In our last post on the SKEW index we looked at how good the index was in pricing two standard deviation (2SD) down moves. The answer: not very. But, we conjectured that this poor performance may be due to the fact that it is more accurate at pricing larger moves, which occur with greater frequency

Machine learning, quantum computing and beyond: cutting-edge quant solutions to finance problems Quant Summit Europe gives you the opportunity to meet with, learn and exchange ideas with over 130 renowned industry quants and data scientists from the world’s leading banks, buy-side institutions and

One of the foundation concepts of the Alpha Architect investment philosophy is the utilization of Enterprise Multiples in the value discovery process. Enterprise multiples are often referred to as the “business buyer metric” and are a key valuation tool used by investment bankers and business

Naïve value factor portfolios have been in a drawdown since 2007. More thoughtful implementations performed well after 2008, with many continuing to generate excess returns versus the market through 2016. Since 2017, however, most value portfolios have experienced a steep drawdown in their relative

Would you like to read a 18-page pounderous law draft titled “Law for introducing a duty to report cross-border tax structuring”? The members of the German Bundestag apparently didn’t. Nothing can be said against reporting cum-ex or similar constructs, so the new law, proposed by finance

I was interested in calculating what the portfolio volatility would be for a portfolio given various correlation assumptions, and also the number of assets. So I took two portfolio of the S&P500 in two very different years: 2008 and 2017. The VIX had one of its highest average levels in 2008, at

Private equity return data should be viewed with caution Returns are likely overstated while volatility is understated Private equity returns are highly correlated to public equities TWO MAGIC WORDS “This time is different” might be the four most dangerous words in investing. “Uncorrelated

It has long been a belief of mine that the industry in which a company operates has a huge impact on its performance, and that most industries simply are not worthwhile for long-term investment consideration. To further this discussion, I took the detailed industry data found in Professor Ken

Macro News and Long-Run Volatility Expectations Anders Vilhelmsson (Lund University) December 10, 2019 I propose a new model-free method for estimating long-run changes in expected volatility using VIX futures contracts. The method is applied to measure the effect on stock market volatility of

Funds flows are frequently analyzed by investors to gauge the demand for investment strategies, but it represents a challenging exercise. Key issues are data availability as few market participants disclose their holdings as well as reporting frequency as limited data is published in real-time. The

With the advent of several machine / deep learning models, there have been several theories emerging in applying these techniques for stock market prediction because of the difficulty and complexity it involves. In this project, we’re trying to solve the problem using a classifier to predict

I have been recently hired to code a series of indicators based on monthly articles in the Stocks & Commodities magazine, and to write here about the details of indicator programming. Looking through the magazine, I found many articles useful, some a bit weird, some a bit on the esoteric side.

In FOREX, both the EURCHF and USDCHF series have outliers that can be a problem when applying Machine Learning techniques to them. So, in this post, the performance of an autoencoder detecting these anomalies is going to be studied. Analyzing the EURCHF and USDCHF returns, it can be seen that there

The American Finance Association Annual Meetings have now come and gone (here is information on the broader conference). The conference was in sunny San Diego this year and I’m told it did not disappoint! 1 This 3-day conference collects the brightest minds in academia to discuss hundreds of new

This is part X of my series of blog posts on skew and kurtosis, where 2 A post on skew: measuring, and it's impact on future returns A post on kurtosis: measuring, it's impact on future returns, and it's interaction with skew. A post on trend following and skew (which I actually wrote

Portfolio Optimisation has always been a hot topic of research in financial modelling and rightly so – a lot of people and companies want to create and manage an optimal portfolio which gives them good returns. There is an abundance of mathematical literature dealing with this topic such as the

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.