Resources

AI INDUSTRY WATCH

  • May 2016 – Some hot on ‘big data,’ others not convinced.
    We could potentially be entering a really enlightened age for quant investing, anchored on the foundation of a strong partnership between quant and big data.
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  • May 2016 – Peterson, Trading on Sentiment: The Power of Minds Over Markets
    Real-time linguistic and psychological analysis of news and social media quantifies how the public regards various asset classes according to dozens of sentiments including optimism, fear, trust and uncertainty.
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  • April 2016 – Andrew Lo Study Says Twitter Can Help You Trade Fed Meetings
    In the social media cacophony, some of the noise rises to the level of stock market signal.
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  • April 2016 – Social media’s ‘stale news effect’ spurs stock volatility, study finds
    Dozens of companies, including Bloomberg LP, have products aimed at analyzing social media sentiment toward equities.
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  • April 2016 – Social media, news media, and the stock market
    Intense social media coverage predicts high volatility of returns and high trading volume over the next month.
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  • March 2016 – Machine Learning as a Service: How Data Science Is Hitting the Masses.
    Machine learning and predictive techniques impact every major industry.
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  • March 2016 – Using Social Media Intelligence to Engage NextGen Investors
    Financial advisors are also taking advantage of the data to add value for their clients and connect with the next generation of investors.
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  • February 2016 – The Big-Data Future Has Arrived
    When you can study billions, even trillions, of data points you begin to uncover forces and trends that until now have always been invisible to human observers.
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  • February 2016 – The World of FinTech Startups for Millennials
    Millennials are investing, lending, and sharing money much differently than their parents, and they are assisted by a growing set of tech-driven tools to do so.
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  • January 2016 – Social media firms make ETF push
    The opportunity to deliver financial services for social media platforms is amazing and potentially disruptive, especially in its ability to engage a Millennial consumer set that’s still emerging.
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  • January 2016 – The “Sixth” Factor – Social media factor derived from tweet sentiments
    Significant evidence that characteristics of securities on social media have significant power in explaining daily returns across sample of fifteen stocks.
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  • January 2016 – Social media analysis for the financial markets
    Examining the Twitter buzz around commodities like oil and gold, exploring the sentiments created based on indexes such as worry, fear, and hope. Concludes that the correlation between sentiments and following-day-prices is quite strong.
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  • December 2015 – Can you beat the stock market in 140 characters or less?
    Hedge funds, banks, asset managers and other investors scour social media for information they can use to guide investment decisions.
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  • December 2015 – How Investors Are Using Social Media to Make Money
    Increasingly, there’s a new technological race in which hedge funds and other well-heeled investors armed with big-data analytics instantly analyze millions of Twitter messages and other non-traditional information sources to buy and sell stocks faster than smaller investors can hit “retweet.”
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  • December 2015- Can sentiment analysis and option volume anticipate future returns?
    Information generated by combining sentiment from the masses and specific traders improve the asset return predictions.
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  • November 2015 – Stock Market Manipulation. Can Fraud Now Be Detected Using Social Media?
    Considering there are over 500 thousand twitter and internet chat messages sent every single minute, investors need a personal analytics platform to filter noise, find relevance, and assist them in making better trading decisions. This is big data, this is the next layer of intelligence for the stock market.
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  • November 2015 – Leveraging social media to predict continuation and reversal in asset prices
    Positive sentiment gradually diffuses into marketplace, leading to continuation, while reversals are sharp as negative sentiment tend to be over-reactionary in nature.
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  • November 2015 – Stock return probability and investor sentiment: A high-frequency perspective
    Strong evidence that changes in investor sentiment have predictive values for intraday market returns.
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  • October 2015 – Structure in the tweet haystack: Uncovering the link between text-based sentiment signals and financial markets
    Statistical methods for natural language are capable of successfully structuring and categorizing the noisy stream of Twitter data.
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  • September 2015 – The effects of Twitter sentiment on stock price returns
    Finds significant evidence of dependence between stock price returns and Twitter sentiment.
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  • August 2015 – Twitter Can Help You Cash In on Corporate Earnings
    The collective ‘wisdom of the crowds’ on Twitter can beat Wall Street in predicting corporate earnings and helping you make smart stock trades, a new report says.
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  • August 2015 – Social media too reflects a negative stock market sentiment
    It may sound a bit far-fetched to believe social media posts can predict stock market behaviour. But research has shown positive correlation between the two.
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  • July 2015 – Quantifying the effects of online bullishness on international financial markets
    Daily Twitter bullishness is found to be a useful investor sentiment indicator. Analysis shows a positive correlation between Twitter bullishness and Google bullishness on a weekly basis.
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  • April 2015 – Big investors say social media influence investment picks
    Big institutional investors not only “like” social media stocks like Facebook, LinkedIn and Twitter, they say information gleaned from social media is shaping their investment decisions.
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  • February 2015 – Local twitter activity and stock returns
    Results find that social media activities have important investment value, can be used to forecast future stock returns.
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  • October 2015 – Exploiting social relations and sentiment for stock prediction
    Demonstrate that topic sentiments from close neighbors of a stock can help improve the prediction of the stock market markedly.
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  • October 2013 – Can Facebook predict stock market activity
    Concludes that Facebook sentiment predicts economically meaningful changes in returns at a statistically significant level.
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  • February 2013 – Twitter and stock returns
    Evidence of the predictive powers of Twitter implies that market participants should pay attention to the information spread on Twitter, and may be able to profit from it.
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  • December 2011 – Social media intelligence: Measuring brand sentiment from online conversations
    Social media monitoring is a useful method for inferring brand sentiment.
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  • October 2010 – Twitter Mood Predicts The Stock Market
    Their extraordinary conclusion is that there really is a correlation between the Dow Jones Industrial Average and one of the GPOMS indices–calmness.
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  • October 2010 – Twitter mood predicts the stock market
    Results indicate a predictive correlation between mood states of the public on Twitter and Dow Jones Industrial Average values.
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BUZZ SOCIAL MEDIA INSIGHTS INDEX
MAY 2016 MONTHLY INDEX REBALANCE

May 2016 Monthly Index Rebalance

BUZZ SOCIAL MEDIA INSIGHTS INDEX
A PRIMER FOR INVESTORS

buzz-whitepaper-thumb

This report explains how social media’s big data has evolved from the consumer products world to the financial world as money managers seek an informational edge to enhance their investment processes.

BUZZ SOCIAL MEDIA INSIGHTS INDEX
GUIDELINE

This document is to be used as a guideline with regard to the composition, calculation and management of the BUZZ Social Media Insights Index.

Licensed to ALPS ETFs