In last week’s commentary, we outline a number of problems faced by tactical asset allocators in actually implementing their views. This week, we explore popular methods for translating a combination of strategic views and tactical views into a single, comprehensive set of views that can be used

This is the third part in a series describing how to approach the creation of a new trading strategy, including everything from idea generation, universe selection, data generation, proper in/out of sample testing, necessary considerations before live trading and the eventual big decision: do I want

Breadth momentum extends traditional absolute momentum approaches for crash protection. Breadth momentum quantifies risk at the universe level by the number of assets with non-positive momentum relative to a breadth protection threshold. Vigilant Asset Allocation matches breadth momentum with a

We study the performance of a comprehensive set of equity premium forecasting strategies that have been shown to outperform the historical mean out-of-sample when tested in isolation. Using a multiple testing framework, we find that previous evidence on out-of-sample predictability is primarily due

Last time, we talked about how to identify stationary time series. Today we continue this line of thought by defining cointegration and looking at its usage in trading. In particular, we will discuss how a pairs trading strategy works. Motivation If we remember that stationarity assumes that a mean

One of the basic principles of technical analysis is that “prices move in trends.” Traders firmly believe that these trends can be identified in a timely manner and used to generate profits and limit losses. Consequently, trend following is arguably one of the most widespread market timing

There have been hundreds of research papers on relative strength momentum since the seminal work by Jegadeesh and Titman in 1993. [1] Relative momentum has been shown to work in and out-of-sample within and across most asset classes. Theoretical results have been consistent, persistent, and robust.

What would you think of a quant strategy that only invests in the most profitable companies? Would it under perform the market or beat the market? If you’re an efficient market person you may think that higher profitability must be priced into equities and therefore at best the strategy would

Continuing from the last post, I will show how using different definitions of passing our out-of-sample test can change our results. How luck can play a role if you use only one strategy to test in out-of-sample. How you split your in-sample(IS) and out-of-sample(OOS) can change results. The

Any frequent reader of our blog knows we are fans of momentum investing. At this point, investment professionals should know that momentum historically works, that momentum is painful, and we have our own opinions on how to implement momentum investing via our Quantitative Momentum Index. Sometimes

We are rather used to reading this disclaimer (or some variation thereof) in mutual fund prospectuses or investment vehicle webpages. Despite warnings, investors and advisors insist on considering past performance (and some other related metrics) as important factors in asset selection. But, are

Newfound specializes in systematic, factor-based approaches to constructing tactical portfolios. While we believe factors like value, momentum, carry, and trend are applicable at the asset class level, care must be taken in designing tactical allocation portfolios. We outline four considerations

What are the research questions? Do factors offer superior diversification benefits relative to assets because factors are less correlated with each other? Does consolidating a larger set of assets into a smaller set of factors reduce noise? Are investors more skilled at relating current information

This article is on the analysis of intra-arrival times of future contract trades, analysing market behaviour, and identification of other particpants’ trading strategy. Intra-arrival time has close relationship with the quantity of each trade. One reason behind this is that many participants use

Compared with machine learning or signal processing algorithms of conventional trading strategies, High Frequency Trading systems can be surprisingly simple. They need not attempt to predict future prices. They know the future prices already. Or rather, they know the prices that lie in the future

In the previous post I revealed a web scraping trick that allows us to defeat AJAX/JavaScript based web pages and extract the tables we need. We also covered how to use that trick to scrape a large volume of options prices quickly and asynchronously using the combination of aiohttp and asyncio. The

As my co-author Andrew Berkin, the director of research for Bridgeway Capital Management, and I explain in our new book, “Your Complete Guide to Factor-Based Investing,” there is considerable evidence of cross-sectional return predictability. Citing more than 100 academic papers, we present the

Traditional portfolio management focuses explicitly on the trade-off between risk and return. Anecdotally, investors often care more about the growth of their wealth. Due to compounding effects, wealth is a convex function of realized returns. Within, we explore geometric mean maximization, an

LASSO stands for Least Absolute Shrinkage and Selection Operator. It was first introduced 21 years ago by Robert Tibshirani (Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B). In 2004 the four statistical masters: Efron, Hastie, Johnstone and

June 2017 Trend Following: DOWN -3.44% / YTD: -16.88% The whole first half of 2017 was negative, with the June result following the same trend. The YTD figure is now well in the red and it would take a good reversal of that equity curve to erase the losses through the second half of the year. Below

This the third in my Hedge Fund Hacks series in which I dig just below the surface of some of the common hedge fund performance statistics. In the previous post I highlighted some of the ways in which Compound Annual Growth Rate can be distorted by chance. In this post I provide a simple hack to

While I am a believer of APT more than of CAPM, I will share some of my findings on CAPM. CAPM has many flaws: there are capital taxes, high transaction cost on illiquid securities with few floating shares, licensed leveraged funds do influence prices with outsized positions, different analyst has a

This is a summary of the recent performance of a wide range of excellent tactical asset allocation strategies. These strategies are sourced from books, academic papers, and other publications. While we don’t (yet) include every published TAA model, these strategies are broadly representative of

In the previous installment, we discussed the use of a popular asset allocation/market timing rule (10M SMA rule hereafter) to size a short option position. The strategy did not work well as it was the case in traditional asset allocation. We thought that the poor performance was due to the fact

Real estate investment trusts (REITs) are often considered to be a distinct asset class. But, do REITs deserve this designation? While exact definitions for asset class may vary, a number of statistical methods can provide strong evidence either for or against the suitability of the designation. The

