Thought Leadership Archives - The Industry Spread

Will CTFs be the new ETF?

December 4, 2018 by George Agathangelou Leave a Comment Filed Under: Thought Leadership Articles Tagged: Blockchain, Cryptocurrencies, CTF, ETF, SEC

Exchange-traded funds (ETFs) are among the most popular methods of investing today, with investors pouring trillions of dollars into an ever-growing pool of funds. Particularly in comparison with expensive to use hedge funds, low-fee ETFs provide a safer, more stable means of investing. One of the benefits of ETFs is that they are infinitely broad in scope and focus; investors can use ETFs to participate in markets ranging from traditional sectors like energy to alternative, newly arising sectors like the cannabis industry.

On the other hand, cryptocurrencies have witnessed an increasing interest and hype in the investment world, especially in 2017. The recently witnessed bull market of 2017, followed by the consequent bear market of 2018 have signalled opportunities for many investors. Some of the most successful cryptocurrencies have witnessed astonishing returns. However, the cryptocurrencies industry is still full of uncertainty and experience heavy volatility on a daily basis. For this reason, many investors could potentially prefer to use a vehicle like an ETF to participate in the cryptocurrency space.

Similar to mutual funds, ETFs gather investor assets and buy stocks or bonds according to a basic strategy spelled out when the ETF is formed. ETFs trade similarly to stocks where you can buy or sell anytime during the trading day. Mutual funds are bought or sold at the end of the day, at the price, or net asset value (NAV), determined by the closing prices of the stocks or bonds owned by the fund. Because they trade like stocks, ETFs can be sold short, and can provide a way to profit when the ETF price drops. Many ETFs also have related options contracts, which allow investors to control large numbers of shares with less money than if they owned the shares outright. The short-selling capability and options are not available with mutual funds. This difference makes ETFs better for day-traders betting on short-term price changes of entire market sectors. For long-term investors, these features don’t matter. Many investors like index products because they are not dependent on the talents of a fund manager who might lose his touch, retire or quit.

A Crypto ETF in theory, works like any other ETF, in the way that it tracks an index or a basket of digital assets. Similarly, they would trade like other ETFs or like common stocks on an exchange, while being subject to price changes throughout the day depending on demand and supply for the underlying instruments. For a Crypto ETF to work properly, it will need to own the underlying assets that it tracks. The ownership of these digital assets would be divided into shares, and investors in the ETF would indirectly own those assets. ETF investors would then receive their share of the profits generated by those underlying assets.

By using a Crypto-ETF, investors might take advantage of the opportunities that tokens present without having the implications of actually owning them. Understanding how to secure cryptocurrencies and digital token exchanges, and actually applying the best security practices can be a daunting task for people. Digital assets are still primarily the targets of thieves and scammers, and as such, we can understand why investors might wish to take this extra precaution, despite the fact that they are giving away the control of their digital assets to a 3rd party custodian. Another benefit of a cryptocurrency ETF is that it can be used to track multiple digital tokens at once. The cryptocurrency world is constantly evolving, and investors looking to hold a basket of, say, 20 different tokens may have to own and operate multiple wallets and accounts across various digital currency exchanges, making sure that their wallet software is up to date, while there are no guarantees that centralized exchanges will not be the target for hackers. The process of investing in these tokens and trading regularly becomes cumbersome and might not be worth the amount of administrative time and effort required. A cryptocurrency ETF could take out much of the time and hassle for investors by simply tracking those same 20 or more tokens. Investors could then buy and sell shares of the ETF to gain the same exposure to that pool of tokens without having to worry about individually managing each of them.

Up to this day, the U.S. Securities and Exchange Commission (SEC) has reported that it will not approve any cryptocurrency ETFs until the markets demonstrable stability and security. Still, that has not deterred many parties from attempting to launch digital currency ETFs.

The Chicago Board Options Exchange (CBOE), which launched bitcoin futures in December 2017, has urged the SEC to reconsider its earlier rejection of digital token funds. Cameron and Tyler Winklevoss, the founders of popular digital currency exchange Gemini, have twice petitioned the SEC to approve a bitcoin ETF, and so far, each attempt has not been successful. Coinbase, another immensely popular digital currency exchange, launched an index fund offering exposure to four of the largest digital currencies, but that again is not quite the same as an ETF.

