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jerryking : alternative_data   12

Rise of machine trading forces data providers to pivot | Financial Times
OCTOBER 30 2019 | Financial Times | Philip Stafford in London.

Financial information suppliers are on the hunt for new markets such as wealth management and corporate audiences....... the days of depending on selling information on fixed workstations, or terminals to a core group of investment bankers and fund managers — the mainstay of the industry over decades — were quickly receding........Cost-conscious banks are reducing the numbers of analysts and traders they employ, cutting research or automating processes that have long been done by humans. That wipes out a once reliable client base for terminals, leaving data specialists to shift their focus elsewhere.....Chief technology officers need to become more efficient more quickly,” “Our fastest growing segment is corporates, business development and investor relations.” This had left rivals such as Bloomberg, Refinitiv and Morningstar “in a race . . . for each others’ customers,........Demand is increasingly being driven by the need for enriched data that can be fed into computers, rather than read by humans, ....“We’re seeing a big shift to data-driven strategies, and fundamental analysis will be more data-driven,” he said. “This is where the industry is headed and where we’re going for it.”....Overall terminal sales are healthy... estimates are that the number of users of terminals, or desktops, in the investment industry will rise to 1.6m this year and hit 1.7m by 2021. Most of that growth is set to come from areas such as investment management rather than trading....Mitko Yankov, global head of platform at Refinitiv, agreed that the old model of selling data as pre-packed bundles of information was disappearing. Customers wanted richer types of data, he said. This means serving developers, data scientists, quantitative traders and even traders and analysts who can write their own code. “They really don’t appreciate monolithic bundles,”...other data providers are hoping to benefit from the rising demand for data as markets apply computing trends such as artificial intelligence and machine learning to trading and analytics. The rush has been exemplified by the London Stock Exchange Group’s $27bn deal to buy Refinitiv.
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This (old - ish) article makes one think of a couple of factors that contributed to a change of balance:
https://www.bloomberg.com/news/articles/2019-02-25/jpmorgan-s-traders-nab-market-share-while-deutsche-bank-s-slip
First is the "exorbitant privilege" of being able to print more than others, but near second is probably information and information processing power advantage By the way, the article cited is a reasonably rich source of information, not to be compared with ft's, where there is no hard data and time perspective. There is another question: How does it square up with market as a "price discovery mechanism"? With barriers to safe entry into this market in billions of investment into technology what are the mere mortals supposed to do? The market seems to be turning into an ever narrower oligopoly.
alternative_data  automation  Bloomberg  coding  CTOs  data  data-driven  data_scientists  Factset  financial_data  fundamental_analysis  investment_management  LSE  Morningstar  Refinitiv  Thomson_Reuters  traders 
8 weeks ago by jerryking
How investment analysts became data miners | Financial Times
Robin Wigglesworth 5 HOURS AGO
Distribution is increasingly focused on pulling readers in rather than pushing content out. Rather than emailing research and praying it gets opened, many banks have built up personalisable research portals. More content is now made public. The websites of most big investment banks now look more like those of think-tanks.....“The evolution of capital markets has put investment research departments in a tricky position,” ..... “The industry has changed dramatically in how it’s done and distributed. But what has not changed is the fundamental job: coming up with great ideas.”
alternative_data  asset_management  charts  data  data-driven  data_scientists  idea_generation  index_funds  investment_research  money_management  passive_investing  sell_side  technology  UBS  unbundling 
11 weeks ago by jerryking
Investment managers need to become coders, says former CPPIB CEO - The Globe and Mail
CLARE O’HARAWEALTH MANAGEMENT REPORTER
DAVID MILSTEADINSTITUTIONAL INVESTMENT REPORTER

Mark Wiseman is learning Python, one of the world’s top computer programming languages.

The former chief executive officer of the Canada Pension Plan Investment Board is not trying to become a master coder, but instead believes investment managers must become proficient in manipulating large data sets to beat the market.

“If you are waiting to get a company’s quarterly or annual report and you think that is how you’re going to make an investment, you are dead meat,”........

“Sources of information are completely different than they were even 10 years ago for investors,” he says.