Be sure to check out our guest post over at research supersite Alpha Architect: Tactical Asset Allocation: Does the Day of the Month Matter? Backtests of long-term strategies like tactical asset allocation are usually shown trading at the end of the month, both because it makes the analysis simpler

This Free Friday, Free Friday #19, is a user submission! It is a long/short strategy for $IWM - the Russell 2000 ETF. Both the long and the short strategy only have two rules each and only hold for 1 day. Below I’ve posted the long strategy on the left and the short strategy on the right. Short

US Sector Rotation with Five-Factor Fama-French Alphas G. Sarwar (University of Greenwich), et al. June 16, 2017 In this paper we investigate the risk-adjusted performance of US sector portfolios and sector rotation strategy using the alphas from the Fama-French five factor model. We find that

Most long-term approaches to investing, like tactical asset allocation or factor investing, are designed to trade infrequently, generally once a month or once a quarter. This is a feature, not a limitation. Trading infrequently forces a strategy to ignore day-to-day noise and focus on long-term

So far, we’ve discussed the importance of investment universe selection and price momentum in designing a robust asset allocation methodology. If you haven’t read those articles, we would strongly encourage you to do so before proceeding with this one. We lay most of the explanatory and

You’ll recall, I had Andy Kershner on the podcast a few episodes back. Towards the end of that episode, Andy briefly mentioned a cloud-based algo development platform and fund, CloudQuant, which is a subsidiary of Kershner Trading Group… I mention this, because with me on this episode is Morgan

This article is the final project submitted by the author as a part of his coursework in Executive Programme in Algorithmic Trading (EPAT™) at QuantInsti™. Do check our Projects page and have a look at what our students are building. Introduction The Dispersion Trading is a strategy used to

Motivation Trading has gotten a lot faster over the last two decades. The term “short-run trader” used to refer to people who traded multiple times a day. Now, it refers to algorithms that trade multiple times a second. Some people are worried about this new breed of short-run trader making

Why Ferrari or Rolex does not price their products at 149.999 or 12.999 but most of the items you see in your supermarket is priced like 4.99? Because Ferrari never likes to position itself as a bargain. Did you know that we tend to chose the price with less syllables even if the two prices have the

I’m very pleased to announce my DataCamp course on Visualizing Time Series Data in R. This course is also part of the Time Series with R skills track. Feel free to have a look, the first chapter is free! Course Description As the saying goes, “A chart is worth a thousand words”. This is why

We've recently been considering the field of deep learning as a modelling methodology for forming new quantitative trading models. Such models have been shown to be 'unreasonably effective' in the fields of computer vision, natural language processing and games of strategy. This

In a rising rate environment, conventional wisdom says to shorten duration in bond portfolios. Even as rates rise in general, the influence of central banks and expectations for inflation can create short term movements in the yield curve that can be exploited using systematic style premia. Value,

What are the research questions? Are crowdsourced earnings forecasts from a source such as Estimize, useful in the capital markets by capturing new information about future earnings? Does a site such as Estimize add incremental accuracy when combined with the conventional, sell-side earnings

STRUGGLING QUANT episode 1: How I lost USD 500.000 while figuring out the link between questions, math, stats, coding and trading Say that you are 30 years old and you have a good 25 years to work hard. Instead of going down the easy way of working for someone else during the day and killing time in

Density estimation using regression? Yes we can! I like regression. It is one of those simple yet powerful statistical methods. You always know exactly what you are doing. This post is about density estimation, and how to get an estimate of the density using (Poisson) regression. The “go-to”

Let’s start an experiment! This post will be the first in a series on going through the process of creating a trading strategy. It will not only detail the steps that I myself curently follow when I am building a strategy, what I’m hoping for is that others contribute to the process by adding

It is a common misconception that the most important thing to have when you're trading, or investing, systematically is good trading rules. In fact it is much, much, much more important to have a good position management framework (as discussed in my first book) and to trade a diversified set

This is the fourth in a new advanced series of posts written by Imanol Pérez, a PhD researcher in Mathematics at Oxford University and an expert guest contributor to QuantStart. In this post Imanol applies the Theory of Rough Paths to the task of predicting which country a company belongs to based

I will be talking on the Factor Investing panel at the upcoming Evidence-Based Investing Conference in Dana Point, CA next Sunday –Tuesday. I am excited for the opportunity to chat, and figured I would highlight a few thoughts we have on the topic going into the event. First, what is “evidence

Last week I posted an article, which formed the first part in a series on Linear Algebra For Deep Learning. The response to the article was extremely positive, both in terms of feedback, article views and also more broadly on social media. Many of you commented that there was "an appetite"

In the last article we looked at the backtest results from 600,912 iron condor trades entered between January 2007 and September 2016. The focus in that article was on win rate and normalized P&L per day for each of the 3024 variations tested. Recall that we looked at combinations of: Trade

Mean reversion strategies rely on the premise that extremes in price eventually revert to the mean price over time. They are effective during established markets – bull, bear or sideways – but unfortunately do not perform well during market regime changes or tail events. Tail events are outcomes

I am frequently asked if I do out-of-sample testing. The short answer is not always and when I do, it is not how most people do the test. There are lots of considerations and pitfalls to avoid when doing out-of-sample testing. Out-of-sample testing is not the panacea it is made out to be. There are

I'm excited to announce my DataCamp course on importing and managing financial data in R! I'm also honored that it is included in DataCamp's Quantitative Analyst with R Career Track! You can explore the first chapter for free, so be sure to check it out! Course Description Financial

Factor investing has experienced a resurgence in popularity under the moniker “smart beta.” Several traditional factors, such as value, size, momentum, and low beta, are well defined and have been heavily researched in academia as return anomalies for many decades. These factors have also been