You will learn why the use of high frequency market data is necessary to be able to measure correctly the risk and rebalance your portfolio adequately. You will also learn how to build strategies to generate alpha. You will study how to build your own portfolio, create a strategy, backtest it,(...)

When we talk about correlations in finance, by default, we assume linear relationships between two time-series “co-moving”. In other words, if one time-series changes its values over a give time period, we seek for a tight correlation reflected within the other time-series. If found, we say they(...)

In a recent post, Long-Term Bonds Behave More Like Stocks Than You Might Think, Lawrence via Fortune Financial fame outlined: It shouldn't be surprising that long-term Treasurys exhibit almost the same degree of volatility as equities. After all, as we discussed in A Better Way to Think of(...)

A few years ago I wrote a summary on a working paper titled “A Lottery Demand-Based Explanation of the Beta Anomaly.” The paper is still a working paper, and has been updated (unfortunately they took out a neat picture from the original paper!). Here is a link to the new version of the paper, and(...)

A few months ago a reader point me out this new way of connecting R and Excel. I don’t know for how long this has been around, but I never came across it and I’ve never seen any blog post or article about it. So I decided to write a post as the tool is really worth it and before anyone asks, I’m(...)

The evolution of a quantitative approach to finance has proceeded through many small but significant steps and occasional large epiphanies. Over the past 70 years financial models have quantified the notion of derivatives, diffusion, risk, diversification, hedging, volatility, replication, and no(...)

Eugene Fama and Kenneth French’s seminal 1992 paper, “The Cross-Section of Expected Stock Returns,” resulted in the development of the Fama-French three-factor model. This model added the size and value factors to the market beta factor. One of the benefits of adding the value factor (the tendency(...)

Trend following is all about following the price. Typically the only input we need for a trend following model is the price. But what if I told that we could make a kind of trend following model which does not use the price direction as an input at all? It also has no stops and no targets. In this(...)

With 10-year rates jumping over 40bp in November, investors are beginning to talk about rising rates again. While rising rates may cause short-term volatility, coupon yield is a much more significant contributor to portfolio return over the long run. Increasing rates actually allow us to reinvest(...)

This article is the final project submitted by the author as a part of his coursework in Executive Programme in Algorithmic Trading (EPAT) at QuantInsti. Do check our Projects page and have a look at what our students are building. About the Author Harish Maranani did his Bachelors in Technology(...)

Text and sentiment analysis has become a very popular topic in quantitative research over the last decade, with applications ranging from market research and political science, to e-commerce. In this post I am going to outline an approach to the subject, together with some core techniques, that(...)

In a previous article the decision tree (DT) was introduced as a supervised learning method. In the article it was mentioned that the real power of DTs lies in their ability to perform extremely well as predictors when utilised in a statistical ensemble. In this article it will be shown how(...)

Most trading strategies have an optimal type of market condition where they work at their absolute best, so having an understanding of market conditions and being able to detect and adapt to them can really have a huge impact on trading performance. But how can we measure market regimes properly?(...)

The 'low-beta' or 'low-volatility anomaly' is one of the most researched in the field of 'alternative beta'. Despite strong published evidence going back to the 1970s that high beta/volatility stocks underperform relative to expectations generated by the Capital Asset(...)

A couple of weeks ago I flew out to Singapore to give a talk at the Quantopian Singapore QuantCon. The event was absolutely fantastic with an incredibly diverse and interesting set of talks. I gave a talk was on the topic of Hunting For Alpha In Alternative Data. Here is a brief summary of the(...)

The process I’m going to follow is based on content from the University of Washington’s CFRM561 course Advanced Trading System Design. “Hypothesis driven development” is the core principle of this course, where each step in the development process involves hypothesizing testable ideas, and(...)

Active investing sounds so easy. But we all know it is extremely difficult. Ask any deep value investor how they have felt over the past few years (although, they are feeling a lot better recently). Certainly, any credible active investor should be able to answer 2 questions: 1) What is the source(...)

