We’ve read a number of outlooks and commentaries lately from firms arguing for investors to take a tactical tilt away from U.S. equities and towards International equities. The logic behind this tilt is largely driven by relative valuations: international equities appear significantly cheaper than

And we’re back for the final episode in this 3-part series on building Mean Reversion strategies with Cesar Alvarez from Alvarez Quant Trading. In the 1st episode we discussed the goal of Mean Reversion trading, how to select a trading universe, a number of effective techniques to measuring Mean

Adaptive Asset Allocation (AAA) is based on the Nobel Prize winning portfolio theory of Markowitz (1952) AAA combines asset’s momentum, volatilities, and cross-correlations for building diversified investment portfolios In a tactical application AAA exploits momentum for crash detection and

So, it’s been some time since you’ve been thinking of making more money out of your successful trading strategy. And why should you not? After all, you’ve worked hard for it and there is only a small % of people who are successful in this business. The idea is to add more funds to your trading

Regression analysis is used all the time to assess how a portfolio “loads” on certain factors. The most common factor loadings examined are the market, size, value, and momentum factors. This can be an informative exercise, and there are nice tools online, such as portfolio visualizer, which

Investing in a Multi-Asset Multi-Factor World Alexandar Cherkezov (Invesco), et al. August 31, 2017 In this article, we advance the use of factor investing across multiple asset classes. It turns out that style factors well established in the equity domain – such as value, momentum or quality –

Why are overnight periods riskier? For one, you can’t use stops to limit your risk. But more importantly, the distribution of overnight returns has far more extreme negative returns than the intraday or close-to-close periods. Let’s take a look at some stats on close-to-open, open-to-close, and

Once we are familiar with the theory surrounding Risk Parity, it’s time to put the strategy into practice and try out the algorithm for ourselves. We discover how it works, analyse the strategy and create our own portfolios. Thanks to the posts written by T.Fuertes and mplanaslasa we already know

This paper presents a new dataset for the annual risk-free rate in both nominal and real terms going back to the 13th century. On this basis, we establish for the first time a long-term comparative investigation of ‘bond bull markets’. It is shown that the global risk-free rate in July 2016

Last month saw a strong upwards performance from the State of Trend Following index, single-handedly reversing half the negative performance for the year, which still stands close to double-digit territory. Please check below for more details. Detailed Results The figures for the month are: October

We track a wide range of tactical asset allocation strategies in near real-time (41 and counting), which members can combine into their own custom portfolios. We provide members with a wealth of data to understand how each strategy fits into a coherent trading plan, but we don’t tell members the

In previous installments of replicating indexes I profiled the style-analysis methodology and presented an example using a hedge fund index. Now let’s turn to a strategy of replicating the S&P 500 Index with a handful of stocks that are considered socially responsible investments (SRI).

When you come in contact with cryptocurrencies, e.g. Bitcoin (BTC), you quickly realise that there is no single price of BTC at any given moment. The reason is that Bitcoin is traded on different markets. It can be worth more on Coinbase exchange and less on Kraken exchange. In particular, the

The differences between correlation and cointegration can often be confusing. While there are some helpful explanations online, I wasn’t satisfied with the visual examples. When looking at a plot of an actual pair of symbols where the correlation and cointegration test results differ, it can be

Changes in earnings are comprised of the expected earnings number plus any seasonal component of earnings. If the seasonal component is expected then it should not affect prices in an efficient market. However, unusual returns have been documented surrounding earnings announcements at the seasonal

Long/short portfolios are helpful tools for quantifying the value-add of portfolio changes, especially for active strategies. In the context of fees, we can isolate the implicit fee of the manager’s active decisions (active share) relative to a benchmark and ask ourselves whether we think that

Integrated Value, Growth & Quality portfolios generated attractive returns year-to-date 2017 Sorting stocks on several characteristics results in relatively smooth performance Mitigates the issue of factor timing, but not of factor selection INTRODUCTION Year-to-date 2017 is shaping up as a

Auquan recently concluded another version of QuantQuest, and this time, we had a lot of people attempt Machine Learning with our problems. It was good learning for both us and them (hopefully!). This post is inspired by our observations of some common caveats and pitfalls during the competition when

October 2017 Trend Following: UP +7.12% / YTD: -15.39% Below is the full State of Trend Following report as of last month, which saw our trend following index post a strong positive performance. Performance is hypothetical. Chart for October: Wisdom State of Trend Following - October 2017 And the

Often in financial markets, as in our daily life, we imitate the decisions of predecessors, instead of analysing available information and making our own decisions. This decision imitation could lead to collective hysteria, and investment calls may be influenced by these panicked situations. Imagine

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

This post is the continuation of the steps for creating a mean reversion strategy from the first part of The ABCs of creating a mean reversion strategy – Part 1. You can also listen to part 2 of my interview on Better System Trader here. A quick recap of the topics covered in part 1. I covered

ack and I will be attending the Evidence-Based Investing Conference tomorrow in NYC. We’re excited to participate and be part of the crowd. Be sure to give us a holler — love to discuss whatever is on your mind! Author rendering of the scene at EBI Historically, the conversations at EBI can end

This article examines a somewhat over-looked, but important, discussion that raged among academic researchers in the late 1990’s and early 2000’s. The topic: factors versus characteristics. What do you mean, “Factors versus characteristics?” We often highlight that the value premium can be

The strategy in this blog will cover no normal technical indicators, but some of my own creation. Also, we will see the difference between strategy performance on test and train data along with respect to the changes in the size of the train data and the prediction length. Unlike in my previous

