Social Sentiment

Sentiment is a score or ranking of how a group of investors or investment professionals “feel” about news and investments. The sentiment comes from multiple sources: Twitter, Blogs, LinkedIn, Facebook, etc. Some sentiment is from market professionals or their Artificial Intelligence equivalents that monitor news stories, public filings, speeches, or other public sources.

The sentiment is quantified on a range from pessimistic to optimistic about the company or the specific news.

Sentiment Data is often referred to as Emerging Data, Alternative Data or AltData. CloudQuant Elite has Sentiment data for: StockTwits,  Alexandria Technologies, and Bloomberg


Alternative Data News. 31, July 2019

Social Sentiment from SMA predicts $GOOGL stock bump with earnings announcement. The growing merger of FINTECH and Data Science and Alternative Data is getting noticed by seed capital providers. Alternative Data derived from images with K-Clustering. Capital One data breach handled well by their fraud team.
Stock Market, Quantitative Strategy, Trading, and Algo Development Industry News

Industry News: Machine Learning and Artificial Intelligence for December 26, 2017

Never Mind Bitcoin. China Loves AI Stocks

…ofit estimates. And yet it’s one of the hottest stocks in China, gaining 117 percent this year as of Dec. 18, thanks to the latest investment craze sweeping the country’s $7.5 trillion equity market: artificial intelligence. Iflytek and other AI-related companies are dominating gains among large-cap Chinese stocks. The buying frenzy has added $61 billion of market value to China’s five most popular AI plays, vaulting some into the ranks of the world’s most actively traded securities. BOE Technology Group Co., a Beijing-based maker of LCD panels that’s developing a system to help drivers avoid traffic accidents, had… 2017-12-20 00:00:00 CloudQuant Thoughts:  “AI-related companies are dominating gains among large-cap Chinese stocks…” Hmmm. Makes one wonder what sort of classifications would allow you to identify a company as “AI-Related”. Our industries are categorized and standardized. For example, Moriningstar uses 11 standard classifications: Basic Materials, Communication Services, Consumer Cyclical, Consumer Defensive, Energy, Financial Services, Healthcare, Industrials, Real Estate, Technology, Utilities) Do these still make sense to you? Where does Amazon properly fit? Are they a Consumer Cyclical because they are a retailer? Should they be considered Technolgy because they host computers? Maybe you as an algo creator or data scientists should think in terms of creating your own classifications based on what the company is doing today. What companies fall into your category of “AI-Related”? How are they doing in comparison to other companies that haven’t launched AI-Realted projects? Looks like there is an opportunity here for you to seek alpha!

Future looking bright for active management

… said Keith Lewis, head of Americas for T. Rowe Price Group Inc.’s global investment services division, Baltimore. Technology on the rise Industry insiders also foresee technology — everything from artificial intelligence and big data to machine learning and risk analytics — will play a significantly increased role within money management. “AI and big data being used to generate alpha is incredibly important. Likewise, with risk analytics to construct portfolios,” said Geraldine Buckingham, BlackRock (BLK) Inc. (BLK)’s global head of corporate strategy in New York. “Every asset manager has to consider how the role of technology plays into their business,” she added. … 2017-12-25 00:00:00 CloudQuant Thoughts: Quantitative portfolio managers have always relied on technology and innovation. We have seen portfolio managers move from using Bloomberg terminals and excel spreadsheets 20 years ago to using Jupyter Notebooks, Python, and sophisticated data science techniques today. Any money manager who isn’t using innovative data science into their daily jobs is likely to fall behind on their ability to keep up with changing markets. The recommendation to diversify into alternative investments in this article provides an interesting and compelling argument. Environmental, Social, and Governance (ESG) in one’s portfolio is a great idea. We strongly believe that portfolio managers should add technical and quantitative skills to their personal skills portfolio to help them grow in capabilities to serve their investors.

FinTech Start-Up ForwardLane’s New APIs for Personalized Insights

…FinTech Start-Up, ForwardLane’s New AI APIs for Personalized Insights ForwardLane, a FinTech start-up based in New York City, is a next generation artificial intelligence (AI) company using machine learning to aggregate and scale firm-specific content, market data news, investment research, product materials, and strategic analytics for financial professionals to deliver personalized stories for their clients. ForwardLane’s two configurable APIs leverage natural language processing and deep learning AI for advisory, sales and distribution professionals. The first… 2017-12-20 00:00:00 CloudQuant Thoughts: At the Newsweek AI conference in early December our CEO pointed out that there are hundreds of Alternative Data (AltData) providers and the number keeps growing. Using natural languge processing to develop trading signals from written and spoken content is on the increase. Numerous data scientists are using some of these AltData sources in their trading algorithms already.

