Qstk Homework 29

This is a summary of survey responses from 1,257 of the 25,589 students who enrolled in this course in Spring 2013.  This review may of interest to students considering to take the third offering of this course starting at coursera on August 16, 2013.

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If you are considering to take the course, please take a look at the results of this survey. I believe they may help you decide if the course is right for you. Completing this course will require a significant investment of your time, and I want to be sure it is a good match!

Important: We assume you have strong programming skills and that you want to learn about finance.

We also use the results of https://citrusnorth.com/installment-loans/ to help us revise the course for the next offering, so some of the issues pointed out by the students will be take into account as we prepare the next session for Fall 2013.

Satisfaction with the course by students who completed the course

Students who completed the course were asked “How much do you agree with each of these statements regarding your learning in the course? (Please rate on a scale of Strongly Disagree to Strongly Agree).” We report the percentage of students who selected Agree or Strongly Agree below improvement from last session in green:

  • Considering everything, the instructor was an effective teacher: 87.9%up 26.8%
  • For the amount of time I invested in this course, I’m happy with what I learned: 90.4%up 22.9%
  • The course materials were presented in an engaging manner: 83.1%up 26.1%
  • I would like to take a more advanced course on this topic: 94.5%up 2.3%
  • I found the course personally fulfilling: 85.5%up 26.5%
  • I learned what I was hoping to learn in this course: 76.4%up 33.6%

Satisfaction with the course by students who did not complete the course

  • Did you find the course useful even though you didn’t complete it? Yes: 93.4% up 3.7%

What was the best thing about the course?

These answers are a combination of responses from the first session (Fall 2012) and the second session (Spring 2013).  Overall the major groups of responses are similar across both sessions.

1. Participation, responsiveness, and engagement by the instructor and TAs in the forums: This was the most frequent positive comment. It seems that the students benefited from and appreciated the efforts by us to respond to questions and problems in the forms.

2. The software: The next most frequent response to this question had to do with QSTK (this is the software package we use for analysis in the course). Students thought both that the library itself was useful, and that using QSTK and the software tools taught in the course helped in applying the concepts in practice. Some relevant quotes:

“The QSTK software provides a great example for implementing quantitative trading.”

“QSTK and the ability to apply the concepts directly in code. During the lectures (which were pretty good), I could see conceptually how to write a program. Having the toolkit handy that handed much of the computation (database load, data cleanup, graphing, event analysis, etc) helped me concentrate more on defining strategies and less on gathering data. “

“There is tremendous value in sharing the software architecture of a quant finance system, and I appreciated this unique knowledge.”

3. Python and libraries: Students liked learning Python, and if they already knew the language, liked dividing office buildings with glass partitions used it to do analysis on financial data. Specific libraries that were mentioned included Pandas, CVXOPT, and Numpy. Some relevant quotes:

“Programming in Python with Yahoo data on stocks – very engaging, interesting”

“Building the project was a fun hands on approach to learning the use of python, pandas and Numpy”

“Getting a usable Quant framework, learning Pandas”

4. The assignments: Many students said they enjoyed the programming assignments, both for the way they were linked together in a course-long project, and as practical applications of the content from the lectures. Some relevant quotes:

“The practical homework and programming assignments based on real approaches.”

“The assignments all helped building one final program, instead of being disconnected”

5. Practical applications: Students said they enjoyed learning about how algorithmic trading is done in practice.Learning about event studies and how hedge funds operate were mentioned specifically.

6. Understanding the stock market: Students also enjoyed learning about the mechanisms behind how the stock market actually works with help from resources like www.insidermonkey.com. Hedge funds were mentioned specifically, as well as High Frequency Trading, CAPM, Portfolio theory, and the Sharpe ratio.

Which aspects of the course need the most improvement?

1. Depth/rigor of finance components: Some students felt that the mathematical rigor of the finance component of the course was too “light.” This is a theme repeated from the first offering. Here are a few quotes:

“The materials on investing are too basic, most of which have been covered in CFA level 1 curriculum.”

“Needs more rigor and clarity in materials. For example first day return (=0) of any study should not be included in average or std. deviation.”

“I would like to see a bigger focus on the financial side rather than the programming side”

Instructor’s response: We absolutely need to fix any errors, (and there are some). However the primary audience for this course is experienced programmers who are interested in a hands-on application of computing to finance. There are other good courses that  go into more depth regarding finance. So bottom line is that I’m probably not going to change the level of material with regard to finance.

2. More gentle introduction to Python and some of the libraries: Some students would like more assignments and video modules devoted to Python, Numpy, and Pandas:

“More support for those with limited programming experience.”

“I would say that the requirements for this course should be a little more clear about the importance of prior programming experience needed to satisfy the assignments and homeworks.”

“The first few weeks did not have enough work and then you get hit by the big homework. We should spend more time in the first few weeks doing Python and Pandas assignments so that the jump in difficulty isn’t as big”

Instructor’s response: I think part of this has to do with setting expectations correctly early on: Namely that you really need to be an experienced programmer. However, I also agree with the idea to have some Python programming exercises early in the course as well.

3. Video and sound production quality: Some students felt that the production quality could use improvement:

“a feeling of “unfinished”, some should be re-recorded (esp. the ones with audio problems) and others would greatly benefit from editing.”

“audio is either too loud or too low, especially when the music is at the beginning. You really need an audio compressor.”

Instructors response: I’ve learned a lot about video production over the course of this experience.  We’ll be reshooting all of the course in 2014.

Participation in online forums

We found that participation in the forums was a strong predictor of success. Of students who completed the course 92.0%  read the forums.  Of those who did not complete the course, only 66.0% read the forums.


Fatima Wirth, Ph.D. assisted with instructional design, and the course was TA-ed by Sourabh Bajaj.

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Portfolio Management and Market Mechanics
In this module, you will understand the course content from a portfolio manager's viewpoint, the incentives for portfolio managers, types of hedge fund, and how to assess fund performance; Also, you will gain insight into market orders, the basic infrastructure of an exchange, and computational components of a hedge fund.

Company Worth, Capital Assets Pricing Model and QSTK Software Overview
In this module, you will learn how to value a company, and an overview of the theory Capital Assets Pricing Model (CAPM), its assumptions, implications and how you can apply it in fund management. Finally, you will learn to install QSTK Software.

Manipulating Data in Python and QSTK
In this module, you will learn how to work with financial data, create a portfolio and optimize a portfolio using Python with Numpy library as well as QSTK and the Pandas library.

Efficient Markets Hypothesis and Event Studies, Portfolio Optimization and the Efficient Frontier
In this module, you will learn about information may affect equity prices and company value, understand efficient market hypothesis and how event studies work; Also, you will learn about the inputs and outputs of a portfolio optimizer, correlation and covariance, Mean Variance Optimization, and the Efficient Frontier.

Digging into Data
We will go into more detail in this module about how to read an event study. We will also talk about the differences between actual and adjusted historical price data, and how to detect and fix wrong data.

The Fundamental Law, CAPM for Portfolios
In this module, you will learn the fundamental law of active portfolio management. We will recap CAPM, and extend it for portfolios. Finally, we're going to look at ways that we can leverage the capital assets pricing model to manage, maybe even reduce market risk.

Information Feeds and Technical Analysis
In this module, we will dive deeper into a few examples of information feeds, and learn about technical analysis, and look at a few example technical indicators. Finally, we are going to learn about Bollinger Bands.

Jensen's Alpha, Back Testing and Machine Learning
In this module, we're going to learn about another measure of a fund performance called Jensen's Alpha, and dig deeper into back testing. We will also take a sneak peek at machine learning.

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