Posts Tagged ‘financial services’

AlphaSense’s Advanced Linguistics Search Engine Could Buy Back Time For Financial Analysts To Do More In-Depth Research

alpha1When Raj Neervannan, CTO and co-founder of financial search engine company AlphaSense, thinks about search, he thinks about it “as a killer app that is only growing…..People want answers, not noise. They want to ask more intelligent questions and get to the next level of computer-aided intelligence.”

For AlphaSense’s customers – analysts at large investment firms and banks or any other industry, as well as one-person shops – that means search needs to get them out of ferreting through piles of research docs for the nuggets of information they really need. Neervannan knows the pain of trying to interpret a CEO’s commentary to understand what he or she was really saying when making the point that numbers were going down when referring to inventory turns. (Jack Kokko, former analyst at Morgan Stanley, is AlphaSense’s other co-founder.)

“You are essentially digging through sets of documents [using keyword search], finding locations of terms, pulling them in piece by piece and constructing a case as to what the company’s inventory turn was really like – what other companies’ similar information was, how that matches up. You have to do quantitative analysis and benchmarks, and it can take weeks,” he says.

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Big Data Challenges In Banking And Securities

Photo courtesy: Johan Hansson, https://www.flickr.com/photos/plastanka/

Photo courtesy: Johan Hansson, https://www.flickr.com/photos/plastanka/

A new report from the Securities Technology Analysis Center (STAC), Big Data Cases in Banking and Securities, looks to understand big data challenges specific to banking by studying 16 projects at 10 of the top global investment and retail banks.

According to the report, about half the cases involved e petabyte or more or data. That includes both natural language text and highly structured formats that themselves presented a great deal of variety (such as different departments using the same field for a different purpose or for the same purpose but using a different vocabulary) and therefore a challenge for integration in some cases. The analytic complexity of the workloads studied, the Intel-sponsored report notes, covered everything from basic transformations at the low end to machine learning at the high-end.

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Thinknum Sees Financial Analysis In A New Light

thinknumpixThinknum is a startup with the mission: disrupting financial analysis.

In his work as a quantitative strategist at Goldman Sachs, Thinknum co-founder Gregory Ugwi saw firsthand the trials and tribulations financial analysts went through to digest companies’ financial reports and then build their own research reports about their expectations for future performance based on past numbers. The U.S. SEC’s mandate that companies disclose their financial data using XBRL (eXtensible Business Reporting Language) was supposed to help them, as well as investors of all stripes and sizes that want to better understand what’s going on at the companies they’re interested in.

“The SEC has mandated that all companies have to release their numbers in a machine-readable format, and that’s XBRL (eXtensible Business Reporting Language),” says Ugwi. The positive side of that is that anyone can now get the stats on companies from Google to Wal-Mart, but the downside is that by and large, they can’t do it in a user-friendly way.

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Machine Learning’s Future: Fortune 500 Buys In, Manufacturing Sees The Light

STServerMartin Hack, CEO and co-founder of machine learning company Skytree, has a prediction to make: “In the next three to five years we will see a machine learning system in every Fortune 500 company.” In fact, he says, it’s already happening, and not just among the high-tech companies in that ranking but also among the “bread and butter” enterprises.

“They know they need advanced analytics to get ahead in the game or stay competitive,” Hack says. For that, he says, they need machine learning algorithms for analyzing their Big Data sets, and they need to be able to deploy them quickly and easily — even if those who will be doing the deployments are coming from at best a background of basic analytics and business intelligence.

“There just aren’t enough data scientists to go around,” he says. It’s very tough to fill those roles in most companies, he says, “so like it or not, we have to make it much, much easier for people to digest and use this.”

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The Office of Financial Research To Look Hard At FIBO For Financial Instrument Reference Database

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Image Courtesy: Flickr/ .reid

Ontologies are getting a thumbs up to serve as the basis for the Office of Financial Research’s Instruments database. Last week, the Data & Technology Subcommittee of the OFR Financial Research Advisory Committee (FRAC) recommended that the OFR “adopt the goal of developing and validating a comprehensive ontology for financial instruments as part of its overall effort to meet its statutory requirement to ‘prepare and publish’ a financial instrument reference database.”

The Instruments database will define the official meaning of financial instruments for the financial system — derivatives, securities, and so on. The recommendation by the subcommittee is that the OFR conduct its own evaluation of private sector initiatives in this area, including the Financial Industry Business Ontology (FIBO), to assess whether and how ontology can support transparency and financial stability analysis.

FIBO, which The Semantic Web Blog discussed in detail most recently here, is designed to improve visibility to the financial industry and the regulatory community by standardizing the language used to precisely define the terms, conditions, and characteristics of financial instruments; the legal and relationship structure of business entities; the content and time dimensions of market data; and more. The effort is spearheaded by the Object Management Group and the Enterprise Data Management (EDM) Council.

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Text Analytics Can Cut A Broad Swath in Capital Markets

tgroupWhat’s next for the capital markets arena when it comes to unstructured content? According to research and consulting firm TABB Group, which specializes in the stock, bond and money markets, it’s time to turn text analytics to internally generated and disseminated unstructured data, which holds a high value for customized intelligence.

