Trader Mood Index ($MIX)

Transcription

1 Trader Mood Index ($MIX) Image: The PsychSignal Mood Index ($MIX). Bull Sentiment = Green, Bear = Red. Blue line is historical price information for the SPDR S&P 500 ETF Trust. (*Note the leading sentiment and price correlation.) ABOUT PSYCHSIGNAL PsychSignal is the leading provider of real time Trader Mood, data, analytics and indices for financial institutions & investment professionals seeking an edge. We created a natural language processing engine which correctly interprets social media text in the context of stock prices. Our technology parses millions of online conversations every day in order to quantify the public s mood about specific stocks and other securities. Our mission is to deliver the underlying market perception as an additional set of indicators for our customers to execute investment strategies. Whereas other news sentiment and event detection systems simply tell you the news faster than you can get it on CNN, what we do is tell you how the market s underlying mood is trending, so that you can predict moves far in advance with or without news events occurring. THE TRADER MOOD INDEX ($MIX) The Trader Mood Index ($MIX) is the first automated datasource in the world for the underlying mood surrounding the S&P 500. The product delivers consistent results transparently, without relying on a black-box. Our goal for the product is to arm our customers with the ability to execute reliable algorithmic trading strategies using the signal as an input. The $MIX allows you to easily visualize what s happening with the public mood surrounding the S&P 500. It s especially useful for asset protection and downside risk aversion. The algorithm can be easily integrated to existing trading algorithms, giving trading strategies a real-time view into the public s psychology surrounding the SPDR S&P 500 ETF Trust. This view can give your trading strategies an edge in advance of bull runs, and keep you out of the pull-ins when they occur. Multiple case studies, scientific papers and trial API access keys are available. Please contact us to learn more.

2 HOW TO VISUALIZE THE $MIX crowd LOVES it = overhyped Key: $SPY PRICE Green = BULLS Red = BEARS Sept. 2, 2014 $SPY= $ crowd HATES it = undervalued crowd LOVES it = overhyped May. 27, 2015 $SPY= $ Oct. 13, 2014 $SPY= $ *NOTE: As pictured here, the $MIX allows a user to easily visulaize the sentiment surrounding the S&P 500 and construct market perception trading strategies using the signal as an input. FREQUENTLY ASKED QUESTIONS 1. What is the Mood Index ($MIX)? PsychSignal s Mood Index ($MIX) is a real time volatility index that measures the change of bullish and bearish sentiment in the S&P How does the Mood Index ($MIX) work? The Mood Index is powered by our proprietary computerized natural language processing engine that has the ability to parse, analyze and quantify the positive or negative mood surrounding specific financial securities from social media platform datafeeds. 3. Where does the Mood Index source its data? We ingest data sourced from public social media data firehoses (Twitter, StockTwits), covering the widest variety of symbols and sources in the industry. 4. What does the Mood Index ($MIX) demonstrate? The Mood Index conclusively demonstrates that changing trader moods, measured properly, can forecast major market price movements. 5. When is the Mood Index ($MIX) data published? The index is published daily at approximately 12:01AM EST, giving you today s trading signal before the open. 6. When did the Mood Index ($MIX) go Live? PsychSignal published the $MIX in June When does the $MIX data history begin? January 18, What is the $MIX measuring? The $MIX is a daily real time index which measures traders changing moods. Said another way what we are measuring with the $MIX is the volatility of change of both bullish and bearish sides. Whichever side, bull or bear, is more volatile will drive the index higher or lower. The theory is that when bearishness is more volatile the index goes negative and you can expect a pull in or at least a choppy market. The bearish index is especially good at foretelling uncertainty, something quite useful for protecting your downside risk exposure. 9. Can I try out the algorithm with my team? The open-source algorithm is available upon request. 10. Can you suggest academic papers that describe the effect that you are picking up and perhaps speak to the rationale for the effect? Yes, we have many technical papers, scientific studies and peer-reviewed whitepapers available upon request.

