{"id":24244,"date":"2020-04-13T07:00:00","date_gmt":"2020-04-13T14:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=24244"},"modified":"2020-04-14T09:20:50","modified_gmt":"2020-04-14T16:20:50","slug":"interview-justin-grossbard-from-compare-for-exbrokers","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2020\/04\/13\/interview-justin-grossbard-from-compare-for-exbrokers\/","title":{"rendered":"Interview: Justin Grossbard, Co-founder and CEO, Compare Forex Brokers"},"content":{"rendered":"\n
\"\"<\/figure><\/div>\n\n\n\n

I recently caught up with Justin Grossbard, Managing Director of Compare Forex Brokers<\/a>, to discuss his perspective for how big data has made a significant impact on financial markets, including Forex. Justin Grossbard is the Managing Director of Compare Forex Brokers and owner of the digital agency ‘Innovate Online.’ He has a Masters In Marketing and is Google Analytics certified.<\/em> <\/p>\n\n\n\n

insideBIGDATA: <\/strong>Big data has made a significant impact on financial markets, including Forex. Can you give us a high-level view for how big data has changed the way trading is done?<\/p>\n\n\n\n

Justin Grossbard:<\/strong> Trading volumes in financial markets, particularly the Forex market have never been higher. We have come a long way from having stock brokers manually placing trade orders on behalf of their clients. The digital revolution brought about by the internet has meant instructions such as buy and sell orders don’t need to be manually entered by a human being. Traders can trade efficiently from wherever they are around the world. All they need is an internet connection and a trading platform with all trading instructions picked up by markets and exchanges with the touch of a button. Traders have the power to call their own shots nowadays and are not reliant on physical brokerage houses or trading floors. This was highlighted as recently as late March as a result of US lockdown measures in the wake of the Covid19 crisis when the New York Stock Exchange decided to close until the crisis passes; currently all trades are being executed online.<\/p>\n\n\n\n

insideBIGDATA:<\/strong> <\/strong>Have you seen that big data helps traders get a complete overview of their trading patterns and generate in-depth reports on profits and losses?<\/p>\n\n\n\n

Justin Grossbard:<\/strong> The modern trading platform is a powerful tool. Not only do traders have global market news at their fingertips, they can also execute trades in mere milliseconds. All trades are recorded instantly and a virtual paper trail of every trade, profitable or otherwise, is stored in the cloud thanks to big data. Traders are able to generate a tremendous amount of reports based on this data that really helps them formulate and hone their trading strategies. None of this was really possible for retail traders until the late 90s and information started to be communicated faster and in real time on the internet. I think this is only going to improve over time as internet speeds and communication becomes more efficient thanks to 5G and beyond.<\/p>\n\n\n\n

insideBIGDATA:<\/strong> How would you assess the success in incorporating machine learning and algorithmic trading techniques to expand upon execution and trading strategies in order to make more informed decisions?<\/p>\n\n\n\n

Justin Grossbard:<\/strong> When it comes to financial markets, sentiment and trader psychology play a massive role. Traders don’t always act rationally and a ‘follow the leader’ approach to trading and investing often contributes to market turmoil. While not perfect, algorithmic and machine based trading techniques not only increase trading volumes and enhance the value or a market or index, they also aim to reduce the impact of human psychology (they’re computers after all). However, since the advent of algorithmic traders market falls (and gains) are much more rapid. This was experienced recently with the biggest and fastest daily drop in the share market since the crash in October 1987. Yes big data in the form of algorithmic trading has helped to create significant wealth but its a double edged sword and sometimes the snowball effect of algo traders is often worse than negative human sentiment.<\/p>\n\n\n\n

insideBIGDATA:<\/strong> It’s been said that when forex trading, it is crucial to anticipate how others react. Can using big data tools help take this into account when developing a trading strategy?<\/p>\n\n\n\n

Justin Grossbard:<\/strong> Given trading derivatives is a zero sum game, traders are always looking for an edge. Even receiving market news a split second ahead of the masses could result in significant gains. Its why most brokers talk up the low latency of their servers and connection speeds. News and pricing communicated fast and in real time to traders is imperative as most money is made in the period of time leading up to and right after sensitive market news is released. For instance, interest rate news, jobs & GDP data. Big data makes it easier for traders to forecast market news and structure their trades accordingly. <\/p>\n\n\n\n

insideBIGDATA:<\/strong> Brokers are heavily regulated in the world of forex. How can big data offer transparency and reporting options to help meet increasingly stringent guidelines?<\/p>\n\n\n\n

