lesson 4: The Capital Assets Pricing Model (CAPM)
- One of the fundamental discoveries/advances in finance
- To estimate how much a stock will go up or down, multiply market movement with beta for the stock
Reading: "What Hedge Funds really do", Chapter 7: Framework for investing: The Capital Assets Pricing Model (CAPM)
Lesson 5: How hedge funds use the CAPM
- Use CAPM to figure out ratio in which you want to buy/sell stocks, go long/short
- Time scale: Change allocations weekly - monthly
- Potentially applicable to high-frequency trading
Lesson 6: Technical Analysis
- Ways to generate some sort of forecast about a stock, looking at recent volume and prices
- Contrast this with using fundamental data about a company
- Indicators (like Bollinger Bands)
Lesson 7: Dealing with data
- How data can be bad
- Actual & adjusted
- Survivor bias
Reading: "What Hedge Funds really do", Chapter 12: Overcoming data quirks to design trading strategies
Lesson 8: Efficient Markets Hypothesis (short)
- Hypothesis: What we want to accomplish in this course is impossible!
- Market responds to any piece of information, so we can never predict a stock movement and use that to our advantage
Reading: "What Hedge Funds really do", Chapter 8: The Efficient Market Hypothesis(EMH) - its three versions
Lesson 9: The Fundamental Law of active portfolio management
- Idea 1: Little intelligence about a lot of stocks
- Idea 2: Deep intelligence about a few stocks
Reading: "What Hedge Funds really do", Chapter 9:The fundamental law of active portfolio management
Lesson 10: Portfolio optimization and the efficient frontier
- Deeper knowledge and philosophy behind portfolio optimization
Reading: "What Hedge Funds really do", Chapter 10: Modern portfolio theory: The efficient frontier and portfolio optimization
Projects
02-P1 Build a market simulator
http://wiki.quantsoftware.org/index.php?title=CompInvesti_Homework_3
Create simulator server
02-P2 Use an indicator to find where good buy & sell points are (with Technical Analysis lesson) Invent your own technical indicator
02-P3 Write a strategy that generates orders Back tester: Test strategy using market simulator Possible peer feedback opportunity (peers back test your strategy)
http://wiki.quantsoftware.org/index.php?title=CompInvesti_Homework_4
Exam
Corresponds to a midterm in the context of the overall course Some Python questions