The world is awash with new data. Satellite imagery, shipping manifests, agricultural sensors, and more can provide untapped insights. To understand how investors might benefit, we decompose investment strategies into three pieces: systematic rules, idiosyncratic decisions, and randomness. We

Buying high yielding and selling low yielding stocks has been an attractive strategy since 2000 However, it has been a highly unattractive strategy over the last century Investors should resist the Siren call of high yielding stocks and focus on other factors INTRODUCTION The search for yield has

After gauging interest from my readers, I’ve decided to open up a subscription service. I’ll copy and paste the FAQs, or my best attempt at trying to answer as many questions as possible ahead of time, and may answer more in the future. I’m choosing to use Patreon just to outsource all of the

In this study, a two-factor risk model is developed assuming differing preferences for “sin” or “no-sin” stocks for two groups of investors. Social screens are built into the model by assuming a small percentage of investors are self-restricting, declining to invest in “sin” stocks.

In the prior post Information In Volatility Structure [1] applied the SABR model to fit noisy raw option price data of approximatelty 700 million prices across a 10 year history of 2700 stocks. The point was to examine a hypothesis: does supply / demand imbalance in the options market express in

In the last two posts (here, and here), we looked at how implied volatility (IV) and price of the option strikes in two broken wing butterfly (BWB) strategies changed with time. In this post, we'll look at another BWB strategy, the centered delta neutral (CDN) BWB. In this strategy, the short

Factor returns, net of changes in valuation levels, are much lower than recent performance suggests. Value-add can be structural, and thus reliably repeatable, or situational—a product of rising valuations—likely neither sustainable nor repeatable. Many investors are performance chasers who in

And we’re back for the 2nd episode in this 3-part series on building Mean Reversion strategies with Cesar Alvarez from Alvarez Quant Trading. In the first episode we discussed the goal of Mean Reversion trading, how to select a trading universe, a number of effective techniques to measuring Mean

HillClimber.ai is a curated machine learning mashup inspired by the quantitative finance blog aggregator Quantocracy. A special shout-out to Mike for all the help he provided in setting up this website. This mashup is very new and I would welcome all feedback from the community. There are many good

Imitation, Oscar Wilde famously observed, “is the sincerest form of flattery that mediocrity can pay to greatness.” The observation echoes the objective for using Professor Bill Sharpe’s style analysis to replicate investment indexes that, for one reason or another, can’t be purchased

Following on from my previous post about downloading historical data, this post shows how previously downloaded data may be updated and appended with new, more recent data without having to re-download all the old data all over again. The main function to do this, HisPricesDates, downloads data

Replicating Anomalies is arguably a “must read” for anyone who thinks about factor investing and is looking to improve their understanding of the space. Lu Zhang, and his colleagues, Kewei Hou and Chen Xue, spent nearly 3 years carefully compiling and replicating 447 “anomalies” identified

One of us (Marcos Lopez de Prado) has been interviewed on the topic of educational training in the finance field by Institutional Investor. A brief synopsis of this interview is below. The full article is HERE. How Universities Are Failing Finance Students With investment shops fighting over

Wes recently challenged me with a unique proposition: Hey Ryan, read through this Replicating Anomalies paper and tell me what you think. Its a bit long, but I’m curious to hear your thoughts. Well, by “a bit long,” Wes really meant 144 pages of equations and reams of quantitative data on

We’re very excited for the launch of an awesome new feature for our members: Meta Strategy. We track a wide range of published tactical asset allocation strategies in near real-time (40 and counting), which members can then combine into their own custom portfolios. Our platform helps members

Note: This is not an official bet. We’re not interested in documenting all the potential details that would be involved, and we don’t have $1million to wager. Moreover, licensed firms are not allowed to make public fund recommendations, so the details of an official bet would have to be private

Are equity multifactor strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider seven ETFs, all currently available (in order of decreasing assets): Goldman Sachs ActiveBeta U.S. Large Cap Equity (GSLC) – holds large U.S. stocks based on good value, strong

This post will be about pulling free data from AlphaVantage, and gauging interest for a volatility trading subscription service. So first off, ever since the yahoos at Yahoo decided to turn off their free data, the world of free daily data has been in somewhat of a dark age. Well, thanks to

When performing multiple-based valuations, which rely on the assumption that perfect substitutes should sell for the same price, it is very important to identify companies that are truly comparable. Most analysts use industry classifications. Lee et al. (2015) note that industry classifications are

Kelley Bergsma & Jitendra Tayal A version of this paper can be found here Want to read our short summaries of academic finance papers? Check out our Academic Research Insight category. What are the research questions Using data from CRSP and Compustat from 1989 to 2015 the research team

A frequent criticism of factor investing is that factor returns are stronger in small caps Our research highlights that this is not uniformly true across factors Value and Size benefit most from including small caps INTRODUCTION Factor investing can be challenged in many ways. Nearly all of the

For active strategies, investors often focus on the potential outperformance the strategy can create. Beyond the potential for outperformance, active strategies also introduce tracking error: extra volatility that comes from active decisions. While most view tracking error as a negative, if derived

In this paper I document that carry trades in commodity markets are subject to potential large and infrequent losses, that is, tail risk. Also, I show that shocks to carry trades and volatility have persistent tail-specific effects which last from four to twelve weeks ahead. The main empirical

Dr Thomas Starke is a Physics PhD who once designed microchips, worked as an engineer for Rolls Royce and lectured at University of Oxford, before applying his know-how of modelling to financial markets… As a trader, Thomas has contracted to various funds and up until recently, he was a