Using a newly constructed unique dataset, this study is the first to document returns of the market portfolio for a long period and with a high level of detail. Our market portfolio basically contains all assets in which financial investors have invested. We analyze nominal, real, and excess return(...)

Similar to some better-known factors like size and value, time-series momentum is a factor that historically has demonstrated above average excess returns. Time-series momentum, also called trend-momentum or absolute momentum, is measured by a portfolio long assets that have had recent positive(...)

In this article series QuantStart returns to the discussion of pricing derivative securities, a topic which was covered a few years ago on the site through an introduction to stochastic calculus. Imanol Pérez, a PhD researcher in Mathematics at Oxford University, and an expert guest contributor to(...)

‘Once you have a system, the biggest obstacle is trusting it.’ Tom Wills ‘What is the last thing you do before you climb on a ladder? You shake it. And that is Monte Carlo simulation.’ Sam Savage, Stanford University Introduction In my early days of looking at trading strategies, getting to the(...)

Andrew Lo is a professor of finance and the director of the Laboratory for Financial Engineering at MIT’s Sloan School of Management. His research spans a wide range of topics, including the empirical validation and implementation of financial asset pricing models; the pricing of options and other(...)

The folks at AQR are top-notch researchers and have written a ton of great papers. Some of their more famous papers are the following: Value and Momentum Everywhere A Century of Evidence on Trend Following Size Matters If you Control Your Junk (my favorite title of any paper ever published) In this(...)

AQR updates it paper on Trend Following performance over the last century. Despite the strategy experiencing poor recent performance, it brings tremendous value to stock and bond portfolios over time by 1) increasing returns and 2) lowering volatility and max loss. A win-win-win in my book. Trend(...)

Compared to the equity market, the options market is a level up in complexity. For each symbol there are multiple expiration dates, strike prices for each expiration date, implied volatilities, and that's before we get to the option greeks. The increased complexity presents us with more(...)

Tail risk conventionally refers to the risk of a large and sharp draw down of the portfolio. How large is subjective and depends on how you define what is a tail. A lot of research is directed towards having a good estimate of the tail risk. Some fairly new research also now indicates that(...)

In spite of several efforts by researchers to overcome the estimation-risk problem (the use of estimate inputs based on sample information as if they were representative of the true population) which produces the so-called “wacky weights”, DeMiguel, Garlappi and Uppal (2009) present striking(...)

Two questions we frequently receive are: “what is global tactical asset allocation?” and “what are style premia (factors)?” In this commentary, we aim to provide a very high-level answer to those questions, incorporating as little math or financial theory as possible and avoiding nuanced(...)

In this article we'll look more deeply at the following iron condor (IC) strategy variations: 38 DTE, 25 pt. wings, 20 delta shorts, 100% stop loss, 50% profit taking 80 DTE, 25 pt. wings, 20 delta shorts, 100% stop loss, 50% profit taking 80 DTE, 75 pt. wings, 12 delta shorts, 200% stop loss,(...)

In his 2012 book, Nicholas Nassim Taleb defined the term antifragile for representing things that benefit from disorder. When this concept is applied to trading, an obvious example can be seen in the relative outperformance of systematic trend following strategies during market crises. In the CTA(...)

In our context, a technical trading indicator can be considered as a combination of a specific technical trading rule with a particular moving average of prices. In two preceding blog posts we showed that there are many technical trading rules, as well as there are many popular types of moving(...)

GMO's latest quarterly commentary is a must read, especially the second half where Jeremy Grantham attempts to model / answer the question "Why Are Stock Market Prices So High?". His first bullet point in the whole piece provides a good summary: Contrary to theory, the market P/E(...)

uly 2017 Trend Following: UP +1.59% / YTD: -15.56% The second half of 2017 starts with a modest gain for our trend following index, while the YTD performance is still well in the red. Below is the full State of Trend Following report as of last month. Performance is hypothetical. Chart for July:(...)

