Dec
7
My flight to Australia will be tomorrow, so this post is the one ahead of schedule.
mlpy - Machine Learning Python: mlpy is a free Python module for Machine Learning. It facilitates classification, regression, clustering and feature selection in Python.
Global Optimization Algorithms – Theory and Application: a free ebook on global optimization, algorithms including Evolutionary Algorithms, Genetic Algorithms, Genetic Programming, Learning Classifier Systems, Hill Climbing, Simulated Annealing... Not easy to understand but worth to save it.
Improved Moving Average Code, and Improved Moving Average?: both posts are aiming to introduce a newly improved moving average trading strategy with detailed codes and examples.
How Expected Shortfall Can Simplify the Equally-Weighted Risk Contribution Portfolio: compare the performance of portfolios under several construction strategies: minimum variance, equally weighted, and Expected Shortfall stable.
mlpy - Machine Learning Python: mlpy is a free Python module for Machine Learning. It facilitates classification, regression, clustering and feature selection in Python.
Global Optimization Algorithms – Theory and Application: a free ebook on global optimization, algorithms including Evolutionary Algorithms, Genetic Algorithms, Genetic Programming, Learning Classifier Systems, Hill Climbing, Simulated Annealing... Not easy to understand but worth to save it.
Improved Moving Average Code, and Improved Moving Average?: both posts are aiming to introduce a newly improved moving average trading strategy with detailed codes and examples.
How Expected Shortfall Can Simplify the Equally-Weighted Risk Contribution Portfolio: compare the performance of portfolios under several construction strategies: minimum variance, equally weighted, and Expected Shortfall stable.
Dec
2
Happy last month of 2011. I will fly to Sydney to present a paper at the 24th Australasian Finance & Banking Conference on next Thursday, so we may not have a review next week. However, feel free to contact me @a_biao for sharing any useful post. This week's review is highly concentrated on R language.
R-code for the algorithm of Ait-Sahalia: the Closed-Form expansion for the transition densities of diffusions by Professor Yacine Aït-Sahalia facilitates the Maximum Likelihood Estimation, the related papers and Matlab package can be downloaded directly at his website at Ait-Sahalia, but in case you are a R user, this is what you need.
R Memory Issue: insights on R memory issue, most of us have met it more or less.
Regression via Gradient Descent in R: detailed simple example demonstrating how to run a regression via Gradient descent in R: principle and codes.
Amelia II: A Program for Missing Data: An excellent R package for multiple imputation of missing data. I had a post introducing its first version at missing data imputation.
The Art of R Programming: A Tour of Statistical Software Design
: the book title tells it, you can't miss it as a R user.
R Cheat Sheets: still having trouble remembering the exact commands in R? here is an excellent collection of R cheet sheets.
How to interpret Johansens' test results: simple while detailed examples guiding you through the basic knowledge how to interpret Johansenss test results for cointegration analysis.
Improving Trend-Following Strategies With Counter-Trend Entries: minor adjustments to strategies that can both improve their backtest performance and also reduce the real costs of trading.
R-code for the algorithm of Ait-Sahalia: the Closed-Form expansion for the transition densities of diffusions by Professor Yacine Aït-Sahalia facilitates the Maximum Likelihood Estimation, the related papers and Matlab package can be downloaded directly at his website at Ait-Sahalia, but in case you are a R user, this is what you need.
R Memory Issue: insights on R memory issue, most of us have met it more or less.
Regression via Gradient Descent in R: detailed simple example demonstrating how to run a regression via Gradient descent in R: principle and codes.
Amelia II: A Program for Missing Data: An excellent R package for multiple imputation of missing data. I had a post introducing its first version at missing data imputation.
The Art of R Programming: A Tour of Statistical Software Design
: the book title tells it, you can't miss it as a R user.R Cheat Sheets: still having trouble remembering the exact commands in R? here is an excellent collection of R cheet sheets.
How to interpret Johansens' test results: simple while detailed examples guiding you through the basic knowledge how to interpret Johansenss test results for cointegration analysis.
Improving Trend-Following Strategies With Counter-Trend Entries: minor adjustments to strategies that can both improve their backtest performance and also reduce the real costs of trading.
