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Written by MarketPsych CEO Dr Richard Peterson, our free newsletter provides commentary on current events from a behavioral perspective. Additional topics include behavioral theories and our latest research results.

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July 03, 2016

How to Predict Brexit - the Human Face of Market Risk

"In fact, it was as a journalist who played around with the facts that Mr. [Boris] Johnson first made his name. He was fired from his first reporting job, at The Times of London, for inventing a quote and attributing it to an Oxford professor (who happened to be is godfather)."
~ "Luck Runs Out for a Leader of 'Brexit" Campaign." New York Times, June 30, 2016.  

Boris Johnson is a former Mayor of London, Member of Parliament (MP), and was a leader of the Brexit campaign.  He was poised to become the next British Prime Minister until engaging in odd behavior last week.  

Johnson started his working life in the media.  In 1989 he was assigned to report on the EU from Brussels by the Daily Telegraph where he misrepresented the dysfunction of the EU in order to gain readership, and he enjoyed the public's shock at his stories:  “I was just chucking these rocks over the garden wall and listening to this amazing crash from the greenhouse next door over in England,” he told an interviewer.  Some of his misrepresentations about the EU became conventional wisdom in the UK, and they may have stoked the euroscepticism behind the Brexit vote (and the subsequent global market declines).

At MarketPsych we research how media information moves investors and market prices.  Some information that hits markets is well-defined, such as the impact of the nonfarm payrolls number described in this past newsletter.  When information fits an understood model, it is taken in stride, and the markets stay calm.  But sometimes events shake our fundamental beliefs about the world, and new frameworks for understanding events are needed.  Today’s newsletter examines human risk in markets - the urge to independence - and a unique media-based trading model that correctly predicted the market direction around the Brexit vote.
 
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June 10, 2016

The World's Greatest Stock Picker, AMZN, and Keeping It Real

Manny introduced himself to me as "the world's greatest stock picker."  He explained that one key to his success was that he only needed two hours of sleep a night.  He pored over details in every significant financial publication and in those quiet morning hours when all others slept, he let information percolate.  By the morning he had brilliant new insights into the industries and companies that were poised to outperform over the following months.  Some of the world's top fund managers subscribed to his research, he told me.

I asked if his clients knew he was housed in prison, in solitary confinement.  He explained that of course they didn't, and he asked that I kindly keep his secret.  He distributed his stock research through his secretary, who kept his office open.

In the intervening days I checked out Manny's story.  Much was true - he was in fact publishing highly-regarded financial research to large AUM clients from prison.

On the surface his research analysis sounded brilliant - the creative ramblings of an out-of-the-box Wall Street-obsessed thinker.  But as we talked in depth it became clear that his thought process was laced with irrational and circumstantial connections. He was often confusing wishful thinking with objective analysis.  He was hypomanic, with grandiose claims and excessively optimistic projections.

As a psychiatrist I've worked with many people with grandiose delusions.  In each case the client has fixed beliefs that are contrary to reality - beliefs that guide much of their waking actions - beliefs that are entirely untrue.  Delusions aren't limited to manic prisoners, in fact we spend most of our days navigating the world based on assumptions, many of which are entirely unfounded.  Because the financial markets are imbued with uncertainty, assumptions are more dangerous in that environment.  Regardless of the fragility in our collective understanding of markets, there are enormous payoffs for those who can discern reality more accurately. 

In fact, academic research on trading models finds that most are delusional.  "Most of the empirical research in finance, whether published in academic journals or put into production as an active trading strategy by an investment manager, is likely false."  ~ Campbell Harvey and Yan Liu, “Evaluating Trading Strategies,” 2014

This quote is particularly relevant to us at MarketPsych because we are restarting our trading business.  We're currently trading a unique media-based machine learning strategy and re-registering as an investment adviser.  It has been a long road to find a strategy worth deploying capital into, and based on our prior experience, trading delusions can easily become enshrined in predictive models.  

Today's newsletter examines the nature of false beliefs among investors, how beliefs shift (with an Amazon case study), honest investment strategy development, and examines what, if anything, we can do to find the truth about what moves markets.

