Developer: John Bollinger (Bollinger Bands®). Concept: Trend-following trading strategy based on Bollinger Bands. Research Goal: Performance verification of the 3-phase model (long/short/neutral). Specification: Table 1. Results: Figure 1-2. Trade Setup: Long Trades: Close[i − 1] >

Everybody know that volatility depends on the measurement frequency: the standard deviation of 5-minute returns is different from that of daily returns. To be precise, if z is the log price, then volatility, sampled at intervals of τ, is volatility(τ)=√(Var(z(t)-z(t-τ))) where Var means taking

Large institutional investors have had access to low-cost "smart beta" for many years. But for retail investors and their financial advisors, "smart beta" ETFs are a welcome innovation. Instead of trying to identify an expensive manager who can pick stocks, a retail investor can

Skill is a slippery concept in finance, courtesy of the shady influence of chance in asset pricing. It's also an awkward topic in just about every corner of money management because discussing it in detail invariably raises serious doubts about our ability to engineer investment results that

As a passionate value investor it took me a long time (and a lot of research) to accept that momentum is a very important factor that you must incorporate in your investment strategy if you want high returns. Momentum simply works The simple reason is that it works. I summarised the most important

With our inaugural March Madness Portfolio Challenge in the books, we’re going to cover three very important takeaways. Takeaway #1: I mean, it wasn’t even close… Yes, in this part we pay homage to our esteemed champion, who has earned the glory due unto him by leading – more or less – the

Following two strong months to start the year, the index was down in March, but still positive overall for 2016. Please check below for more details. Detailed Results The figures for the month are: March return: -5.90% YTD return: 5.16% Below is the chart displaying individual system results

This study documents persistent, net-of-fees, alpha-generating commodity trading advisor funds focused on commodity investment ("Commodity Funds"). The baseline for performance measurement is a new benchmark model that includes factors established in the literature. A nonparametric

A common tactic for some traders is to scale out of successful positions. The logic is this: I’ve already made some money, so I want to hold onto some of that. I’ll cash out a portion of my trade now, and see how the trade continues, but with reduced risk. You see this behavior with day traders,

This post looks at the consequences of ETF rebalancing. These funds follow pre-announced rules that involve discrete thresholds. The well-known SPDR tracks the S&P 500, but there are over 1400 different ETFs tracking a wide variety of different underlying indexes. When any of these underlying

Jack and I published, “Analyzing Valuation Measures: A Performance Horse-Race Over the Past 40 Years,” in the 2012 Journal of Portfolio Management. horse race Here is a summary of the research paper on our own blog. The paper asked a simple question: “Which valuation metric has historically

Outliers are annoying. The analysis would be easier if they did not exit. Then, why not to remove them? As libesa told us in her last post titled “Machine Learning: A Brief Breakdown”, world is going crazy with Machine Learning and now we use it in all domains. In this post, we will see another

This post presents an appealing proof for the progressing domination of algorithmic trading over human trading. By analysing the US stock market between 1960 and 1990, we estimate a human engagement (human factor) in live trading decisions taken after 2000. We find a clear distinction between

In my previous post I published a bunch of R Scripts that will enable a reader of Taleb's "Silent Risk", Chapter 3, Section 3.2 "Problems and Inverse Problems" to play with the ideas he presents. I thought I should discuss one of the results those scripts produce that does

GARCH models are very responsive in the sense that they allow the fit of the model to adjust rather quickly with incoming observations. However, this adjustment depends on the parameters of the model, and those may not be constant. Parameters’ estimation of a GARCH process is not as quick as those

It is estimated that 180,000 Chinese immigrated to the United States in the latter half of the 19th century; many of them worked on the Transcontinental Railroad. Deeply routed in Chinese culture, the immigrants brought with them various medicinal remedies for common ailments. It was believed that

I've taken a bit of a break from posting options strategy research, but before I dive back in I'm going to revisit some material I posted on Momentum Rotation systems last year. If you're new to my blog you may have missed my posts related to rotation system results and data. For the

