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Nasdaq Turns to AI to Detect Market Manipulation

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Nasdaq undertakes 14 million trades a day on its exchange making it nearly impossible to detect market abuses taking place. However, with the help of artificial intelligence (AI), Nasdaq is cracking down on the bad behavior.

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There are several things that Valerie Bannert-Thurner, senior vice president and head of risk and surveillance at Nasdaq, considers when it comes to bad behavior on the exchange.

Speaking to the American Banker, she said:

If people are excessively profitable given how they trade and in comparison to everybody else trading the same instruments with similar styles, then we ask, is this luck or something else?

Yet, with an excessive amount of trades taking place each day at Nasdaq, keeping track of who’s doing what can seem a daunting task.

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In a bid to lighten the workload, the exchange is turning its attention to AI to help, and is one of the first exchanges to use AI as well.

Maintaining the Exchange’s Integrity

As Nasdaq is a well-known exchange that operates 25 exchanges in Canada, Europe, and the U.S. it needs to maintain its trustfulness or else companies will cease from using it.

According to Bannert-Thurner, Nasdaq exists because people believe in its abilities to detect market manipulation and abuse. So-much-so, that 45 outside exchanges and 13 regulators use its SMART trade surveillance platform.

Typically, Nasdaq has large teams of people using the SMART platform to detect bad behavior on the exchange. They do this while also connecting what traders are saying in emails and chats with what their actual trades are doing for them to catch them in the act.

However, it was in 2014, that the Libor scandal uncovered the manipulation of Forex markets, with U.S. regulators investigating many incriminating chats to fix rates.

To prevent situations like this taking place again, Nasdaq is employing AI software from Digital Reasoning.

AI to Detect Market Abuses

Using language processing and machine intelligence, the software can detect the language traders are using and identify any key triggers, which will alert those seeking out market abuse to look at what trades were made.

To make sure the software can detect what is going on, Nasdaq is feeding it alerts and chats from previous cases of manipulation.

Bannert-Thurner said:

We think machine learning will make a significant difference in how we detect behaviors, how we help people review and investigate.

The Digital Reasoning software will screen the threat indicators first before humans see them. It is hoped that the software will reduce the number of alerts down to only the relevant ones.

Featured image from Shutterstock.

Important: Never invest (trade with) money you can't afford to comfortably lose. Always do your own research and due diligence before placing a trade. Read our Terms & Conditions here. Trade recommendations and analysis are written by our analysts which might have different opinions. Read my 6 Golden Steps to Financial Freedom here. Best regards, Jonas Borchgrevink.

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Artificial Intelligence

The End of Human Money Managers

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robot

Quantitative Easing by central banks around the world has led to dramatic changes in the money management industry over the past six years. Not only have we seen increasing regional differences, but stock picking has also become more difficult as the money injected into the markets by central banks has lifted pretty much everything, regardless of valuation and the future potential of the asset.

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Investors have become impatient and highly demanding as a result of years of low interest rates. Old mutual funds are being swapped out for new, better ones at a record pace as investors hunt for higher ROI. Passive income has become a trend, and ETF’s and automated investment strategies are getting more and more popular as a result.

How do money managers attract capital?

There are three main factors that determine how much capital a money manager is able to attract from investors:

  1. Track record
  2. Strategy
  3. Technology

Changes in any of these factors can have a big impact on investors’ willingness to let the fund manager keep the money they have already invested with him, or receive new money.

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Technology has been a very important driver over the past few years. Data-driven, or quantitative funds are gaining an ever-increasing market share in the money management space. This is happening because more and more people are realizing the obvious benefits that this type of money management has to offer.

Investors increasingly prefer the robustness, speed, and predictability that automated money management can provide. When it comes to robustness, we are referring to both the physical and psychological aspect of it.

Humans vs. robots

Humans are pretty much the opposite of “robust,” in the true sense of the word. Our emotional state on any given day can make us react to things in different ways than we otherwise would have done, potentially leading to critical mistakes for a trader.

As humans, we may miss trading opportunities in the market because we came in late, took a day off, or simply didn’t pay attention at any given moment.