Sophisticated cyber threats and being ready for GDPR are major concerns for finance sector IT pros

…ted to modernisation. Respondents were also asked to rank the relevance of various emerging technologies, and cited Intelligent Process Automation as the most useful to their business, followed by Artificial Intelligence (35 per cent) and Predictive Modelling (33 per cent). Blockchain ranked lower in terms of perceived relevance (25 per cent) and was only currently used by a fifth of those surveyed within their organisation. Fintan Galvin, chief executive officer at Invotra, says: “It’s clear from our study that finance technology professionals understand the need to drive change. But, they are charged with pr… 2017-12-21 00:00:00 CloudQuant Thoughts: Cyber threats aren’t going away. This has been a major initiative for the financial industry over the past 5 years and will continue to grow.

Automation Will Create More Fulfilling Work

…cale unemployment. In fact, SpaceX and Tesla co-founder Elon Musk, along with Bill Gates and Stephen Hawking, have expressed such concerns. Last month, amidst North Korean tensions, Musk tweeted that artificial intelligence (AI) posed a greater risk than the country’s nuclear capabilities. This is not the first time Musk has voiced caution on the topic; he’s repeatedly said that AI and automation are the “biggest risk we face as a civilization” and that “robots will do everything better than us.” In response, Facebook CEO Mark Zuckerberg has challenged Musk, calling “people who are naysayers and try to drum up these… 2017-12-25 10:56:05-05:00 CloudQuant Thoughts: Fearing innovation has never helped grow an economy. We all value the innovations that advance our society. Yes, in some cases that technology is a “job killer” but at the same time, it opens opportunities in other areas. I was recently reminded of how innovation changed the world in watching Hidden Figures. The movie showed the introduction of the computer to NASA. While the mathematicians could have resisted and complained about the “job-killing” possibilities of the computer they didn’t. They saw that the computer brought opportunity. With a little re-tooling, a little effort, and without fear they learned the new skills needed to be valuable. Robots may do some things better than us. Ok. We all know that. So What? The rate of change is ever increasing. Let the robots have the old stuff as we focus on the future! Robots are incapable of automating innovation.

Facebook to demote posts fishing for Likes

… Facebook worries it might be bad for you, adds a mute button “To help us foster more authentic engagement, teams at Facebook have reviewed and categorized hundreds of thousands of posts to inform a machine learning model that can detect different types of engagement bait,” the company said in a blog post. “Posts that use this tactic will be shown less in News Feed.” The company said it will have stricter demotions for repeat offenders. It added that page administrators looking to increase brand reach on the site should “focus on posting relevant and meaningful stories.” The update will be rolled out over … 2017-12-18 00:00:00 CloudQuant Thoughts: Nice. Hope that their machine learning and data science team have truly found a way to have a more authentic user engagement. Did you notice the link to the previous link? In this case Facebook has created new opporutinities using AI. The jobs created here didn’t exists last year. Learning how to use big data to improve the user experience is a high growth job opportunity.

Generational Tech Shift to Transform Trade Lifecycle

…tion of new technologies. As global financial markets continue to evolve, the real opportunities are yet to be delivered in transparency and efficiency for investors.” Distributed ledger technology, machine learning, portfolio optimisation techniques and the cloud are being used today but the paper said the challenge for markets and policy-makers is to harness this potential while managing its new risks. “Neither legacy systems nor policy inertia should be allowed to stifle this generational opportunity,” added NEX. Mark Whitcroft, founding partner at venture capital firm Illuminate Financial Management, s… 2017-12-18 03:39:54+00:00 CloudQuant Thoughts: The idea of using best of breed components to put together a trading system, or a regulatory system isn’t new. Making use of third-party components without violating license agreements or intellectual property is going to be a real challenge in our trading industry.

Today in Technology: The Day the Horse Lost Its Job

…erviced cars and that utilized motorized vehicles for transportation and delivery. [17] There are interesting insights to be gleaned from the shift from horses to automobiles. As with automation and artificial intelligence in our own day, it was a shift driven by new technology. But it wasn’t driven by technology alone, and its ultimate impact in many ways was by no means predictable at the time. As one author has noted, what appears today as obvious – the transition from horses to cars – was in many respects far less than inevitable. As she points out, “the replacement of animal power took a particular form that … 2017-12-21 00:00:00 CloudQuant Thoughts: Another historical reference, written rather nicely, to remind us that the innovations caused by AI and Big Data shouldn’t be feared and should be embraced. Plus, who can resist reading an article that includes firemen and horses?

