Sell Side

The investment and trading industry is commonly broken down into “Buy Side” and “Sell Side.”

The Buy Side is the industry participants who typically make up the investment and speculation firms. These include institutions such as proprietary traders, mutual funds, hedge funds, pension funds and insurance firms that tend to invest.

The Sell Side is the industry participants who typically provide brokerage or liquidity services. These include broker-dealers, Future Commission Merchants, exchanges, and other marketplaces.

CloudQuant and our parent Kershner Trading Group, as a family office fund and proprietary traders, is considered to be Buy Side.


Machine Learning, Quantitative Investing News

Industry News: Machine Learning and Artificial Intelligence for December 18, 2017

Generational Tech Shift to Transform Trade Lifecycle

…tion of new technologies. As global financial markets continue to evolve, the real opportunities are yet to be delivered in transparency and efficiency for investors.” Distributed ledger technology, machine learning, portfolio optimisation techniques and the cloud are being used today but the paper said the challenge for markets and policy-makers is to harness this potential while managing its new risks. “Neither legacy systems nor policy inertia should be allowed to stifle this generational opportunity,” added NEX. Mark Whitcroft, founding partner at venture capital firm Illuminate Financial Management, s… 2017-12-15 06:39:54+00:00 CloudQuant Thoughts: We really like the following quote in this article:

“A huge part of financial services built technology in-house or used a small number

of vendors and that old model is dying,” Whitcroft added. ‘Financial services is being componentisized.”

This gets to the heart of new business models that include innovation and crowdsourcing. We have found that innovators, with atypical backgrounds and education, when given access to institutional grade tools and datasets can create something unique. There is significant value that can be derived. Our business model is proving this out daily.

Deutsche Bank Upgrades Equities Trading With AI

…Deutsche Bank today announced the implementation of an upgraded equities trading platform with artificial intelligence (“AI”) capabilities, setting a new benchmark for best execution, a key focus for financial institutions ahead of MiFID II. Under MiFID II, financial institutions will have to demonstrate they have taken sufficient steps to obtain the best possible result when executing client orders. Using a unique combination of next-generation algorithms, Deutsche Bank’s enhanced equities trading platform is o… 2017-12-14 11:02:41+00:00 CloudQuant Thoughts: Innovation on the sell side to provide brokerage capabilities is always welcome. So much of the sell side innovations have been the integration of vendor technologies as demonstrated by the rapid growth of technology like Fidessa into the listed derivatives market and new vendors like Vertex Analytics. DB’s Autobahn has a good reputation among the fund managers who utilize it for research and trade execution. DB’s claim that “Autobahn 2.0 will also help us to reduce technology risk and save costs for our clients” may be a bit optimistic though. The growth of internally developed systems inside the sell-side is sometimes dangerous if the firm does not keep a strong focus on meeting the continued needs of the buy-side. Sometimes this is difficult to maintain for a longer period of time with additional risk being transferred to the buy side clients as management, engineers, and support staff turn over with the passing of time. We hope that DB’s German engineering approach won’t allow this to happen!

World’s Biggest Pension Fund Sees AI Replacing Asset Managers

…: What are the biggest changes in the investment world you see coming in the next five to 10 years? Mizuno: Adoption of technology, including AI and ESG integration into all asset classes. I believe artificial intelligence will be able to either replace or enhance the asset managers’ work, particularly for short-term trading. Question: What are the implications? Mizuno: Asset managers have to adjust their conventional business model. Investors will be more focused on the long-term investment theme, as AI will take over the short-term trading. In other words, investors will shift their focus to the long-term susta… 2017-12-15 09:43:52-05:00 CloudQuant Thoughts: The prediction (in this article) that Amazon and Google may at some point become Fund Managers is very interesting. When you consider that they have access to huge amounts of Alternative Datasets from their core businesses there is a strong probability that they would be successful. The concern is that none of the “portal” giants have ever been above average in providing trading data. Their financial portals miss out on critical data points that FINTECH firms like Bloomberg, Interactive Data, CQG, Thompson Reuters, don’t. Amazon, Google, and others would have to go through a learning curve or acquire those skills and that focus. Fund management and trading companies run differently than other businesses.

Microsoft levels up Word, Excel, and Outlook with more AI capabilities

…Microsoft is adding a host of new capabilities to its Office productivity suite that are aimed at using machine learning to help people get their work done more efficiently. Outlook, Excel, and Word will all benefit, with new features rolling out to a limited set of users in the coming months and then expanding to a broader set of people later on. Outlook’s web client will provide users with an interface that will automatically offer them responses to questions layered inside emails, while Excel has a new feature … 2017-12-13 00:00:00 CloudQuant Thoughts: Excel is still one of the strongest data analytics tools.

