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%,

We demonstrate a computer program that designs a portfolio consisting of common securities, such as the constituents of the S&P 500 index, that achieves any desired profile via in-sample backtest optimization. Unfortunately, the program also shows that these portfolios typically perform

What is an Order book? With the growing popularity of Algorithmic and High Frequency Trading, study of order books has grown manifolds. “Order book” is essentially an electronic list of all Buy and Sell orders, arranged as per price time priority. This means that a person having higher price on

Summary As a stand-alone asset class, dividends may make an interesting alternative to fixed income: they offer low volatility, are generally robust to market crises, and may serve as an inflation hedge. Accessing dividend strips was previously restricted to institutional investors, using

A hedge fund is simply a go anywhere investment vehicle that attempts to provide excess returns to cash with a low correlation to traditional asset classes (i.e. vehicles that provide alpha). Hedge funds and liquid alternatives have taken a lot of heat recently, much of it deserved, but in this post

It’s hard enough deciding which Machine Learning technique to use, but after selecting an appropriate clustering algorithm the next challenges begin: how good is the separation and into how many groups should you divide the data? Maybe three is not always a crowd… First, let’s set the scene We

I recently came across a graph similar to the following while doing some market research. 1 Source: Yahoo! Finance. Analysis by Newfound Research. Data from January 1951 – December 2015. The argument was that the markets are getting more volatile. While this certainly looks to be the case based on

This is more of a "How to build your own algotrading strategy - the Ethereum edition" and not a "make money fast" blog post. It is also a real example with real returns (and real production errors that cost me money) where you can see how to identify opportunities, why

We present extensive evidence that "risk premium" is strongly correlated with tail-risk skewness but very little with volatility. We introduce a new, intuitive definition of skewness and elicit an approximately linear relation between the Sharpe ratio of various risk premium strategies

The range over the last week has been extremely tight. On 7/20/16 SPY closed at a 50-day high. Every SPY close in the 5 days since 7/20 has been within the intraday range of that 7/20/16 bar. (And it wasn’t even that big of a range.) It is said that consolidations are often resolved in the

I had the pleasure of finally meeting Gary Antonacci earlier this year. Gary is the creator of the momentum strategy that I follow and have been discussing on this blog. I first came across his work in 2011 on the blog Abnormal Returns (which should be a daily read for investors). Gary and I have

The majority of machine learning posts to date on QuantStart have all been about supervised learning. In this post we are going to take a look at unsupervised learning, which is a far more challenging area of machine learning. Supervised learning involves taking a number of data observations, each