In new research,  “Inner Voices: Harvesting Text Analytics from Proprietary Data,” research analyst Valerie Bogard and senior analyst Paul Rowady discuss that there are more use cases than initially undertaken for text analytics tools. “Although ultra-low latency trading strategies were an early use case in this space, text analytics is no longer limited to just that,” Bogard said in an email to The Semantic Web Blog. “The use of machine readable news has been widely adopted and all major market data providers incorporate market moving news content into their feeds.”

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Interest Grows In Riding The Semantic Wave

Image Courtesy: Flickr/ Peter Kaminski

Image Courtesy: Flickr/ Peter Kaminski

Industry leaders in sectors including banking and financial services look to have high hopes for semantic technology. They’re thinking about FIBO (Financial Industry Business Ontology) and leveraging semantic technology for more traditional types of data integration and analytics projects. At Cognizant, Thomas Kelly, a director in its Enterprise Information Management practice – and the author of this white paper on How Semantic Technology Drives Agile Business – sees the positive development that clients in the Fortune 500 space like these “are maturing in their use of semantic technology, from a project focus to more enterprise initiatives.”

The interest in FIBO, he says, is representative of an overall interest across in industries in leveraging industry ontologies as mechanisms to help companies better standardize, align and learn from the output of industry-wide efforts. The attention that industry analysts, including Gartner, have put on the semantic web in the last year – not to mention regulators beginning to consider its use in sharing information on a regulatory basis – have helped increase interest by commercial organizations, Kelly notes. That’s also evident in the life sciences sector, as another example, with the efforts of the FDA/PhUSE  Semantic Technology Working Group Project to include a draft set of existing CDISC standards in RDF.

The pickup in attention to many things semantic ties to the different perspectives that organizations need to manage about their data, which include “how they currently think of their data, how it is currently perceived in managing business operations; and where they are looking to go in the future that makes it more inclusive of what’s going on in the world outside their walls – that is, how the rest of the industry looks at this data and uses it to support their business processes,” he says.

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QuantDesk Event Analyzer Module For Bloomberg App Portal Delivers Market — And Machine Learning — Magic

rsz_ucenapixLucena Research is the company behind the machine-learning based investment technology QuantDesk (see our original story here). Its five modules are designed to give non-quants in family investment offices, hedge funds, endowments and other registered investment advisors a scientific approach to investment decision- making to augment their own strategies with the tools to figure out market trends and patterns.

But CEO Erez Katz says there’s another market its technology now is ready to address: Those who’d like to have the research done for them. To that end, it’s deploying a new module on the Bloomberg App Portal, dubbed the QuantDesk Event Analyzer, that provides subscribers with professionally researched market & equity trends,  and equity price forecasts based on machine learning pattern analysis.

The company already has three modules on the portal, and the latest provides a collection of studied and researched events-based decision support strategies. “We do the research, form the strategy, and deploy our findings in the form of entry/exit signals on their dashboard on the Bloomberg App Portal,” says Katz, whereas previously its products deployed only toolsets and technology for users to deploy their own strategy. “This new edition takes the research of our internal quants and exposes the output and exclusivity of these unique strategies to our premium client base – that is, buy and sell signals not available to the rest of the world.”

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Digital Reasoning’s Synthesys Puts The Focus On Compliance Via RealTime Email Analytics

Synthesys, Digital Reasoning’s machine learning platform that ferrets out meaning in unstructured data at scale, is bringing its smarts to compliance use cases for organizations, such as financial institutions. (See this article for more insight into the technology behind the company’s software.)

This week, the vendor delivered Version 3.7 of the Synthesys software, which brings with it the capability to monitor and analyze all email communications in near real-time. That matters to many compliance program use cases, among them insider trading, money laundering and reputation management. “They all go back to finding information inside of communications, like who are the people and organizations mentioned in email, and what is being discussed about them,” says Tim Estes, chairman and CEO. “Synthesys can take essentially millions of emails and winnow them to maybe a hundred that are problems.”

That means fewer things are falsely flagged as issues, there’s less privacy treading into innocent emails, and there’s more return on time for the people charged with protecting customers and enforcing compliance requirements.

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Sentiment Analytics Tech Helps Gauge TRust In Financial Services Sector

How do you feel about the financial institutions you deal with? A report earlier this week of a survey of Wall Street financial services industry professionals, conducted by Labaton Sucharow LLP, might have you a bit leery.

The report notes, for example, that 23 percent of those polled said they’d observed or had firsthand knowledge of wrongdoing in the workplace. Twenty-nine percent believe that financial services professionals may need to engage in unethical or illegal activity in order to be successful. More than one-quarter think the compensation plans or bonus structures at their companies incentivize employees to compromise ethical standards or violate the law, and 24 percent would engage in insider trading if they could make $10 million and get away with it. Twenty-eight percent say the financial services industry does not put the interests of clients first.

Says the report, “We see a powerful and frightening pattern that threatens an already fragile marketplace.” Yikes. Now, on the heels of that, comes Thomson Reuters’ Q2 TRust Index that aims to gauge trust in the top 50 global financials. It leverages technology including its own news and social media sentiment analytics solution, Thomson Reuters News Analytics (TRNA), and its MarketPsych Indices, which provides real-time psychological analysis of news and social media.

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