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About MarketPsych

MarketPsych LLC is a leader in behavioral finance, applying the competitive advantages from modern psychology into finance through data, research, and consulting.

DATA
World's most comprehensive sentiment data produced in partnership with Thomson Reuters
RESEARCH
Predictive analytics underlying economic research, trading strategies, and investment recommendations
CONSULTING
Best-in-class coaching for financial advisors, traders and investors

Latest News

May 01, 2016

Dr. Peterson is speaking in Budapest on May 11th at Global Derivatives, the world's largest quant finance gathering.

April 30, 2016

MarketPsych Research Analysts Brandon Becker published new sentiment research on the Japanese Yen, Chipotle, Starbucks, and Volkswagen while Tate Hayes published on Ford and the Australian market and Sean Reilly on Tesla.

April 25, 2016

Dr. Peterson is presenting on "The Sentimental Markets Hypothesis" to the Santa Barbara CFA Institute on April 25, 2016.

Recent Press

May 01, 2016

Book Review: Trading On Sentiment -- Brenda Jubin Investing.com

March 28, 2016

The ‘Brussels’ effect in investing -- Judy Martel Bankrate.com

February 28, 2016

Beleggen met big data wordt langzaam gemeengoed (Dutch) -- Lenneke Arts, Jeroen Groot Dutch Financial Daily


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.

continue reading...

World Markets

- (30 day data)

Price Buzz Sntment Econunc Govinst
United States -0.1%
United Kingdom +1.6%
Germany +2.5%
Japan +3.1%
China -2.4%
India +1.3%

updated 2016-05-01

Currencies

- (30 day data)

Price Buzz Sntment Prcfrc Volatil
US Dollar -0.6%
Euro +0.6%
Brazilian Real +4.4%
Japanese Yen +5.8%

updated 2016-05-01

Commodities

- (30 day data)

Price Buzz Sntment Prodvol Cnsmvol
Crude Oil +24.8%
Natural Gas +11.3%
Gold +5.5%
Corn +10.2%

updated 2016-05-01

US Sectors

- (30 day data)

Price Buzz Sntment Innovtn Earnfct
Technology -5.7%
Energy +10.5%
Utilities -2.8%
Financials +2.7%
Industrials +0.8%

updated 2016-05-01

US Large Cap

- (30 day data)

Price Buzz Sntment Mktrisk Fundmstr
Apple Inc -14.8%
Bank of America Corp +7.4%
General Electric Co -3.7%
JPMorgan Chase & Co +6.3%
Microsoft Corp -10.3%
Wal-Mart Stores Inc -3.2%

updated 2016-05-01


Countries Top Lists

- (30 day data)

Conflict
Highest Lowest
Brazil Singapore
Turkey Denmark
Israel Switzerland
Philippines Canada
Belgium Norway
Sanctions
Highest Lowest
Russia Netherlands
South Korea Brazil
India Singapore
France Australia
Israel Ireland

updated 2016-05-01

Currencies Top Lists

- (30 day data)

Relative Buzz
Highest Lowest
US Dollar Norwegian Krone
Japanese Yen United Arab Emirates Dirham
Chinese Yuan Renminbi Iranian Rial
Euro Argentine Peso
Australian Dollar Swedish Krona
Price Forecast
Highest Lowest
Thai Baht Argentine Peso
Turkish Lira Iranian Rial
Brazilian Real Egyptian Pound
Israeli Shekel South Korean Won
Indian Rupee Hong Kong Dollar

updated 2016-05-01

Commodities Top Lists

- (30 day data)

Supply vs Demand
Highest Lowest
Corn Hogs
Wheat Cattle
Crude Oil Silver
Soybeans Coffee
Natural Gas Copper
Production Volume
Highest Lowest
Rice Crude Oil
Coffee Platinum
Corn Soybeans
Silver Hogs
Cattle Heating Oil

updated 2016-05-01

Equities Top Lists

- (30 day data)

Market Risk
Highest Lowest
Coach Inc VF Corp
C.H. Robinson Worldwide Inc Computer Sciences Corp
Cimarex Energy Co Halliburton Co
Danaher Corp Hartford Financial Services Group Inc
Edwards Lifesciences Corp Aetna Inc
Time Urgency
Highest Lowest
Western Union Co Parker Hannifin Corp
CF Industries Holdings Inc Boston Properties Inc
U.S. Bancorp KeyCorp
Fiserv Inc Health Care REIT Inc
Motorola Solutions Inc Adobe Systems Inc

updated 2016-05-01