In this article the concept of unsupervised clustering will be considered. In quantitative finance finding groups of similar assets, or regimes in asset price series is extremely useful. It can aid in the development of filters, or entry and exit rules. This helps improve profitability for certain(...)

We all use models. We all continuously working to improve and validate our models. Constant effort is made trying to estimate: how good our model actually is? A general term for this estimate is error rate. Low error rate is better than high error rate, it means our model is more accurate. By far(...)

In our previous post on sentiment analysis we briefly explained sentiment analysis within the context of trading, and also provided a model code in R. The R model was applied on an earnings call conference transcript of an NSE listed company, and the output of the model was compared with the(...)

780%. That’s the amount of gains you would have missed had you followed the market timing strategy I’m going to describe in the following article that utilizes the CAPE ratio. Yes, that’s significant. But there’s far more to this story, and I suspect that had you acted on this strategy, you’d have(...)

Every month of this second half of the year seems to have a recurring theme and/or unilateral direction, rendering the YTD performance quite clearly negative. November was no different and produced a variation on the same theme, as you can see below. Below is the full State of Trend Following(...)

This is a summary of the recent performance of a number 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 TAA(...)

In this post I’m going to take a look at performance as a whole of a group of TAA strategies and how that performance has varied over time. I’ll then compare it to the classic 60 40 US stock US bond portfolio and a more globally diversified and modern portfolio, the All Weather Portfolio. There’s(...)

When you drive a car, you need to look out your front window and not the rear-view mirror. The same should be true for estimating risk in financial markets. Ironically, most of the “low volatility” products use backward looking information regardless of whether they emphasize low beta or low(...)

Like most investors, those using momentum are often guilty of chasing performance. In fact, momentum requires that we do this. But it should be done in a disciplined and systematic way. Performance chasing should not be due to myopia, irrational loss aversion, or other psychological biases.(...)

This paper explains the idiosyncratic risk puzzle in a novel test setting with a combination of arbitrage risk and arbitrage asymmetry as in Stambaugh/Yu/Yuan (2015). We utilize the popular investment strategy pairs trading to identify a different kind of mispricing and find a dominant negative(...)

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(...)