3 $MIX AORITHM $MIX CALCULATIONS HOW THE AORITHM WORKS 1. The algorithm counts all the messages our system recognizes as referencing the $SPY cashtag. This is total message count including neutral messages. COLUMN D 2. The algorithm counts all messages our system recognizes as referencing the $SPY cashtag and being bullish. This is bullish message count. COLUMN B 3. The algorithm counts all messages our system recognizes as referencing the $SPY and being bearish. This is bearish message count. COLUMN C 4. The $MIX algorithm finds the percent % bullish messages to the total. COLUMN E 5. The percent % bearish messages to the total. COLUMN F 6. It then looks at the difference of yesterday s % bullish messages from today s % bullish messages. COLUMN G 7. Then algorithm measures the difference of yesterday s % bearish messages from today s % bearish messages. COLUMN H 8. Then calculate a rolling 9 day standard deviation of the difference of yesterday s % bullish messages from today s % bullish messages which is in column G. COLUMN I 9. The algorithm calculates a rolling 9 day standard deviation of the difference of yesterday s % bearish messages from today s % bearish messages which is column H. COLUMN J 10. Divide the bullish standard deviation [COLUMN I] by the bearish standard deviation [COLUMN J]. COLUMN K 11. Subtract 1 from the result of COLUMN J and you have the mood index. [ EXAMPLE USE CASE SCENARIO ] USING $MIX TO AVOID MARKET CORRECTIONS Using Quantopian online backtest modeling software, we created a very basic trading strategy using the Mood Index ($MIX) daily sentiment signal for the $SPY as the only input. Our strategy was to enter a trade if the bullish sentiment signal provided by a mix of Twitter and StockTwits exceeded 0.4, and then exit the trade when the bullish sentiment signal was less than 0.3 without using leverage and a relatively small cash position of $100,000 in order to prevent moving the market by our own activity. Using only this strategy from July 2013 to July 2015, we were able to generate a 20.2% return, underperforming the market benchmark of 32.1% during this period. However, the goal of this strategy was not to beat the benchmark, but to reduce risk exposure. Note in the image below, by using only the $MIX as a signal without any other strategies in place, our strategy was able to nearly eliminate exposure to market volatility and pull-ins as shown in the strategy s (blue line) smoothed out performance versus the benchmark s (red line) volatile movement shown below. We believe that the risk-protection implications from being able to avoid market corrections from this study are compelling. Please contact us for access to the strategy s source code and trial access to the API to conduct your own analysis. USING THE $MIX TO AVOID MARKET CORRECTIONS *NOTE: The use of the $MIX in this simple strategy as the only input in avoiding almost every single market pull-in over the course of a two year period.

4 SCIENTIFIC REVIEWS The Prediction of Stock Price, Volatility and Volume with Intraday Social Media Sentiment Metrics (summary) - M.S. Checkley, M.A Anon Higon, H. Alles Computing Trading Strategies Based on Financial Sentiment Data Using Evolutionary Optimization (summary) - Dr. R. Hochreiter Our head scientist, Dr. Matthew Checkley specializes in networks, automated financial market trading and data science, centered around linking social media sentiment metrics and financial market prediction. He recently released a study currently under peer review of how sentiment metrics predict several market behaviors - stock returns, volatility and trading volume - by demonstrating that a change in sentiments is consistently correlated to a subsequent change in market behavior. The paper explores the ability of sentiments extracted from the micro-blogging sites Twitter and StockTwits to predict stock markets. The selected content concerns bloggers feelings about five major stocks. Taking two indicators derived from bullish and bearish sentiment metrics, the team modeled their ability to forecast three attributes of the stocks: price direction; volatility; and trading volume. They add to knowledge in this field by employing two years of every two-minutes price data, matched to concurrent sentiment metrics. We find robust evidence for a causal link from sentiments to stock price returns, volatility and volume over a period of a few minutes or more. The