March 2016 Trend Following: DOWN -2.84% / YTD: +6.86% The index gave back some of its gains from the beginning of the year, last month. The performance is still positive Year-To-Date and over the last 12 months. Below is the full State of Trend Following report as of last month. Performance is

ection 3.2 in Chapter 3 of "Silent Risk", a draft of a book by Nassim Nicholas Taleb defines the "inverse problem" as follows: Definition 3.4 (The inverse problem). There are many more degrees of freedom (hence probability of making a mistake) when one goes from a model to the

I’m sure we all want to create trading strategies that perform better and last for longer but there are a number of issues we need to look out for when developing robust trading strategies, some are well-known and some perhaps aren’t. In this episode we’ll be talking with Perry Kaufman about

This part of the tutorial on using NEAT algorithm explains how genomes are crossed over in a meaningful way maintaining their topological information and how speciation (group genomes into species) can be used to protect weak genomes with new topological information from prematurely being eradicated

This is the another post of the series: How to build your own algotrading platform. Last time we talked about The "for-looper" backtester (as I love to call them). Now it's time to see some code! We said that we have something like that:

To date on QuantStart we have introduced Bayesian statistics, inferred a binomial proportion analytically with conjugate priors and have described the basics of Markov Chain Monte Carlo via the Metropolis algorithm. In this article we are going to introduce regression modelling in the Bayesian

If you read the paper, watch the news, and listen to investment experts you are doing it all wrong. There are no market wizards; the emperors have no clothes; most people are ‘swimming naked’. The following paragraphs offer abundant and incontrovertible evidence condemning expert judgment for

Deep Blue was the first computer that won a chess championship, in 1996. It took 20 more years until another computer program, AlphaGo, could defeat the best human Go player. Deep Blue was a model based system with a fixed chess library and hardwired chess rules. AlphaGo is a data-mining system, a

In this post we are talking about autoregressive models and their application to a financial world. This model follows the idea that the next value of the serie is related with the p previous values. Definition of p-order autoregressive model An autoregressive model or AR is a type of modelling that

Last week’s post on analyzing US equity value and momentum risk premia ended with a question: How much, if any, improvement should we expect by adding a dynamic system for managing exposure to these risk factors vs. a buy-and-hold strategy? What follows is a preliminary effort in searching for an

It has been a while since my last post and in the intervening time I have been busy working on the code of my previous few posts. During the course of this I have noticed that there are some further improvements to be made in terms of robustness etc. inspired by this Master's thesis, Improved

While much is known about the financialization of commodities, less is known about how to profitably invest in commodities. Existing studies of Commodity Trading Advisors (CTAs) do not adequately address this question because only 19% of CTAs invest solely in commodities, despite their name. We

Exotic options have always been a hobby of mine. One of the curious things about Dodd-Frank was it started to push swap trading onto exchanges. As such, a cottage industry of exchange traded exotics (in the US they're technically swaps) has popped up over the last few years. The biggest of

I recently listened to an interesting interview at Better System Trader with Jay Kaeppel on Seasonality, a topic which I hadn’t done much backtesting on previously. Jay outlined 3 rules for constructing a seasonal trading strategy on the stock index: - Stay long the last 4 days and first 3 days of

In this post on bettersystemtrader.com, Andrew Swanscott interviews Kevin Davey from KJ Trading Systems who discusses why looking at your back-test historical equity curve alone might not give you a true sense of a strategy’s risk profile. Kevin Davey also writes on the topic here for

In the last post we covered Machine learning (ML) concept in brief. In this post we explain some more ML terms, and then frame rules for a forex strategy using the SVM algorithm in R. To use ML in trading, we start with historical data (stock price/forex data) and add indicators to build a model in

There’s very strong historical evidence to support the existence of a value premium in equity markets. While there’s no dispute over the existence of the value premium (value stocks have provided an annual average return 5% higher than growth stocks over the long term), there is much debate over

