Motivated by McLean and Pontiff (2016), we study the pre- and post-publication return predictability of 138 anomalies in 39 stock markets. Based on more than a million anomaly country-months, we find that the United States is the only country with a statistically significant and economically

In the 1950s, Harry Markowitz proposed a method to identify the optimal trade-off between risk and return for a portfolio. The theory is broadly termed, “Mean-Variance Optimization (MVO).” Sam Wittig, a Drexel graduate I advised and who did some research for Alpha Architect, shared with us his

In our previous article on Introduction to Zipline package in Python, we created an algorithm for moving crossover strategy. Recall, Zipline is a Python library for trading applications and to create an event-driven system that can support both backtesting and live-trading. In the previous article,

Summary We have noticed the market reaching a broad consensus that equities are overvalued, implying a drag on forward expected returns as valuation multiples contract. While there is often great wisdom in the crowd, there can also be great madness. We believe it is prudent to consider how the crowd

In this article we are going to consider a stastical machine learning method known as a Decision Tree. Decision Trees (DTs) are a supervised learning technique that predict values of responses by learning decision rules derived from features. They can be used in both a regression and a

Precious metals have been in free-fall for several years, as a consequence of the Fed’s actions to stimulate the economy that have also had the effect of goosing the equity and fixed income markets. All that changed towards the end of 2015, as the Fed moved to a tightening posture. So far, 2016

Pyfolio is a Python library that takes a return series of an asset, hedge fund, trading strategy, anything with daily returns and automatically generates some really cool statistics and charts. There is a LOT of cool stuff to explore in the library, have fun! Performance statistics Backtest

As we have discussed before, one of the major problems for the first formal asset pricing model developed by financial economists, the capital asset pricing model (CAPM), was that it predicts a positive relation between risk and return. But empirical studies have found the actual relation to be

My book The Missing Risk Premium is a steal at only $15, but my first book, Finding Alpha, is a $65, which is a bit much for anyone not expensing their books. Finding Alpha goes over why the current asset pricing model fails, with lots of evidence, explains why economists still like it, and then in

Eric Crittenden was recently on Meb Faber’s podcast and he tells a compelling story about the perils of survivor bias in backtesting. Eric’s story begins when he is an undergraduate working on a project for a quantitative finance course. The professor asked that the students develop a systematic

When testing trading strategies a common approach is to divide the initial data set into in sample data: the part of the data designed to calibrate the model and out of sample data: the part of the data used to validate the calibration and ensure that the performance created in sample will be

We are going to play around with a mixture distribution made up of a large proportion of ~N(0, 1) and a small proportion of ~N(0, 1+a). The wider distribution is "polluting" the standard normal distribution. We are going to see that mean absolute deviation is a more efficient estimator of

Is your high dividend investment strategy based on buying companies with a high dividend yield and high dividend cover? Saving_chalkIf so you can do a lot better. In this article I summarise an interesting research paper that found the normal way most investors look at dividend income investing is

With the recent announcement at Point72 of a $250MM investment in quantitative trading platform Quantopian and a recent FT article, there has been a surge in interest in Do-It-Yourself quant strategies. Here’s one from WSJ. There are some significant challenges for these startups in my opinion*:

Bank of America Merrill Lynch recently released a research note suggesting that Risk Parity investment strategies currently represent a substantial source of systematic risk in global markets. The note was picked up breathlessly by several media outlets and posted under sensationalist headlines

Anyone who has spent time reading this blog has become familiar with research involving asset pricing anomalies that generate excess returns. In particular, the academic literature has addressed the following: “size,” or a portfolio of small minus big stocks (SMB) (see here for background)

Today's guest is a trader that has been requested quite a few times actually, I've had a lot of requests to have this person as a guest on the show, and the guest is Adam Grimes. Adam has two decades of experience in the industry as a trader, analyst and system developer and is currently