Today, BlackRock has already begun using “alternative data sources” to gain more in-depth information on companies such as sales predictions, customer traffic and inventory......“As we look at data in industry and how fast it’s moving, there is going to be an increasing bifurcation between proprietary and non-proprietary data."

Non-proprietary data is information that is readily available on the internet and can easily be used by competitors. Now, money managers are increasingly looking for proprietary data to win a competitive advantage.

For BlackRock’s equities business alone, Mr. Wiseman says the firm has tripled the budget for data over the past two years and holds between 400 and 500 proprietary data sets at a time.......learning Python is a more important skill for a young investment manager than learning foreign languages, or even some of the curriculum taught to chartered financial analysts.

“But this is what investing is about today,” he said. “So those of you who are spending your time on your CFA Level III, that is really nice to have the letters after your name on the business card. But you probably would have been better off spending your time learning how to code Python.”
alternative_data  BlackRock  coding  commoditization_of_information  CPPIB  information_sources  investment_management  Mark_Wiseman  massive_data_sets  proprietary  software_developers  software_development 
october 2019 by jerryking
DE Shaw: inside Manhattan’s ‘Silicon Valley’ hedge fund
March 25, 2019 | Financial Times Robin Wigglesworth in New York.

for a wider investment industry desperately trying to reinvent itself for the 21st century, DE Shaw has evolved dramatically from the algorithmic, computer-driven “quantitative” trading it helped pioneer in the 1980s.

It is now a leader in combining quantitative investing with traditional “fundamental” strategies driven by humans, such as stockpicking. This symbiosis has been dubbed “quantamental” by asset managers now attempting to do the same. Many in the industry believe this is the future, and are rushing to hire computer scientists to help realise the benefits of big data and artificial intelligence in their strategies........DE Shaw runs some quant strategies so complex or quick that they are in practice almost beyond human understanding — something that many quantitative analysts are reluctant to concede.

The goal is to find patterns on the fuzzy edge of observability in financial markets, so faint that they haven’t already been exploited by other quants. They then hoard as many of these signals as possible and systematically mine them until they run dry — and repeat the process. These can range from tiny, fleeting arbitrage opportunities between closely-linked stocks that only machines can detect, to using new alternative data sets such as satellite imagery and mobile phone data to get a better understanding of a company’s results...... DE Shaw is also ramping up its investment in the bleeding edge of computer science, setting up a machine learning research group led by Pedro Domingos, a professor of computer science and engineering and author of The Master Algorithm, and investing in a quantum computing start-up.

It is early days, but Cedo Crnkovic, a managing director at DE Shaw, says a fully-functioning quantum computer could potentially prove revolutionary. “Computing power drives everything, and sets a limit to what we can do, so exponentially more computing power would be transformative,” he says.
algorithms  alternative_data  artificial_intelligence  books  D.E._Shaw  financial_markets  hedge_funds  investment_management  Manhattan  New_York_City  quantitative  quantum_computing  systematic_approaches 
march 2019 by jerryking
BlackRock bulks up research into artificial intelligence
February 19, 2018 | FT | Robin Wigglesworth in New York and Chris Flood in London.

BlackRock is establishing a “BlackRock Lab for Artificial Intelligence” in Palo Alto, California.....The lab will “augment our current teams and accelerate our efforts to bring the benefits of these technologies to the entirety of the firm and to our clients”.....The asset management industry is particularly interested in the area, as they try to improve the performance of their fund managers, automate back-office functions to cut costs and enhance their client outreach by analysing vast amounts of internal and external data....\quantitative managers are “engaged in an arms race” as data analysis techniques that work today will not necessarily be relevant in five years.

“Big data offers a world of possibilities for generating alpha [market beating returns] but traditional techniques are not good enough to analyse the huge volumes of information involved,” .....The data centre is looking for another dozen or so hires for its launch, underlining the ravenous appetite among asset managers to snap up more quantitative analysts adept at trawling through data sets like credit card purchases, satellite imagery and social media for investment signals.
alpha  artificial_intelligence  asset_management  arms_race  automation  alternative_data  BlackRock  back-office  quantitative  Silicon_Valley 
february 2018 by jerryking
Algos know more about us than we do about ourselves
NOVEMBER 24, 2017 | Financial Time | John Dizard.