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May 02, 2016

Machine Learning, Big Data, and Finding Alpha in the Noise

In quantitative investing, deep learning could be dismissed as a surefire way to overfit on data.  However, as we will describe in more detail below, appropriate set up of deep learning can improve results significantly.  In particular, learning algorithms that first identify the market context in which their strategies are deployed (the regime), are better prepared to learn how markets dynamically adapt to information flow.  Academic research in finance does not yet use deep learning (interdisciplinary research is often slow in coming), but it does support the value of understanding context.  For example, research by Elijah DePalma at Thomson Reuters demonstrates that the performance of common investment strategies differs across market regimes, and these differences may be rooted in the divergent mental states of traders in each context (e.g., optimism in a bull market versus pessimism in a bear market).

Historically, many investors have used the VIX to define market regimes as calm or volatile. As DePalma did in the whitepaper linked to above, sentiment can define market regimes.  Our own data product - the Thomson Reuters MarketPsych Indices (TRMI) - was built to address the problem of dimension reduction in media flow, in part to improve regime detection.  The TRMI quantify and aggregate the information that is directly meaningful and impactful to traders in the form of granular sentiment indexes like "fear" and "joy" as well as macroeconomic indexes like "earningsForecast" and "fundamentalStrength" suggested by a review of the academic literature.   

In the new world of machine-learned strategies, most algorithms use a switching mechanism to change algorithms as regimes shift.  Given that deep learning is based on the neural basis of human decision making, it helps to consider how such human decision making changes depending on the context.  For example, in the midst of market panic, traders think and behave very differently than in the midst of a gradual bull market.  A network that generalizes information like a human mind under stress will behave superiorly during a market panic. However, when markets are quiet, a more complex network architecture can ascertain the nuances of information flow and price behavior. Research supports the use of such regime-dependent approaches in more primitive forms (e.g., switching from value to momentum strategies depending on the VIX level). 

With the recent explosion of such machine-readable and granular data sets, deep learning is better able to show its value.  To support the surge of interest in applying machine learning to vast financial datasets, a new ecosystem - including data such as the TRMI - has arisen.

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April 11, 2016

How Fund Managers Trade on Sentiment

Information sometimes hits market prices hard.  The non-farm payrolls number, released the first Friday of each month, has a significant impact on the value of the U.S. Dollar and Treasury bonds.

Elijah DePalma, Senior Quantitative Research Analyst at Thomson Reuters, analyzed the millisecond impact of the nonfarm payrolls (NFP) on the U.S. dollar future contract (DXZ4) on one day—December 5, 2014. A minutely chart of the December dollar index future contract below shows that the price impact is nearly instantaneous with the news release.


SOURCE:  Courtesy of Elijah DePalma, Senior Quantitative Research Analyst at Thomson Reuters

Dr. DePalma notes that on December 5, 2014, $5.7 million of USD contracts (DXZ4) were traded within 63 milliseconds of the NFP release, and $29 million was transacted within 100 milliseconds.

Information that is not numerical (as Nonfarm payrolls is) that is conveyed in text is more difficult to measure.  MarketPsych’s expertise in text analytics allows us to tackle the non-numerical side of information flow – the concepts that influence and bias investors.   Our sentiment-based data feed allows us to deeply understand how information causes herding, and when it doesn’t.  This feed is called the Thomson Reuters MarketPsych Indices, and it is consumed by the world’s largest quant funds and banks for trading and risk applications.

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March 06, 2016

The Trump Effect, Media Attention, and Stock Price Patterns

There's no such thing as bad publicity.
~ Associated with P.T. Barnum, the 19th century American showman and circus owner

Before the premiere of her first reality TV show - a show called The Simple Life, set on a farm - Paris Hilton was a little-known American socialite.  Little-known, that is, until someone released an amateur sex tape of her and her then-boyfriend three weeks before the premiere of the show.  

Despite the painful inanity of The Simple Life and the crassness of releasing a sex tape 3 weeks before her show’s premiere, Hilton became a media star and a business success who is now worth around $100 million (per Wikipedia).  

There's nobody in the world like me. I think every decade has an iconic blonde, like Marilyn Monroe or Princess Diana and, right now, I'm that icon.
~ Paris Hilton

Any publicity - even the moral outrage over a (probably deliberate) sex tape release - was good publicity for her brand.   Hilton’s strategy was later repeated by Kim Kardashian and most recently Donald Trump (minus the tape, so far).

If a strategy of grabbing media attention with morally outrageous acts boosts celebrity brands and sways voting patterns, might media attention to companies also boost stock prices? Today’s newsletter looks at the power of attention to drive stock returns.  While celebrities appear to be boosted by publicity - any type of publicity - studying the repeating effects of media attention on stock prices reveals more nuanced but similarly broad patterns over time.  But before diving into that, a quick plug for our book, launching this month!

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