The magnitude and importance of random numbers in finance does not have to be explained. We need them. Either it is an option pricing or a Monte Carlo simulation, random numbers are with us. However, we make a trade-off: the speed in their generation versus uniqueness. That is why a widely accepted

Over several time horizons op-ex week in December has been the most bullish week of the year for the SPX. The positive seasonality actually has persisted for up to 3 weeks. I’ve shown the study below in the blog many times since 2008. It looks back to 1984, which was the first year that SPX

As markets become more mature and more efficient, it can be become increasingly difficult to find sustainable edges. Many traders are looking at the same data and using the same techniques, so what are our options here? 2 of the obvious options we have are: Try to find a unique approach to the

This survey paper will discuss the (potential) structural sources of return for both CTAs and commodity indices based on a review of empirical research articles from both academics and practitioners. The paper specifically covers (a) the long-term return sources for both managed futures programs and

Recently I read a blogpost and someone was recommending the book "DIY Financial Advisor "by Wesley R. Gray, Jack Vogel and David Foulke. I believe it was the QuantStrat blog but I might be wrong. The book is a good read and also suggest a couple of simple systems any investor can implement

How Should Investors Respond to Increases in Volatility? Alan Moreira (Yale University) andn Tyler Muir (UCLA) December 2, 2016 They should reduce their equity position. We study the portfolio problem of a long-horizon investor that allocates between a risk-less and a risky asset in an environment

What can an interesting and surprising experiment with finance students and finance professionals tell us about financial decisions and how to maximise extracting returns from low information content systems? Introduction It is well known that humans are bad at estimating probabilities. We

This article is the final project submitted by the author as part of his coursework in Executive Programme in Algorithmic Trading (EPAT™) at QuantInsti. You can check out our Projects page and have a look at what our students are building after reading this article. About the AuthorEPAT student

Conditional Value-at-Risk (CVaR), also referred to as the Expected Shortfall (ES) or the Expected Tail Loss (ETL), has an interpretation of the expected loss (in present value terms) given that the loss exceeds the VaR (e.g. Alexander 2008). For many risk analysts, CVaR makes more sense: if VaR is a

In this post, I provide R code that enables the replication of the Center for Research in Security Prices (CRSP) Volatiliy Deciles using Yahoo! Finance data. This post is related to my last blog post in that it will generate the CRSP low volatility decile portfolio, thereby facilitating the

In most of my mean reversion posts, I use RSI(2) to determine if a stock has sold off. In this post, I will explore how to use a stock’s recent return to determine if it has sold off. This will be done in way to normalize the return between low and high volatile stocks. This basic strategy has

Following up on my last post, I’d like to take a deeper dive into the performance of TAA strategies. In particular, I’ll take a look at the differences between the top performing TAA strategies and the bottom performing ones. There are some important points that come out of this analysis which I

A couple of months ago, we published a study on the performance of trend following after drawdowns, as the State of Trend Following index was hitting high levels of drawdown (about 2/3 of the historical maximum). We showed that in 80% of cases, the post-drawdown performance is positive, showing that

This is a test of a number of popular approaches to portfolio optimization. Each seeks to answer the question: given a universe of assets, how much should we allocate to each? We’ve intentionally made these tests as simple and fair (read: unoptimized) as possible in order to best represent each

When I start to write a blog post, usually my process is this: Come up with a really bad pun for the title. Write the rest of it. Bad puns are an important part of finance, and life in general. A blog reader contacted me recently to chat about various technical analysis indicators, and one he

Investors tend to have some pretty engrained misconceptions of what “winning” funds look like. For instance, winning funds lay waste to the index and category peers; they do so over the short- and long-term; they corner really well, deftly avoiding big drawdowns and rocking during rallies; they

In practice, no one trading model will ever be that good on its own. Luckily statistics has come up with a lot of theory about how you can combine weaker models to create better overall predictions. We’ll discuss how to combine many different trading signals into overall models and some of the

Summary In a persistent, low interest rate environment, dividend strategies have rapidly increased in popularity. In theory, investors should be indifferent to dividends. In practice, they are not. As a strategy, a focus on high dividend yield may simply be a (poor) value strategy in drag. A focus

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,

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 report

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

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.

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,

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,

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

Factors are at the core of a modern quant equity workflow. This episode introduces the notion of alpha and risk factors at a high level, and delves into some of the use cases which include: understanding how the market is moving, understanding how a portfolio is exposed to sources of risk, and

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 at

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 have

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 trip,

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

The worst case in finance is when you think you’re right, but you’re actually wrong. This can be especially dangerous when you’ve used some methodology or statistics to justify a decision, but are unaware of all the subtle biases that can cause false results. In this episode we’ll cover many

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