Month: October 2010

Learning To Take Small Losses Quickly (Revisited)

Saturday, October 16, 2010

While taking small losses may seem like a simple matter of discipline, there are a number of reasons why this is not the case. To summarize briefly, holding a losing position tends to spur a set of psychological processes which interrupt the rational choice of realizing the loss.

There are a few posts from my old tumblog that continue to get a bunch of hits and, god bless the $GOOG, they look to me like some of the best ones i did. So I figured I would edit them a bit and repost them here on the new blog.  This first one fits beautifully into the StockTwits University Project and might be useful to some readers.

I have had the opportunity to talk with a number of very successful traders over the years and have found that even while their trading styles and asset classes of choice differ, they all tend to share a few common attributes which separate them from less successful traders and which I believe contribute to a significant portion of their profitability.

I have traded equities profitably for a number of years myself and have applied similar rational behaviors to my own routine that have served me well.

One in particular stands out as crucial and so I will begin there.

Every winning trader that I know habitually takes losses quickly before they become outsized.

Continue reading “Learning To Take Small Losses Quickly (Revisited)”

Twitter Mood Predicts the Stock Market (But Don't Bet On It Yet)

Friday, October 15, 2010

since feeling is first who pays any attention to the syntax of things

– ee cummings

By now, there is enough evidence suggesting mood materially affects individual decision making that we can assume this to be the case.  With regard to markets, so much depends upon one’s break even point as prospect theory has shown us and as losses loom larger than gains. Indeed, one can make the case that this is why support and resistance levels seem to work as these tend to be areas where a large number of positions break even.

The leap though from empirical validations of the individual’s behavioral-affective dynamic to making assumptions with regard to the collective market psychology is not so easy.  For one thing, it is much easier to construct a sound scientific method for measuring complex relationships within individuals and for another, measuring the market mood alone is fraught with construct validity problems.

This brings us to a wonderful study published by Bollen, Mao and Zeng in which they measure the correlation between mood states derived from large-scale Twitter feeds and the $DJIA over time.

Instinctively, I love this study because I have observed qualitatively a possible relationship but also because it provides a new potential framework for measuring near real time collective market sentiment that might be less encumbered by indecipherable motivations than current popular measures.  Here is a copy of the study with some concluding comments below:

Twitter Mood Predicts the Stock Market

There is a big difference between discovering a method to measure close to real time collective sentiment and its relationship to market behavior and exploiting it for market gains.  For one thing, the methodology described in the study above, while provocative, will require scrutiny from the scientific community, reproduction and refinement.

But, and perhaps more importantly, the individuals who will attempt to trade off of such data will be subject to not only market structural limitations but their own emotions as well.

Twitter Mood Predicts the Stock Market (But Don’t Bet On It Yet)

since feeling is first who pays any attention to the syntax of things

– ee cummings

By now, there is enough evidence suggesting mood materially affects individual decision making that we can assume this to be the case.  With regard to markets, so much depends upon one’s break even point as prospect theory has shown us and as losses loom larger than gains. Indeed, one can make the case that this is why support and resistance levels seem to work as these tend to be areas where a large number of positions break even.

The leap though from empirical validations of the individual’s behavioral-affective dynamic to making assumptions with regard to the collective market psychology is not so easy.  For one thing, it is much easier to construct a sound scientific method for measuring complex relationships within individuals and for another, measuring the market mood alone is fraught with construct validity problems.

This brings us to a wonderful study published by Bollen, Mao and Zeng in which they measure the correlation between mood states derived from large-scale Twitter feeds and the $DJIA over time.

Instinctively, I love this study because I have observed qualitatively a possible relationship but also because it provides a new potential framework for measuring near real time collective market sentiment that might be less encumbered by indecipherable motivations than current popular measures.  Here is a copy of the study with some concluding comments below:

Twitter Mood Predicts the Stock Market

There is a big difference between discovering a method to measure close to real time collective sentiment and its relationship to market behavior and exploiting it for market gains.  For one thing, the methodology described in the study above, while provocative, will require scrutiny from the scientific community, reproduction and refinement.

But, and perhaps more importantly, the individuals who will attempt to trade off of such data will be subject to not only market structural limitations but their own emotions as well.

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