The SEC has indicated its openness to the possibility of cryptocurrency funds in the future, and this could perhaps continue to fuel investor optimism if cryptocurrency ETFs do become a reality, in other parts of the world. Various markets in Europe and Asia, for instance, have introduced cryptocurrency ETFs thanks to differing levels of regulation. For the time being, though, U.S. investors will have to wait with only a few options at hand.

One of the options could be a related group of ETFs: blockchain funds. Blockchain technology supports the cryptocurrency space and is closely linked with digital tokens. There are a growing number of ETFs focused on blockchain-related companies and may include crypto related brands like computer processor developers and manufacturers. While bitcoin-based ETFs have been rejected by regulators owing to questions of control, those based on the underlying blockchain technology are receiving a better treatment. Two popular blockchain ETFs include the Reality Shares Nasdaq NexGen Economy (BLCN) ETF and the Amplify Transformational Data Sharing (BLOK) ETF. Such blockchain ETFs come with the inherent risk of investing funds on technology-based start-ups while the blockchain industry is still evolving, and regularly hitting regulatory roadblocks across the globe.

Simultaneously, as the entire world is waiting for the US SEC decision regarding Bitcoin ETFs, Switzerland is pioneering in the space. The country’s financial regulator, FINMA, confirmed that the exchange-traded product will arrive in Switzerland’s principal stock exchange, on November 21st. The specific ETP will track five top cryptocurrencies by market capitalization. ETPs are exchange-traded products which are being approved by Switzerland’s stock exchange called Six Swiss Exchange. The ETP is being issued by Amun AG, headquartered in Zug.

ETPs are different from ETFs and the main reason is the fact that ETPs are not subject to the CISA (Collective Investment Schemes Act). As a result, they are not supervised by FINMA. Alternatively, ETFs would be subject to the CISA, as they are funds that are traded and are normally tracking an index’s performance. On the other hand, ETPs are similar to ETFs in a way that they trade in a multi market-making segment. However, they are not funds in a legal way. Instead, they are collateralized and noninterest-earning bearer debt securities. They can replicate an underlying asset which is usually from the sector of commodities.

In their document, Six Swiss Exchange shows that ETPs will include ETNs (exchange-traded notes) as well as ETCs (exchange-traded commodities). While ETNs are a kind of debt security that can be traded on exchanges with a promise of returns linked to a market index, ETCs provide exposure to various commodities. In addition, there are also different risks involved with ETFs and ETPs. ETFs are separate asset pools which can be segregated in case of the provider’s bankruptcy, which means that there is no issuer risk involved.

While it’s far from a sure thing, many cryptocurrency enthusiasts see it as only a matter of time before the SEC approves digital currency ETFs. If and when this happens, it is expected to have a significant impact on the performance of the cryptocurrency space itself. With the increasing amount of volatility surrounding the cryptocurrency marketplace, investors are looking for new ways to operate in this unexplored area of finance. CTFs allow investors to purchase a basket of cryptocurrencies through a single investment vehicle, and by doing so investors will immediately be able to lower their inherent risk without having to pull their investment for fiat currency. This will allow investors to naturally lower their risk exposure without having to pay excessive diversification fees and will allow them to continue to operate in a deregulated market.

About the Author – George Agathangelou

George Agathangelou

George is a leading figure in the crypto-space within Cyprus, with ten years of experience in financial services and strong connections throughout the FX and Cryptocurrency industry. He specializes in FX, cryptocurrency trading, and alternative assets investments while maintaining an incredible operational track record in management positions for regulated investment firms. Primarily an economist, George believes passionately in the future of cryptocurrencies and aspires to help create the infrastructure required for vast adoption of this new financial paradigm. Simultaneously he is an advisor for Cyprus Blockchain Technologies, a non-for-profit organization with the aim to conduct edge-cutting research in the field of Blockchain and Distributed Applications and to organize transfer of knowledge sessions, round-table discussions and executive education seminars. Lastly, George is the Chief Business Development Officer for the first blockchain consultancy firm in Cyprus, Universal Crypto. As an active member of Bitcoin Club Limassol, George attends local meetups to share his enthusiasm. George also founded and maintains the Facebook community Digital Currency Group. Being a renowned public speaker, he’s been discussing recent trends and topics of the cryptocurrency space around the globe. He’s also contributed to a number of white paper preparations and audits for various companies focusing on financial services.

