I get most of my quant research done while trying to avoid other less interesting work. So I was curious when I saw my friend John Hussman writing about an obscure technical indicator called the Coppock Curve. I filed it away as “interesting”, sent it to the Idea Farm list, then moved on. But

We are really excited that Quantopian & Quantpedia Trading Strategy Series continues with a second article focused on Reversal Effect during Earning Announcements (Strategy #307). Click on a "View Notebook" button to read a complete analysis:

This is a test of a tactical asset allocation strategy from David Varadi of CSS Analytics. I’ve been a long-time fan of David’s work. David always devises unique approaches to trading, swimming just outside the mainstream. This strategy is a good example of that. The strategy is notable for its

A year ago, bear-market risk looked elevated for the US stock market, based on a Hidden Markov model (HMM). The warning, which was discussed on these pages at the time (see here, for instance), has had a mixed record. Although stocks swooned in late-2015 and early 2016, the growling was relatively

Value has a long history as an investing style, backed up by empirical evidence that portfolios of the cheapest stocks outperform the broad market. The strategy behind value investing is simple: buy stocks with a low price relative to their current financial metrics like earnings, EBITDA or cash

There is a cool new website that tracks the performance of well-known tactical strategies. AllocateSmartly has collected an extensive list of strategies from well-known hedge fund managers like Ray Dalio along with several other portfolio managers and financial bloggers. The backtests for these

The world is awash with backtests that lay claim to new portfolio techniques that provide superior results for managing risk, juicing return, or both. What’s often missing is a robust stress test to confirm that the good news is more than a statistical anomaly. Crunching the numbers on a single

Quantitative Trading for Non-Quantitative Traders This title might seem like a contradiction. You might think that non-quantitative traders are incapable of quantitative trading. This isn’t true. They might not be able to discover quantitative insights or read scholarly papers but they can

Over USD$2 trillion is held in exchange-traded equity funds, just in the U.S., with hundreds of new funds added each year. Strategies vary from simple index-tracking funds to funds that follow sophisticated strategies (e.g., “smart beta”) designed to yield impressive results, based on backtests.

Time-series momentum examines the trend of an asset with respect to its own past performance. This is different than cross-sectional momentum (often referred to as Carhart momentum), which compares the performance of an asset with respect to the performance of another asset. Research into

Sometimes even the best evidence-based active investment strategies can create a formidable challenge to investors seeking to exploit them. Case in point — momentum investing. On the one hand, stock-selection momentum strategies (here is a link to more information) can have the potential to

A reader sent this interesting link about Up/Down Capture. The great part about this article it is something I have intuitively known about my trading strategies but never tried to quantify. This was the bump I needed to investigate this concept. When the SPY moves up on average how much does my

It is common for large asset management firms to publish their capital market assumptions: long-term global asset expected return and covariance assumptions. Yet many firms do not draw the link between what published capital market assumptions say and what they mean when carried through the

One of my favorite Twitter follows @LadyFOHF shared the below scatter chart from Morgan Stanley that attempted to map areas of the global market that were both cheap (valuation ranks at the lower end of its 10-year history) and defensive (a low or negative correlation to global equities). One of the

As the casual reader of this blog (or my book) will be aware, I like to delegate my trading to systems, since humans aren't very good at it (well, I'm not). This is quite a popular thing to do; many systematic investment funds are out there competing for your money; from simple passive

In the first and second articles in the series we looked at the courses that are taken in the first half of a four-year undergraduate mathematics degree - and how to learn these modules on your own. In the first year we discussed the basics - Linear Algebra, Ordinary Differential Equations, Real

Trading algorithmically based on sentiment data is a relatively new field compared to more established approaches. With the explosion of social media and computing power, the analysis of sentiment data has also increased, with some hedge funds committing considerable resources to researching the

We investigate the maturity-structure of roll strategy returns in the energy futures markets. Our innovation is to report and analyze the risk/return profile, the Sharpe ratio, and the asset pricing loadings of rollover strategies based on futures contracts of the same underlying commodity but with

In this post, we play again little bit around with python and the pandas-library. You may want to read the first part of this series. There we have backtested a simple crossing moving average strategy in pandas. We had a long/slow moving average over the last 40 days and a fast/short moving average

It has been over three months since my last post, due to working away from home for some of the summer, a summer holiday and moving home. However, during this time I have continued with my online reading and some new thinking about my conditional restricted boltzmann machine based trading system has

Launched only recently, AllocateSmartly.com tracks the industry’s best tactical asset allocation strategies with thorough, up-to-date backtests. As of writing 16 (sub) strategies are tracked and benchmarked on near real-time basis. All of the tracked strategies are both quantitative and

The academic standard for intermediate-term momentum measurement is “12_2 momentum:” simply sort all stocks based on a stock’s total return over the past twelve months, ignoring the last month. (a discussion is here and here) However, a few years ago Robert Novy-Marx wrote a paper titled “Is

It is Fall in Summer!… The trend following index had a big move to the downside last month, taking the YTD performance to a negative level as well. The results are similar, over on the Wisdom State of Trend Following, a sort of version 2.0 of this report, which I write for them too. Please check

August 2016 Trend Following: UP -7.32% / YTD: -7.95% August was mostly one-sided, sliding down to a strong negative performance, and taking with it the Year-To-Date performance to a similar level. Interesting to note the shorter timeframes weighing on the index while the longer timeframes are still

This is a summary of the recent performance of a number of excellent asset allocation strategies. These strategies are sourced from books, academic papers, and other publications. They range from simple, static portfolio allocations, to complex and dynamic portfolio optimization. Read more about our

NO SIGNAL is a regular column where we examine various snafus in the trading, particularly the automated trading, world. We look at errors in application logic, mistakes by overzealous co-workers, failures in technology and temporary losses of power to both infrastructure as well as craniums. These

WHAT IS QUANTLER? Quantler is an open source cloud-based trade automation software designed for individual traders of FX and CFDs. Our goal is to help individual traders optimize their trading performance through innovative but simple-to-use trading technology. Quantler makes it easy to build your

The evaluation of volatility models is gracefully complicated by the fact that, unlike other time series, even the realization is not observable. Two researchers would never disagree about what was yesterday’s stock price, but they can easily disagree about what was yesterday’s stock volatility.

