What would happen if you took $5,000 out of your $100,000 permanent portfolio and allocated it to Bitcoin? From 3.6% annual to 15% annual returns? Got to love the Permanent Portfolio I have been somewhat obsessed with the simplicity and fundamental thinking behind the permanent portfolio. I have

We have done a fair amount on the investment merits of ESG investing, but the question of how ESG affects the fundamental performance of a firm (in a causal fashion) is addressed in this study. For example, this paper askes questions such as, “Are high ESG scoring firms more adept at managing

Sources of long-term historical data are few and far between. Because it’s been generously provided for free, one of the most often used is data from Professor French (of Fama-French fame). Others include Shiller and Ibbotson. These data sets are fine for a first pass at testing out ideas, but

Highly ranked ESG stocks trade at higher valuation multiples than the stock market However, the difference in multiples is minor and far less than extreme than for Growth stocks ESG ETFs generated lower returns than the stock market, but were also less volatile INTRODUCTION Europeans seem far more

Welcome to another installment of Reproducible Finance! Inspired by a great visualization in Hands on Time Series with R by Rami Krispin, today we’ll investigate some market structure data and get to know the Midas data source provided by the SEC. Let’s start by importing data from the SEC

As trading becomes automated, we have seen that traders seek to use as much data as they can for their analyses. But we all know that adding more variables leads to more complications and that in turn might make it harder to come to solid conclusions. Think about it, we have more than 3000 companies

One of the interesting puzzles in finance is that stocks with greater idiosyncratic volatility (IVOL) have produced lower returns (see an earlier post here). This is an anomaly because idiosyncratic volatility is viewed as a risk factor—greater volatility should be rewarded with higher, not lower,

Through R or Python we can nowadays apply a wide range of methods for predicting financial market variables. Key concepts include penalized regression, such as Ridge and LASSO, support vector regression, neural networks, standard regression trees, bagging, random forest, and gradient boosting. The

Recently I’ve had several of my consulting clients come with a strategy that uses Inverse Volatility Position Sizing. The basic idea is that the more volatile positions have smaller size while the less volatile ones get a larger size. I have always been a fan of equal position sizing for several

By reader request, this is a test of a tactical strategy from Harrison Schwartz that considers various economic yields in order to rotate among asset classes. Strategy results versus the 60/40 benchmark follow. We’ve extended Schwartz’s original test by an additional 6+ years, and accounted for

Shiller’s CAPE ratio is a popular and useful metric for measuring whether stock prices are overvalued or undervalued relative to earnings. Recently, Vanguard analysts Haifeng Wang, Harshdeep Singh Ahluwalia, Roger A. Aliaga-Díaz, and Joseph H. Davis have written a very interesting paper on

A stop-loss order protects profit or limits risk on an investor’s open position by exiting at a predetermined price. Placing an order to sell a long stock position if the price drops 5% below the purchase price is an example of a stop-loss order. In this post, we’re going to dig into what a stop

This post will be about analyzing SVIX–a proposed new short vol ETF that aims to offer the same short vol exposure as XIV used to–without the downside of, well, blowing up in 20 minutes due to positive feedback loops. As I’m currently enrolled in a Python bootcamp, this was one of my capstone

Variance and covariance are widely accepted risk measures for liquid assets that trade in public markets. Illiquid assets are not part of this framework because of their lack of regular price quotes and thus time variance. Due to the difficulty in using standard risk measures to assess non-traded

The difference between the cheapest and most expensive smart beta ETF in the US is 59 bps on average Some smart beta ETFs offer negative factor exposure, which requires explanation Factor scores can be used to identify which smart beta ETFs offer the best ratio of factor exposure per dollar in fees

Factors play an important role for quantitative portfolio construction. How a factor is defined and how a factor portfolio is constructed play important roles in the results achieved. Naively constructed portfolios – such as most “academic” factors – can lead to latent style exposures and

