Managed futures strategies have historically provided meaningful positive returns during left-tail equity events. Yet as a trading strategy, this outcome is by no means guaranteed. While trend following is “mechanically convex,” the diverse nature of managed futures programs may actually prevent

Machine learning can improve macro trading strategies, mainly because it makes them more flexible and adaptable, and generalizes knowledge better than fixed rules or trial-and-error approaches. Within the constraints of pre-set hyperparameters machine learning is continuously and autonomously

Stock sentiment can be aggregated from public sources using a big data approach Results indicate that sentiment has some predictability for short-term factor performance Positive sentiment resulted in higher subsequent returns than negative sentiment INTRODUCTION Albert Einstein famously stated that

It is no secret that artificial intelligence (AI) systems have made enormous strides in recent years, partly due to the adoption of Bayesian (probability-based) machine learning techniques rather than the rule-based techniques used until about 20 years ago. AI systems have advanced in lockstep with

Factor investing is hard and some factors make it harder than others. A value strategy results in a portfolio of stocks that exhibit temporary or structural issues and are usually rated “Sell” by brokers, which makes these emotionally challenging to hold. Small caps are companies that are

Open-High-Low-Close (OHLC) bars are a type of financial data typically used to represent daily movements in the price of a financial instrument. They give us more information about certain characteristics of the series than line charts, such as intraday volatility or daily momentum. Could Generative

The literature in portfolio optimisation has been around for decades. In this post I cover a number of traditional portfolio optimisation models. The general aim is to select a portfolio of assets out of a set of all possible portfolios being considered with a defined objective function. The data:

When I was in graduate school at Northwestern in the early 90s the hot financial topics were all related to finding and estimating risk factors: Arbitrage Pricing Theory via latent factors (Connor and Koraczyk 1986), Kalman filter state-space models (eg, Stock and Watson 1989), and method of moment

The focus of this study is on the response of typical or systematic risk premia to crowding (large inflows of capital). In particular, the paper focused on documenting the response of commonly recognized systematic risk premia strategies to periods, following the identification of crowded

This is a test of a new paper from Dr. Wouter Keller titled Growth-Trend Timing and 60-40 Variations: Lethargic Asset Allocation (LAA). This is primarily a buy & hold strategy that’s roughly based on the classic “Permanent Portfolio”, but it includes an element of tactical asset

The term mutual information is drawn from the field of information theory. Information theory is busy with the quantification of information. For example, a central concept in this field is entropy, which we have discussed before. If you google the term “mutual information” you will land at some

U.S. equities have out-performed international equities for 8 of the past 10 years, but this trend has tended to flip-flop historically and persist for multi-year stretches. Home country bias is a real phenomenon that investors have to deal with, especially during these streaks where U.S. equities

Most institutional investors can only trade the largest, most liquid stocks Introducing minimum liquidity requirements impacts factors differently Factor portfolio construction with liquidity constraints is especially challenging in small stock markets INTRODUCTION Index funds have breached $11

Say you’ve built a little nest egg thanks to some discipline and frugality. And now you realize that you should probably invest that money so that you’ve got something to live off of in retirement. Or perhaps you simply want to earn a better return than stashing your cash underneath your bed, I

A point of confusion for many new quant momentum investors is the difference between Time- Series Momentum and Cross-Sectional Momentum: Time-series (TS) looks at each individual stock’s momentum and owns assets with positive momentum while shorting those with negative momentum; Cross-sectional

Investment-based capital asset pricing looks at equity returns from the angle of issuers, rather than investors. It is based on the cost of capital and the net present value rule of corporate finance. The q-factor model is an implementation of investment capital asset pricing that explains many

The financial independence, retire early (FIRE) movement has gained a lot of traction. “We retired at 30” headlines get clicks and have made people question the typical retirement timetable: A main goal for those pursuing FIRE is to reach a portfolio balance that can reasonably fund their

Welcome to a year-end installment of Reproducible Finance with R, a series posts that will be a little bit different from the norm on Alpha Architect (see here for my last post). We will search for and hopefully unearth some interesting market conditions, but we’ll primarily focus on the code that

There are many calendar / seasonal anomalies by which we can enhance our overall investment strategy. One of the least frequent but still very interesting anomalies is for sure the Pre-Election Drift in the stock market in the United States. This year is the election year, and public discussion is

Return correlations for the major asset classes have edged down in recent years, which implies that diversification opportunities have increased, if only marginally. The correlation readings are only modestly softer overall and for several asset class pairings it’s fair to say that nothing much

Last week I noted the current rally was reaching historical extremes for persistency. Here I will look at another study from the subscriber letter, and then update last week’s study. In last night’s letter I looked at all times back to the inception of the NASDAQ in 1971 in which both SPX and

The Cboe’s VIX® (30-day) and VIX3M (93-day) indexes enable us to quantify volatility term structures but until now, historical analyses between VIX style indexes have been limited to dates after December 2001. This post introduces the results of VIX3M style calculations back to 1990, and reviews

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