Financial Trading for Behavioral Scientists

Todd A. Ward, PhD, BCBA-D

bSci21Media, LLC

For behavioral scientists interested in trading stocks, commodities, Forex, cryptocurrency, and the like, you are in luck.  Trading, particularly “technical” trading, is all based on time-series data, the type of data in which most of you already have extensive training.  The data itself (i.e., price movement) is comprised of the behavioral products of millions of traders in various markets, set in a field of setting factors in the global economy.

The main goal of technical trading is to watch for price trends and profit from them.  You can profit from rising prices by “going long”, which means to buy at a low price and sell at a higher price.  However, you can also profit from falling prices by shorting the market, which involves borrowing stocks, selling them at a higher price, then buying them back at a lower price and taking the profit.

Moreover, trading comes with a variety of descriptive analytics, many of which are free, and are fairly simple to wrap your head around.  Supporting analytics, or technical indicators, are used to help determine the probability that changes in price are indicative of a larger trend or if they are simply “noise” or random variation.  Some indicators are designed to predict price movements before they happen.

In the example below, I will introduce you to a basic technique in trend analysis using moving averages.

The graph above shows the price of Bitcoin cryptocurrency from approximately September 2017 to September 2018.  The blue line is the price.  The higher the line the higher the price.  At its peak in December of 2017, Bitcoin was trading at nearly 20,000 US Dollars.

The two other lines are moving averages, or Exponential Moving Averages (EMAs) to be more precise.  A moving average is simply the average value across a period of time relative to the current period.  An EMA is weighted slightly heavier to the most recent price movements such that it tracks more recent movements more closely.

In this case, we have a 9-Day EMA in green, and a 20-Day EMA in red.  So, for any given day, we can see the price of Bitcoin, the average price of Bitcoin across the past nine days, and the average price of Bitcoin across the past 20 days.

When the shorter-term EMA is above the longer-term EMA, you can be more confident that price movements are part of a larger upward trend.  When the shorter-term EMA is below the longer-term EMA, you can be more confident that price movements are part of a larger downward trend.

Many traders use EMAs to give them quantitative buy and sell signals.  In this case, if the 9-day EMA crosses above the 20-day EMA it is a signal to buy a long position.  Conversely, if the 9-day EMA crosses below the 20-day EMA, it is a signal to sell a long position.

In the graph above, every green arrow indicates a buy signal, and every red arrow indicates a sell signal, and in each case the price indicated at the green arrow is lower than the price at the red arrow.  Note that this method may not always maximize profits, but it is a way to be more confident that you are buying at the start of an uptrend and selling at the start of a downtrend.  And if you were shorting the market, you would buy at the red arrows and sell at the green arrows to profit from falling prices.

I will also note that moving averages are no guarantee that you will profit from trading and I am not offering trading advice.  My goal here was to introduce you, the behavioral scientist, to data and supporting analytics that are readily amenable to your training.  If you are interested in “going down the rabbit hole” to teach yourself everything you wanted to know about trading, I recommend  The graph above came from, which is free.

In the future, I plan to discuss several more data analytics for behavioral scientists who have an interest in trying their hand at trading.  If you want to get started, the Internet has a variety of trading simulators such that you can develop and test your own trading strategy without using real money, such as the simulator at Investopedia.

Remember, trading is all based on measuring behavioral products and predicting their future, something we do already.

Todd A. Ward, PhD, BCBA-D is the President and Founder of bSci21Media, LLC, which owns the top behavior analytic media outlet in the world,  bSci21Media aims to disseminate behavior analysis to the world and to support ABA companies around the globe through the Behavioral Science in the 21st Century blog and its subsidiaries, bSciEntrepreneurialbSciWebDesignbSciWriting, and the ABA Outside the Box CEU series.  Dr. Ward received his PhD in behavior analysis from the University of Nevada, Reno under Dr. Ramona Houmanfar.  He has served as a Guest Associate Editor of the Journal of Organizational Behavior Management, and as an Editorial Board member of Behavior and Social Issues.  Dr. Ward has also provided ABA services to children and adults with various developmental disabilities in day centers, in-home, residential, and school settings, and previously served as Faculty Director of Behavior Analysis Online at the University of North Texas.  Dr. Ward is passionate about disseminating behavior analysis to the world and growing the field through entrepreneurship. Todd can be reached at

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1 Comment

  1. I was just speaking with a fellow BA not too long ago about trading and behavior analysis, so it was great receiving this article in my inbox! Bullish Bears is another amazing resource for those interested 🙂

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