A senior researcher at Goldman Sachs stated the banks increase in its research effort. Putting this down to hardware advances that suggest the technology is on the verge of a breakthrough. More importantly, he mentions the “possibility this becomes a critical technology.” This is the thought process behind many businesses effort to build a quantum computer including the algorithms and software to fully utilise it.

William Hartnett, managing director at Citi, realises the power of this new technology is ground-breaking in risk management and trading and went on to say “banks need to start learning how to harness it now.”

The main focus is aimed the Monte Carlo Simulations. A Monte Carlo Simulation is a prediction and forecasting tool where multiple outcomes are simulated with varying random variables. On a standard supercomputer, the algorithms are well known and documented. However, a quantum computer requires new algorithms.

A traditional supercomputer has a strict logic path it obeys when calculating the outcomes, whereas a quantum computer can compute multiple outcomes at once and select the most desirable outcome.

In terms of the finance industry, banks carry out these complex calculations to assess their overall risk positions and option pricing.

“Together, these calculations account for the bulk of the computing power currently used by JPMorgan Chase”, said Ning Shen, managing director of quantum research.

The hope that instead of overnight calculations for risk assessment, they will be completed instantly. The advantage being that real time quotes grant opportunities in a larger market.

Mr Shen added: “If you can recalibrate your models fast, you can give better execution to clients.” The same technology could also make it possible to optimise the investment portfolios of wealthy clients on a case-by-case basis, he said.

Quantum computing is far from the only technological advancement that could shake up the industry. AI can analyse huge amounts of data quicker than humanly possible making it efficient at spotting anomalies or opportunities.

Source: FT.com

Modelomni has been quicker in spotting the potential of Artificial Intelligence and developing highly sophisticated models. The fact remains larger Wall Street Companies struggle to adapt to newer technologies and do it fast, while the more dynamic and disruptive Tech start-ups are more agile and flexible and less burdened by hierarchy.

XTX employs no human traders while Ken Griffith’s, Citadel Securities has spent the last 4 years focusing on technology to challenge Wall Street Market Makers. Both viewed as huge success with technology the key factor.

Modelomni Technology Lab offers an efficient Portfolio Diversification Solution for Market Professionals, uncorrelated to Equities and Bonds.

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Citadel Securities, the market-making firm, generates $3.5 Bio. The Ken Griffin firm outpaced similar non-banks and is also catching up to highly successful Wall Street Rivals.

Citadel Securities may be even more lucrative than Griffin’s hedge fund because of reports that profit margins will exceed 30%. His wealth, long estimated at about $10.3 billion, based largely on his investments in, and ownership of, the hedge fund business, is $5.1 billion higher, according to new calculations by the Bloomberg Billionaires Index.

Citadel Securities is an award-winning global market maker across a broad array of fixed income and equity products. The unique set of capabilities and tools are designed to drive down the cost of transactions, helping to meet the liquidity needs of asset managers, banks, broker-dealers, hedge funds, government agencies, and public pension programs. They strive to provide the most efficient execution and the highest caliber of services, making markets more fair and accessible for all, taken from Citadel Securities website.

Eight years ago, Citadel Securities was beginning to look like a failed experiment by Griffin as he looked for opportunities caused by the financial crisis. The newly developed firm was a full-service investment bank that would compete with Wall Street by offering research, underwriting and mergers-and-acquisitions advice. However, this was ended two years ago as dozens of employees were dismissed and the firm focused on electronic trading.

It began as a high frequency market-maker in options before moving into equities. According to Bloomberg, it handles more than 1 of every 5 shares traded in the U.S.

Technology has fueled the rise of nonbank market-makers, according to Larry Tabb, founder of Tabb Group LLC. Their smaller size and highly targeted business models make it easier to respond to changes and improve technology.

In 2016, Citadel gained a presence on the floor of the New York Stock Exchange by acquiring a division of KCG Holdings Inc. it’s now the largest market-maker, winning mandates from Uber Technologies Inc., Spotify Technology SA and Virgin Galactic Holdings Inc. The firm could be in line for more lucrative assignments as startups bypass traditional public offerings for so-called direct listings.

Bloomberg referred to Citadels Securities as a “Money Machine” due to its rising revenue and ability to muscle banks out of markets they once dominated. The success came from a focused mission on technology.

Employees also have investments in the hedge fund, so they benefit when the funds perform well. A memorandum tied to a recent bond offering shows that about 20% of Citadel LP’s $32.2 billion of assets under management come from principals and employees. Griffin has about $5.4 billion invested in Citadel funds, according to the Bloomberg Billionaires Index.

For now, those investments are thriving. The flagship Wellington Fund posted returns of 13% in 2017 and 9.1% in 2018, and is up 16.7% through the first 11 months of this year. That performance stands out in a challenging environment for hedge funds. As seen by the recent news of several hedge funds struggling.

The securities business continues to expand rapidly and is now vying to become one of the Fed’s primary dealers.

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Research and Development never stops or decelerates.

No emotions. Relentless 24 hours per day self-learning and self-correcting Supervised Machine Learning, Unsupervised Machine Learning, Deep Learning Neural Networks algorithms, 100% focused on performing one single task efficiently: Trade under strict conditions and with Risk Management at its core.

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