If you’ve been following me on Twitter you know that I’ve finally had it with Amazon. There is a silver lining of course, and the good news for my readers is that all of my books are now free to download.(Sadly I cannot control the two I didn’t self publish…) If you care to understand why

Yield. Within almost any asset class, investors want to know, what is the “yield” on the investment? For some investors, this is the most important and only screen used when sorting funds. Mutual fund companies have found ways to feed the beast by “juicing” the dividend yield on equity

There are not too many creative opportunities to leave a person applying for a quant role dumbfounded with the interview question directly related to his CV. First of all, we want to test his quant skillset, check his ability to read a code, analyse the model, listen to his judgement on the

Pim van Vliet posted a link to my 1994 dissertation, noting it was an early documentation of the low-vol effect. One may wonder, why did this early evidence fall flat? Clearly, lots of things, but I'll try to highlight the keys. Here's my lead paragraph, which makes clear I saw the low vol

Benchmarks can be a very difficult subject to pin down. Choosing different ones can create drastically different backdrops to frame both short and long-term results. This was even true in our 2018 March Madness Bracket Challenge, with the value-weighted benchmark taking the top place. As investors,

A growing number of investors are seeking to construct portfolios that simultaneously capture the 1) long-term factor premia ( value, momentum, size etc.) and 2) have attractive ESG profiles. The main research questions of the paper are as follows: Is the relationship between ESG and factor stable

The interest rate-sensitivity of the Low Volatility factor has increased in recent years Mainly due to the sectoral biases from the long portfolio Sector-neutrality reduces the interest rate-sensitivity, albeit at the cost of performance INTRODUCTION Low Volatility strategies have become popular

Time flies, and it's time for another annual update on the performance of my own investment and trading. Previous updates can be found here, here and here. These updates follow the UK tax year; from 6th April to 5th April, as I have to do my taxes anyway it makes sense to analyse everything at

This paper examines the day of the week effect in the crypto currency market using a variety of statistical techniques (average analysis, Student's t-test, ANOVA, the Kruskal-Wallis test, and regression analysis with dummy variables) as well as a trading simulation approach. Most crypto

Last month I shared a table that showed performance of opex weeks by month. April was one of the most bullish. The study below looks specifically at April opex week. I last showed it on the blog in 2016. Results are all updated. 2018-04-15 The numbers are impressive, and suggest a bullish edge.

First, the bad news: Google Finance no longer provides data for historical prices or financial statements, so we say goodbye to getSymbols.google() and getFinancials.google(). (#221) They are now defunct as of quantmod 0.4-13. Now, the good news: Thanks to Steve Bronder, getSymbols() can now import

My favorite moment of the film The Social Network takes place when Mark Zuckerberg asks his roommate Eduardo Saverin to explain the Elo system to him. This algorithm is used to rank chess players. At that time, Zuckerberg was developing the just-for-fun website Facemash, an early predecessor of

Friday the 13th is upon us. Known for bad luck and horror movies, I decided to go back and see how SPX has performed on this day. What I found was scary… 2018-04-13 Good luck trading today!

For those of you wanting to get started/familiar with the Julia language. I wrote a small script to import .csv data using the Alphavantage API. In this example we demonstrate how to download a single .csv file as well as batch download multiple .csv files and export as .csv. First lets download a

Constructing a portfolio means allocating your money between few chosen assets. The simplest thing you can do is evenly split your money between few chosen assets. Simple as it is, good research shows it is just fine, and even better than other more sophisticated methods (for example Optimal Versus

Long-horizon return regressions have effectively small sample sizes. Using overlapping long-horizon returns provides only marginal benefit. Adjustments for overlapping observations have greatly overstated t-statistics. The evidence from regressions at multiple horizons is often misinterpreted. As a

A fairly neutral month for the index after the up-and-down start of the year, leaving the index in slight negative mode for the year. Please check below for more details. Detailed Results The figures for the month are: March return: 0.9% YTD return: -1.1% Below is the chart displaying individual

How will systematic (“coordinated”) investing affect prices? What is the risk of increasingly coordinated holdings (crowding)? How does coordinated investing affect market microstructure and optimal execution? Is factor timing possible? What are the issues in the design of factor-based

Momentum is one of the most researched topic in financial market literature. A search of the SSRN database on momentum will turn up around 1000 papers written over the past three years and 3000 papers in total. With so much information available, it should not be surprising that many analysts have

Smart beta ETF investors seem to ignore empirical evidence Excess returns from smart beta are substantially different from factor returns Smart beta ETFs offer little diversification for an equity-centric portfolio INTRODUCTION Assets under management in smart beta products surpassed $1 trillion in

For most investors, long-term “failure” means not meeting one’s financial objectives. In the portfolio management context, failure comes in two flavors. “Slow” failure results from taking too little risk, while “fast” failure results from taking too much risk. In his book, Red Blooded

In a quest for speed enhancements over R. I opted to look at the Julia language. It is a high level programming language touting similar speed to C. I find the syntax not at all that different from Python and R. If you have knowledge of the ‘how’ to solve many problems using those languages, the

A different strategy for each day of the week? Sounds crazy, but is it? I have wrote about “Turnaround Tuesday” before: https://www.seeitmarket.com/turnaround-tuesday-wall-street-cliche-media-fiction-17013/ This simple strategy is actually at new highs since this was published over 10 months

Returns to the simplest domestic capitalization weighted indexes have dominated virtually all active strategies over the nine years since the Global Financial Crisis. It’s not hard to understand why many investors have opted to eschew active strategies altogether, and instead have migrated en