Guest: David Varadi. David is the Co-Founder and Portfolio Manager of QuantX Funds and has been the Director of Research at Blue Sky Asset Management since 2014. Previously, David was the Vice President of Economic Research and Strategic Development for Flexible Plan Investments overseeing(...)

A user commented on ETF Sector Rotation post about a simple idea for trading the sector ETFs, which I can’t believe I have never tried. I like keeping things simple just like my Brazilian Jiu-Jitsu game. Rules If the Select Sector SPDR ETF (XLY, XLP, XLF, XLE, XLV, XLI, XLB, XLK, XLU) is above its(...)

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 the(...)

This post is a token of appreciation for Faisal Habib who taught us structured products this summer. As commonly known among people who are familiar with leveraged ETFs, the tracking error of those products tend to be larger than what we intuitively expected. This phenomenon has been explored and(...)

The article provides new empirical evidence on the state of market efficiencies in ETFs by studying the following questions: What is the magnitude of the ETF premiums across all US-listed ETFs and all underlying asset class and over time? In particular, what is the magnitude of “true” ETF premiums(...)

So far in this series, we’ve covered faulty benchmark construction, the wide array of fundamental drivers, and the critical importance of quality in cutting through the noise among micro cap stocks. Now, we turn to the largest factor spreads I’ve come across in any segment of the market, real world(...)

In keeping with our recent theme of providing useful tidbits of algo trading practicalities, here’s an elegant solution that resolves Yahoo’s unceremonious exit from the free financial data space. Regular readers would know that I use various tools in my algo trading stack, but the one I keep(...)

This is a test of the “Vigilant Asset Allocation” (VAA) strategy from the recently published paper: Breadth Momentum and Vigilant Asset Allocation, by Dr. Wouter Keller and JW Keuning. This is an aggressive momentum trading model, similar in spirit to Keller and Keuning’s popular Protective Asset(...)

Simple rules on macro assets can create very attractive returns. We present a simple trend system and a simple carry system and show how the combination of the two return streams appears very attractive. Summary Trend following in one form or another has been an investment style for decades.(...)

In this new article series QuantStart returns to the discussion of pricing derivative securities, a topic which was covered a few years ago on the site through an introduction to stochastic calculus. Imanol Pérez, a PhD researcher in Mathematics at Oxford University, and an expert guest contributor(...)

The financialization of crude oil markets over the last decade has changed the behavior of oil prices in fundamental ways. In this paper, we uncover the gradual transformation of crude oil from a physical to a financial asset. Although economic demand and supply factors continue to play an(...)

Designing and managing asset allocation strategies based on factors is promoted in some corners as a better way to build portfolios. Not surprisingly, there’s no shortage of studies that support this view. But the jury’s still out on whether it’s prudent to throw out the standard asset-class(...)

I’m here today to talk about the Stochastic Portfolio Theory (SPT). SPT is a relatively new portfolio management theory. It was first introduced in 1999 by Robert Fernholz. In my opinion, SPT is very attractive for several reasons: it’s theoretical, it’s not very well known and, most importantly,(...)

Wednesday is a Fed Day. Fed Days have historically shown an upside tendency. I have documented this tendency in great detail over the years, with the most complete documentation coming in The Quantifiable Edges Guide to Fed Days. Based on what the market did Tuesday, this does not seem to be the(...)

If you ask your typical long-only investor (or financial advisor) how momentum is doing this year they’ll likely say, “Amazing!” This statement will almost surely be based on the fact they own (or know about) the iShares “Momentum Factor” Fund (Ticker: MTUM). MTUM is on fire year to date (through(...)

The most recent "analysis" seemingly spreading like wildfire across the perma-bear community was performed by Horizon Kinetics in their most recent quarterly commentary. Their claim is that the price-to-earnings of the Nasdaq (or any index really) is much higher than reported because we(...)

What are the research questions? Market globalization is said to be the culprit of decreased benefits of international diversification. In 1995, a US investor investing in Vodafone had exposure to 99% of UK based sales. The same investor in 2012 is exposed to UK sales only for 8% while at the same(...)