New Hedge Funds Next Year Will Embrace High Tech

…industry may be getting one step closer to its robot-guided future. Seventy percent of new hedge funds that will start next year will include investment processes that use computer models, including artificial intelligence and machine-learning technologies, according to a prediction in a Deloitte report released Thursday. That’s a jump from 47 percent in 2015. The new technologies process large, alternative data sets and hedge funds have increasingly turned to them to generate higher returns. That doesn’t mean 2018 will be easy for the industry overall. Investment firms will continue to be pressured by regulatory… 2017-12-15 00:00:00 CloudQuant Thoughts: No surprise here. We used AI, and alternative data sets to launch our crowdsourcing trading strategy incubator. Others have seen success in innovating and application of technology.

Trends: Rich Data or only Data Rich? Have banks figured out Big Data? | Fintech Recap 2017

…g the great mass of customer data jealously guarded in siloes, with no access or use. And when the fintechs come to the bank boards with proposals, they’re bringing the latest buzzwords in tow – AIMachine Learning and Advanced Analytics; it’s lost on them that all those words come under the same banner. Anyone can compile a few transactions in a glorified Excel sheet, apply a few analytics and sell it off as an ‘AI enabled recommendation’ product. The term Big Data is troublesome in itself as a misnomer. Computing vast oceans of data which, in itself can be complex, does not do justice to the actual compl… 2017-12-15 00:00:00 CloudQuant Thoughts:  The idea that Clean Data and Fast Data combined equal Rich Data is compelling. One used to hear terms like data mining or data research. Institutional banks need to compete. To compete they need to continue to monetize assets including data. This path to monetizing data will include working across silos to build their own internal alternative data sets for use by new business lines that have yet to be formalized within the traditional bank.

Essentials of Deep Learning : Introduction to Long Short Term Memory

…te Text generation using LSTMs. 1. Flashback: A look into Recurrent Neural Networks (RNN) Take an example of sequential data, which can be the stock market’s data for a particular stock. A simple machine learning model or an Artificial Neural Network may learn to predict the stock prices based on a number of features: the volume of the stock, the opening value etc. While the price of the stock depends on these features, it is also largely dependent on the stock values in the previous days. In fact for a trader, these values in the previous days (or the trend) is one major deciding factor for predictions. … 2017-12-10 12:22:43+05:30 CloudQuant Thoughts: This article is the most technical posting included in this week’s post. It is well worthwhile reading. The concept that your trading algorithim may need to understand context from something else that happened much earlier in time sequence has a lot of very interesting applications in trading.  

How banks can build better engagement with business customers

…ds their financial needs well or fairly well”, 40 per cent cite a lack of personalisation as a major reason for leaving their current provider. This highlights the clear opportunity that the rise of Artificial Intelligence presents for providers to enhance and personalise the customer experience for SMEs. Chatbots, in particular, could move beyond a customer service role to one where they offer real engagement with a small business’ needs. In Sweden, Swedbank has developed a web assistant called Nina, which has an average of 30,000 conversations per month and can handle more than 350 different customer questions. … 2017-12-13 12:16:33 CloudQuant Thoughts: Does anyone want to help us with our ChatBot project? We need to develop one too.

15 Trending Data Science GitHub Repositories you can not miss in 2017

…siast, I have curated a list of repositories that have been particularly famous in the year 2017. Enjoy and Keep learning! Table of Contents Repositories for Learning Resources Awesome Data Science Machine Learning / Deep Learning Cheat Sheet Oxford Deep Natural Language Processing Course Lectures PyTorch – Tutorial Resources of NIPS 2017 Open Source Softwares TensorFlow TuriCreate – A Simplified Machine Learning Library OpenPose DeepSpeech Mobile Deep Learning Visdom Deep Photo Style Transfer CycleGAN Seq2seq Pix2code 1. Learning Resources 1.1 Awesome Data Science This GitHub repository is an ultimate r… 2017-12-15 00:00:00 CloudQuant Thoughts: We don’t understand why they left our GitHub repository off the list. Our GitHub repository is increasingly growing with sample python trading algorithms and scripts that can be used to help trading strategy developers grow in their capabilities. You can find our repository at