As every year, I checked my analytics so that I can let you know what was popular. This year I have also experimented with a survey where I asked one question at the end of each relevant post. About 120 replies recieved, but the free Survey Monkey account (the survey provider I went with) only lets

In an influential piece, Sharpe (1991) 1 put forward the proposition that active investing must be a losing pursuit in aggregate, as it amounts to a zero-sum game in gross terms and hence must be a negative-sum game after costs. I take a critical look at the underlying concepts and assumptions

As in 2018, Low Volatility produced the best and Value the worst performance Value did not recover significantly further after a short rally in Q3 2019 However, Momentum broke its upward trajectory since then INTRODUCTION We present the performance of five well-known factors on an annual basis for

In 2019, we published 45 research notes (not including video + audio commentary), totaling over 100,000 words. Our research spanned a number of topics, including: ensemble techniques, deep dives on trend following, factor and sector rotation, fixed income analysis, and – of course – rebalance

We are closing 2019 with much gratitude to our clients, collaborators and online visitors. We have launched this blog less than a year ago and have had the pleasure of seeing many visitors from all over the world ranging from buy-side investors, financial advisors, asset owners, thought leaders,

The end of the year is a good time for a short recapitulation. Apart from other things we do (which we will summarize in our next blog in a few days), we have published around 50 short blog posts / recherches of academic papers on this blog during the last year. We want to use this opportunity to

We’ve taken a lot of time reviewing multi-factor allocation techniques within the equity portion of a portfolio here and here. But thus far we have only written on the concept of utilizing a multi-factor investment technique in contrast with traditional asset allocation here. In this post, we are

Liquidity is a critical force behind market price distortions (and related trading opportunities). First, the cost of trading in and out of a contract gives rise to a liquidity premium. Second, the risk that transaction costs will rise when market conditions necessitate trading commands a separate

Google dictionary defines the word robust thusly: sturdy in construction able to withstand or overcome adverse conditions … and offers the following definitions for the word fragile: easily broken or damaged flimsy or insubstantial; easily destroyed not strong or sturdy; delicate and vulnerable

In this post I take a look a popular and quite simple quant strategy that combines value and profitability, the Greenblatt Value Strategy. Results are impressive and the strategy has held up better than most value strategies over the last 10 years. And even more impressive it has even outperformed

Klaus Grobys contributes to the literature on asset pricing models with his October 2019 paper, “Another Look on Choosing Factors: The International Evidence.” Using bootstrap simulations, Grobys examined international markets, specifically the four regions of North America (NA), Europe, Japan

Transaction costs are a major concern for practitioners attempting to implement factors investing strategies identified in academic literature. Naturally, this is a subject that has been covered before here, here, and here, but a new look at transaction costs never hurts. The authors of this paper

Author: Wes Gray. Wes is the CEO/CIO of Alpha Architect. He has published multiple academic papers and four books, including Quantitative Value (Wiley, 2012), DIY Financial Advisor (Wiley, 2015), and Quantitative Momentum (Wiley, 2016). After serving as a Captain in the United States Marine Corps,

Over the last several years, we have written several research notes demonstrating the potential benefits of diversifying “specification risk.” Specification risk occurs when an investment strategy is overly sensitive to the outcome of a single investment process or parameter choice. Adopting an

For the quant community, it was arguably the most awaited book of 2019. Finally a peek behind the curtains into the most successful hedge fund manager in history. The +66% average (gross) returns that Jim Simons and his army of data scientists produced over the last 17 years in their Medallion fund

In 2019 we published more than 50 research notes on mostly factor investing and smart beta ETFs, but also on topics like ESG, activist investors, hedge fund replication, and artificial intelligence. The Research Compendium 2019 contains all of our research published this year. We would like to thank

This post introduces a quant trading model based on volatility. More specifically it uses the prices of volatility futures contracts based on the SP500 to make risk-on and risk-off decisions that can be used to trade various risk-assets. Why Volatility? There is a bunch of research that shows that

Trend-following funds and strategies were once extremely popular after the 2008/2009 crisis. They offered attractive performance, and diversification properties made them a nice addition to investor’s portfolios. Ten years later, “trend-following strategy” is not such a popular word.