Order book imbalance strategies have been a big alpha source in automated market making. Tick by tick observations provide important information about general market sentiment and direction, and high frequency trading firms (HFTs) have been very efficient at trading on this information at very low(...)

Most trading models I see are missing an important concept. It’s not a terribly difficult concept, but it is an important one. It’s not at all strange that most traders, in particular on the retail side, are missing this point. Most trading books skip over it. Most books gloss over it, or just(...)

Calculating an optimal portfolio from a set of capital market assumptions (CMAs) is a straightforward quantitative exercise, but the results are highly dependent on the assumptions holding in the future. Any portfolio that is initially assumed to be optimal will be sub-optimal if any single assumed(...)

In my previous blog post we considered the general weighted moving average. In this post we aim to give an overview of some specific types of moving averages. Specifically, we cover “ordinary” moving averages and mention some examples of exotic moving averages. Ordinary Moving Averages These are(...)

I’ve long been interested in the behavior of corporate insiders and how their actions may impact their company’s stock. I had done some research on this in the past, albeit in a very low-tech way using mostly Excel. It’s a highly compelling subject, intuitively aligned with a company’s equity(...)

Earlier this year, I attended the Google Next conference in San Francisco and gained some first hand perspective into what’s possible with Google’s cloud infrastructure. Since then, I’ve been leaning on Google Cloud Platform (GCP) to run my trading algorithms (and more) and it has become an(...)

The accuracy of U.S. stock return forecasts based on the cyclically-adjusted P/E (CAPE) ratio has deteriorated since 1985. The issue is not the CAPE ratio, but CAPE regressions that assume it reverts mechanically to its long-run average. Our approach conditions mean reversion in the CAPE ratio on(...)

RCM Alternatives has a great piece (HT Tadas) out outlining what the VIX is, the market for VIX related products, and how to think about volatility as an asset class. It also happens to contain my new favorite quote for anyone thinking about trading volatility: Still, if you cannot see the VIX(...)

In this article Frank Smietana, one of QuantStart's expert guest contributors describes the Python open-source backtesting software landscape, and provides advice on which backtesting framework is suitable for your own project needs. Backtesting is arguably the most critical part of the(...)

What are the research questions? Do variations in aggregate measure of size and activity of sell-side analysts affect the quality of research produced by that industry? Do those same variations in aggregate measures of size and activity of sell-side analysts affect optimism bias in the research(...)

In last week’s commentary, we outline a number of problems faced by tactical asset allocators in actually implementing their views. This week, we explore popular methods for translating a combination of strategic views and tactical views into a single, comprehensive set of views that can be used as(...)

This is the third part in a series describing how to approach the creation of a new trading strategy, including everything from idea generation, universe selection, data generation, proper in/out of sample testing, necessary considerations before live trading and the eventual big decision: do I(...)

Breadth momentum extends traditional absolute momentum approaches for crash protection. Breadth momentum quantifies risk at the universe level by the number of assets with non-positive momentum relative to a breadth protection threshold. Vigilant Asset Allocation matches breadth momentum with a(...)

We study the performance of a comprehensive set of equity premium forecasting strategies that have been shown to outperform the historical mean out-of-sample when tested in isolation. Using a multiple testing framework, we find that previous evidence on out-of-sample predictability is primarily due(...)

Last time, we talked about how to identify stationary time series. Today we continue this line of thought by defining cointegration and looking at its usage in trading. In particular, we will discuss how a pairs trading strategy works. Motivation If we remember that stationarity assumes that a mean(...)

One of the basic principles of technical analysis is that “prices move in trends.” Traders firmly believe that these trends can be identified in a timely manner and used to generate profits and limit losses. Consequently, trend following is arguably one of the most widespread market timing(...)

There have been hundreds of research papers on relative strength momentum since the seminal work by Jegadeesh and Titman in 1993. [1] Relative momentum has been shown to work in and out-of-sample within and across most asset classes. Theoretical results have been consistent, persistent, and robust.(...)