Equity Market Innovation Leads to Venue Proliferation

May 25, 2018 by Ivy Schmerken Leave a Comment Filed Under: Thought Leadership, Thought Leadership Articles Tagged: ATS, Buy Side, dark pool, Equity Market, equity trading, FINRA, Imperative Execution, Intelligent Cross, Long Term Stock Exchange, Luminex Trading, M-ELO, NASDAQ, Stock Exchange, trading strategies, trading system, Venue Proliferation

Several startups are planning to launch either new venues or order types, and even listing standards, to solve problems in US equity trading.

At last month’s SIFMA Equity Market Structure conference, executives with the following three startups and one established exchange discussed their particular market innovations and how each one fits into the existing equity market structure.

Imperative Execution is on the brink of launching a dark pool to actively solve the problem of minimizing market impact.
Nasdaq Stock Market introduced a new order type to help institutions gather more liquidity.
Luminex Trading & Analytics is running a buy-side only block execution facility, which has gained traction over the past three years.
Long Term Stock Exchange is in registration to become a stock exchange to help companies access the public markets and discourage short-term trading.

But each of these venues is entering a complicated marketplace. With 12 exchanges and more than 30 ATSs, any new entrant is facing hurdles of convincing buy-side and sell-side firms to connect to their venues to send order flow.

“Market impact is a problem that a significant segment of the market cares about across the board,” said Roman Ginis, founder and CEO of Imperative Execution, a startup based in Stamford, Conn., which is preparing to launch a dark pool known as Intelligent Cross in May. “The market makers care about this, the long-onlys care and the strategy funds care, even though they each have different holding periods,” said Ginis.

Roman Ginis

“Instead of looking for a silver bullet that solves the problem in the marketplace, we look at it as an optimization problem,” said Ginis, who has a PhD in computer science from the California Institute of Technology and is a former quantitative trader at Cubist Systematic Strategies, a business of Point72 Asset Management. Ginis said he spent many years designing algorithms for different strategies.

The startup is seeded by Steve Cohen’s Point 72 Ventures arm to combat slippage associated with certain high frequency trading strategies, as first reported in The Wall Street Journal in Steven Cohen Targets High-Frequency Trading with ‘Dark Pool’ Venture. However, Ginis said the problem is not specific to HFT and is much broader.

“The problem is we have a market great for small orders. The US market is deep and fast and highly liquid for retail. We don’t have an efficient market for those who want to trade a few thousand shares,” said Ginis. “This is why all the algorithmic desks exist,” he said. We built this platform to be additive to everyone’s algorithm that want to execute over time,” he said.

Unlike other ATSs, it will not work “parent” orders; instead, it will only see the “child orders” that are executed through algorithms, he said. About 80% of the market is algorithms working orders in the market.

Intelligent Cross will run a midpoint cross whose objective is to get the market impact immediately after the print as close to zero as possible, said Ginis.

Need for Differentiation

What sets the dark pool apart from other rivals is that it uses machine learning to optimize its matching engine’s decision of when to trade based on the liquidity of each stock.

Rather than design a continuous trading system, Intelligent Cross is a venue that optimizes at discrete times. It will measure its own performance, learn from empirical data and “recalibrate” the match time for each stock, said Ginis.

As a near continuous match, it matches every couple of milliseconds, which is different for every stock based on its liquidity. To find the best time to trade, the ATS measures the market response typically 5-10 milliseconds after its own print. This is typical since algorithms also measure the book before and after, and know exactly how much slippage they received, said Ginis.

If there is too much market reaction that means somebody learned some information about the direction of the trade. In that case, the ATS would adjust the matching periods to make them further apart, explains Ginis. Instead of matching every 7 milliseconds, it can match every 3-10 milliseconds or 5-12 milliseconds. Then it can start shrinking the period between matches. “What the matching engine does is constantly find the optimal frequency in which to match such that it matches as often as possible, yet while the market response goes as close to zero as possible,” said Ginis. What you see coming out of the venue is a series of midpoint prints at randomized times, he said.

Slowing Down Executions

Chuck Mack, Deputy Head of US Equities, on the SIFMA panel

Other venues are also looking at reducing the frequency of trading to help institutions aggregate liquidity.