Robots are obviously not affected by fatigue and lack of focus. For example, a robot can monitor the stock or cryptocurrency market and trade just like a human trader would do, with the only difference being that the former (arguably) does it better and never needs to rest.

Thanks to the high computing power available today, robots can collect, verify, analyze, and react to opportunities long before a human will even understand that such opportunities exist.

Data-driven approach to fund management is taking over

A recent ranking by Institutional Investor Magazine revealed that out of the world’s 100 biggest hedge funds, five of the top six spots were held by data-driven funds.

On first place was Ray Dalio’s Bridgewater Associates with $122.3 billion under management. In 2016, Bridgewater grew the amount of money under management by 17%.

Renaissance Technologies, the company known for having hundreds of mathematicians, physicists, and coders on their payroll, came in fourth with $43 billion.

Two Sigma, which is also well-known for using technologies like AI and machine learning, came in fifth with $39 billion under management. Their increase from the year before was 28%.

According to Barclays, $500 billion are now invested in purely data-driven funds, while JP Morgan claims that data-driven trading strategies accounts for a whopping 90% of global trading volumes in stocks.

The core objective of any money manager is always to follow the money. That’s why we are seeing a race right now by the big players in the industry to use words like “technology-driven,” “artificial intelligence,” and so on. Whether or not that is true is not always a concern for them.

Money managers are destined to unemployment

Those who are really in trouble because of this huge change are the money managers themselves. Most of them will likely lose their jobs over the next few years. There is simply very little need for their very expensive services anymore, as robots are able to do the same thing in a much cheaper and more consistent way.

As legendary investors Jim Rogers predicted a few years ago, the stock brokers will become broke and the farmers are going to be driving Lamborghinis. Maybe there will finally be some truth to this.

Featured image from Pixabay.

Important: Never invest (trade with) money you can't afford to comfortably lose. Always do your own research and due diligence before placing a trade. Read our Terms & Conditions here. Trade recommendations and analysis are written by our analysts which might have different opinions. Read my 6 Golden Steps to Financial Freedom here. Best regards, Jonas Borchgrevink.

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4.2 stars on average, based on 20 rated postsFredrik Vold is an entrepreneur, financial writer, and technical analysis enthusiast. He has been working and traveling in Asia for several years, and is currently based out of Beijing, China. He mainly follows the stock and forex markets, and is always looking for the next great alternative investment opportunity.




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Bitcoin Giant Bitmain Enters the High Stakes AI Race

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Artificial Intelligence

The Sophon, named for a fictional proton-sized supercomputer, could be the tool to train neural networks in data centers worldwide. It is the latest project being developed by Bitmain Technologies Ltd., the bitcoin mining giant that has carved out a dominant position in bitcoin mining.

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Such chips, called application-specific integrated circuits (ASICs), could unleash a new wave of distributed computing, according to Michael Bedford Taylor, a University of Washington professor who studies bitcoin mining and chips.

Sophon is due to debut before the end of the year.

Bitmain Has The Know-How

Bitmain has the background to play a role in the expanding artificial intelligence industry. The company designs the silicon that goes in bitcoin mining equipment, assembles the machines and sells them worldwide, in addition to its own bitcoin mining operation and the ones that it manages for other mining pools.

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Bitmain’s founders are not averse to playing a spoiler role.

Jihan Wu, the co-founder of Bitmain, supports the New York Agreement that seeks to double the bitcoin block size under the SegWit2X proposal, a move that some in the bitcoin community view as an attempt to give the miners control over bitcoin.

Some also believe Wu was behind the recent bitcoin split known as bitcoin cash, which at least one of Bitmain’s miners supported, a contention that Wu has denied. Wu points out that he was among the supporters of Bitcoin Unlimited, an earlier bitcoin scaling proposal that did not get activated.

Why Wu Supports Forks

Wu nonetheless said splits should be allowed. He said a fork is inevitable since people in the bitcoin community do not agree on how to best scale bitcoin.

Wu met Micree Zhan, Bitcoin’s co-founder, when Zhan was running DivaIP in 2010, a company that made a device that allowed a user to stream a TV show on a computer screen.

