Toronto, Ontario--(Newsfile Corp. - December 9, 2019) - RIWI Corp. (CSE: RIW) (OTC PINK: RWCRF) (the "Company" or "RIWI"), a global trend-tracking and prediction technology firm, is pleased to report that an independent, expert review of the Company's proprietary datasets on US employment trends has revealed that RIWI data outperformed all other benchmarks in predictive accuracy for a surprise in nonfarm payrolls. A "nonfarm payrolls surprise," according to the paper's authors - quantitative hedge fund researchers Radu Ciobanu, PhD, and Ernest Chan, PhD - is when official US monthly nonfarm payrolls data diverge from the consensus prediction of Wall Street economists.

"The financial market mainly reacts to surprise: the difference between the actual announced number and the Wall Street consensus. This surprise can move not only the US markets, but foreign markets as well," observe Dr. Ciobanu and Dr. Chan in their published review of RIWI's historic datasets.

"Traders who have an information advantage on whether or not to expect a nonfarm payrolls surprise can invest with much more confidence on trades," said Neil Seeman, RIWI's Chief Executive Officer.

For example, US data released on December 6, 2019 showed that a nonfarm payrolls surprise occurred in November: nonfarm payrolls rose by 266,000, yet the consensus of economists had predicted a gain of only 183,000. The authors note how the German DAX index moved sharply higher last week as a direct result of this huge positive surprise.

Nonfarm payrolls measure the number of jobs added or lost in the US economy over the prior month, excluding the farming industry. Nonfarm payrolls data are released monthly by the United States Bureau of Labor Statistics as part of a comprehensive review of the ongoing state of the US labor market.

The paper, entitled, "US nonfarm employment prediction using RIWI Corp. alternative data", showed:

• After weighting the data and performing seasonal adjustment, the application of monthly averages of daily, continuous RIWI sentiment data from December 2013 - October 2017 outperformed all other benchmarks available in predictive accuracy for the "sign" (i.e., positive or negative) of any surprises in official nonfarm payrolls data;
• After cross-validation tests, the average predictive accuracy of "the RIWI Score" for the sign of nonfarm payrolls surprises was 63%; and
• When predicting both the magnitude and the sign of a nonfarm payrolls surprise, combining the RIWI score with public data was critical to enhance predictive insights.

A summary of the analysis of RIWI's predictive power for the signs and magnitude of nonfarm payroll surprises, by co-authors Ernest P. Chan, PhD and Radu Ciobanu, PhD of E. P. Chan & Associates ("CHAN"), may be found at: .

Dr. Ernest Chan, an investment manager and quantitative consultant, is a former quantitative researcher at IBM, Morgan Stanley, Credit Suisse and at other finance firms. He also serves as an adjunct faculty member of Northwestern University's Master of Advanced Data Science program and is the author of three books on quantitative and algorithmic trading to generate alpha for traders. His most recent book is Machine Trading: Deploying Computer Algorithms to Conquer the Markets (Wiley). Dr. Radu Ciobanu is a quantitative researcher at CHAN, and its affiliate, QTS Capital Management, LLC.

RIWI's Potential to Predict Essential Economic Indicators Beyond Nonfarm Payrolls

"Beyond predicting NFP surprises, RIWI's data have the potential to be a more accurate gauge of the actual US employment situation, and therefore economic growth, than the NFP number," the paper notes. "One can imagine an ambitious researcher using RIWI data to directly predict GDP growth and achieving better results than using the traditional economic indicators such as NFP," the paper's co-authors state.

"It has been an exciting year for our finance business line," said Mr. Seeman. "After winning the Battle of the Quants 'Rising Star' Award in New York City for our patented, unique, continuous, real-time sentiment data across China, we have successfully attracted sales leaders with deep finance expertise, and we have won new contracts for excellent customers, such as BofA Securities."

About RIWI

RIWI is a global trend-tracking and prediction technology firm. On a monthly or annual subscription basis, RIWI offers its clients tracking surveys, continuous risk monitoring, predictive analytics and ad effectiveness tests in all countries - without collecting any personally identifiable data. .

About E.P. Chan & Associates

E.P. Chan & Associates builds quantitative models for investors and asset managers, serving individual and institutional clients in Australia, Canada, China, France, India, Israel, Italy, Russia, Singapore, South Africa, the United Kingdom, and the United States since 2006. Dr. Ernest P. Chan, a co-author of the paper described in this News Release, is also a Managing Member of QTS Capital Management, LLC, which has managed a commodity pool as well as individual clients' accounts using quantitative trading approaches since 2011. He has led research in the fields of Big Data, artificial intelligence and quantitative trading algorithms at IBM and Morgan Stanley, and has been a quantitative trader at Credit Suisse and other Wall Street firms. Dr. Chan is the author of Quantitative Trading: How to Build Your Own Algorithmic Trading Business and Algorithmic Trading: Winning Strategies and Their Rationale, both published by Wiley. His latest book, Machine Trading: Deploying Computer Algorithms to Conquer the Markets, was published in 2017. He was an Adjunct Associate Professor of Finance at Nanyang Technological University in Singapore, and an Industry Fellow of the NTU-SGX Centre for Financial Education. He is an adjunct faculty member of Northwestern University's Master's in Data Science program. Dr. Chan holds a Bachelor of Science degree from the University of Toronto (1988), a Master of Science (1991) and a Doctor of Philosophy (1994) degree in theoretical physics from Cornell University. Dr. Radu Ciobanu, a co-author of the paper described in this News Release, is a consultant to E.P. Chan & Associates, received his Ph.D. in Computer Science from the University of Edinburgh, and did research on probabilistic systems. He is currently a quantitative researcher at E. P. Chan & Associates, and its affiliate, QTS Capital Management, LLC.

RIWI Corp.

Signed: "Neil Seeman"
Neil Seeman, Chief Executive Officer

For more information, please contact:
Daniel Im, Chief Financial Officer

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CAUTION REGARDING FORWARD-LOOKING INFORMATION:

Information and statements contained in this news release that are not historical facts are "forward-looking information" within the meaning of Canadian securities legislation that involves risks and uncertainties. Forward-looking information included herein is made as of the date of this news release and RIWI does not intend, and does not assume any obligation, to update forward-looking information unless required by applicable securities laws. Forward-looking information relates to future events or future performance and reflects management of the Company's expectations or beliefs regarding future events. This forward-looking information is based, in part, on assumptions and factors that may change or prove to be incorrect, thus causing actual results, performance or achievements to be materially different from those expressed or implied by forward-looking information.

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