News 20 February 2018

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  • Algorithms are dominating the Investment and Trading landscape. But What is An Algorithm In The First Place? Algorithms are Mathematical Instructions providing a Step-By-Step Procedure For Calculations. Algorithms are used for calculation, data processing, and automated reasoning. From dating websites and trading floors, through to online retailing and internet searches, Algorithms are the “new god” from the machine powering them all, increasingly determining our collective futures


  • According to Dr Panos Parpas, a lecturer in the quantitative analysis and decision science (“quads”) section of the department of computing at Imperial College London, “algorithms, explained simply, follow a series of instructions to solve a problem. It’s a bit like how a recipe helps you to bake a cake. Instead of having generic flour or a generic oven temperature, the algorithm will try a range of variations to produce the best cake possible from the options and permutations available.” Parpas stresses that algorithms are not a new phenomenon and that they have been used for decades. The difference is that the current interest in them is due to the vast amounts of data now being generated and the need to process and understand it


  • Scott Patterson, a Wall Street Journal reporter and author of The Quants, likens the use of algorithms on trading floors to flying a plane on autopilot. The vast majority of trades these days are performed by algorithms, but when things go wrong, as happened during the flash crash, humans can intervene


  • Dr Panos Parpas/Imperial College London: “By far the most complicated algorithms are to be found in science, where they are used to design new drugs or model the climate,” says Parpas. “But they are done within a controlled environment with clean data. It is easy to see if there is a bug in the algorithm. The difficulties come when they are used in the social sciences and financial trading, where there is less understanding of what the model and output should be, and where they are operating in a more dynamic environment. Scientists will take years to validate their algorithm, whereas a trader has just days to do so in a volatile environment.”


  • Most investment banks now have a team of computer science PhDs coding algorithms, says Parpas, who used to work on such a team. “With City trading, everyone is running very similar algorithms,” he says. “They all follow each other, meaning you get results such as the flash crash. They use them to speed up the process and to break up big trades to disguise them from competitors when a big investment is being made. It’s an on-going, live process. They will run new algorithms for a few days to test them before letting them loose with real money. In currency trading, an algorithm lasts for about two weeks before it is stopped because it is surpassed by a new one. In equities, which is a less complicated market, they will run for a few months before a new one replaces them. It takes a day or two to write a currency algorithm. It’s hard to find out information about them because, for understandable reasons, they don’t like to advertise when they are successful. Goldman Sachs, though, has a strong reputation across the investment banks for having a brilliant team of algorithm scientists. PhDs students in this field will usually be employed within a few months by an investment bank.”


  • Dr Ian Brown, the associate director of Oxford University’s Cyber Security Centre, says that algorithms are now programmed to look for “indirect, non-obvious” correlations in data. “For example, in the US, healthcare companies can now make assessments about a good or bad insurance risk based, in part, on the distance you commute to work,” he says. “They will identity the low-risk people and market their policies at them


  • Viktor Mayer-Schönberger, professor of internet governance and regulation at the Oxford Internet Institute, presents a real-life scenario to illustrate how algorithms are being used. He explains how the analytics team working for US retailer Target can now calculate whether a woman is pregnant and, if so, when she is due to give birth: “They noticed that these women bought lots of unscented lotion at around the third month of pregnancy, and that a few weeks later they tended to purchase supplements such as magnesium, calcium and zinc. The team ultimately uncovered around two dozen products that, used as proxies, enabled the company to calculate a ‘pregnancy prediction’ score for every customer who paid with a credit card or used a loyalty card or mailed coupons. The correlations even let the retailer estimate the due date within a narrow range, so it could send relevant coupons for each stage of the pregnancy.”


  • Christopher Steiner, author of Automate This: How Algorithms Came to Rule Our World argues that we should not automatically see algorithms as a malign influence on our lives, but we should debate their ubiquity and their wide range of uses. “We’re already halfway towards a world where algorithms run nearly everything. As their power intensifies, wealth will concentrate towards them. They will ensure the 1%-99% divide gets larger. If you’re not part of the class attached to algorithms, then you will struggle. The reason why there is no popular outrage about Wall Street being run by algorithms is because most people don’t yet know or understand it.”