This article proposes tail risk hedging (TRH) as an alternative model for managing risk in investment portfolios. The standard risk management approach involves a significant allocation to hiqh-quality bonds. However, this approach has historically reduced expected returns over the long term (see

This post is a bit of an experiment. My good friend Steven Wood and I started discussing some collaborations a few months ago. We hope that it ups the quality of our research and also brings some new insights into each of our philosophies. Below is his recent post, for which I helped provide some

When traders set in-sample and out-of-sample periods for their backtests, it’s common just to pick some dates that split the data into a pre-defined percentage or range. The trouble with this approach is that it often doesn’t take into account the type of market environments that exist in those

We have talked about outliers several times in this blog. Examples include how to detect them or how to transform the data to remove them. Here we have another technique to detect outliers in our big data set: the isolation forest algorithm. The idea behind the isolation forest method The name of

We wanted to take a moment to highlight two must read posts from Newfound Research. Newfound is a thought leader in the TAA space and we highly recommend following them now. The Diversification Dangers of DIY Tactical – and – Diversifying the What, How, and When of Trend Following Newfound

We develop a strong diagnostic for bubbles and crashes in bitcoin, by analyzing the coincidence (and its absence) of fundamental and technical indicators. Using a generalized Metcalfe’s law based on network properties, a fundamental value is quantified and shown to be heavily exceeded, on at least

Buy cheap. This is a motto many live by, not only in their daily lives but also in their investment philosophy. Historically, “buying cheap” stocks was a good idea (i.e., the so-called “value” premium). But how might valuation matter when it comes to country allocations? In other words,

What are the research questions? By studying tweets on thirty companies in the Dow Jones Index, the authors ask the following research question: Is it possible to develop a system for the detection and discovery of the popularity of special events on Twitter that may influence the financial markets?

In this post we look at the one plot that proves that technical analysis (TA) is useless. TECHNICAL ANALYSIS AND HOROSCOPES As volatility has made a come back in recent months, investors have sought advice to asset managers. In many instances, such advice consists in technical indicators. Charles

Naïve and simple long/flat trend following approaches have demonstrated considerable consistency and success in U.S. equities. While there are many benefits to simplicity, an overly simplistic implementation can leave investors naked to unintended risks in the short run. We explore how investors

2018 started negative for the majority of factors Momentum, Quality and Growth showed the strongest performance Low Volatility, Dividend Yield and Value generated negative returns INTRODUCTION We present the performance of seven well-known factors on an annual basis for the last 10 years and the

This is a summary of the recent performance of a wide range 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

In this post, we are going to examine a trading system with the goal of using it as a hedge for long equity exposure. To this end, we test a simple, short-only momentum system. The rules are as follows, Short at the close when Close of today Cover at the close when Close of today > lowest Close

Factor Momentum Robert D. Arnott (Research Affiliates), et al. January 31, 2018 Past industry returns predict the cross section of industry returns, and this predictability is at its strongest at the one-month horizon (Moskowitz and Grinblatt 1999). We show that the cross section of factor returns

I have written the difficulty in trading and testing short strategies. I had stopped trading my short strategy because it was too hard to trade psychologically for me. About nine months ago, I revisited my short strategy to see how it had been doing since I stopped and of course it has been doing

Stock market performance leading up to and around many holidays has often been bullish. This is something I have written about several times over the years. Holy Thursday is one such day that has done quite well. I have shown Holy Thursday stats a few times in the past. The chart and statistics

We outlined the full set of rules for the Newfound 2018 March Madness Brackets here. After two more rounds, we are down to the Final Four in our Newfound 2018 March Madness Bracket Challenge, and while the right hand side of the bracket looks predictable with two number 1 seeds facing off (Kansas

In this post I will be using R and data.table to extract all trend up / down days. The following method was used to quantify a trend day: 1. Trend up = Close price closes within 25% of the days high 2. Trend down = Close prices closes within 25% of the days low 3. Exclusive of gaps, if open is above

A key issue raised by the rapid growth of computerised algorithmic trading is how it responds in extreme situations. Using data on foreign exchange orders and transactions that includes identification of algorithmic trading, we find that this type of trading contributed to the deterioration of

Most of the literature on the momentum factor has focused on behavioral explanations, generally either investor underreaction or overreaction. For example, in his paper “Explanations for the Momentum Premium,” Yale University professor Tobias Moskowitz points out: Underreaction results from

Trend following is an investment strategy that buys assets exhibiting strong absolute performance and sells assets exhibiting negative absolute performance. Despite its simplistic description, trend following has exhibited considerable empirical robustness as a strategy, having been found to work in

Factor exposure should be considered a source of returns as well as of risk Factor biases can be measured top-down or bottom-up The results of the two approaches do not necessarily reconcile INTRODUCTION Factor investing has become immensely popular in recent years and assets in smart beta products

New to Tactical Asset Allocation? Learn more: What is TAA? In our previous post, we looked at the tax impact of TAA for investors trading in taxable accounts. Using our database of more than 40 published TAA models we concluded that, while TAA as a whole has been relatively tax efficient, the

A momentum-based investing approach can be confusing to investors who are often told that “chasing performance” is a massive mistake and “timing the market” is impossible. Yet as a systematized strategy, momentum sits upon nearly a quarter century of positive academic evidence and a century

There are a hundred ways to evaluate whether an investment is cheap--discounted cash flows, competitor multiples, mean reversion, multiple of projected earnings--the list goes on...and on. To each his own on what is the "best" valuation methodology, but suffice it to say that the

I came across this excellent article lately “Machine learning at central banks” which I decided to use as a basis for a new cheat sheet called Machine Learning Modelling in R. The cheat sheet can be downloaded from RStudio cheat sheets repository. As the R ecosystem is now far too rich to