Networks of Equities in Financial Markets

Network Perspective of the Stock Market

Emergence of Statistically Validated Financial Intraday Lead-Lag Relationships

Evolution of Worldwide Stock Markets, Correlation Structure and Correlation Based Graphs

Equity Returns at Turn of the Month

The best quant mashup links for the week ending Saturday, 10/31 as voted by our readers: The Cold Blood Index [Financial Hacker] How to Write a Great Quant Blog [Quant Start] High Frequency Market Microstructure: Part 1 (Microstructure Noise) [Portfolio Effect] Buy the Winners [Systematic Relative

This post builds on work done by jcl over at his blog, The Financial Hacker. He proposes the Cold Blood Index as a means of objectively deciding whether to continue trading a system through a drawdown. I was recently looking for a solution like this and actually settled on a modification of jcl’s

Many traders use technical and/or fundamental data, but few traders have discovered the unique benefits of using sentiment data in their investment process. Sentiment data attempts to quantify the emotional mood of investors and traders and can be used as a very effective contra-indicator. When

Elixir is the new hot programming language on the block. The bastard child of Ruby and Erlang. Syntax Ruby is designed like Apple designs phones. It looks and feels right. I love that the goal of Ruby language design is to reduce cognitive dissonance when implementing features. Everything has to fit

A fund must take active risk to generate active returns in excess of fees. However, some managers charge active fees but manage their funds passively. Managers also tend to become less active as they accumulate assets. This problem of hedge fund closet indexing is widespread. Over a third of capital

We draw a significant amount of inspiration for the material we cover on this blog from the publications of our financial brethren. Unfortunately, given the non-stop firehouse of information that increasingly characterizes the digital age, it’s nearly impossible to consume anything longer than a

Microstructure noise describes price deviation from its fundamental value induced by certain features of the market under consideration. Common sources of microstructure noise are: bid-ask bounce effect order arrival latency asymmetry of information discreteness of price changes Noise makes high

Anna Cieslak and Pavol Povala—authors of the paper “Expected Returns in Treasury Bonds,” which was published in the September 2015 issue of The Review of Financial Studies—examined the time variation in the risk premium that investors require for holding Treasury bonds. While most of the

Marisa Mayer’s recent announcement that she is again pregnant, and does not plan to take maternity leave after her twins arrive, has once again raised the age-old question about how far women have really come in making a gender equitable workplace. While women are undoubtedly making progress,

Over the last five blog posts we looked at the automated backtest results for 4160 options straddles sold on the S&P 500 Index (SPX) at 73 days-to-expiration (DTE). Eight different loss approaches were tested on these straddles. On top of these eight loss approaches, tests were conducted with no

The Fama-French value factor HML exhibits a fairly reliable 4 year cycle. Growth and Value out-performance oscillates with a 4 year period (see my previous post on this). Liew and Vassilou (1999), show that annual change in HML is related to future GDP change (see my blog post here). Therefore

Most investors know of the VIX Index, but not as many understand what information the VIX provides an investor. Here is my attempt to provide an initial outline of what it is and why the information embedded within the figure is so powerful. VIX Defined The CBOE has a white paper that provides a ton

Imagine two NBA teams. Team one starts Anthony Davis, Russell Westbrook, Stephen Curry, Kevin Durant, and James Harden. Team two starts Hassan Whiteside, DeMarcus Cousins, Brook Lopez, Marc Gasol, and Nikola Vucevic. Who wins? The two teams above determined by a stat called PER (player efficiency

Generating trading ideas could be a frustrating process, especially so if there isn’t a structured framework for it. To assist us in our idea generation, we break down algorithmic trading ideas into 4 main categories. These 4 categories are essentially types of market inefficiencies. As mentioned

Trend-following strategies take long positions in assets with positive past returns and short positions in assets with negative past returns. They are typically constructed using futures contracts across all asset classes, with weights that are inversely proportional to volatility, and have

I just came across a great post on sector investing by Dave Mazza, Head of Research for SSGA's ETF and mutual fund businesses. There is a lot of great information he walks through, but I thought there were three tidbits particularly interesting to us as risk managers. First, he points out that

Today's post is a guest post from Jacques Joubert, who runs QuantsPortal. Jacques emailed me recently and asked if I'd be willing to contribute to a post about how to get started in quant blogging. I was more than happy to do so, and Jacques wondered if it would make a good guest post for

Okay, we're sold on a closet-indexing approach to the markets. Now we're investigating a variety of smart-beta products available in the market that weigh a large portfolio of stocks with some algorithm. But a natural question arises when trying to pick smart beta ETFs: What is the optimal

Do not commence working over your blog without the vision. “If you don’t know where you are going, any road will get you there!” You want to avoid that mistake. Spend some time dreaming of the final form of your site. Highly sought after content is important but not as much as your commitment

In due diligence, we often evaluate summary statistics like annualized return, volatility, alpha, beta, up-capture, and down-capture. These statistics can unify years of returns into a single number. While this can be convenient for comparing different strategies, it fails to provide adequate

People come up with all kinds of reasons not to buy stocks with strong momentum. Some of the most common reasons that I hear: Stocks with high momentum are risky Stocks with high momentum are overvalued Stocks with high momentum are susceptible to reversals As for the first point, yes, buying stocks

You’ve developed a new trading system. All tests produced impressive results. So you started it live. And are down by $2000 after 2 months. What now? Carry on in cold blood, or pull the brakes in panic? This is a situation all too familiar to any algo trader. There can be several reasons why a