forecast errors attributed to the models built on sentiment metrics show modest improvements over the errors made when forecasting price as simply-related to the last known price. In the context of these market data, we find selective evidence of sentiments predictive value for market behavior illustrates how strong bull and strong bear measures, when appearing together, tend to foreshadow more turbulence in market prices. This effect is robust and consistent. Similarly, sentiments show potency in predicting price direction and trading volumes. This study finds good evidence for PsychSignal s social media sentiment metrics, extracted from salient micro-blogging activity, being Granger-causal of stock price returns, volatility and stock trading volume. The typical prediction window is less than 20 minutes, albeit with small variation between the five stocks analysed. We note, however, our tests concern the use of pure sentiment metrics without addition of market trends, companylevel accounting data, etc.- to predict market behavior. We anticipate both more accurate and longer-term predictive ability for market behaviors with the addition to the model of data besides sentiment metrics. Another researcher in the field, Ronald Hochreiter of WU Vienna University of Economics and Business conducted an independent study entitled: Computing trading strategies based on financial sentiment data using evolutionary optimization in April, The study applies evolutionary optimization techniques to compute optimal rule-based trading strategies based on financial sentiment data. The sentiment data was extracted by PsychSignal from the social media service StockTwits to accommodate the level of bullishness or bearishness of the online trading community towards certain stocks. Numerical results for all stocks from the Dow Jones Industrial Average (DJIA) index are presented and a comparison to classical risk-return portfolio selection is provided. The tests were conducted using a buy-and-hold portfolio over the year The evolutionary trading portfolio exhibits better risk properties than both other portfolios in all five metrics. Especially important is the reduction of the maximum drawdown, which is of importance to asset managers nowadays, because investors are increasingly looking to this metric if they are searching for secure portfolios, independently verifying what we can demonstrate using the Quantopian study on the previous page. The paper concludes that an evolutionary optimization approach to compute optimal rule-based trading strategies based on financial sentiment data has been developed. It can be shown that a portfolio composed out of the single trading strategies outperforms classical risk-return portfolio optimization approaches in this setting. The next step is to include transaction costs to see how this active evolutionary strategy loses performance when transaction costs are considered. Future extensions include extensive numerical studies on other indices as well as using and comparing different evaluation risk metrics or a combination of metrics. One may also consider to create a more flexible rule-generating algorithm e.g. by using genetic programming. Finally, to achieve an even better out-of-sample performance the recalibrating of the trading strategy can be done using a rolling horizon approach every month. Please contact us for access to the original document. Please contact us for access to the original document.

5 TM IN SVAC GEVT AVEW AAC JRNE TSLA WETY AEG SA HWVW QQQ NUDH AATV OENY G JUY UTDB IN SVAC GEVT AVEW AAC JRNE TSLA WETY AEG SA HWVW QQQ NUDH AATV OENY G JUY UTDB EXAMPLE REQUEST \ &update=1d&sources=stocktwits&symbols=market,&from= &to= &format=cs v EXAMPLE RESPONSE SOURCE,SYMBOL,TIMESTAMP_UTC,BULLISH_INTENSITY,BEARISH_INTENSITY,BULL_MINUS_BEAR,BULL_SCOR ED_MESSAGES,BEAR_SCORED_MESSAGES,BULL_BEAR_MSG_RATIO,TOTAL_SCANNED_MESSAGES stocktwits,, t05:00:00z, , , ,56,35,1.6,335 stocktwits,, t05:00:00z, , , ,276,97,2.8454,945 stocktwits,, t05:00:00z, , , ,432,202,2.1386,1716 stocktwits,, t05:00:00z, , , ,367,188,1.9521,1552 stocktwits,, t05:00:00z, , , ,453,213,2.1268,1840 stocktwits,, t05:00:00z, , , ,440,249,1.7671,1822 stocktwits,, t05:00:00z, , , ,42,18,2.3333,250 stocktwits,, t05:00:00z, ,2.11, ,16,3,5.3333,49 stocktwits,market, t05:00:00z, , , ,2692,1469,1.8325,16700 stocktwits,market, t05:00:00z, , , ,5368,3134,1.7128,32258 stocktwits,market, t05:00:00z, , , ,10451,5910,1.7684,53218 stocktwits,market, t05:00:00z, , , ,10520,5892,1.7855,52553 stocktwits,market, t05:00:00z, , , ,13079,6943,1.8838,62637 stocktwits,market, t05:00:00z, , , ,11154,6091,1.8312,5464 HOW THE TECHNOLOGY WORKS 1. Ingest Social Media Firehoses We take 100% FULL FIREHOSE DATA from social media, and only let in conversations with a $Cashtag, Security Name, Stock Symbol, Company Name, Market Sectors or other relevant information. 