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Robots now are solving banks’ very expensive research problem

Human-friendly Robot
Human-friendly Robot

As lawmakers in Brasilia debated a controversial pension overhaul for months, a robot more than, miles away in London kept a close eye on all of them. The algorithm, designed by technology startup Arkera ., tracked their comments in Brazilian newspapers and government web pages each day to predict the likelihood the bill would pass.

Weeks before the legislation cleared its biggest obstacle in July, the machine’s data-crunching allowed Arkera analysts to predict the result algae experiment to the letter, giving hedge fund clients in Fresh York and London the insight to buy the Brazilian real near eight-month lows in May. It’s since rallied more than %.

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This is the kind of edge that a fresh generation of scientists is betting will upend the research marketplace. For Arkera’s clients on Wall Street and in the City of London, that means getting robots to filter through the noise in faraway lands.

There are too many people to follow on Twitter, too many websites, too many articles, noted Nav Gupta, the -year-old co-founder of Arkera, which says its software have to process as much information as, human analysts. That’s a very expensive problem and everybody faces it.

The firm raised million pounds a previous year from investors including Alan Howard of hedge fund Brevan Howard Asset Management.

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Applying so-called artificial intelligence to automate swathes of the research process is quickly gaining traction because cost-conscious investment banks are downsizing. In the U.K. alone, there was a % drop in research budgets previous year, Financial Conduct its data show. At the biggest banks, there’s been a % drop since in the number of front-office staff covering currencies, such as traders and researchers, according to London-based research analytics consultancy Coalition Development Ltd.

That means it’s even harder than it used to be to afford analysts on the ground in developing nations, concerning the only places in the world where investors have to get yield right now.

Data-science firms like Arkera and Fresh York-based Sigmoidal say they have to solve this problem applying machines that learn as they go to dredge through tens of thousands of news articles, government statements and social media accounts in languages as varied as Spanish, Arabic, and Chinese.

After an initial investment of up to certain USD, banks have to save millions of dollars over seven years applying such systems because they don’t need to hire as many data analysts, noted Marek Bardonski, who was a chief executive officer of Sigmoidal when he spoke within July. He has since left the firm. Before now, Bardonski was a computer scientist at graphics chipmaker Nvidia.

Take this year’s protests in Hong Kong. Bardonski noted Sigmoidal’s software was able to track developments in the Cantonese-language press and even identify the non-verified Twitter feeds of pro experiment leaders to monitor the risk of further unrest. The technology is useful for far-away countries wracked by political turmoil, places where investors are keen to put money, however, don’t have easy access to information.

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The scheme has to give an edge over traditional analysts working for financial institutions, noted Bardonski, who noted typical reports will include charts on sentiment, keyword statistics, and short written summaries. Instead of getting, news articles, clients have to get all the insights on one page.

Neither Sigmoidal nor Arkera would let see an instance of an automated report to see how readable it is compared with one produced by a human, citing rules against sharing proprietary data.

In Europe, the way investors consume research has evolved fast since fresh rules previous year forced investors for the first time to pay separately for the analysis they receive. The so-called MiFID II legislation stopped a widespread practice of having the cost of research built into the fees that the likes of Goldman Sachs or Morgan Stanley got paid to execute trades.

The irony is that a year and a half since the rules came into force, many investment banks still offer research for free because clients aren’t willing to pay for it, according to Sarah Jane Mahmud, a senior Intelligence analyst who specializes in regulation. They get around the rules by publishing research on their websites for public consumption.

However, the quality has gone downhill because mid-level analysts have left or been pushed out, leaving junior analysts to do the function so their more senior colleagues have to go to client meetings. This is giving investors even more impetus to seek out bespoke research, like paying cash to speak with experts in the field of investing in automated research to support their senior fund managers and strategists.

Asset managers now need to assess the value of every single research operation to assess if it’s worth paying for, how much they should pay for it, and trying to filter the good from the bad, Mahmud noted.

Under MiFID II, asset managers must be prepared to demonstrate they 've done due diligence on all investments they make for their clients, something that’s always been tricky in developing countries.

It was that very problem that inspired Gupta and his business partner Vinit Sahni, whose careers spanned firms including Citadel LP, DE Shaw & Co., and Goldman Sachs, to set up Arkera in. During their -year careers in investment banking, trying to find information to substantiate something felt like pulling teeth, Sahni,, noted.

So the pair set up a team of data scientists and engineers to design a search engine that investors have to apply to give them an edge in places like Turkey, Mexico, and Egypt. It functions kind of like Google, only it’s programmed to choose the great experiment relevant sources from tens of thousands of articles, social media feeds and government releases.

As good as robots are getting at deciphering market jargon, even their developers admit they’ll never fully replace humans. In the next decade, Sahni noted smart machines will significantly enhance the capabilities of human analysts.

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We will see advancements in cognitive abilities, communication and the physical potential of humans as we collaborate closely with machines and algorithms, he noted.

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