With IoT, any company can enter the SaaS market

…team generator manufacturer sells a small batch of units to a pilot customer, connecting them all through an IoT platform that exports the real-time status of more than 50 operational parameters to a machine learning model for three months. The resulting machine learning model quantifies some initial assumptions and creates some new insights for the operation usage. The steam generator company can now sell a monitoring service to new customers that improves serviceability with predictive maintenance, boosts supply chain efficiency with more accurate demand information, and lowers customer costs with demand-ba… 2017-12-16 00:00:00 CloudQuant Thoughts: It is no big secret that quantitative analysts want access to the Alternative Datasets that IoT will provide. The capabilities of predicting stock movements will greatly affect trading and investment strategies.  
Machine Learning, Quantitative Investing News

Industry News: Machine Learning and Artificial Intelligence News for the week ending October 9, 2017

AI & ML FINTECH perspective: Chicago startups with chatbots, JP Morgan, Hedge Funds, OCBC, UBS, Morgan Stanley, Google, Zillow, GoPro, Snap, …
Researcher reading iPad

Big Data, Data Analytics, Data Science in the News October 5, 2017

Data Science Topics: UBS, data quality, cloud-native, Morgan Stanley, wildlife monitoring drones, targeted marketing, retirement planning, Boeing, Google AI
Quantitative Strategies and Capital for Trading

Quantitative Trading and Data Science in the News August 14 2017

Topics include: GeoLocation Alternative Data, robotic revolution, buy side, sell side, hot jobs, financial crime, …
Innovation in Trading

The next wave of broker innovation will be Crowdsourced algos


Are Institutional Brokers Innovative?

Markets Media is reporting that “47% of buy-side firms, mostly those with modest levels of assets under management, did not think their brokers were doing anything innovative.” Andy Kershner, the CEO of CloudQuant’s parent company was interviewed.  He believes:

The next wave of broker innovation likely will be geared toward democratizing quantitative trading, according to Kershner Trading Group Founder and CEO Andy Kershner. That would vastly expand the universe of high-level quant traders globally, which Kershner roughly estimated stands at perhaps 5,000 today.

Andy Kershner, Kershner Trading

“It will be very similar to what you saw when you had access to the market open up in the mid-90s for day traders,” Kershner said. “You had lots of innovation, lots of people coming in with lots of ideas, and lots of software that really changed and knocked out all the market makers. Then high-frequency came along and killed all the specialists and also knocked out some of the day traders. I think now we’ll see a reversion.”

“You’ve got ‘big data’ out there everywhere that everybody talks about, but the moat is access to the data, access to capital, and access to some knowledge of what to do with it,” Kershner continued. “I think in the next three years if you do not have an auto- or quantitative-trading system that’s more than just APIs — you’ll need back-testing, forward-testing, live, the whole package — if you don’t have that for people to sign up for, your brokerage will be left behind.”

Read the full story on Markets Media

Stock Market, Quantitative Strategy, Trading, and Algo Development Industry News

Morgan Stanley’s 16,000 Human Brokers Get Algorithmic Makeover

Call them cyborgs. Morgan Stanley is about to augment its 16,000 financial advisers with machine-learning algorithms that suggest trades, take over routine tasks and send reminders when your birthday is near. The project, known internally as “next best action,” shows how one of the world’s biggest brokerages aims to upgrade its workforce while a growing number of firms roll out fully automated platforms called robo-advisers. The thinking is that humans with algorithmic assistants will be a better solution for wealthy families than mere software allocating assets for the masses. At Morgan Stanley, algorithms will send employees multiple-choice recommendations based on things like market changes and events in a client’s life, according to Jeff McMillan, chief analytics and data officer for the bank’s wealth-management division. Phone, email and website interactions will be cataloged so machine-learning programs can track and improve their suggestions over time to generate more business with customers, he said. … Read the full story on Bloomberg
Quantitative Strategies and Capital for Trading

Citigroup taps quant trader Thomas Chippas

According to MarketWatch: By  Published: June 2, 2017 1:13 p.m. ET
Citigroup Inc. has tapped a quantitative trading veteran to help the bank vault into that hot category, as part of a broader build-up of its equities unit.
Thomas Chippas will join as global head of quantitative execution, the bank said Friday. Mr. Chippas previously led quant-trading units at Barclays PLC and Deutsche Bank AG, before leaving for jobs beyond Wall Street, most recently as chief operating officer of blockchain-technology startup Axoni Inc. Read the full article on MarketWatch