Happy new year! It’s that time again, when everyone with a blog does a wrap up of the previous year. Here’s my look-back. Many of you follow along with the “+/-30% per quarter wider-market breadth indicator”. Which is too much of a mouthful, so I’ve humbly named it after myself instead. I

This code tries to show how to use genetic algorithms to create a simple trading strategy. It is intended as a proof of concept, rather than trying to provide a ready-to-use strategy. The historical data used is the following: SP500.dat. It is the closing prices of S&P 500 from 2006/12/18 to

This is a stylized implementation of the strategy described in the research paper titled "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns?" by Yuhang Xing, Xiaoyan Zhang and Rui Zhao. The authors show that their SKEW factor predicts individual equity

A recent post on Bloomberg website entitled Rising VIX Paints Doubt on S&P 500 Rally pointed out an interesting observation: While the S&P 500 Index rose to an all-time high for a second day, the advance was accompanied by a gain in an options-derived gauge of trader stress that usually

Happy New Year, one day early. Here’s wishing 2017 is successful for you in whichever way you define success. Aren’t calendars wonderful? A couple of days ago, up pops a reminder on my calendar to revisit a post I did a year ago. At the very beginning of 2016, I wrote a post on whether yearly

As we approach the end of 2016, the Shiller CAPE 10 stands at about 28, a level rarely exceeded (with the exception of in the late-1990s technology-driven bull market). Such heights cause many investors to worry about what current valuations may mean for future expected returns. I’ll try to

This is a test of the “Golden Butterfly”, the homegrown buy & hold strategy from PortfolioCharts.com. PortfolioCharts is to buy & hold what AllocateSmartly is to tactical asset allocation, an independent and unbiased catalog of strategy performance, so when they put their stamp of

Quantopian & Quantpedia Trading Strategy Series continues ... Now with a 4th article, again written by Matthew Lee, focused on Cross-Sectional Equity Mean Reversion (Strategy #13):

Another year. Looking at my google analytics reports I can’t help but wonder how is it that I am so bad in predicting which posts would catch audience attention. Anyhow, top three for 2016 are: On the 60/40 portfolio mix The case for Regime-Switching GARCH Most popular machine learning R packages

On behalf of the entire Newfound Research team, we would like to wish you and yours a happy holiday season. We treat this weekly research commentary as a sacred part of our investment process. We continue to be honored and humbled by the vast and growing number of readers it reaches, a sign of the

As 2016 nears its finish line, here we are with the list of recommended reading on our blog with the top-rated blog posts, as voted by you! Enjoy the last few days doing what you love most! Read on. System Architecture of Algorithmic Trading This one is straight out of a lecture in the curriculum of

Ever wondered if you can design a profitable trading strategy by trading volatility ETFs ? Well, yes you can. Those ETFs are highly ineffective vehicles on a long term investment horizon. However short term strategies have shown to be a rewarding way to trade these ETFs. Before we move onto strategy

There are instances where I "borrow" an idea from someone (actually... most of my posts were at a minimum inspired by someone else). In this case, I am stealing the initial concept from Ryan Detrick who posted the following chart of annual U.S. stock returns going back ~200 years as there

If you couldn’t tell from our recent monster commodity futures post, we’ve been thinking a lot about futures recently. The futures research area is relatively “fresh,” and a lot more exciting than hacking through equity stock selection research where we already understand the basic answer

When talking about quantitative trading, there are a large number of indicators and operators we can use as a buy/sell rule. But apart from deciding what indicator we will follow, the most important part would be setting the correct parameters. So, one method we can use to find adequate parameters

This paper investigates the relation between monetary conditions and the excess returns arising from an investment strategy that consists of borrowing low-interest rate currencies and investing in currencies with high interest rates, so-called "carry trade". The results indicate that carry

As Professor John Cochrane observed, the literature on investment factors now fills a veritable “factor zoo” with hundreds of options. How do investors select from among this huge array of possibilities? Noah Beck, Jason Hsu, Vitali Kalesnik and Helge Kostka, authors of the paper “Will Your

The introduction to this series, here, described the different variations of SPX iron condors (IC) and exits that were tested at 38 days to expiration (DTE). Recall, the tests covered 9 IC variations, with short strike deltas at four locations, utilizing 12 exits. In all, there were 432 test runs (9

In previous posts, we presented 2 volatility trading strategies: one strategy is based on the volatility risk premium (VRP) and the other on the volatility term structure, or roll yield (RY). In this post we present a detailed comparison of these 2 strategies and analyze their recent performance.