These are the best quant mashup links for the week ending Saturday, 03/26 as voted by our readers: FX: multivariate stochastic volatility – part 2 [Predictive Alpha] Predicting Stock Market Returns—Lose the Normal and Switch to Laplace [Six Figure Investing] Momentum for Buy-and-Hold Investors

There are many investors who prefer to remain invested in stocks at all times. Perhaps they think tactical allocation is some kind of voodoo. Maybe they have a strong psychological bias against occasional whipsaw losses and do not mind bear market drawdowns. Maybe they have institutional constraints

In a recent blog post, I rather glibly stated that the market tends to revert to a mean. A reader called me out about the time frame I was using, which raises a good point. A market can tend toward both mean reversion and momentum over different time frames. Many traders would argue that different

Recently there has been a lot of research on the question of whether higher moments of return other than volatility (specifically, the skewness of returns) helps to explain equity returns. (I’ve included a brief definition of skewness and a demonstrative example of it below.) For instance, the

If you've been a regular reader of our blog, you already know that we recently published our first book Adaptive Asset Allocation: Dynamic Portfolios to Profit in Good Times - and Bad. As of this writing, it still stands as the #1 new release in Amazon's Business Finance category.

Adjusted stock prices are the foundation for time-series analysis of equity markets. Good analysts insist on properly-adjusted stock data. But the best analysts understand the adjustment process from first principles. This is Quandl's guide to the creation and maintenance of accurate adjusted

In previous discussions of Bayesian Inference we introduced Bayesian Statistics and considered how to infer a binomial proportion using the concept of conjugate priors. We discussed the fact that not all models can make use of conjugate priors and thus calculation of the posterior distribution would

Obsession with short-term performance against market cap benchmarks preordains the dysfunctionality of asset markets. The problems start when trustees hire fund managers to outperform benchmark indexes subject to limits on annual divergence… Benchmarking causes, first, the inversion of the

If you've been a regular reader of our blog, you already know that we recently published our first book Adaptive Asset Allocation: Dynamic Portfolios to Profit in Good Times - and Bad. As of this writing, it still stands as the #1 new release in Amazon's Business Finance category.

The value and momentum factors have earned high praise in recent years as complementary sources of risk premia for designing and managing equity portfolios. AQR’s widely cited paper “Value and Momentum Everywhere” a few years back helped popularize the idea, pointing to applications in

In the introductory text for Part I of their 2016 book, Adaptive Asset Allocation: Dynamic Global Porfolios to Profit in Good Times – and Bad, Adam Butler, Michael Philbrick and Rodrigo Gordillo state: “…we have come to stand for something square and real, a true Iron Law of Wealth Management:

"Smart beta" investing is an alternative to the traditional active and passive approaches to funds management, whereby investors adopt a systematic method that provides exposure to factors that are argued to be related with expected returns at low cost. Therefore, the question of how smart

The “practitioner Black-Scholes delta” for hedging options is a delta calculated from the Black-Scholes-Merton model (or one of its extensions) with the volatility parameter set equal to the implied volatility. As has been pointed out by a number of researchers, this delta does not minimize the

Not sure why is that, but traditionally we consider 60% stocks and 40% bonds to be a good portfolio mix. One which strikes decent balance between risk and return. I don’t want to blubber here about the notion of risk. However, I do note that I feel uncomfortable interchanging risk with volatility

Justin spoke at the Investing in Smart Beta conference this week in Fort Lauderdale, FL. He spoke alongside Research Affiliates in a session titled "The Smart Beta Checklist: Choosing The Best Strategy & Risk/Return Profile For Your Portfolio." Here's a quick description:

In part 2 our mean-variance optimal FX portfolio is allowed to choose from multiple models each week based on a measure of goodness (MSSE). The risk-adjusted return improves as a result with the annualized Sharpe Ratio rising to 0.86 from 0.49. In part 1 we estimated a sequential multivariate