In 2011, Wu needed a chip designer to build a mining operation and approached Zhan. Zhan first designed an ASIC to run SHA-256, the cryptographic calculation used in bitcoin, at maximum efficiency. It took him six months to finish the job. His first rig, Antminer S1, was ready in November 2013.

Bitmain felt the sting of the 2014 Mt. Gox meltdown. But by 2015, bitcoin’s price bottomed out and later recovered. In the meantime, Bitmain introduced its Antminer S5.

Bitmain now employs 600 people in Beijing.

Also read: Bitmain clarifies its ‘bitcoin cash’ fork position

Ready To Take On Google

Bitmain has since developed a deep learning chip with improved efficiency. Users will be able to build their own models on the ASICs, enabling neural networks to deliver results at a faster pace. Google’s DeepMind unit used this technique to train its AlphaGo artificial intelligence.

Bitmain plans to sell the chips to any company looking to train its own neural nets, including firms like Alibaba, Tencent and Baidu. Bitmain could build its own data centers with thousands of deep learning rigs, renting out the computation power to clients the way it does with bitcoin mines.

Professor Taylor said companies like Bitmain that have excelled in bitcoin mining could take on the Googles and Nvidias since they have developed the skills to survive in an ultra-competitive and highly commoditized industry, and have the system level design expertise and the ability to reduce data center costs.

Important: Never invest (trade with) money you can't afford to comfortably lose. Always do your own research and due diligence before placing a trade. Read our Terms & Conditions here. Trade recommendations and analysis are written by our analysts which might have different opinions. Read my 6 Golden Steps to Financial Freedom here. Best regards, Jonas Borchgrevink.

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4.8 stars on average, based on 4 rated postsLester Coleman is a veteran business journalist based in the United States. He has covered the payments industry for several years and is available for writing assignments.




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Artificial Intelligence

Dutch Police Use Augmented Reality to Investigate Crime Scenes

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Dutch police are undertaking an experiment to see if augmented reality can help officers at a crime scene, according to the New Scientist.

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Using an AI system video from the body cameras worn on the officers at the scene will relay back to experts who can guide the officers by making virtual notes which the officers will be able to see via a smartphone or head-mounted device.

Dragos Datcu, principal researcher at augmented reality (AR) company Twnkls in Rotterdam, the Netherlands said:

We now have good enough software and hardware to use augmented reality at crime scenes.

What’s great about the new AI system is the fact that experts can get involved with what the crime scene investigators are doing regardless of where they are located.

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So by viewing the footage that is sent from a camera on the police vest, a chemical specialist in one location can view it while a forensic scientist in another location can too. The system is similar to the popular Pokémon Go smartphone game that has grabbed the attention of millions of people around the world.

Not Suitable for Making an Arrest

However, while the technology may prove beneficial in providing an extra pair of eyes for investigating crime scenes, when it comes to making an actual arrest the technology is not suitable for that just yet.

According to Nick Koeman, innovation adviser from the National Police of the Netherlands, the officers undertaking the AI system trial found the extra information distracting.

Of course, some may simply say that ensuring a complete team is at the scene of a crime would be more beneficial for an investigation; however, that is not always possible due to budget cuts and time constraints.

As such the use of an AI system that can cut down on the number of people involved at a crime scene without sacrificing on the required thoroughness could potentially provide the answer that many police departments are searching for.

Not only that, but by reducing the number of people at a scene it cuts the potential possibility of contaminating evidence. The use of AI gives people the chance to assess the evidence and discover additional clues without being at the crime scene.

AI could also help in court cases by helping to recreate a scene for a jury, but as Michael Buerger, professor of criminal justice at Bowling Green State University in Ohio states, legal challenges are likely to raise when augmented reality (AR) is used in the courts.

Featured image from Shutterstock.

Important: Never invest (trade with) money you can't afford to comfortably lose. Always do your own research and due diligence before placing a trade. Read our Terms & Conditions here. Trade recommendations and analysis are written by our analysts which might have different opinions. Read my 6 Golden Steps to Financial Freedom here. Best regards, Jonas Borchgrevink.

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