Nov
25
This week is very quiet (despite of the poor performance in Eurozone), since people are busy preparing Thanksgiving & Black Friday. As always, I appreciate if you come across some interesting articles and like to share with us. Simply @a_biao via twitter or drop me a line at [email protected]
Style analysis: Detailed examples and R codes to "guess" the asset allocation of a fund by style analysis: a procedure attributing funds performance to the performance of asset classes;
Asynchrony in market data: solutions to diminish the covariance matrix estimation error caused by trading asynchrony: markets around the world being open at different times;
A New Simple Approach for Constructing Implied Volatility Surfaces: a new calibration method goes directly from implied volatility dynamics to implied volatility surface, said to be better than existing implied volatility surface models?
Style analysis: Detailed examples and R codes to "guess" the asset allocation of a fund by style analysis: a procedure attributing funds performance to the performance of asset classes;
Asynchrony in market data: solutions to diminish the covariance matrix estimation error caused by trading asynchrony: markets around the world being open at different times;
A New Simple Approach for Constructing Implied Volatility Surfaces: a new calibration method goes directly from implied volatility dynamics to implied volatility surface, said to be better than existing implied volatility surface models?
Nov
24
Time flies really quickly, I didn't realize today is the thanksgiving day until reading a post of my subscribed blog. Following last year's thanksgiving, I'd like to give my special thanks to:
1, my supervisor Prof. David Newton for supporting my research and co-authoring a submitted paper.
2, my colleagues & co-authors: Fangyi Jin, Qian Han, Doojin Ryu, and Songtao Wang for your consistent help and encouragement.
3, my blog readers for not giving up reading my posts. I have to admit that the number of posts I have written in 2011 is much less than that of last year, due to the increasing workload of my PhD research. 2012 is a more challenging year as I will graduate by Sep, 2012, I expect my PhD thesis and job hunting will occupy most of time. Hope you keep staying tuned, I will try to write whenever I can.
4, Kai dai, Joanne, Ting Qiu for your kindness to invite me to dozens of dinners, I suddenly realize I had less than 5 dinners at my home in the last month, the left were cooked by you guys.
5, ...
1, my supervisor Prof. David Newton for supporting my research and co-authoring a submitted paper.
2, my colleagues & co-authors: Fangyi Jin, Qian Han, Doojin Ryu, and Songtao Wang for your consistent help and encouragement.
3, my blog readers for not giving up reading my posts. I have to admit that the number of posts I have written in 2011 is much less than that of last year, due to the increasing workload of my PhD research. 2012 is a more challenging year as I will graduate by Sep, 2012, I expect my PhD thesis and job hunting will occupy most of time. Hope you keep staying tuned, I will try to write whenever I can.
4, Kai dai, Joanne, Ting Qiu for your kindness to invite me to dozens of dinners, I suddenly realize I had less than 5 dinners at my home in the last month, the left were cooked by you guys.
5, ...
Nov
18
Resampling and Shrinkage : Solutions to Instability of mean-variance efficient portfolios: we know mean-variance portfolio highly depends on the input of expected return and covariance matrix, a post demonstrates with full R codes two common techniques to make portfolios in the mean-variance efficient frontier more diversified and immune to small changes in the input assumptions.
Improving Portfolio Selection Using Option-Implied Volatility and Skewness: is option-implied information useful for improving the out-of-sample performance of a mean-variance efficient equity portfolio? this paper tells you an answer.
SDE Matlab Toolbox: a nice Matlab toolbox for simulation and estimation of stochastic differential equations, it supports both univariate and multivariate SDEs.
Black-Litterman Model: Black-Litterman model is used to overcome a few shortcomings of Markowitz efficient frontier method, here is a post with full R codes demonstrating how to implement Black-Litterman model.
Using Neural Network For Regression: compare the performance of Artificial Neural Network (ANN) and OLS for a simple linear regression. Not surprisingly, ANN wins.
Improving Portfolio Selection Using Option-Implied Volatility and Skewness: is option-implied information useful for improving the out-of-sample performance of a mean-variance efficient equity portfolio? this paper tells you an answer.
SDE Matlab Toolbox: a nice Matlab toolbox for simulation and estimation of stochastic differential equations, it supports both univariate and multivariate SDEs.
Black-Litterman Model: Black-Litterman model is used to overcome a few shortcomings of Markowitz efficient frontier method, here is a post with full R codes demonstrating how to implement Black-Litterman model.
Using Neural Network For Regression: compare the performance of Artificial Neural Network (ANN) and OLS for a simple linear regression. Not surprisingly, ANN wins.