Value Bubbles Messaoud Chibane and Samuel Ouzan (Neoma Business School) February 27, 2019 According to several extended behavioral theories, value profits should mirror momentum profits, and vary over time. We test these theories in the cross section of returns. Value returns depend on market

The Current Ratio is a vital metric for understanding a company’s liquidity position as well as its ability to pay its obligations on time. It is defined simply as the company’s total current assets divided by its current liabilities. Current assets are defined as assets that are cash or

Robert Carver worked in the City of London for over a decade. For seven years he was a portfolio manager at AHL — one of the world’s largest systematic hedge funds — before, during and after the global financial meltdown of 2008. In this interview, we talk about many trading topics, mostly

It has been more than a year since my last post, I’ve been super busy with consulting assignments working on algorithmic/electronic trading. The workload is still heavy but I managed to find a few hours to write this post as I came across a new great tool: LOBSTER (and before anyone asks I’ve no

In Part 1 of this article, we reviewed the performance of using a more complex form of simple trend following (i.e. adding a channel breakout rule alongside a simple time-series momentum rule). The simple trend signal (S) used was based on the sign of the trailing 12-month return of the asset. The

This is our third post in our series on “net-nets” having previously analyzed “Benjamin Graham’s Net Current Asset Values: A Performance Update” by Henry R. Oppenheimer and “Graham’s Net-Nets: Outdated or Outstanding?” by James Montier. The focus of this post is the research paper

The Fama French three-factor model provides a powerful tool for assessing exposures to equity risk premia in investment strategies. In this note, we explore alternative specifications of the value (HML) and size (SMB) factors using price-to-earnings, price-to-cash flow, and dividend yield. Running

The outlook for US equity and bond returns is low based on historical data The return assumptions of US public pension funds are difficult to achieve Only an extreme allocation to alternatives would meet the expected rate of return THE GLOBAL PENSION FUND CRISIS Tens of thousands of Dutch workers

I have written many times over the years about the bullish tendency of the market during opex week in December. I’ve even referred to it as “The Most Wonderful Week of the Year”. And it was…up until last year. So below is an updated look at the stats and profit curve for owning SPX from the

This post will be a quickie detailing a rather annoying…finding about the pandas package in Python. For those not in the know, I’ve been taking some Python courses, trying to port my R finance skills into Python, because R seems to have fallen out of favor in the world of finance. (If you know

Price distortions are an important source of short-term trading profits, particularly in turbulent markets. Here price distortions mean apparent price-value gaps that arise from large inefficient flows. An inefficient flow is a transaction that is not motivated by rational risk-return optimization.

ESG Investing (sometimes called Socially Responsible Investing) is becoming a current trend, and its proponents characterize it as a modern, sustainable, and responsible way of investing. Some people love it, others see it as just another fad that will soon be forgotten. We at Quantpedia have

In a previous post, Trend-following vs. Momentum in ETFs, I compared trend-following and momentum to see which produced better results on a basket of ETFs. In the post, I mentioned combining trend-following and momentum into one strategy to see if combined they can beat buy and hold more often.

A large body of evidence demonstrates that investment strategies focused on buying stocks that are cheap relative to measures of fundamental value have achieved higher long-term returns than the broad market. Motivated by such legendary investors as Benjamin Graham, David Dodd, and Walter Schloss,

We’ve already talked about dimensionality reduction long and hard in this blog, usually focusing on PCA. Besides, in my latest post I introduced another way to reduce dimensions based on autoencoders. However, in that time I focused on how to use autoencoders as predictor, while now I’d like to

I have written about Fed Day edges for years. Much of the research can be found in the Fed Study category blog posts. Today I decided to share a chart showing historical performance on Fed Days over the course of the last 5 Fed chairpeople. 2019-12-10 Have a happy Fed Day tomorrow!