A well-designed investment plan is an important part of achieving investment objectives, but even the best investment plan is useless if you cannot stick to it. Rolling relative performance can give context to the size of short-term portfolio fluctuations while looking at risk exposures can give(...)

The time around Thanksgiving has shown some strong tendencies over the years – both bullish and bearish. In the table below I show SPX performance results based on the day of the week around Thanksgiving. The bottom row is the Monday of Thanksgiving week. The top row is the Monday after(...)

Whilst working on some code for my Masters I kept thinking, "it would be really awesome if there was an R package which just consumed a price series and produced a data.frame of results from multiple randomness tests at multiple frequencies". So I decided to write one and it's named(...)

As I have been discussing in a series of articles (which you can find here, here and here), we now have a substantial body of evidence demonstrating that individual investors possess a preference for low-priced equities. This is anomalous behavior, because the level of a company’s stock price is(...)

Time for a little myth busting. Recently, the Motley Fool posted an article that argued the following: when market volatility is low, protective put options are cheap. From the article: Smart investors know that the time to buy most investments is when most investors aren’t paying attention to(...)

Much has been written about the Post-Earnings Announcement Drift (PEAD) strategy (see, for example, my book), but less was written about pre-earnings announcement strategies. That changed recently with the publication of two papers. Just as with PEAD, these pre-announcement strategies do not make(...)

In this post i take an R-view of “Backtesting – Harvey & Liu (2015).” The authors propose an alternative to the commonly practiced 50% discount that is applied to reported Sharpe ratios when evaluating backtests of trading strategies. The reason for the discount is due to the inevitable data(...)

For today’ s post and the next few I’ll be going back to my favorite topic, quant investing. In this post I want to explore pure momentum quant portfolios and in particular ways to make pure momentum investing tolerable and implementable to more investors. Note: for a refresher on momentum and its(...)

I have done several posts about trading XIV & VXX. In these posts (here, here and here) I refer to using synthetic data before these ETFs started trading. I supported the use of the data due to the very high correlation of daily returns during the overlap period. With a correlation of .97, I(...)

Jack and I had the honor of attending the Evidence-Based Investing conference, hosted by the team at Ritholz Wealth Management. Wow. What a great event and a great group of inspiring investors and thinkers. Abe, Meb, John, Mike, and I had the opportunity to chat about systematic investing. Mr.(...)

Using a transaction cost model, and an assumption for the smart beta premium observed in data, we estimate the capacity of momentum, quality, value, size, minimum volatility, and a multi-factor combination of the first four strategies. Flows into these factor strategies incur transaction costs. For(...)

Many traders who managed to design and implement a mean reversion system ‘correctly’ made a fortune. Fact is that financial markets move in patterns and especially in cycles. In simple words everything that goes up must come down and everything that goes down must come up. Nothing moves in one(...)

Does this graph look like a kid’s drawing? Maybe a piece of art from the monkey Jeff? No, of course Jeff draws better than this. Actually, it is a representation of what is known as a Lévy flight, a mathematical concept that shows up in nature, marketing, cryptography, astronomy, biology, physics(...)

As an investment strategy, momentum focuses solely on prior returns. Being valuation agnostic, however, does not mean that a momentum strategy does not have first-order valuation effects on portfolio construction. Using historical US sector data, we find that both cross-sectional and time-series(...)

Over the past several decades, academics have identified numerous variables that seem to predict future expected returns. This has led to a proliferation of so-called “factors” identified in the literature, and created what John Cochrane has labeled the “factor zoo.” Now we we have a zoo of new(...)

While it is well known that risk parity strategies typically allocate more weight or apply leverage to asset classes with lower risk, it is not well understood how higher volatility affects the Sharpe ratios exhibited by the assets that get over- or under- weighted. We find that in practice the(...)

Sometimes I read academic literature, and often times those papers contain some proofs. I usually gloss over some innocent-looking assumptions on moments’ existence, invariably popping before derivations of theorems or lemmas. Here is one among countless examples, actually taken from Making and(...)