2. Categorize by Security The system then organizes the conversations by Company or Financial Security being discussed according to a comprehensive database. The output is a quantity of conversations by each investment security topic. -4 BEAR ±0 NEUTRAL +4 BULL 3. Analyze Mood The Mood Analyzer processes the language surrounding the security s name the way a professional trader would. The output is the mood intensity for each security on a scale of -4 (extreme bear) to +4 (extreme bull). 4. Aggregate Mood Scores The Sentiment Quantifier aggregates the bullish or bearish sentiment score for each security based upon the total volume and mood intensity. This score is referred to as The Stock s Mood. 5. Output Signal via API Our customers access the data via a secure API access key available with a monthly license subscription. The API integrates easily with algo trading platforms and can be custom tailored for each customers specific needs. PSYCHSIGNAL TEAM Mr. James Crane-Baker Co-Founder, CEO James Crane-Baker is a serial startup veteran based in Miami, Florida. Beginning his career on Wall St. in 1995 as one of the first 5 employees of Broadway Trading (BT), he was a pioneer in direct access electronic trading, which revolutionized stock trading execution access to the inside spread. He was instrumental in helping the firm grow from a handful of traders working on folding tables in a cramped office to having thousands of traders scattered across the country in nearly a dozen offices. He was featured and interviewed as a member of The New Trading Elite in the New York Times Bestseller Electronic Day Traders Secrets. In the interview he says that the secret to day trading is anticipating where the crowd will be going next, and knowing how to position ones self ahead of the curve to yield the best results which became the genesis behind PsychSignal many years later. After ten years on Wall Street, James moved to Rio de Janeiro Brazil to learn Portuguese and taught himself how to hack website code. With an independent Brazilian web development team they built out a classified ad network called which they sold four years later to The Chena Group in Summer James initial thoughts for the PsychSignal project began in 2010 as a way to recreate the Squawk-Box he used at Broadway Trading, but listening to social media instead of the trading floor. After selling RedeParede, he put together a team of advisors, mentors and software big-data engineers to explore the concept. By October 2011, the original API was able to predict the Gallup US Mood Index a full day in advance with 85+% accuracy. The work on that project has continued to gain momentum since that milestone and is attracting investment and interest from Quant Funds, Hedge Funds and trading desks worldwide. Crane-Baker holds Bachelor degrees in finance and history from Tulane University, graduated from The Kent School in CT, and studied at the London School of Economics. James also completed the prestigious NOLS Patagonia semester expedition. He is the sole listed inventor and holder of US Patent # 8,311,883. Mr. Leonardo Almeida Co-Founder, Chief Technology Officer Leonardo Almeida is an information retrieval, back-end product development, and infrastructure engineer based in Rio de Janeiro, Brasil. Almeida oversees execution of the software engineering, infrastructure, and database systems. From July 2003 to February 2011 Almeida worked in a similar capacity for TeleListas.net prior to joining Crane-Baker at RedeParede. Dr. Matthew Checkley, Ph.D. Data Analysis & Modeling Dr. Checkley creates the mathematical algorithms that use real-time sentiments in financial market prediction and trading algorithm development, employing recent developments in Artificial Neural Networks for PsychSignal as an engineering consultant. His models generate the sentiment-based trading algorithms for the Company. Dr. Bob Nisbet, Ph.D. Data Analysis & Modeling Dr. Nisbet is a data