In a recent post I showed that the momentum factor has been mean-reverting in the short-term, and that this effect can be used to trade both the factor and momentum strategies effectively. An obvious extension is to see whether trend-following as a factor is also mean-reverting. After all,

Yes, Artificial Intelligence (AI) is here to stay. Previously on this blog, I have written about the Basis of the Scientific Trading System as well as the Artificial Intelligence Trading Systems. Since then, I have designed a trading system model which I believe could satisfy all requirements of the

There is a substantial debate on the topic of factor investing and whether or not the “backtested” excess returns are actually achievable in practice. Much of the research on the topic suggests that practitioners in the field are unable to capture any of the so-called “factor premiums”. For

Hardly a month goes by without some pundit trumpeting that the VIX is broken. But before you worry too much, consider some of the non-obvious characteristics of the Cboe’s Fear Gauge. First a Summary These charts list possible explanations for perceived VIX “brokenness” Complaint Market in Low

It’s no secret that value has had a bad bout of performance in recent memory. This underperformance has been thoroughly examined by multiple research teams and we’ve done some of our own work on the subject. We’ve also done in-depth rebuttals to “value investing is dead” articles in the

Value and momentum equities exhibited significant performance last week raising short-term questions about factor crowding and long-term questions about appropriate factor diversification. We explore the idea of appropriate factor diversification through the lens of a retiring investor, asking the

In Part I Risk Parity, I discussed the“Chasing Diversifiers” problem that harms investors’ performance. This week, I apply the “Relevance of the Long-Run” concept to Risk Parity. First, let me acknowledge upfront that deep historical data is messy and is not precise. Depending on the

As we explained in an earlier Mathematical Investor blog, “target-date funds” are currently the rage in the finance world. The term refers to a mutual fund that targets a given retirement date, and then steadily shifts the allocation of assets from, say, a 80%/20% mix of stocks and bonds at the

The Low Volatility factor exhibited significant exposure to Value since 1989 The factors were highly correlated in the 1990s, but less after the financial crisis Quantitative easing was positive for Low Volatility, but negative for Value INTRODUCTION Riding the Ferris wheel in an amusement park is

In general, machine learning is a form of artificial intelligence that allows computers to improve the performance of a task through data, without being directly programmed. Reinforcing learning is a specialized application of (deep) machine learning that interacts with the environment and seeks to

Chart patterns have long been a favourite of the technical analysis community. Triangles, flags, pennants, cups, heads and shoulders…. Name a shape, someone somewhere is using it to predict market behaviour. But, is there a grain of truth or reliability in these patterns? Can it really give you a

In the last post we used the data available for the momentum factor using an ETF (ticker: MOM) which seeks to replicate The Dow Jones Thematic Market Neutral Momentum Index to time when to be in or out of high momentum stocks. Alpha Architect recently did some interesting analysis of the

new concepts in quantitative research Home CSSA Investor IQ When Should You Buy Momentum? Mean-Reversion in The Momentum Factor September 12, 2019 by david varadi Recently there was a good post by Bespoke Research highlighting the “Momentum Massacre” that we recently witnessed in the market.

Value and Momentum each had back to back extreme returns (five sigma) days on Monday, September 9th and Tuesday, September 10th. The Dow Jones Thematic Market Neutral Value Index (“Value”) started the week up 3.45%, its best day since inception on December 31st, 2001. The Value Index followed

Risk management has become a high priority for many investors over the past decade. The worst financial crisis and recession since the Great Depression in 2008-2009 clearly has the power to focus minds. Research shops have moved heaven and earth to search for solutions that attempt to limit risk

From showing related articles at the end of the article you have browsed through to creating a personalised recommendation based on your viewing habits, you would be surprised of the number of times you have been interacting with the K-means algorithm without even realising it. The above examples

The market is driven by two emotions: greed and fear. Have you ever heard that quote? It is quite popular in financial circles and there may just be some truth behind it. After all, when people, with short-term investments, think are going to lose a lot of money, many of them sell as fast as they

When tinkering with trading ideas, have you ever wondered whether a certain variable might be correlated with the success of the trade? For instance, maybe you wonder if your strategy tends to do better when volatility is high? In this case, you can get very binary feedback by, say, running

To understand the Sequential Bootstrapping algorithm and why it is so crucial in financial machine learning, first we need to recall what bagging and bootstrapping is – and how ensemble machine learning models (Random Forest, ExtraTrees, GradientBoosted Trees) work. It all starts from a Decision

CTAs, mostly trend followers, have historically delivered meaningful diversification to both traditional and alternative asset classes. However, CTAs have struggled over the last ten years. There have been various explanations such as low volatility, increased correlations, and suppressed interest

We exploit the idea that long-only strategies are “long/short portfolios all the way down,” we demonstrate how to isolate the active bets of portfolio managers. Using the example of a momentum / low-volatility barbell portfolio, we construct a simple long/short portfolio using ETFs and S&P

Purpose: The purpose of this article is to introduce the reader to some of the tools used to spot stock market trends. Materials and Methods: We will utilize a data set consisting of five years of daily stock market data for Analog Devices. The time period we consider starts on January 1, 2013 and

The low-risk effect refers to the empirical finding that within an asset classes higher-beta securities fail to outperform lower-beta securities. As a result, “betting against beta”, i.e. leveraged portfolios of longs in low-risk securities versus shorts in high-risk securities, have been

Marcos Lopez de Prado, who was named “Quant of the Year” for 2019 by the Journal of Portfolio Management, and who has recently formed his own investment firm True Positive Technologies, was recently interviewed by KNect365, an organization that sponsors numerous conferences and other exchanges