When intelligence collectors and analysts take an interest in you, they usually start not by monitoring the content of your calls or messages, but by looking at the patterns of your communications. Who are you calling, how often and in what sequence? What topics do you comment on in social media?

This is called traffic analysis, and it can give a pretty good notion of what you and the people you know are thinking and what you are preparing to do. Traffic analysis started as a military intelligence methodology, and became systematic around the first world war. Without even knowing the content of encrypted messages, traffic analysts could map out an enemy “order of battle” or disposition of forces, and make inferences about commanders’ intentions.

Traffic analysis techniques can also cut through the petabytes of redundant babble and chatter in the financial and political worlds. Even with state secrecy and the forests of non-disclosure agreements around “proprietary” investment or trading algorithms, crowds can be remarkably revealing in their open-source posts on social media.

Predata, a three-year-old New York and Washington-based predictive data analytics provider, has a Princeton-intensive crew of engineers and international affairs graduates working on early “signals” of market and political events. Predata trawls the open metadata for users of Twitter, Wikipedia, YouTube, Reddit and other social media, and analyses it to find indicators of future price moves or official actions.

I have been following their signals for a while and find them to be useful indicators. Predata started by creating political risk indicators, such as Iran-Saudi antagonism, Italian or Chilean labour unrest, or the relative enthusiasm for French political parties. Since the beginning of this year, they have been developing signals for financial and commodities markets.

The 1-9-90 rule
1 per cent of internet users initiate discussions or content, 9 per cent transmit content or participate occasionally and 90 per cent are consumers or ‘lurkers’

Using the example of the company’s BoJ signal. For this, Predata collects the metadata from 300 sources, such as Twitter users, contested Wikipedia edits or YouTube items created by Japanese monetary policy geeks. Of those, at any time perhaps 100 are important, and 8 to 10 turn out to be predictive....This is where you need some domain knowledge [domain expertise = industry expertise]. It turns out that Twitter is pretty important for monetary policy, along with the Japanese-language Wiki page for the Bank of Japan, or, say, a YouTube video of [BoJ governor] Haruhiko Kuroda’s cross-examination before a Diet parliamentary committee.

“Then you build a network of candidate discussions [JK: training beds] and look for the pattern those took before historical moves. The machine-learning algorithm goes back and picks the leads and lags between traffic and monetary policy events.” [Jk: Large data sets with known correct answers serve as a training bed and then new data serves as a test bed]

Typically, Predata’s algos seem to be able to signal changes in policy or big price moves [jk: inflection points] somewhere between 2 days and 2 weeks in advance. Unlike some academic Twitter scholars, Predata does not do systematic sentiment analysis of tweets or Wikipedia edits. “We only look for how many people there are in the conversation and comments, and how many people disagreed with each other. We call the latter the coefficient of contestation,” Mr Shinn says.

The lead time for Twitter, Wiki or other social media signals varies from one market to another. Foreign exchange markets typically move within days, bond yields within a few days to a week, and commodities prices within a week to two weeks. “If nothing happens within 30 days,” says Mr Lee, “then we say we are wrong.”
algorithms  alternative_data  Bank_of_Japan  commodities  economics  economic_data  financial_markets  industry_expertise  inflection_points  intelligence_analysts  lead_time  machine_learning  massive_data_sets  metadata  non-traditional  Predata  predictive_analytics  political_risk  signals  social_media  spycraft  traffic_analysis  training_beds  Twitter  unconventional 
november 2017 by jerryking
Wall Street’s Insatiable Lust: Data, Data, Data
By BRADLEY HOPE
Updated Sept. 12, 2016

One of his best strategies is to attend the most seemingly mundane gatherings, such as the Association for Healthcare Resource & Materials Management conference in San Diego last year, and the National Industrial Transportation League event in New Orleans.