Whenever constructing a quant portfolio or managing portfolio risk, the risk model is at the heart of the process. A risk model, usually estimated with a sample covariance matrix, has 3 typical issues. Not positive-definite, which means…[not invertible] Exposed to extreme values in the sample,

With significant research into factor rotation in 2016, we expect to see more factor rotation strategies in the market in 2017. Using six popular factors (Value, Size, Reinvestment, Operating Profitability, Momentum and Beta), we explore both switching and rotation based strategies. We find

Machine learning is a hot topic right now, with a lot of people wondering how it could be used in finance and trading. Used naively, machine learning poses a great deal of risk. We’ll discuss why that’s the case and also some good ways to use it carefully. Sponsored by DataCamp: Learning to code

Taking a look at Vix single day spikes, since there have been two rather significant and rare single day spikes of 39 and 49% in 2016. Note im not talking about Vix daily swings, but single day spikes from close to close. The intention here is to gauge how Vix has historically behaved after

This is a test of two variations of the Protective Asset Allocation (PAA) strategy from Wouter Keller and JW Keuning’s paper: PAA: A Simple Momentum-based Alternative to Term Deposits. The strategy is notable for its aggressive use of a “crash protection” asset that has resulted in extremely

Robert Shiller shows that Cyclically Adjusted Price to Earnings Ratio (CAPE) is strongly associated with future long-term stock returns. This result has often been interpreted as evidence of market inefficiency. We present two findings that are contrary to such an interpretation. First, if markets

In this piece, we compare U.S. equity mutual funds’ annual expenses to our estimate of their potential future pre-fee excess returns. We demonstrate that many funds are priced to fail—their fees approach or exceed their potential future pre-fee excess returns. Whereas investors might have

On this episode, I’m joined by a quant trader who works at a high frequency trading firm—though you might be surprised to hear, he started out on the same path that many retail traders do—his name is; Dave Bergstrom. The thing that makes Dave unique from most traders who’ve been on this

Some of the tactical asset allocation strategies that we track have significant exposure to rising interest rates, or more specifically, to the types of assets that are most negatively affected by rising rates. While we don’t (yet) track every published TAA model, the strategies that we do track

POST OUTLINE Part-1 Recap Part-1 Error Corrections Part-2 Implementation Details, Deviations, Goals Prepare Data Setup PYMC3 Generalized Linear Models (GLM) Evaluate and Interprate Models Conclusions References part-1 recap In part 1 We discussed the theorized underpinnings of Ying Wu of Stevens

Just how perfect do circumstances need to be going forward for an investor in the S&P 500 to make money? Let's take a look at one measure. The first chart plots forward 10-year returns for the S&P 500 at various 5 point "CAPE" valuation buckets (i.e. less than 10x P/E all the

When applying Machine Learning tools to market prediction, the internet is saturated with academic papers and lacking in practical code examples. In this post, it’s my goal to translate one such paper from text to code. Mark Dunne’s Undergraduate Thesis, “Stock Market Prediction“, approaches

The Shiller CAPE (cyclically adjusted price-earnings ratio) is typically regarded as a stock market valuation measure. When the CAPE is high stocks are supposed to be expensive, and vice-versa. The CAPE itself is not a good stock market timer. However, the CAPE can indirectly be used for market

When one has a price model that they think will work well for forecasting returns, the next step is to actually trade it. This isn’t that simple for a variety of reasons. For one thing, you need to define how much risk you’re okay with taking on in a portfolio, and then try to maximize your

