This is a test of the Quint Switching Filtered strategy from Lewis Glenn. On the surface this is a run-of-the-mill tactical asset allocation strategy based on short-term momentum, not unlike several strategies that we track. But digging a little deeper, we’ll highlight qualities that make this

Over the past few years, financial-news sentiment analysis has taken off as a commercial natural language processing (NLP) application. Like any other type of sentiment analysis, there are two main approaches: one, more traditional, is by using sentiment-labelled word lists (which we will also refer

This is a unique article in that it directly assesses the feasibility and effectiveness of protecting equity portfolios using traditional passive means and more contemporary active strategies. It is jam-packed with information and analysis that is best consumed in two parts; however, a good summary

The rise and fall (?) of Risk Parity is a great case study of the frameworks I have been writing about so far. We start with the concept of “Chasing Diversifiers.” Chasing Diversifiers (link) Although Risk Parity is as close as you get to a pure risk diversification play, just like other

After stumbling across a set of old research notes from 2009 and 2012, we attempt to implement a Cyclicals versus Defensives sector trade out-of-sample. Post-2012 returns prove unconvincing and we find little evidence supporting the notion that PMI changes can be used for constructing this trade.

A classic long-short, multi-factor portfolio has lost close to 20% since 2018 The drawdown is within expectations, but the recovery period is abnormally long However, it’s difficult to argue for structural changes that make factor investing unattractive SEEKING DIVERSIFICATION THROUGH MULTI-FACTOR

There are as many strategies for extracting alpha from the markets as there are traders. Unfortunately, this article will be discussing none of them. If that’s what you’re looking for, I suggest you check out the very sophisticated techniques covered in this video. OK. If you’re still reading,

The R environment makes statistical estimation and learning accessible to portfolio management beyond the traditional quant space. Overcoming technicalities and jargon, managers can operate powerful statistical tools by learning a few lines of code and gaining some basic intuition of statistical

The variance risk premium (VRP) refers to the fact that, over time, the option-implied volatility has tended to exceed the realized volatility of the same underlying asset. This has created a profit opportunity for volatility sellers—those willing to write volatility insurance options, collect the

This post is a summary of a more detailed Jupyter (IPython) notebook where I demonstrate a method of using Python, Scikit-Learn and Gaussian Mixture Models to generate realistic looking return series. In this post we will compare real ETF returns versus synthetic realizations. To evaluate the

This is a test of Movement Capital’s Composite Strategy. It combines tactical asset allocation with passive buy & hold. This balance between strategy risk and asset risk may be psychologically easier to trade, encouraging investors to stick with a smart investment plan when either style finds

I’d argue that a typical investor believes the following–In the past and over the long run, stocks outperformed bonds.(1) However, as highlighted here, an academic paper last year shows that the majority of individual U.S. stocks actually lost compared to Treasury Bills (i.e. the return to

One of the most effective performance reporting formats I know is a Cone Chart, popularized by Bridgewater Associates. Here are some reasons why a Cone Chart is so effective: It clearly establishes ex-ante expectations of both return and volatility. When actual outcomes deviate within expectations,

New research from Axioma suggests that tilting less – through lower target tracking error – can actually create more academically pure factor implementation in long-only portfolios. This research highlights an important question: how should long-only investors think about factor exposure in

The live performance of quant strategies is significantly worse than in backtesting Factor investing returns from research are frequently challenged as being overstated However, the performance of smart beta and long-short multi-factor funds match theoretical returns INTRODUCTION When pitching an

It is options expiration week this week. Options expiration weeks often have a bullish tendency. You can see it broken down by month in this post from March. But the summer months of June, July, & August have not seen that same bullish tendency. August’s performance has actually been net

Towards the end of completing my masters in data science, I started picturing myself doing clever things with machine learning and automated trading. If like me, you have run into the “how do I get historical free tick data” connundrum, then this post is for you. I have structured my post in

Successful and long-lasting quantitative research programs require a solid foundation that includes procurement and curation of data, creation of building blocks for feature engineering, state of the art methodologies, and backtesting. In this project we create a open-source python package

R is an object-oriented programming language and work environment for statistical analysis. It is not just for programmers, but for everyone conducting data analysis, including portfolio managers and traders. Even with limited coding skills R outclasses Excel spreadsheets and boosts information

As an option seller you want the market to stay within the range prognosticated by implied volatility. But what is the historic probability that markets behave as expected? And what other analysis could be done to enhance your chances and find the periods when it is wise to sell an at the money

One of the more popular equity strategies over the past decade is low volatility investing. Simply put, this is a systematic strategy that invests in stocks with lower volatility, either measured by Beta or standard deviation. Why? Well, the low-beta anomaly is the fact that in the past, academics

In my last two posts, Market Timing with a Canary, Gold, Copper, LQD, IEF and much more and Day of Month and Market Timing, I assumed that we earned no interest in cash. Most methods did a good job of telling us when to be in the SPY and when to be in cash. How much could we boost returns by

In a previous post, from way back in August of 2017, we explored the relationship between the VIX and the past, realized volatility of the S&P 500 and reproduced some interesting work from AQR on the meaning of the VIX. With the recent market and VIX rollercoaster, this seemed a good time to

What are the research questions? This paper is the first to examine the impact of including an active share target into the mean-variance optimization process of constructing portfolios. They use the Ceria and Stubbs (2006) approach to robust portfolio optimization as methodology. Monte Carlo

The bond risk premium is the return that investors earn by investing in longer duration bonds. While the most common way that investors can access this return stream is through investing in bond portfolios, bonds often significantly de-risk portfolios and scale back returns. Investors who desire