Justin Grossbard:<\/strong> Just this morning we had a request from one of our clients to have a trade of theirs looked into. He was concerned that his stop loss order wasn’t honoured which resulted in a substantial loss for him. This issue was immediately communicated to his broker’s Trade Queries team for auditing. He is waiting on an answer which will be communicated to him shortly. However, audits such as these are easy to do given all trade data is recorded in real time on the various trading platforms provided by brokers. <\/p>\n\n\n\n

In\n fact, many of the most popular brokers in the market boast Electronic \nCommunications Networks (ECNs) which provides clients direct access to \nother participants in the market allowing for more price and information\n transparency. While the cost to use an ECN platform is often higher, \naccess to readily available current and past information helps to reduce\n the occurrence of price manipulation.<\/p>\n\n\n\n

insideBIGDATA:<\/strong> How is big data analytics able to allow the forex broker to estimate crisis prevention situations in advance so they can alert traders about the risk?<\/p>\n\n\n\n

Justin Grossbard:<\/strong> Using a series of historical information brokers can forecast forthcoming news and market events that are likely to impact the overall performance of the market. How these events will impact liquidity, pricing fluctuations of tradable assets etc allows them to better inform their clients so that they can prepare to maximise their returns or minimise their losses. It also allows the brokers to mitigate risk. One common risk mitigation tactic brokers utilise is to alert their traders ahead of a volatile market event. They will then increase margin requirements and reduce leverage afforded traders to protect the downside of market forces turning against them. In January 2015 a forex broker called Alpari went out of business due an unforeseen market event. Had they been a bit more scrupulous in terms of risk they would have avoided bankruptcy like the overwhelming majority of other brokers.<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n","protected":false},"excerpt":{"rendered":"

I recently caught up with Justin Grossbard, Co-founder and CEO of Compare Forex Brokers, to discuss his perspective for how big data has made a significant impact on financial markets, including Forex. He also assesses the success in incorporating machine learning and algorithmic trading techniques to expand upon execution and trading strategies in order to make more informed decisions.<\/p>\n","protected":false},"author":37,"featured_media":24254,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[65,115,87,72,180,191,67,56,97,1],"tags":[868,280,867,277,95],"acf":[],"yoast_head":"\nInterview: Justin Grossbard, Co-founder and CEO, Compare Forex Brokers - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2020\/04\/13\/interview-justin-grossbard-from-compare-for-exbrokers\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Interview: Justin Grossbard, Co-founder and CEO, Compare Forex Brokers - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"I recently caught up with Justin Grossbard, Co-founder and CEO of Compare Forex Brokers, to discuss his perspective for how big data has made a significant impact on financial markets, including Forex. 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After all, money launderers are humans and therefore display consistent\u2026","rel":"","context":"In "Big Data"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":12886,"url":"https:\/\/insidebigdata.com\/2015\/03\/18\/data-science-101-deep-learning-intelligence-from-big-data\/","url_meta":{"origin":24244,"position":1},"title":"Data Science 101: Deep Learning – Intelligence from Big Data","date":"March 18, 2015","format":false,"excerpt":"From the Stanford Graduate School of Business, we have a compelling panel discussion - Deep Learning: Intelligence from Big Data.\" A machine learning approach inspired by the human brain, Deep Learning is taking many industries by storm.","rel":"","context":"In "Big Data"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":5902,"url":"https:\/\/insidebigdata.com\/2013\/11\/14\/h20-open-source-memory-machine-learning-app-big-data\/","url_meta":{"origin":24244,"position":2},"title":"H20: An Open Source, In-memory Machine Learning App for Big Data","date":"November 14, 2013","format":false,"excerpt":"\"We developed H2O to unlock the predictive power of big data through better algorithms,\" said SriSatish Ambati, CEO and co-founder of 0xdata. \"H2O is simple, extensible and easy to use and deploy from R, Excel and Hadoop.\"","rel":"","context":"In "Analytics"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2013\/11\/oxdata.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":5162,"url":"https:\/\/insidebigdata.com\/2013\/10\/23\/skytree-extends-machine-learning-hadoop-big-data-environment\/","url_meta":{"origin":24244,"position":3},"title":"Skytree Extends Machine Learning to Any Hadoop Big Data Environment","date":"October 23, 2013","format":false,"excerpt":"Skytree, the Machine Learning Company, recently announced that its flagship product Skytree Server seamlessly integrates with Apache Hadoop, providing predictive analytics with unified management on any Hadoop environment. 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