Over or Under? Momentum, Idiosyncratic Volatility and Overreaction

I. Trading Strategy Concept: Short-term momentum patterns with a trend filter. Source: Dahlquist, J. R., Bauer, R. J. (2012). Technical Analysis of Gaps. New Jersey: Pearson Education, Inc. Research Goal: Performance verification of the Gap Pattern. Specification: Table 1. Results: Figure 1-2. Trade

1. Motivating Example Jegadeesh and Titman (1993) show that, if you rank stocks according to their returns over the previous 12 months, then the past winners will outperform the past losers by 1.5{\scriptstyle \%} per month over the next 3 months. But, the authors don’t just test this particular

A large body of literature examines whether managers of actively managed funds add value to their investors by generating abnormal returns. Unfortunately, not only do the vast majority fail to do so, but the evidence, as presented in my book, “The Incredible Shrinking Alpha,” demonstrates that

I have a ton of respect for the way in which GMO manages money (their guts to be massively contrarian if that is their view) and I think their thought leadership is about as good as it gets in the industry. That said, I have long had an issue in the way in which they think about investor behavior

Well, at least as far as I can tell. To understand what I am talking about consider the following results generated using daily open/high/low/close data for ticker GDX (an ETF that tracks gold mining stocks). Figure 1 displays the cumulative $ gain/loss achieved by holding 100 shares of ticker GDX

We study several aspects of the so-called low-vol and low-beta anomalies, some already documented (such as the universality of the effect over different geographical zones), others hitherto not clearly discussed in the literature. Our most significant message is that the low-vol anomaly is the

Over the last five blog posts we looked at the automated backtest results for 4040 options straddles sold on the S&P 500 Index (SPX) at 80 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

We examined 15 famous asset allocation strategies in my last book Global Asset Allocation. (If you haven’t read it yet I’ll send you a free copy.) I would like to have included a lot more tactical ideas in the book but there is a constant struggle between keeping an idea/book simple, but

I am not yet a Bitcoin advocate. Nevertheless, I am impressed with the amount of Bitcoin activity and the progress that advocates are making towards having Bitcoin recognized as a legitimate currency. Right now, I am mostly interested in the technology behind bitcoin and the possibility of working

In Part I of this series, we explored Grinold's Fundamental Law of Active Management, and why the theory leads to misguided conclusions in the presence of asset correlations. In this article we will offer a primer on a useful tool for portfolio evaluation, Principal Component Analysis (PCA),

As you might expect I spend quite a lot of my time using real financial data - asset prices and returns; and returns from live and simulated trading. It may surprise you to know that I also spend time examining randomly created financial data. This post explains why. I also explain how to generate

Backtests have become the weapon of choice for rationalizing various forms of tactical asset allocation, which has become increasingly popular as a risk-management tool since the 2008 crash. The hazards of backtesting—studying how a strategy performed in the past–are well known, which leads some

Many of you are probably aware of the paper from AQR entitled, “Size Matters: When you control for your junk.” We loved the title so much we considered it one of our Top 5 Geeky, Yet Funny, Economic Paper Titles. Of course, great papers often go unread beyond the abstract because they are a bit

Good article on False Discovery Rate – the probability that a significant p-value indicates a true effect and one of the reasons that quant tests and systems generally underperform. For example, Market Stress and Market Stress Automations generally test out at around 85% wins. In real life they

Below is a piece of Python code allowing to download option chains from NASDAQ website. It is basically a big main function relying heavily on BeautifulSoup and wrapped into a class (‘c’est chic’). User provides the ticker, expiry, option type, etc… and is returned option data for all

Serious volatility watchers are always attending a three ring circus. The left ring holds the general market. Center ring has options on the S&P 500 and the various CBOE VIX® style indexes and to the right are VIX futures, Volatility Exchange Traded Products like VXX, UVXY, TVIX, and XIV plus

In the previous post on this topic, we discussed the challenges and statistics involved in selecting a pair of stocks for statistical arbitrage. We understood how by using the co-integration tests we can say within a certain level of confidence interval that the spread between the two stocks is a

October 2015: Trend Following DOWN -9.19% - YTD: +3.36% A strong downward move at the beginning of the month weighed heavily on the index, which did not recover and went on to post its worst return of the year. The YTD performance is still in the black. Below is the full State of Trend Following

The State of Trend Following index posted a strong negative return in October. Enough to take the YTD performance in the red, with another volatile oscillation around the neutral line for 2015. Please check below for more details. Detailed Results The figures for the month are: October return:

The Ivy Portfolio spreadsheet track the 10 month moving average signals for two portfolios listed in Mebane Faber's book The Ivy Portfolio: How to Invest Like the Top Endowments and Avoid Bear Markets. Faber discusses 5, 10, and 20 security portfolios that have trading signals based on

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