The Health of Stock Mean Reversion: Dead, Dying or Doing Just Fine

My second post on this blog was a look at mean reversion, Is mean reversion dead? Given I am using a new data provider(Norgate Data), it has been almost two years since that post and there have been other articles on this recently, I figured it was time to check again. The research will focus on Russell 1000 stocks since 1995. The test is back to 1995 covers 3 bull markets and 2 bear markets.

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Maximum Loss Stops: Do you really need them?

We hear it all the time. “You must use stops.” And most of us use them. But do you know how they change your strategy results? Are they improving your results by giving you higher CAR or lower maximum drawdown? Recently I was speaking with a reader about this topic and he insisted that it you had to have stops to trade. Well, does one?

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Multiple Time Frames for Scoring ETF Rotational Strategies

Today we have a guest post from David Weilmuenster who I worked with while at Connors Research.

A widely applied technique for scoring assets in rotational systems is to rank those assets by their price momentum, or return, over a given historical window and to rotate into the assets with higher momentum. This approach seeks to capitalize on the well-demonstrated tendency for price momentum to persist. But, it begs some questions:

1. “What is an appropriate historical period for measuring price momentum?” Clearly, the momentum of a given asset can rank quite differently compared to the tradable universe over 1 month, 3 months, or 6 months.
2. “Is one historical period sufficient?” If relative momentum can vary widely depending on the historical window, would it be better to consider multiple slices of history?
3. Is higher momentum always preferable to lower momentum, especially if the system rules filter the tradable universe before scoring the ETFs for rotation?

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What I am reading: July 15, 2015

Recent articles that I found interesting and made me think. For more articles see the quant mashup Quantocracy.

Five Myths About Data-Mining Bias

“Data-mining is widely used nowadays for trading algo development. There are several myths about how to deal with data-mining bias.”

Average Returns, Rarer Than You Think

“During the course of the 89 years covered by the chart, we never had a single year when the annualized compound return was simply the average!”

All Strategies “Blow Up”

“In this article, we will explain why even good strategies must test investors’ ReSolve every now and then in order to deliver long-term excess returns.”

Annual Asset Class Returns

I always love seeing this chart.

Good Quant Trading,

XIV a heart attack waiting to happen

A research friend recently sent me a link to The #1 Stock In The World. Besides being a blatant title to get one’s attention (and it worked on me), I found the idea interesting along with my research friends. I have been trying to add either XIV or VXX to my trading in some small way. The article is only doing a buy and hold on XIV but it peaked my interest to try some other ideas.

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Mutual Fund Sector Rotation – Ideas from readers

The post ETF Sector Rotation generated good ideas on what to try differently. This post will research two ideas using Fidelity sector mutual funds. The previous post focused on two ideas on the Select Sector SPDR ETFs.

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What I am reading: June 10, 2015

Recent articles that I found interesting and made me think. For more articles see the quant mashup Quantocracy.

Tactical Asset Allocation: Beware of Geeks Bearing Formulas

“The first time one can actually realize how good (bad) his chosen backtesting solution is when the strategy is traded live. However I am always amazed how little some traders pay attention to how closely their backtest match their live results.”

Torturing Historical Market Data

“There’s no such thing as right or wrong data, just better or worse. Stock market data looks spotless when you just see the performance numbers, but looks can be deceiving.”

Screw It, I’m All In, Baby

A little humor for your day but oh so true.

Improving the Simple ETF Rotational Trading Model

“What I love about trading models like this is the simplicity. So often simplicity trumps complication. Simple systems often have one important characteristic. They often get you out of the market during bear markets and get you back in to ride the next bull cycle. That is, if you are disciplined enough to actually follow the rules, which of course is another entire topic.“

ETF Sector Rotation – Ideas from readers

The post ETF Sector Rotation generated some good ideas on what to try differently. This post will focus on two ideas on the Select Sector SPDR ETFs. The next post will look at two ideas using Fidelity sector mutual funds.

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ATAA Conference Trip Report

I am back from the Australian Technical Analysis Association meeting in the Gold Coast, Australia. I had a great time meeting readers of the blog and other traders. Lots of good presentations. My favorites include those by Alan Clement and Andrew Gibbs, which provided me with new research ideas. These include trend following and using fundamental data. Rande Howell talked about our emotions, which as quant traders we believe we can ignore but we cannot.

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How good is Smart Beta?

A popular topic lately has been “Smart beta” ETFs. What is smart beta? It is using different ways to weight an index and the ETF that tracks it. For example, the S&P500 index is a capitalization weighted index. Bigger companies have a larger portion of the index. If you look at the SPY, Apple which is the largest company, accounts for 4% of the index (https://www.spdrs.com/product/fund.seam?ticker=spy). Other ways one can weight an index are equal weight, by volatility, by fundamental measures, by technical measures and so on. Why would you do this? To beat the returns of the S&P500 index . But are these other ways better?

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