The performance profile of Mean Reversion is extremely desirable to a lot of traders. Mean reversion trading strategies can produce high win rates and a smooth equity curve, however there are risks, which can result in giving back a large portion of profits, or of your trading account, some times(...)

The table below, taken from the newly released book I co-authored with Andrew Berkin, “Your Complete Guide to Factor-Based Investing,” shows the annual premium and Sharpe ratio for the equity factors of market beta, size, value, momentum, profitability and quality. It also shows the odds that each(...)

In this post, we will explore a feature of Python pandas package. We usually find queries about converting tick-by-tick data into OHLC (Open, High, Low and Close) frequently. This can be accomplished with minimal effort using pandas package. The OHLC data is used for performing technical analysis(...)

At a time when demand for income generating assets is at an all-time high, the yields on income generating assets are at, or near, all-time lows. While the headlines often speak to the number of Baby Boomers entering retirement, the more important statistic is actually the amount of wealth entering(...)

A quick look at annual returns over the 100+ years of daily percent change (close to close) data that we have on dow jones 1 2 3 4 5 6 7 import matplotlib.pyplot as plt import pandas as pd import numpy as np import datetime dj = local_csv("DjiaHist.csv", date_column = "Date",(...)

This post will address what pairs trading is, how you can test for a pairs trading opportunity, and how to implement a pairs trading strategy. For information on the libraries I’ve used and how I structured my trading methods, I recommend starting with my previous post on the subject. Pairs Trading(...)

One of the TAA strategies that I have often been asked about is Antonacci’s Composite Dual Momentum (ACDM from now on). I never got around to tracking or writing about it but now the the folks at Allocate Smartly have it covered. In this post I’ll highlight the key details of the strategy and it’s(...)

Since my last post on the currency strength indicator I have been conducting a series of basic randomisation tests to see if the indicator has better than random predictive ability. The first test was a random permutation test, as described in Aronson's Evidence Based Technical Analysis book,(...)

Research Affiliates has been churning out some great content lately. In their recent piece titled “Timing “Smart Beta” Strategies? Of Course! Buy Low, Sell High!” they examine some value based factor rotation strategies. Namely, they examined rotating among the factors that had the worst 1,3,5,and(...)

If you want long-term outperformance, you must be able to stomach short-term underperformance. As William Bernstein said, “The most important investment ability is an emotional discipline.” Investing is a team sport that requires this discipline from both the investment manager (to stick to his(...)

The results from last month’s trend following index were only slightly negative, which is quite surprising as most of other indices were sharply down, including The Wisdom Trading State of Trend Following report, which I write as a version 2 of this report. The principles for the index are the(...)

I wanted to send you this article shortly after the end of the third quarter 2016 but, like a lot of things, it slipped my mind. What has worked in 2016 – value is not dead I will get right to the point about what strategy would have given you the best return so far in 2016. Here is a short(...)

A picture is worth a thousand words or said a wise woman a hundred years ago. True to every word of the idiom, the beauty of visualization lies in how clearly it might convey multiple messages. Visualization of data is one of the key functions of a data scientist and decoding the visual messages is(...)

Measuring Factor Exposures: Uses and Abuses Ronen Israel and Adrienne Ross (AQR Capital Management) September 19, 2016 A growing number of investors have come to view their portfolios (especially equity portfolios) as a collection of exposures to risk factors. The most prevalent and widely(...)

Election year is shaping up to be a bad year for trend following. October saw the State of Trend Following index post another successive down month. The current drawdown is still within the limits of the max value from the historical back-test run, but the Year-To-Date performance is now well into(...)

Principal Component Analysis (PCA) is a technique used to reduce the dimensionality of a data set, finding the causes of variability and sorting them by importance. >How? If you have a set of observations (features, measurements, etc.) that can be projected on a plane (X, Y) such as: DataSet(...)