My fellow quant nerds, we need to vote more. Less than 2% of clickthroughs result in a vote. I know we can do better. Higher rated links are read significantly more often, so help me to encourage bloggers to write quality content by registering to vote and making your voice heard. — Mike @

This weeks show is going to be a little different to previous episodes. A couple of weeks ago I went back through all the guests we’ve had on the show so far and realised how very fortunate we’ve been to have so many fantastic guests on the show, sharing their knowledge and experience, some of

Academic researchers have presented theory, as well as empirical evidence, suggesting certain linkages between equity risk and the Treasury bond market, a relationship that clearly has important implications for investors’ understanding of markets and portfolio design. Studies, for example, have

Below is a list of all of the books written by all of the folks on our quantitative trading blogroll, sorted by Amazon sales rank from most to least popular. I did the legwork for a new page I’m putting together for the site (similar to what we used to have at The Whole Street), and thought it

In a sustained effort to try too hard, investors are constantly analyzing and assessing the growth rates of various markets around the world. The key assumption behind this analysis is that knowledge of these growth rates enhances their ability to predict the future and expected returns. This

And let’s be completely straight here: they do. Occasionally we hear about “The disappearance of the X factor”. This is just people mistaking the transient nature of the factor outperformance for a permanent decline. People seem to have a very strong tendency to confuse short term performance

Over the last five blog posts we looked at the automated backtest results for 4160 options straddles sold on the S&P 500 Index (SPX) at 66 days-to-expiration (DTE). Eight different loss approaches were tested on these straddles. On top of these eight loss approaches, tests were conducted with no

Back in August I outlined why I thought the plain-vanilla value premium had been compressed to the point growth had and was likely to continue to outperform in my post Death of (Plain Vanilla) Value - Long Live GARP. This post is meant as a follow up and suggests a few frameworks as to how an

Spurned by reading an account of a trader who swears by machine learning, few days ago I wrote about aesthetics in finance. Maths and tech without a narrative is pointless. My own attempts at providing a narrative foundered a few months back. Suspend Your Disbelief! I started using the Sharpe as a

SEC rules allow companies to delay the public disclosure of significant corporate events for up to 4 business days. This information is reported on an 8-K. This 4-day gap between an event and the disclosure creates an interesting situation. As an insider, if I know an 8-K is going to report news, I

This post will deal with a quick, finger in the air way of seeing how well a strategy scales–namely, how sensitive it is to latency between signal and execution, using a simple volatility trading strategy as an example. The signal will be the VIX/VXV ratio trading VXX and XIV, an idea I got from

The two charts above are from recent trades I have taken. Charts created in AmiBroker. On July 20, 2015 IBB, iShares Nasdaq Biotechnology ETF, made a closing high of 398. About three months later it closed at 289 for 27% loss. A very common thing I hear from traders is that they “don’t trade

Is it possible to classify and predict (yes, predict!) if market trends will be bullish, bear or ranged by using a method called “naïve” and based on something as simple as Bayes’ theorem is? Let’s see! Predicting trends with naïve Bayesian classifier Our main objective is to explore

Understanding the volatility of Treasury bond returns, as well as the volatility of both the level and slope of the Treasury term-structure, are fundamental issues in finance. What’s more, they have important implications for investors and portfolio design. Researchers have offered both theory and

To be instructive, I always use very few tickers to describe how a method works (and this tutorial is no different). Most of the time is spent on methods that we can easily scale up. Even if exemplified using only say 3 tickers, a more realistic 100 or 500 is not an obstacle. But, is it really

First draft of 'JavaScript for Financial Analysts' Chapter 6. ~ Before I start - mulling over a change in function syntax for the book. So that for example, [0,1,2,3].map(function(i){return i+1;}) becomes [0,1,2,3].map( i => i+1 ) If you have an opinion, let me know! ~ So far we have

Introduction For the overwhelming majority of investors, portfolios are broadly organized into strategic silos of stocks and bonds, such as the ubiquitous 60/40 balanced portfolio. By design, the strategic proportions of stocks and bonds in the portfolio change very little over time. However, within

To install Systematic Investor Toolbox (SIT) please visit About page. The purpose of a back-test is to show a realistic historical picture of strategy performance. One might use back-test results and corresponding statistics to judge whether a strategy is suitable one. Hence, it is best to structure

This is a short post to let you know that I'll be speaking at QuantCon on the April 9th 2016 in New York City. What is QuantCon? Over the past five years the software landscape for retail quant traders, and researchers at quant funds, has changed dramatically. The uptake of open source

Algorithmic trading risks can be categorized into the following: Access Consistency Quality Algorithm Technology Scalability There are 2 places where Risk Management is handled – Within the application – We need to ensure that wrong parameters are not set by the trader. It should not allow a

Multi-factor investing that combines value, momentum, quality/profitability, or low volatility factors is today’s hot new investment approach. There has been an explosion of multi-factor ETFs recently with nine of the fourteen existing U.S. multi-factor funds coming to market this year, and five

There is ample research that shows that sorting government bonds on yield works great. The outperformance has been very consistent over the years at about 2% a year. The Dimson, Marsh, Staunton crew examined this in their 2012 GIRY issue, and graphic is below. These are returns from going long high

The best quant mashup links for the week ending Saturday, 10/17 as voted by our readers: Returns clustering with K-means algorithm [Quant Dare] Absolute Strength Momentum Investing Strategy [Alpha Architect] Guns, Bombs and eSports: Applying Data and Portfolio Analytics to Counter-Strike Gambling