AI Industry Watch

AI INDUSTRY WATCH

  • March 2017 – Acadian Starts Experimenting with Bing on Predictive Investing
    Microsoft’s Bing applies machine learning techniques using data from social media and trending topics on its search engine to predict outcomes on a multitude of topics. Companies have begun to explore the possibility of utilizing this data within finance to make objective, AI-based investment decisions.
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  • March 2017 – How Hedge Funds Use Twitter to Gain an Edge in Trading
    Investors have long been using social media data to help make investment decisions. With the growth of Twitter and the amount of influential people on the platform, hedge funds have been increasingly devoting resources towards using Twitter data in their trading.
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  • February 2017 – Revolutionizing Stock Predictions Through Machine Learning
    The use of computers within finance is evolving beyond algorithmic order execution. Hedge funds are applying artificial intelligence and machine learning to the process to gain an edge. A new study shows that computers may be able to accurately predict stock price movements based on online data such as Google searches.
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  • February 2017 – Earnings Releases & Social Media: Listening to the Crowd
    Once regarded as noise, the sentiment of the crowd is now being recognized as potentially economically significant. The information gathered by crowdsourcing the sentiment on social media platforms can often lead to better predictions on events such as earnings releases.
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  • February 2017 – Silicon Valley Hedge Fund Takes On Wall Street With AI Trader
    While the finance world is no stranger to the concept of automated trading machines, the use of artificial intelligence within them is revolutionizing the industry. Hedge funds recognize the ability of AI to evolve and adapt to new information and are increasingly using the technology within their decision making.
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  • December 2016 – Big Data = Big Success at Top Investment Fund
    The world of investing is moving away from traditional techniques and principles and embracing big data. The Renaissance Technologies’ Medallion fund, one of the top performing funds in the world, embraces this concept by using quantitative methods and supercomputers to generate stunning returns.
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  • December 2016 – ‘Early Days’ For Alternative Data
    The use of alternative data as an input in investment decision-making is increasing and improving. Those who do dismiss the potential of big data will be left behind.
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  • November 2016 – The Next Generation of Hedge Fund Stars: Data-Crunching Computers
    Quantitative hedge funds are recognizing the potential of using computers to analyze big data to make investment decisions. This technology, widely regarded as a major part of the future of the financial industry, is helping these funds bring in new money at a time when the industry is lagging.
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  • November 2016 – Art, Science and Technology: Investing In The Age of Big Data
    Institutional investors such as CPPIB utilize big data extensively in their quantitative and electronic trading strategies. With technology constantly improving, the investing world is recognizing the potential for big data to provide meaningful advantages.
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  • November 2016 – Blackrock Is Making Big Data Bigger
    BlackRock seeks data sources that are not well known but are in fact predictive of future returns. Social media activity, satellite images of big retailers’ parking lots in developing markets, and company research reports are just a few examples of fertile sources of information.
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  • October 2016 – How Smart Beta ETFs of the Future Will Use AI
    The next generation of “Smart Beta” ETFs will feature artificial intelligence, machine learning, and big data analytics. Traditionally limited to big institutional firms, the investing strategies from this technology are now available to retail investors through the BUZ ETF.
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  • October 2016 – Machine still needs man for accurate social media analysis
    Despite the enormous advances in artificial intelligence, there are nuances in the way humans communicate and express themselves that machines cannot fully decipher yet. Many of these nuances are based on cultural influences that vary widely between groups of people. Machines need human support to augment the sentiment analysis process.
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  • October 2016 – ‘Siri, catch market cheats’: Wall Street watchdogs turn to A.I.
    Financial market regulators are now recognizing the value of artificial intelligence and machine learning in analyzing big data. New surveillance tools utilizing A.I. technology are being developed to catch manipulative trading behavior.
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  • October 2016 – There’s a new breed of trader on Wall Street, and they’re becoming the new ‘masters of the universe’
    Big data and quantitative analysis are becoming increasingly integrated with traditional investment techniques. This hybrid approach is being rapidly adopted across Wall Street within hedge funds and investment firms.
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  • October 2016 – IBM Aims Watson at the Financial Services Industry
    IBM’s supercomputer, Watson, is being deployed in the finance industry to analyze vast amounts of data. Artificial technology is becoming an increasingly important part of the decision-making process.
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  • September 2016 – My Favorite Robot
    Artificial intelligence and big data analytics becoming increasingly integrated into the financial world. The next generation of investment management will be a hybrid of human decision-making and AI technology.
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  • September 2016 – Looking for an Edge on China Stocks? Try Weibo
    Retail investors are heavily involved in China’s growing financial markets. Artificial intelligence tools that extract sentiment from China’s numerous social media platforms are growing in popularity.
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  • August 2016 – What is Social Media’s Role in Investing?
    Social media is having a large influence on the investment habits of Millennials, who are using social platforms to research and make personal finance decisions.
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  • August 2016 – Businesses can no longer ignore social media sentiment analysis.
    Sentiment analysis is the critical advantage social media has over opinion polling. It allows analysts to gather the real opinions of consumers rather than what they want telephone surveyors to know.
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  • July 2016 – Data, analytics grow in importance amid slow markets.
    Technologies such as machine learning, artificial intelligence, natural-language processing, data visualization and predictive reasoning are required for managers looking to supplement investment strategies.
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  • July 2016 – AI and machine learning on social media data is giving hedge funds a competitive edge.
    Unicom conference on ‘AI, Machine Learning and Sentiment Analysis Applied to Finance’ examined the state of the art processes.
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  • June 2016 – Make way for the robot stock pickers: Artificial intelligence and automation are expected to revolutionise the investment industry.
    Artificial intelligence can replace stock pickers in many funds. Why employ hundreds of asset managers, each selecting stocks and implementing investment strategies, when a few programs can do it for you?
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  • June 2016 – Innovative Artificial Intelligence System Developed by UNIST.
    The results outperformed the system of the MIT and the University of Cambridge by 40% in terms of stock price prediction accuracy.
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  • June 2016 – Risk Premium Of Social Media Sentiment.
    Sentiment extracted from social media is a valid additional risk factor. The results suggest that sentiment can be harnessed in a predictive analytics framework to realize positive residual alpha after adjusting for market effects.
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  • June 2016 – There’s a hot new trend that could change the face of investing.
    Alternative data is increasingly being used by traditional long-short, quantitative hedge, pension, and mutual funds to track industries ranging from construction and retail to tech and real estate.
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  • June 2016 – Tech moguls declare era of artificial intelligence.
    AI, which combs through large troves of raw data to predict outcomes and recognize patterns, is already used in web search systems, marketing recommendation functions and security and financial trading programs.
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  • 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 – This is the future of investing, and you probably can’t afford it.
    Mining raw and unstructured data for investment insights is going mainstream. Investors are hiring data specialists and putting projects in place to make sure they aren’t left behind.
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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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