This is a summary of links featured on Quantocracy on Thursday, 12/01/2016. To see our most recent links, visit the Quant Mashup. Read on readers!
New Book Added: Python Machine Learning Blueprints [Amazon]
Key Features Put machine learning principles into practice to solve real-world problems Get to grips with Python's impressive range of Machine Learning libraries and frameworks From retrieving data from APIs to cleaning and visualization, become more confident at tackling every stage of the data pipeline Book Description Machine Learning is transforming the way we understand and interact with
Tactical Asset Allocation in November [Allocate Smartly]
This is a summary of the recent performance of a number of excellent tactical asset allocation strategies. These strategies are sourced from books, academic papers, and other publications. While we dont (yet) include every published TAA model, these strategies are broadly representative of the TAA space. Read more about our backtests or let AllocateSmartly help you follow these strategies in
Looking Forward Not Backward When Estimating Volatility [Blue Sky AM]
When you drive a car, you need to look out your front window and not the rear-view mirror. The same should be true for estimating risk in financial markets. Ironically, most of the low volatility products use backward looking information regardless of whether they emphasize low beta or low historical volatility. Typically, this estimation window is contained within a range of 6 to 12 months.
Common Mistakes of Momentum Investors [Dual Momentum]
Like most investors, those using momentum are often guilty of chasing performance. In fact, momentum requires that we do this. But it should be done in a disciplined and systematic way. Performance chasing should not be due to myopia, irrational loss aversion, or other psychological biases. Behavioral Challenges It is not always easy adhering to a disciplined approach. If you are not vigilant,
TAA strategy performance over time [Investing For A Living]
In this post Im going to take a look at performance as a whole of a group of TAA strategies and how that performance has varied over time. Ill then compare it to the classic 60 40 US stock US bond portfolio and a more globally diversified and modern portfolio, the All Weather Portfolio. Theres some interesting things to note in the analysis. Lets get to it. The data Im using is from
From Backtesting to Live Trading by Dr. Vesna Straser [Quantopian]
Dr. Vesna Straser, an optimal trade execution and algorithmic trading professional, presented "From Backtesting to Live Trading" this past April at QuantCon NYC. During this talk, Dr. Vesna Straser discussed the differences in expected slippage between live trading, simulation trading, and backtesting. Typically when backtesting signals, order fill simulations are simplified to obtain
An Impact of Correlation and Volatility on a Pairs Trading Strategy [Quantpedia]
This paper explains the idiosyncratic risk puzzle in a novel test setting with a combination of arbitrage risk and arbitrage asymmetry as in Stambaugh/Yu/Yuan (2015). We utilize the popular investment strategy pairs trading to identify a different kind of mispricing and find a dominant negative (positive) relationship among overpriced (underpriced) stocks between idiosyncratic volatility and
Chicago Python Workshop [Portfolio Effect]
You will learn why the use of high frequency market data is necessary to be able to measure correctly the risk and rebalance your portfolio adequately. You will also learn how to build strategies to generate alpha. You will study how to build your own portfolio, create a strategy, backtest it, optimize it, and use vol forecasting with PortfolioEffect hft Python package. Prerequisite Beginner
Non-Linear Cross-Bicorrelations between Oil Prices and Stock Fundamentals [Quant at Risk]
When we talk about correlations in finance, by default, we assume linear relationships between two time-series co-moving. In other words, if one time-series changes its values over a give time period, we seek for a tight correlation reflected within the other time-series. If found, we say they are correlated. But wait a minute! Is the game always about looking for an immediate feedback
The post Quantocracy’s Daily Wrap for 12/01/2016 appeared first on Quantocracy.