Summary Low volatility equity ETFs have seen huge inflows this year, driven by both a compelling story (risk-managed equity investing) and significant outperformance. Critics have raised concerns that short-term performance chasers have driven up the valuations of these strategies, increasing the

History does not repeat itself, but it often rhymes – Mark Twain You certainly wouldn’t know it from a reading of the CBOE S&P500 Volatility Index (CBOE:VIX), which printed a low of 11.44 on Friday, but there is a great deal of uncertainty about the prospects for the market as we move

In this paper, we analyse the relationship between the currency carry return and volatility and liquidity risk factors. We find that both categories of risk factors are relevant to understanding and explaining carry return, with an outperformance for volatility ones especially the global FX

This post relates to a talk I gave in April at QuantCon 2016 in New York City. QuantCon was hosted by Quantopian and I was invited to talk about some of the topics discussed on QuantStart. I decided to talk about whether it is worth building your own backtesting system. This post goes into more

It's time for the 5th and final part of the Build Better Strategies series. In part 3 we've discussed the development process of a model-based system, and consequently we'll conclude the series with developing a data-mining system. The principles of data mining and machine learning

Today I wanted to share a link to my first post on Quantopian, in which I describe a strategy that uses social data to switch between mean reversion and momentum based trading regimes. This feat is accomplished using a new social factor I’ve created. I believe this strategy presents a novel

If you are into consumption-based asset pricing theory and the associated empirical attempts to reconcile the theory with the data from the realized equity premium, garbage is a fascinating subject. So let’s talk about asset pricing both with–and without–garbage. garbage First the original

You probably know that even a trading strategy which is actually no different from a random walk (RW henceforth) can perform very well. Perhaps you chalk it up to short-run volatility. But in fact there is a deeper reason for this to happen, in force. If you insist on using and continuously testing

Milind began his career in Gridstone Research, building earnings models and writing earnings notes for NYSE listed companies, covering Technology and REITs sectors. Milind has also worked at CRISIL and Deutsche Bank, where he was involved in modeling of Structured Finance deals covering Asset Backed

Low-volatility strategies have quickly become the darling of many investors, thanks largely to trauma caused by the bear market that arose from the 2008-2009 financial crisis combined with academic research showing that the low-volatility anomaly exists in equity markets around the globe. Earlier

Intermediate-Term Price momentum, originally researched by Jegadeesh and Titman in 1993, documented a how recent stock returns tended to continue in the future. Stocks that were past winners (on average) continue to do well, while stocks that were past losers (on average) continue to perform poorly.

Back in December I wrote that It's Generally Smart to Avoid Credit Risk outlining that more than 100% of credit's excess performance over time has come when the level of credit spread was extreme. What if the same were true for well known investment factors? Taking a Look at the Small Cap

A long-time reader asked that we examine the performance and process associated with the Dorsey Wright Focus Five ETF (ticker: FV). For those who are unfamiliar with the product, FV is a $3B+ sector rotation fund. The fund is designed to provide targeted exposure to five sector- and industry-based

It would be great if machine learning were as simple as just feeding data to an out-of-the box implementation of some learning algorithm, then standing back and admiring the predictive utility of the output. As anyone who has dabbled in this area will confirm, it is never that simple. We have

One of the problems for the first formal asset pricing model developed by financial economists, the Capital Asset Pricing Model (CAPM), was that it predicted a positive relationship between risk and return. However, empirical studies have found the actual relationship to be flat, or even negative.