Given the popularity of Low Volatility, investors might expect structural shifts in the factor characteristics Betas, valuations, sector biases, interest rate sensitivity, and factor exposures are highly time-varying Although these are worth monitoring from a risk perspective, none seem particularly

Are broad measures of public sociopolitical sentiment relevant to investors? Do they predict stock returns as indicators of exuberance and fear? To investigate, we relate S&P 500 Index return and 12-month trailing S&P 500 price-operating earnings ratio (P/E) to the percentage of respondents

The Two Centuries Investments blog from Mikhail Samonov has become a new favorite of mine. More “thought heavy” than “numbers heavy”, Mikhail is a fount of novel ideas. In this piece he describes something I apply in my own investing (though never defined so succinctly): the need to balance

No pain, no premium: risks are the currency that investors need to pay in order to earn excess returns in the long run. Over a period of almost 100 years the Fama-French US market index achieved approximately 10% annualised return but temporarily lost more than 30% on multiple occasions. For no

Moving average is one of the most commonly used smoothing method, basically the go-to. It helps us detect trend in the data by smoothing out short term fluctuations. The computation is trivial: take the most recent k points and simple-average them. Here is how it looks: Moving average example with

This is a summary of the recent performance of a wide range of excellent Tactical Asset Allocation (TAA) strategies, net of transaction costs. These strategies are sourced from books, academic papers, and other publications. While we don’t (yet) include every published TAA model, these strategies

For those of you that don’t know, the idea of creating an open-source package based on Advances in Financial Machine Learning stemmed from Ashu and my master’s project at WorldQuant University. The initial goal was to build out a package that we could leverage to help us extend the literature

My Advisor Perspective article of June 17, 2019 discussed the regime shifting nature of the low volatility anomaly—low volatility stocks have outperformed high volatility stocks, providing both higher returns while experiencing lower volatility. For example, in his 2012 paper, “Enhancing a

Let’s talk about tail risk modelling today. In this blog, I want to introduce Extreme Value Theory (EVT) which concerns itself with modelling of the tails of a distribution, and its key results. As we go along we will work through a toy example with basic R implementation. There are two popular

Alternative Title: What to Do if Your Boss is Terrified of New Ideas? Several younger quants have asked this question: “The culture of our quant group is very skeptical about new ideas. They are terrified of data-mining, and random factors. How can we innovate in such environment?” My thoughts

We have shown many times that timing luck – when a portfolio chooses to rebalance – can have a large impact on the performance of tactical strategies. However, fundamental strategies like value portfolios are susceptible to timing luck, as well. Once the rebalance frequency of a strategy is set,

In financial markets, two of the most common trading strategies used by investors are the momentum and mean reversion strategies. If a stock exhibits momentum (or trending behavior as shown in the figure below), its price on the current period is more likely to increase (decrease) if it has already

For the first time, we will use the features of multiple financial instruments. In this case, we will use the main Forex pairs and the SP500 to perform our forecasts on Gold. This Jupyter notebook will be your guide for more complex calculations. Obviously, you can change the features and

Generative Adversarial Networks are a quite powerful tool for generating synthetic samples. Visual inspection has been used as a traditional measure of performance. However, it is quite hard to inspect when a time series looks realistic or not! Which methodology can be used then? In order to measure

Value and Size have a positive relationship with the PMI, similar to the S&P 500 Indicates that risk sentiment is a core driver of factor performance Investors can consider incorporating variables like the PMI in a risk management framework INTRODUCTION A physicist, a chemist, and an economist

Today’s quantitative investors seem split between innovation on one hand and engineering on the other. The prior group is constantly looking for new factors that predict returns in areas like alternative data and machine learning - yet often fail to find them. The latter camp is focused on

TL;DR: We built a profitable Bitcoin sentiment strategy yielding 2400% returns over 24 months. Adding trading fees made the strategy more realistic while finding optimal sentiment combinations and window sizes increased returns dramatically. In the previous article, we described how to build a

New to Tactical Asset Allocation? Learn more: What is TAA? There are two hard costs that investors must consider when comparing a tactical asset allocation strategy to conventional buy & hold: (1) increased tax liability (if trading in a taxable account), and (2) increased trading costs

Stocks are risky investments. Let's be very clear, stocks are risky with positive skew. Of course, everyone knows that but some data published about two years really drove that home. (See my earlier post "Most stocks are losers - Median and skew tell an important story" about the

In this article I wanted to concentrate on some basic time series analysis, and on efforts to see if there is any simple way we can improve our prediction skills and abilities in order to produce more accurate results. When considering most financial asset price time series you would be forgiven for

We explore a representative multi-asset momentum model that is similar to many bank-based indexes behind structured products and market-linked CDs. With a monthly rebalance cycle, we find substantial timing luck risk. Using the same basic framework, we build a simple ensemble approach, diversifying

Simple value and momentum strategies often end up with opposite market positions. One strategy succeeds when the other fails. There are two plausible reasons for this. First, value investors regularly bet against market trends that appear to ‘have gone too far’ by standard valuation metrics.

The date on which a portfolio is rebalanced can have a tremendous impact on realized performance. We demonstrate that a strategy rebalanced on different dates using the exact same investment process can exhibit return differentials of over 20% across short periods of time. These differences are

A year in the making, my third book is now complete. Trading Evolved is quite different from my previous books, and substantially more information packed. This book is a practical, in-depth guide on how to backtest and analyze strategies using powerful Python techniques. To my knowledge, no such

In this week’s post, we discuss four articles. The size, written by the folks at AQR, is titled “Fact, Fiction, and the Size Effect” and is a deep dive into the Size effect–I highly recommend everyone read the underlying paper as well. The second article examines the baseline historical