“I walk the floor, try to talk to companies and get a sense within an industry of who collects data that could provide a unique insight into that industry,” he said.....Data hunters scour the business world for companies that have data useful for predicting the stock prices of other companies. For instance, a company that processes transactions at stores could have market-moving information on how certain products or brands are selling or a company that provides software to hospitals could give insights into how specific medical devices are being used......A host of startups also are trying to make it easier for funds without high-powered data-science staffers to get the same insights. One, called Quandl Inc., based in Toronto, offers a platform that includes traditional market data alongside several “alternative” data....
alternative_data  conferences  data  data_hunting  hedge_funds  insights  investors  exhaust_data  market_moving  medical_devices  mundane  private_equity  Quandl  quants  sentiment_analysis  unconventional  unglamorous  Wall_Street 
september 2016 by jerryking
Meet the SEC’s Brainy New Crime Fighters - WSJ
By SCOTT PATTERSON
Updated Dec. 14, 2014

The SEC is mustering its mathematical firepower in its Center for Risk and Quantitative Analytics, which was created last year soon after Mary Jo White took charge of the agency to help it get better at catching Wall Street misconduct. The enforcement unit, led by 14-year SEC veteran Lori Walsh, is housed deep within the warrens of the SEC’s Washington headquarters, and staffed by about 10 employees trained in fields such as mathematical finance, economics, accounting and computer programming.

Ms. Walsh says access to new sources of data and new ways of processing the data have been key to finding evidence of wrongdoing. “When you look at data in different ways, you see new things,” she said in an interview
alternative_data  analysis  analytics  arms_race  data  data_driven  enforcement  fresh_eyes  hiring  information_sources  mathematics  misconduct  models  modelling  patterns  perspectives  quantitative  quants  SEC  stockmarkets  Wall_Street 
december 2014 by jerryking
Traders Seek an Edge With High-Tech Snooping - WSJ.com
Dec. 18, 2013 | WSJ | By Michael Rothfeld and Scott Patterson.

A growing industry uses surveillance and data-crunching technology to supply traders with nonpublic information.

Genscape's clients include banks such as Goldman Sachs Group Inc., J.P. Morgan Chase & Co. and Deutsche Bank AG, hedge funds including Citadel LLC and large energy-trading outfits such as Trafigura Beheer BV. Surveillance and analysis of the oil, electricity and natural-gas sectors can run Genscape clients more than $300,000 a year.
surveillance  data_driven  slight_edge  traders  hedge_funds  sleuthing  Genscape  sensors  commodities  corporate_espionage  competitive_intelligence  scuttlebutt  due_diligence  market_research  exclusivity  investment_research  research_methods  LBMA  nonpublic  primary_field_research  banks  Citadel  oil_industry  natural_gas  snooping  alternative_data  informational_advantages  imagery  satellites  infrared  electric_power 
december 2013 by jerryking
The Financial Bonanza of Big Data
March 7, 2013 | WSJ | By KENNETH CUKIER AND VIKTOR MAYER-SCHÖNBERGER:
Vast troves of information are manipulated and monetized, yet companies have a hard time assigning value to it...The value of information captured today is increasingly in the myriad secondary uses to which it is put—not just the primary purpose for which it was collected.[True, but this secondary or exhaust data has to be placed in the right context in order to maximize value]. In the past, shopkeepers kept a record of all transactions so that they could tally the sums at the end of the day. The sales data were used to understand sales. Only more recently have retailers parsed those records to look for business trends...With big data, information is more potent, and it can be applied to areas unconnected with what it initially represented. Health officials could use Google's history of search queries—for things like cough syrup or sneezes—to track the spread of the seasonal flu in the United States. The Bank of England has used Google searches as a leading indicator for housing prices in the United Kingdom. Other central banks have studied search queries as a gauge for changes in unemployment.

Companies world-wide are starting to understand that no matter what industry they are in, data is among their most precious assets. Harnessed cleverly, the data can unleash new forms of economic value.
massive_data_sets  Amazon  books  Google  branding  Facebook  Wal-Mart  Bank_of_England  data  data_driven  value_creation  JCK  exhaust_data  commercialization  monetization  valuations  windfalls  alternative_data  economic_data  tacit_data  interpretation  contextual  sense-making  tacit_knowledge 
march 2013 by jerryking

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