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There’s A New Unicorn In Town…

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NC-based tech startup Tresata just scored a $1B valuation, helping it raise $50M from GCP Capital Partners. It’s not an IPO, but the money’s flowing.

What’s surprising is that this is the first time the company has raised outside funds across its seven-year history, making that $1B valuation all the more significant.

“We have had tremendous interest from investors ever since we started the company and never found the right partner,” CEO Abhishek Mehta said in an interview.

Mehta highlighted that revenue came as the cheapest form of capital, allowing the company to fuel its growth—a model that’s worked exceptionally well for the business. But now it’s time to change that, Mehta said.

“This time around, we found that partner,” he said. “With GCP, we have someone on our side who believes strongly in our vision — that in data lies the power to enrich life.

“We are excited about this investment, as it is a validation of the confidence our clients have placed in us from the beginning.”

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The Rise Of Algorithmic Trading: How Manned Trading Desks Are Seeing Their Demise

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One of the last human points of resistance to the algorithmic take-over of professional trading can be found in the fixed-income trading desk.

The deft relationship business is known for having participants with Ivy League pedigrees and a lacrosse playing background, a data point that a machine learning algorithm recently used when making hiring recommendations.

With an algorithmic transition, profit margins are being squeezed as human relationships are becoming less important. Benchmarking the transition are new hiring mandates at some of the largest banks, a recent Greenwich Associates report pointed out.

Relationships and fundamental market understanding are less important, the report noted, and algorithmic experience and data science skills are now in demand.

In this emerging environment, what are best practices for an integration framework?

When combining fundamental investment thinking with quantitative thinking unique conflicts often arise. Fundamental, discretionary fund managers often creatively connect non-correlated dots and sometimes challenge consensus thinking.

Quantitative logic, on the other hand, is often driven by if-then Boolean logic at its core and looks for mathematical consensus to guide decisions. Mixing these two disciplines can be like expecting oil and water to enjoy each other’s company.

“Relationships and balance sheet still matter, of course,” Greenwich Associates Managing Director Kevin McPartland wrote in a report title that highlights a dichotomy, “Trust and Data Drive Fixed-Income Dealer Growth.”

Trust is a human concept and data is often associated with empirical, mathematically discovered understanding is the point where a melding of the minds might occur.

The integration of the two highlights the new path forward. “But the ability to manage both in a more quantitatively driven way can mean the difference between profit growth and a year-on-year decline,” McPartland observed.

Technology is increasingly becoming an important feature on bond trading desks, particularly for sell-side banks. Fully 91% of bulge bracket banks said automating parts of the trading process was one of their “top technology priorities for 2018,” the Greenwich Associates report pointed out.

Only 44% of middle market participants, meanwhile, felt the same. This group was having slightly more difficulty “complying with new/changing regulations” and was more focused on improving client management tools and data.

Knowing what technology to integrate so as to deliver a near-term positive return on investment while positioning the firm for a longer-term shift in intelligent automation can prove challenging and, itself, might have variables that don’t easily fit into a math formula.

“Assessing the process of tech integration is always a challenge because no technology is perfect, but it is often much better than human performance,” said Evan Schnidman, CEO and Co-Founder, Prattle, a firm that uses machine learning techniques to analyze human speech patterns to provide a market edge.

Electronic equities trading has proven to be highly reliable, but very rare missteps can have catastrophic cascading effects. With bond trading, and any other technology-driven process in financial services, it is vital to build appropriate circuit breakers and human checkpoints. Simply, technology is a valuable tool, but humans still need to be in the loop.”

When integrating technology into a bond trading workflow it is important to set proper expectations that can be measured on a project plan timeline.

Often times expecting the unexpected is a discretionary skill that requires understanding technology’s limits.

“The best practice in tech integration is to remember that technology will not solve all of your problems, but it may be able to streamline the human workflow and optimize decision making,” Schnidman said. “If institutions keep that in mind, they are likely to adopt technology more readily, thereby making each additional innovation less jarring from a tech integration standpoint.”

With technological change coming at an increasingly rapid pace, the imperative is in understand what aspects of human trading can be modeled and what should remain discretionary.

“Instead of fully relying on quantitative approach at the beginning, it is a good starting point to integrate the output of quants and AI models in the human decision making process of position taking and dealing,” said Tsuyoshi Yokokawa, founder and CEO of San Francisco based Alpaca Markets, a firm that in parts helps integrate quantitative trading models into fundamental trading methodologies.

As the last refuge of human-based trading turns algorithmic, the processes and methodologies that drive these projects are likely to determine success or failure to a large degree.

In bond trading, the sell side is taking a different approach to that of the middle market.

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Alibaba’s Singles Day Records $31B Sales, Trumps Amazon’s Biggest Day

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Chinese tech and e-commerce behemoth, Alibaba, saw its one-day shopping event draw sales worth over $31B, setting a new record for the company. Labelled as its Singles Day event, the day records sales that’s bigger than Black Friday and Cyber Monday combined.

The record sales in under 24 hours dwarfs the $4B in sales Alibaba’s rival Amazon garnered during its Prime Day sale earlier this year.

Here’s why Alibaba’s CEO believes the sale is a huge winner for the company.

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[CHART] The Amazon Effect: Retail AI Startups See Record Activity in 2018

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Amazon’s meteoric rise has basically transformed the retail AI landscape.

Amazon’s expansion into grocery, the acquisition of Whole Foods, and promises of 1-hour grocery delivery have forced supermarkets to experiment with AI-run “micro-fulfillment” robots just to stay in the game.

Yes, supermarkets are really turning to robots.

Yes.

Robots.

Anyway…

Since Q1’13, retail AI startups raised $1.8B across 374 deals. In Q2 of his year, however, deal flow saw a record spike. Check it out.

 

 

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