For those who prefer a little more rigor in their quantitative research, I can offer more a somewhat more substantive statistical argument in favor of the IBS indication discussed in my previous post. Specifically, we can show quite convincingly that the IBS process is stationary, a highly desirable

Internal Bar Strength (IBS) is an idea that has been around for some time. IBS is based on the position of the day’s close in relation to the day’s range: it takes a value of 0 if the closing price is the lowest price of the day, and 1 if the closing price is the highest price of the day. More

There are a plethora of seasonal anomalies documented in academic research. For equities these include the Halloween effect (“Sell in May”), January effect, turn-of-the-month effect, weekend effect and holiday effect. For example, Bouman and Jacobsen (2002) and Jacobsen and Visaltanachoti (2009)

In this post I want to discuss ways to make use of signals from relevant market indices in your trading. These signals can add value regardless of whether you trade algorithmically or manually. The techniques described here are one of the most widely applicable in the quantitative analyst's

Almost at the very moment I published a post featuring some interesting research by Glabadanidis (“Market Timing With Moving Averages” (2015), International Review of Finance, Volume 15, Number 13, Pages 387-425 – see Yes, You Can Time the Market. How it Works, And Why), several readers wrote

I apologize in advance for this rather "wonkish" post, which is aimed chiefly at the high frequency fraternity, or those at least who trade intra-day, in the equity markets. Such minutiae are the lot of those engaged in high frequency trading. I promise that my next post will be of more

Pretty obviously, he had been making creative use of the "money management" techniques so beloved by futures systems designers. I invited him to consider how it would feel to be trading a 1,000-lot E-mini position when the market took a 20 point dive. A $100,000 intra-day drawdown might

Carnegie Mellon's Steve Shreve is out with an interesting post on careers in quantitative finance, with his commentary on the changing landscape in quantitative research and the implications for financial education. I taught at Carnegie Mellon in the late 1990's, including its excellent

There is a great deal of market lore related to the US presidential elections. It is generally held that elections are good for the market, regardless of whether the incoming president is Democrat or Republican. To examine this thesis, I gathered data on presidential elections since 1950,

One of the most commonly cited maxims is that market timing is impossible. In fact, empirical evidence makes a compelling case that market timing is feasible and can yield substantial economic benefits. What’s more, we even understand why it works. For the typical portfolio investor, applying

Is Jeremy Grantham, co-founder and CIO of GMO bullish or bearish these days? According to Myles Udland at Business Insider, he’s both. He quotes Grantham: “I think the global economy and the U.S. in particular will do better than the bears believe it will because they appear to underestimate the

Jeff Swanson's Trading System Success web site is often worth a visit for those looking for new trading ideas. A recent post Seasonality S&P Market Session caught my eye, having investigated several ideas for overnight trading in the E-minis. Seasonal effects are of course widely recognized

In this post I am going to take a look at what an investor can do to improve a hedge fund investment through the use of dynamic capital allocation. For the purposes of illustration I am going to use Cantab Capital’s Aristarchus program – a quantitative fund which has grown to over $3.5Bn in

Summary Leveraged ETFs suffer from decay, or “beta slippage.” Researchers have attempted to exploit this effect by shorting pairs of long and inverse leveraged ETFs. The results of these strategies look good if you assume continuous compounding, but are often poor when less frequent compounding

One of the challenges faced by investment strategists is to assess whether a strategy is continuing to perform as it should. This applies whether it is a new strategy that has been backtested and is now being traded in production, or a strategy that has been live for a while. Fig 6All strategies

A perennial favorite with investors, presumably because they are easy to understand and implement, are trades based on a regularly occurring pattern, preferably one that is seasonal in nature. A well-known example is the Christmas effect, wherein equities generally make their highest risk-adjusted

For many years now the “gold standard” in factor models has been the 1996 Fama-French 3-factor model: Fig 1 Fig 5Here r is the portfolio’s expected rate of return, Rf is the risk-free return rate, and Km is the return of the market portfolio. The “three factor” β is analogous to the

Recently I have been working on the problem of how to construct large portfolios of cointegrated securities. My focus has been on ETFs rather that stocks, although in principle the methodology applies equally well to either, of course. My preference for ETFs is due primarily to the fact that it is

MOVING FROM RESEARCH TO TRADING I have written recently about the comparative advantages of different programming languages in the context of research and trading (see here). My sense of it is that there is no single “ideal” programming language – the best strategy is to pick an appropriate

I tend not to get involved in Q&A with readers of my blog, or with investors. I am at a point in my life where I spend my time mostly doing what I want to do, rather than what other people would like me to do. And since I enjoy doing research and trading, I try to maximize the amount of time I

Towards the end of last year I wrote a post (see here) about the advent of modern programming languages, including the JIT compiled Julia and visual programming language ADL from Trading Technologies. My conclusion (based on a not very scientific sample) was that we appear to be at the tipping

I was asked by a reader if I could illustrate the application of the Kalman Filter technique described in my previous post with an example. Let’s take the ETF pair AGG IEF, using daily data from Jan 2006 to Feb 2015 to estimate the model. As you can see from the chart in Fig. 1, the pair have been

One of the challenges with the cointegration approach to statistical arbitrage which I discussed in my previous post, is that cointegration relationships are seldom static: they change quite frequently and often break down completely. Back in 2009 I began experimenting with a more dynamic approach

In his latest book (Algorithmic Trading: Winning Strategies and their Rationale, Wiley, 2013) Ernie Chan does an excellent job of setting out the procedures for developing statistical arbitrage strategies using cointegration. In such mean-reverting strategies, long positions are taken in

In a post on LinkedIn I referred to the concept of CrashMetrics and how it can be used for portfolio protection. It’s a simple approach to the management of extreme risk that works rather well. It can be summarized as “CAPM for crashes”. Here’s how it works. Let’s