ETF Trends (hat tip Josh) showed the following "quilt" of large cap factor calendar year returns in the post Low Volatility is Not a Buy and Hold Strategy. Author John Lunt's takeaway (bold mine): It is reasonable to conclude that low volatility is not a buy and hold strategy. This is

Summary­­ Economic growth is a key driver of long-term stock and bond returns. Economic growth comes from two main sources: demographic changes (i.e. increases in the number of workers) and productivity growth (i.e. each worker producing more output). Historically, approximately 55% of growth has

There is still no value in bonds today. Many readers just had a knee-jerk reaction and they’ve determined that I fall into one of two categories: A total idiot A total genius But let’s dig a bit deeper into the claim that bonds lack “value,” even with this quarter’s 85 basis point back-up

The magnitude and importance of random numbers in finance does not have to be explained. We need them. Either it is an option pricing or a Monte Carlo simulation, random numbers are with us. However, we make a trade-off: the speed in their generation versus uniqueness. That is why a widely accepted

Over several time horizons op-ex week in December has been the most bullish week of the year for the SPX. The positive seasonality actually has persisted for up to 3 weeks. I’ve shown the study below in the blog many times since 2008. It looks back to 1984, which was the first year that SPX

As markets become more mature and more efficient, it can be become increasingly difficult to find sustainable edges. Many traders are looking at the same data and using the same techniques, so what are our options here? 2 of the obvious options we have are: Try to find a unique approach to the

This survey paper will discuss the (potential) structural sources of return for both CTAs and commodity indices based on a review of empirical research articles from both academics and practitioners. The paper specifically covers (a) the long-term return sources for both managed futures programs and

Recently I read a blogpost and someone was recommending the book "DIY Financial Advisor "by Wesley R. Gray, Jack Vogel and David Foulke. I believe it was the QuantStrat blog but I might be wrong. The book is a good read and also suggest a couple of simple systems any investor can implement

How Should Investors Respond to Increases in Volatility? Alan Moreira (Yale University) andn Tyler Muir (UCLA) December 2, 2016 They should reduce their equity position. We study the portfolio problem of a long-horizon investor that allocates between a risk-less and a risky asset in an environment

What can an interesting and surprising experiment with finance students and finance professionals tell us about financial decisions and how to maximise extracting returns from low information content systems? Introduction It is well known that humans are bad at estimating probabilities. We

This article is the final project submitted by the author as part of his coursework in Executive Programme in Algorithmic Trading (EPAT™) at QuantInsti. You can check out our Projects page and have a look at what our students are building after reading this article. About the AuthorEPAT student

Conditional Value-at-Risk (CVaR), also referred to as the Expected Shortfall (ES) or the Expected Tail Loss (ETL), has an interpretation of the expected loss (in present value terms) given that the loss exceeds the VaR (e.g. Alexander 2008). For many risk analysts, CVaR makes more sense: if VaR is a

In this post, I provide R code that enables the replication of the Center for Research in Security Prices (CRSP) Volatiliy Deciles using Yahoo! Finance data. This post is related to my last blog post in that it will generate the CRSP low volatility decile portfolio, thereby facilitating the

In most of my mean reversion posts, I use RSI(2) to determine if a stock has sold off. In this post, I will explore how to use a stock’s recent return to determine if it has sold off. This will be done in way to normalize the return between low and high volatile stocks. This basic strategy has

Following up on my last post, I’d like to take a deeper dive into the performance of TAA strategies. In particular, I’ll take a look at the differences between the top performing TAA strategies and the bottom performing ones. There are some important points that come out of this analysis which I

A couple of months ago, we published a study on the performance of trend following after drawdowns, as the State of Trend Following index was hitting high levels of drawdown (about 2/3 of the historical maximum). We showed that in 80% of cases, the post-drawdown performance is positive, showing that

This is a test of a number of popular approaches to portfolio optimization. Each seeks to answer the question: given a universe of assets, how much should we allocate to each? We’ve intentionally made these tests as simple and fair (read: unoptimized) as possible in order to best represent each