scientist/manager with a solid history of managing teams in analytical research, statistical analysis & modeling of complex data systems for Retail, Banking, Telecommunications, Credit, and Membership clubs. He has been Co-Principal Investigator on numerous government grants, serves as an external consultant with Aviana Global, teaches in the UC Irvine Extension Predictive Analytics Certification Program, and serves on the editorial board of the new Cambridge University Press book series on Predictive Analytics. Dr. Nisbet assists PsychSignal with data mining project work; scientific writing, and speaking. Dr. James Pennebaker, Ph.D. Linguistics & Language processing Dr. Pennebaker is Regents Centennial Professor of Liberal Arts in the Psychology Department at the University of Texas at Austin. His work with Linguistic Inquiry and Word Count (LIWC) KredStreet, Inc , All Rights sales@psychsignal.com

6 text analysis software to determine the degree any text uses positive or negative emotions, self-references, causal words, and 70 other language dimensions advises PsychSignal in its Natural Language Processing engine. Dr. Pennebaker s work is also used by Facebook to gather insight into their users emotional state. Dr. Pennebaker has remarked that PsychSignal s technology is one of the smartest approaches and implementations he has seen. He advises on prediction improvement techniques. Dr. Pennebaker has been on the faculty at the University of Virginia, Southern Methodist University, and The University of Texas since Author or editor of 10 books and over 250 articles, Pennebaker has received numerous awards and honors. His resume is 32 pages long. Dr. Erin Olivo, Ph.D. Crowd Psychology Dr. Olivo is a private-practice Clinical Psychologist in New York City, a Clinical Psychologist at Columbia University medical Center and an Assistant Clinical Professor of Medical Psychology at Columbia University and a noted speaker on the topic of Crowd Psychology Sentiment Analysis. She is a faculty member in the Behavioral Medicine Division of the Psychiatry Department at Columbia where she conducts clinical research focused on the use of mind-body interventions with a variety of patient populations. Dr. Olivo worked with Crane-Baker at Broadway Trading in the late 1990 s as the psychological advisor to the members of the trading floor. Mr. Henk Alles Artificial Intelligence Mr. Alles is the developer of our Artificial Neural Network ( ANN ), and soon to be launched trading alerts tools. A Netherlandsbased software engineer formerly of Xerox Parc, MediaLabs, Rank Corporation Atos/Origin, ExoVision and Seven miles, Mr. Alles is the founder and managing director of Infolution, a company that makes automatic machine based text analysis focusing on solving information complexity. For the past 25 years, his work has focused on information retrieval technology, natural language search, semantic mapping, and various other tools for managing large document and database archives. Infolution s client list is extensive, including Intel, ING Bank, Pfizer, John Deere, Heineken, Shell, United Nations, Unilever, Dutch Police and the Department of Homeland Security. His Artificial Neural Network helps power the deep learning AI and smart alert filtering portions of the PsychSignal API and SquawkrBox systems. Mr. Lars von Thienen Analysis Algorithms Mr. von Thienen is the developer of proprietary stock cycle regressive analysis algorithms that identify and quantify the underlying natural trading cycles of particular stocks. His works can be found in a wide array of technical papers, analyst publications, magazines and published books on the topic. Mr. von Thienen received his degree in Industrial Engineering and Business Management from the Nordakademie, University of Applied Sciences in Hamburg. He has worked as a scientist, trader and business consultant for over 25 years. He has extensive experience in combining technical engineering knowhow with economics and business-related issues. He is a published author of multiple financial analysis research books, is appointed as commercial judge at the regional, court of Hamburg and the CEO of whentotrade.com. Mr. von Thienen s stock cycle analysis algorithms provide PsychSignal with key regressive analysis insight for specific stocks, and are used in parallel with PsychSignal s sentiment analysis algorithms to provide unparalleled insight into trading patterns.