The SPDR S&P 500 ETF (SPY) is one of the widely traded ETF products on the market, with around $200Bn in assets and average turnover of just under 200M shares daily. So the likelihood of being able to develop a money-making trading system using publicly available information might appear to be

Value has broadly been accepted as an investing style, and historically portfolios formed on cheap valuations outperformed expensive portfolios. But value comes in many flavors, and the factors(s) you choose to measure cheapness can determine your long-term success. In particular, several operating

This blog post is available as a PDF here. Summary Over the last year, we’ve written about how low interest rates and high equity valuations point to a low return rates for traditionally allocated portfolios. In a State Street survey of over 400 institutional investors, the expected return rate

The purpose of this article series is to introduce a very familiar technique, Linear Regression, in a more rigourous mathematical setting under a probabilistic, supervised learning interpretation. This will allow us to understand the probability framework that will subsequently be used for more

I have generated simulated end-of-day close indicative share values (4:15 PM ET) for VelocityShares' BSWN, LSVX, and XIVH Exchange Traded Notes (ETNs) from March 31st, 2004 through July 14th, 2016. BSWN VelocityShares VIX Tail Risk ETN LSVX VelocityShares VIX Variable Long/Short ETN XIVH

Pairs trading strategy’s return depends on the divergence/convergence movements of a selected pair of stocks’ prices. However, if the stable long term relationship of the stocks changes, price will not converge and the trade opened after divergence will close with losses. We propose a new model

I. Trading Strategy Source: Kaufman, P. J. (2013). Trading Systems and Methods. New Jersey: John Wiley & Sons, Inc. Concept: Trend following trading strategy based on Simple Moving Average (SMA) filters. Research Goal: To benchmark the Simple Moving Average (SMA) against the Hull Moving Average

SEBI issued a discussion paper today with inputs from all stakeholders such as investors, infrastructure institutions and intermediary to understand how Algorithmic Trading has led to fairness, concerns and changes in market quality in recent years. It states that more than 80% of the orders placed

As with many people these days, most of my investment information comes from the internet. It has taken me years to compile a group of research-oriented blogs and websites that I have found most useful. Here is my list: Investment Blogs Quantocracy: This is an aggregator of quantitative trading

The Sharpe ratio was introduced half a century ago and it’s still going strong. Although the world is now awash with competitors, the granddaddy of quantitative risk metrics endures. Its longevity and widespread use drives some analysts batty, but for good or ill the SR is deeply embedded into the

June 2016 Trend Following: UP -1.34% / YTD: -0.92% Not much volatility for the index in the month of July. It started around +1% and finished around -1% with little amplitude in the middle. The YTD just turned slightly negative. Below is the full State of Trend Following report as of last month.

The indexes that power VelocityShares new BSWN, LSVX, and XIVH funds have been live since 2011, but they haven’t been directly accessible via exchange traded products until July 2016. The goals of these new funds are pretty straightforward, on the long side BSWN & LSVX track upside volatility

This episode features Dr. Yves Hilpisch—the founder of The Python Quants. TPQ do a lot of good for those involved in quantitative finance, they; frequently host meet-ups and workshops, have developed platforms and analytics libraries, and often contract to exchanges, banks and hedge funds for

In my last post I showed research on how optimization results can be mean reverting. Sometimes, my research keeps getting side tracked as I think of random ideas to look at. In this post, we look at the random walk my research took starting from my mean reverting optimization research. I will show

Introduction Inspired by another blogpost I decided to experiment with trading arbitrage between different exchanges. Not so long ago there was a hardfork in the blockchain-based cryptocurrency Ethereum. This means that we now have two Ethereums: Ethereum (ETH) and Ethereum Classic (ETC). The

I was emailed recently with a career-related question about jumping from one quant role to another. The question posed was "How can I make the jump from being a quant/software developer to a quant trader/researcher in a fund or investment bank?". This is certainly possible and does happen

While the explosion of noSQL database offerings of late can be daunting, each of them is typically suited for a particular purpose. Most CRUD web-applications can be comfortably done with either noSQL or a RDBMS, however for true high performance applications, the choice of database is of

Here we are in August, a great time to drink–and think–about wine. Of course, as a research-focused finance blog, our angle on wine is a bit different than that of Dr. Vino. A summary of the discussion: …we estimate a real financial return to wine investment (net of storage costs) of 4.1%,