There is a new paper published in the Journal of Financial Markets that digs a bit deeper into the Moskowitz, Ooi, and Pedersen “Time Series Momentum” paper (some background here). ts paper The paper is behind a pay firewall, but luckily there is a 4 part lecture by the authors explaining the

A common quant trading technique involves taking two assets that form a cointegrating relationship and utilising a mean-reverting approach to construct a trading strategy. This can be carried out by performing a linear regression between the two assets (such as a pair of ETFs) and using this to

SUMMARY We have previously discussed many problems associated with the measurement of alpha. Measurement uncertainty, the choice of model risk factors, and the analysis timeframe can all have significant impacts on the calculation and applicability of alpha, and investors are often hard-pressed to

Looking for a great challenge on a Saturday morning on September 24th? 2016-08-26 14_58_14-March for the Fallen Come join some members of the Alpha Architect team and our tribe of friends/clients when we take part in the “March for the Fallen” on Saturday, September 24, 2016. We’re aiming to

Do Stock Market Trading Activities Forecast Recessions? Ujjal Chatterjee (University of Wisconsin-Milwaukee, American University of Sharjah) August 9, 2016 This paper re-examines the existing recession forecasting models with stock market liquidity as an additional forecasting variable. We

I used to update an old post on free data sources and stock screeners for investors. I thought I’d summarize a handful of websites that focus on tactical asset allocation software, tools, and backtesters. For a long time I was going to build this on tacticalassetallocation.com, but there are now

To those on my new blog notification list, I sent out the opportunity to join me in a one hour webinar where people could ask me anything about trading. I had a ton of fun answering lots of great questions. See the bottom of the post for links to download the mp3 files of the webinars. Some

In my two previous blog posts (here and here), I analyze the performance of bonds during really bad months for US stocks (“Crisis Alpha” months), and I analyze the performance of US stocks during really bad months for US bonds. A quick summary of the results from those prior studies: Bonds have

This is a summary of the recent performance of a number of excellent asset allocation strategies. These strategies are sourced from books, academic papers, and other publications. They range from simple, static portfolio allocations, to complex and dynamic portfolio optimization. AllocateSmartly is

Mark Hulbert has compiled an interesting list of recent market panics: August 2015: Concerns about the Chinese economy and stock market led to panic selling, with the Shanghai index plunging 8.5% in one day. Soon after in the U.S., on August 24, 2015, the DJIA plunged over 1,000 points in just a few

After I covered some basics about WCF Services and setup a server, we need to connect a client. In this post I want explain a little more the overall design philosophy. Than in followup posts we come closer to coding and bring it all up. First, lets talk about some basic design stuff. There are many

Motivated by McLean and Pontiff (2016), we study the pre- and post-publication return predictability of 138 anomalies in 39 stock markets. Based on more than a million anomaly country-months, we find that the United States is the only country with a statistically significant and economically

In the 1950s, Harry Markowitz proposed a method to identify the optimal trade-off between risk and return for a portfolio. The theory is broadly termed, “Mean-Variance Optimization (MVO).” Sam Wittig, a Drexel graduate I advised and who did some research for Alpha Architect, shared with us his

In our previous article on Introduction to Zipline package in Python, we created an algorithm for moving crossover strategy. Recall, Zipline is a Python library for trading applications and to create an event-driven system that can support both backtesting and live-trading. In the previous article,

Summary We have noticed the market reaching a broad consensus that equities are overvalued, implying a drag on forward expected returns as valuation multiples contract. While there is often great wisdom in the crowd, there can also be great madness. We believe it is prudent to consider how the crowd

In this article we are going to consider a stastical machine learning method known as a Decision Tree. Decision Trees (DTs) are a supervised learning technique that predict values of responses by learning decision rules derived from features. They can be used in both a regression and a

Precious metals have been in free-fall for several years, as a consequence of the Fed’s actions to stimulate the economy that have also had the effect of goosing the equity and fixed income markets. All that changed towards the end of 2015, as the Fed moved to a tightening posture. So far, 2016

Pyfolio is a Python library that takes a return series of an asset, hedge fund, trading strategy, anything with daily returns and automatically generates some really cool statistics and charts. There is a LOT of cool stuff to explore in the library, have fun! Performance statistics Backtest

As we have discussed before, one of the major problems for the first formal asset pricing model developed by financial economists, the capital asset pricing model (CAPM), was that it predicts a positive relation between risk and return. But empirical studies have found the actual relation to be

My book The Missing Risk Premium is a steal at only $15, but my first book, Finding Alpha, is a $65, which is a bit much for anyone not expensing their books. Finding Alpha goes over why the current asset pricing model fails, with lots of evidence, explains why economists still like it, and then in

Eric Crittenden was recently on Meb Faber’s podcast and he tells a compelling story about the perils of survivor bias in backtesting. Eric’s story begins when he is an undergraduate working on a project for a quantitative finance course. The professor asked that the students develop a systematic

When testing trading strategies a common approach is to divide the initial data set into in sample data: the part of the data designed to calibrate the model and out of sample data: the part of the data used to validate the calibration and ensure that the performance created in sample will be