Action Item: Please let us know your trip details so we can support you as much as possible. We are a little over 3 weeks away from March for the Fallen (#MFTF). NOTE: There is a monster training event occurring simultaneously to MFTF this year so be prepared to dodge humvees and watch out for stray

You are probably wondering how a technical topic like Neural Network Tutorial is hosted on an algorithmic trading website. Neural network studies were started in an effort to map the human brain and understand how humans take decisions but algorithm tries to remove human emotions altogether from the

Following on from my post yesterday, this post presents some preliminary results from the test I was running while writing yesterday's post. However, before I get to these results I would like to talk a bit about the hypothesis being tested. I had an inkling that the dominant cycle period might

The study examined the performance of securities that were trading at no more than two-thirds of its Net Current Asset Value (“NAV”) during the 1970-82 period in the US Net nets, on a gross basis, more than tripled the returns of the market (as measured by the S&P 500 TR) Net nets, on a net

Earlier this month, Bloomberg published a news article about the launch of a new Risk Parity ETF in the US. The RPAR Risk Parity ETF plans to allocate across asset classes based on risk. The fund would be the first in the U.S. to follow this quantitative approach, allotting more money to securities

Principal component analysis (PCA) is one of the earliest multivariate techniques. Yet not only it survived but it is arguably the most common way of reducing the dimension of multivariate data, with countless applications in almost all sciences. Mathematically, PCA is performed via linear algebra

Chinese tariffs. Tesla to 420. Trump tweets. With so much unpredictability in the markets these days, one short look away from the market could take a toll on your portfolio. Unfortunately, the market does not wait for people to get off work to become volatile. In fact, much of the volatility can

We explore “top N” sector rotation strategies based upon momentum signals. We find that too much concentration (i.e. N is too small) leads to poor performance, whereas performance does not appear to materially degrade for larger N. We find that short- to long-term signals all appear to generate

This is part 2 (part one is here) of an excellent article that examines 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;

In part 1 of this series, we talked about how a market-savvy systematic trader would approach a period of drawdown in a trading strategy. Specifically, they’d: do the best job possible of designing and building their trading strategy to be robust to a range of future market conditions chill out

A couple of years ago on the blog I showed a study suggesting that Labor Day week performance has been somewhat dependent on whether the market has rallied over the 20 trading days leading up to it. I decided to update that study today. Below is a look at post-Labor Day performance when the previous

Value investors earn a premium for holding undesirable stocks The yield curve may identify periods where the premium is more attractive Since 1971, the performance of the Value factor was negative when the yield curve was flattening INTRODUCTION Imagine a portfolio of companies that are plagued by

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

Classic pairs trading strategies have suffered deteriorating returns over time. Can a research pipeline that facilitates the identification and selection of ETF pairs make pairs trading viable again? This post investigates such a pipeline. The problem: pairs wander away Source: Ernie Chan,

In this Article, we will show how to obtain free financial data including: End-of-day and real-time pricing; Company financials; Macroeconomic data. Data sources utilized in this Article include: U.S. Securities and Exchange Commission (SEC); Quandl; IEX; Alpha Vantage. We also provide code to

One of the big problems for the first formal asset pricing model developed by financial economists, the CAPM, was that it predicts a positive relation between risk and return. But empirical studies have found the actual relation to be flat, or even negative. Over the last 50 years, the most

It is funny that my last post, Brazilian Jiu-Jitsu & Trading – Shiny New Toy, because this post is definitely chasing a shiny toy. I was reading the August 2019 Technical Analysis of Stocks & Commodities issue and came across the article “Swing Trading 10-Point Breakouts.” The basic

Investors can harvest returns from common equity factors on country level Returns are consistent when combined into a multi-factor portfolio Performance of some factors is comparable to those on single stock level, indicating common drivers INTRODUCTION Factor investing strategies like Value are

In a previous post, we explored the dividend history of stocks included in the SP500. Today we’ll extend that anlaysis to cover the Nasdaq because, well, because in the previous post I said I would do that. We’ll also explore a different source for dividend data, do some string cleaning and

Building trading strategies on futures contracts has the unique problem that a given contract has expiration date, example the 3 month contract on wheat. In order to build a continuous time series across the different contracts we stitch them together, most commonly using an auto roll or some other

Systematic value strategies have struggled in the post-2008 environment, so one that has performed well catches our eye. The Barclays Shiller CAPE sector rotation strategy – a value-based sector rotation strategy – has out-performed the S&P 500 by 267 basis points annualized since it

With a growing availability of filtered (news) and unfiltered (social media) information, the author investigates the following question: Do news or social media contain any information that is of relevance for investment decision making and if so are the two sources are complementary or

Roughly speaking, a tensor is an array (generalization of a matrix) of numbers that transform according to certain rules when the array’s coordinates change. Fixed-income returns across countries can be seen as residing on tensor-like multidimensional data structures. Hence a tensor-valued

Developer: Toby Crabel (Narrow Range N-Day Pattern; Note: Wide Range N-Day Pattern applies a reverse logic of Narrow Range N-Day Pattern). Source: Crabel, T. (1990). Day Trading with Short Term Price Patterns and Opening Range Breakout. Greenville: Traders Press, Inc. Concept: Volatility cycles

We’ve all heard the say “correlation does not imply causation”, but how can we quantify causation? This is an extremely difficult and often misleading task, particularly when trying to infer causality from observational data and we cannot perform controlled trials or A/B testing. Take for