‘Decision intelligence platform’ Element Data raises another $2.6M to fuel growth

…n Element Data is now $7.8 million. The fresh cash will help the year-old company increase sales and marketing efforts for its flagship product, Decision Insights Computing Engine (DICE), which uses artificial intelligence and machine learning to help people make decisions based on a broad range of criteria. It’s been a busy 2017 for Element Data, which bought three companies this year and now employs 25 people. In April, it acquired the technology and team of PV Cube, a Seattle machine learning startup led by two vets of Microsoft Cortana and Bing. In October, it bought the assets and patents of BehaviorMatrix, a… 2017-12-24 20:05:46+00:00 CloudQuant Thoughts: We wonder what sort of AltData is available from them. Is anyone in the crowd interested?
Business charts

Industry News: Machine Learning and Artificial Intelligence News November 20, 2017

AI & ML news covering: Overvalued Markets, crypto-currencies, Quantamental funds, Social Sentiment Deceptions, Hiring of Buy-Side Quantitative Trading, …
Daily ROIC Prior to improvements

Improving A Trading Strategy

TD Sequential is a technical indicator for stock trading developed by Thomas R. DeMark in the 1990s. It uses bar plot of stocks to generate trading signals. … Several elements could be modified in this strategy. Whether to include the countdown stage, the choice of the number of bars in the setup stage and countdown stage, the parameters that help to decide when to exit and the size of the trade will affect strategy performance. In addition, we could use information other than price to decide whether the signal should be traded.
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Social Sentiment in Trading Algorithms

Bloomberg recently wrote that “It’s no secret that hedge fund managers are always looking for new sources of data that will help them in their never-ending quest to beat the market.” (1) One of the most interesting new sources of data is social sentiment. We have found that the incorporation of social sentiment data is definitely improving the quality of algorithms as shown in our backtesting on CloudQuant. Over the next couple of weeks an intern from the University of Chicago who is mastering in Financial Mathematics is working on incorporating social signals into a DeMark Indicators script that is available for all registered users to see in the CloudQuant base working scripts. I look forward to seeing how this improves. And I look forward to seeing her quantitative reasons for why social sentiment and other changes to the TD Sequential script improves. (1) Finding Novel Ways to Trade on Sentiment Data | Tech At Bloomberg
Quantitative Strategy, Trading, and Algo Development Industry News

Finding Novel Ways to Trade on Sentiment Data

It’s no secret that hedge fund managers are always looking for new sources of data that will help them in their never-ending quest to beat the market. Quantitative researchers at Bloomberg have been developing innovative methods to help reveal embedded signals in one of the more popular sources of unconventional financial data: sentiment analysis of news stories and social posts. “Everyone is looking into alternative data sets, sometimes without really understanding their value,” says Dr. Arun Verma, Ph.D., a researcher who leads the Quant solutions team within Bloomberg’s Quantitative Research group, which is headed by Bruno Dupire. “They are looking at data like sentiment, supply chain relationships, and even things like satellite imagery. Often Machine Learning methods are applied to optimize alpha from such data, but a lack of scientific rigor can lead to poor out of sample performance. We avoid the trap of extreme data mining by using robust statistics.” Read the full story on Tech at Bloomberg, June 14 2017 Alternative Data sets from Bloomberg for social sentiment making its way into algorithmic trading. At Cloudquant have been using it in our backtesting with the intention of improving quantitative trading strategies.     
Quantitative Strategies and Capital for Trading

Funds Face ‘Alt’ Data Challenge

MarketsMedia by By Rob Daly on 5/18/2017
Although alternative data sets are helping funds with systematic investment strategies, those funds that employ discretionary strategies are finding it harder to separate the new trading signals from the noise.

Much of it comes from the structure of the discretionary funds, which have separated their data science/quant research teams away from their portfolio managers, according to Leigh Drogen, CEO of Estimize and who participated in an alternative data panel hosted by Wall Street Horizon.

“They are left sending reports and Excel spreadsheets to the portfolio managers and asking them to buy in with P&L,” he said. “It’s almost like they were a sell-side shop.”

Even if a managing partner and head quant are convinced that new data sets can capture further alpha, the portfolio managers have to buy into it.

Read the full story on MarketsMedia