Investors are slowly becoming more and more interested in ethical investing. Part of the reason is the industry is starting to care more, but the other reason is that there is a lot of evidence to show that it can produce better or at least equivalent returns. One subset of this type of investing is

In my June 4, 2019 article “The Re-Death of Value, or Déjà Vu All Over?” I noted that one possible explanation for at least part of the poor performance of value stocks over the past decade has been the sharp fall in both the real interest rate (due to weak global growth) and unexpected

In recent years, machine learning, more specifically machine learning in Python has become the buzz-word for many quant firms. In their quest to seek the elusive alpha, a number of funds and trading firms have adopted to machine learning. While the algorithms deployed by quant hedge funds are never

Intraday trading strategies offer great promise as well as great peril. This post explores an intraday trading strategy for crude oil calendar spreads and highlights the impact of transaction costs on its profitability. Background In a previous post, I explored an end-of-day pairs trading strategy

We all have our favorite momentum indicators. One of mine is percent off 1 year high. This requires 252 data points and comparisons, plus a division. Another one is the 200-day moving average. This requires 200 closing prices, 199 additions and a division. A simple momentum indicator is Rate of

Concept: Volatility clustering: Large price moves tend to be followed by large price moves, and small price moves tend to be followed by small price moves. Research Question: Is there a tendency of large price moves in one direction to be followed by large price moves in the opposite direction?

In our previous post, we looked into implementing a Kalman filter in R for calculating the hedge ratio in a pairs trading strategy. You know, light reading… We saw that while R makes it easy to implement a relatively advanced algorithm like the Kalman filter, there are drawbacks to using it as a

e have already traveled a good part of the trip, but there is still an important part. In this post, I tell you where we are and how much we have left. Courage, we sure got it! The Machine Learning Workflow The following diagram provides a high-level overview of the stages in a machine learning

One dirty little secret that has been hiding behind the curtains of finance for a long time, is that high-risk stocks do not have higher returns than low-risk stocks. Back in 1975 Haugen and Heins first recognized the low-risk anomaly: Our emperical efforts do not support the conventional hypothesis

Over the last few months, much of the financial press expressed concerns about the impact of inverted yield curves on financial markets, in particular, the stock returns. Some previous academic literature has shown that there exists a link between yield curves and economic growth (see references

Recent research suggests that equity factors exhibit positive autocorrelation, providing fertile ground for the application of trend-following strategies. In this research note, we ask whether the same techniques can be applied to the active returns of long-only style portfolios. We construct

Investment portfolios can be simplified by separating alpha from beta Alpha + beta portfolios offer higher risk-adjusted returns than smart beta The main hurdle for better portfolios is investor behaviour, not a lack of products INTRODUCTION In Buddhist teaching, the primary obstacles that prevent

Financial markets are not information efficient with respect to macroeconomic information because data are notoriously ‘dirty’, relevant economic research is expensive, and establishing stable relations between macro data and market performance is challenging. However, statistical programming

As I have shown many times in the past, there isn’t a more reliable time of the year to have a selloff than this upcoming week. I have often referred to is as “The Weakest Week”. Since 1960 the week following the 3rd Friday in September has produced the most bearish results of any week. Below

In the last post we showed that trend-following tends to be mean-reverting in the short-term. Data analysis also shows that trend-following has an even stronger mean-reverting effect using a 6-month or 120-day window using the same methodology. Take a look at the chart below using the BarclayHedge

Anyone who’s tried pairs trading will tell you that real financial series don’t exhibit truly stable, cointegrating relationships. If they did, pairs trading would be the easiest game in town. But the reality is that relationships are constantly evolving and changing. At some point, we’re

Finding complex chart patterns has never been an easy task. This article will give you a simple indicator for complex chart pattern recognition. You will have the freedom to detect any pattern with any pattern length. Not just 2-bar candlestick formations, but complex stuff like V-Tops spread over

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