  • What Is Machine Learning? Machine learning is a technique that researchers, and now firms, have begun using to design more intelligent computer systems. Instead of attempting to hard-code a computer with all possible scenarios and actions a machine may encounter when trying to perform a certain task, machine learning allows the computer to ‘learn’ the necessary relationships and actions involved in completing a task intelligently. This ‘training’ involves using a large data set that the computer algorithm can repeat (typically with guidance and supervision) to learn through trial and error how to connect input data (e.g., credit history, employment history, assets, purchasing history) with the desired output (e.g., the correct identification of a suitable risky portfolio). Once this is learned, the algorithm can be used on real-world data with surprisingly good (in some cases) results


  • The most popular approach to machine learning is using neural networks, computer algorithms designed to mimic the way we believe the brain processes information. The networks are created by connecting millions, if not billions, of artificial neurons (essentially input/output switches) in all possible combinations. Through the process of supervised trial and error, certain connections are strengthened, others weakened, and some removed until the correct network of switches and connections is left that can accept an input and identify the correct output


  • In the world of fintech, common use cases for machine-learning algorithms are credit-scoring models for fintech credit companies (e.g., marketplace lending or peer-to-peer lenders) and robo-advisors. In the former, hundreds of input parameters about the individual (including, in some cases, Facebook and Twitter usage) are fed into a neural network that attempts to find patterns unobservable to humans but that are correlated with credit-worthiness. Whether these types of neural networks really work has yet to be tested by a full credit cycle. In the case of robo-advisors, similar input parameters about the individual investor are considered by a neural network designed to assess suitability, risk tolerance, and appetite. Alpha-generating algorithms are a separate angle in which machine learning is being developed in the field of investment management


  • One issue relating to neural network-based AI applications in investment management is the black box, in which the workings of an algorithm are not understood by its user and lead to potentially unintended actions or consequences. It’s a well-known headache for regulators trying to ensure market stability. Although some attempts have been made to check the source code of algorithmic traders, the most effective protection against algorithmic errors are circuit breakers on markets that limit the damage a failing algorithm can cause


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  • UBS: “Wealthy millennials and other private clients have expressed growing interest in innovations like digital platforms and sustainable and impact investing,” says Mark Haefele, global CIO at UBS Wealth Management. “This gives wealth managers and financial advisors a renewed opportunity to improve their digital capabilities as well as using private capital to help make the world a more sustainable place.”


  • In a new report released jointly by FactSet and Scorpio Partnership, High Net Worth Individuals are looking for accurate, insightful quality strategies and information delivered in a technologically savvy way. It ranks as the top criteria clients use to judge a wealth management firm’s credibility: Read Full Report: goo.gl/H61z2Z


  • UBS says  that millennials are likely to be worth up to $24 trillion by 2020 – about 1.5 times the US economy


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  • The European Union’s MiFID II regulations, enforced from Jan. 3, require money managers to separate the trading commissions they pay from investment-research fees. This means banks in turn have to be more transparent, providing specific charges for their analysts’ time and work in order to comply


  • Barclays is proposing three levels of service bronze, silver and gold — with the premium package comprising unlimited reports, field trips and “occasional” one-on-one meetings with analysts and corporate executives, according to a pricing document seen by Bloomberg News. At the bottom end of the scale, read-only access to European research will start at 30,000 pounds


  • Alliance Bernstein LP’s sell-side unit has quoted some firms about $150,000 a year to access equity analyst reports and other basic services


  • Credit Agricole SA and Nomura Holdings Inc. are pitching as much as 120,000 euros ($140,000) a year for their premium credit and macro-economic research packages, Bloomberg News has reported. Some money managers have said they’re getting quoted $50,000 for a basic package from JPMorgan Chase & Co.’s fixed-income analysts


  • McKinsey & Co. estimates investors will slash more than $1 billion of spending as they become pickier about what they pay for, with most only willing to fork out for analysts with the best track records. This may force banks to shrink or eliminate their research arms, potentially triggering hundreds of job losses


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