Nasdaq Stock Market has created the Midpoint Extended Life Order (M-ELO) “for participants for whom a millisecond or microsecond doesn’t matter,” said Chuck Mack, Deputy Head of US Equities, on the SIFMA panel. The dark order-type “is for participants or investors who are willing to be patient for just half-a-second and in return for that, anyone who wants to trade against them has to wait half a second,” he said. M-ELO orders will only execute against other M-ELO orders, which means they won’t be interacting with intermarket sweep orders (ISOs) or be subject to pinging, he said. “It was created in a simple way to protect participants as much as we can from adverse selection and market impact,” said Mack.

Simplicity of Block Trading

In the public markets, where many traders are representing 100 share orders to buy or sell, institutions are vulnerable to adverse selection and information leakage. “It can be frustrating to assemble a block,” said Jonathan Clark, CEO of Luminex Trading & Analytics, an ATS that is owned and operated by a consortium of buy-side institutions.

“Governance and ownership structure is what makes us unique,” said Clark. Launched in 2015, Luminex was founded by nine asset managers, including Fidelity Investments, to serve as a utility-like venue for buy-side institutions. The other investors include Bank of New York Mellon Corp., BlackRock, Capital Group Cos., Invesco, JP Morgan Asset Management, MFS Investment Management and State Street Global Advisors and T. Rowe Price.

“We aren’t catering to broker-dealers. They don’t have access to the platform,” said Clark, adding that asset managers must come in through their OMS or EMS, so it’s a closed environment.

Jonathan Clark, CEO of Luminex Trading & Analytics

The formation of the buy-side dark pool was a reaction to some of the scandals that occurred with dark pools, such as the blow-up of Pipeline Trading after it leaked information about institutional clients to its market-making arm. “That sort of stuff gives pause to the buy side,” said Clark, a former buy-side trader on a quantitative team at Merrill Lynch Investment Management, which was acquired by BlackRock in 2006. Clark became co-head of equity trading at BlackRock until he joined Luminex.

Though skeptics had claimed the venue would not get matches since buy-side firms are on the same side of the trade, Clark said the ATS has data to prove otherwise. With a minimize size of 5,000 shares, Clark said Luminex’s average execution size is 30,000 shares, and the average quantity submitted is 160,000 shares.

Based on FINRA data across all ATSs in the 10,000-share category, Luminex’s average trade size is 50,000 to 60,000 shares. Currently, Luminex has 180 subscribers in the market and feels that 200 will put it in good shape.

Despite the proliferation of order types in the market, Luminex is focused on the simplicity of block trading and only has two order types, said Clark. Cost is also a factor in Luminex’s growth. While some other block venues charge one to two cents per share, Luminex charges 25 mils (or .25 per 100 shares). He attributes some recent success to conditional orders where clients have an opportunity to rest an order in multiple places simultaneously. “Clients have an opportunity to rest with us and to rest with other venues,” he said. Through an OMS or EMS, they can check a box and expose a one-million share order to BIDS and Luminex, he said. “I can be in multiple places at the same time, and I can be patient,” said Clark.

Long Term Value Creation

By contrast to public exchanges that focus on high-speed trading, Long-Term Stock Exchange (LTSE) is in registration with the SEC to launch a stock exchange that allows listed companies to focus on long-term growth plans.

The exchange is looking to fight short-term thinking by creating new voting standards and disclosure rules which reward long-term shareholding. The idea was borne out of a book, The Lean Startup, in which author Eric Ries focused on how to help companies with innovation. Ries found out that startups and established companies are waiting longer to access the public markets. Ries proposes that startups will sell more shares to the public on LTSE because the rules will insulate them from short-term distractions and pressure from activist investors.

Michelle Greene, chief policy officer at LTSE

“As soon as companies enter the public marketplace they are facing quarterly scrutiny,” commented Michelle Greene, chief policy officer at LTSE. However, in the private marketplace, there was interest in how a company was executing on the long-term plan and growth, said Greene. Among the ideas is to provide disclosures that would give long-term investors more insight into what a company is planning for the long-term, and how they are measuring progress toward longer-term goals, said Greene, who is a former head of corporate responsibility at the New York Stock Exchange. Though it would be easier to register as an ATS, LTSE decided to register as an exchange. Greene said it would take the authority of an SRO [self regulatory organization] to make an ecosystem change. In addition, LTSE struck an arrangement in December of 2017 with IEX, whereby companies that list on LTSE can trade on the IEX platform. The partnership, known as “LTSE Listings on IEX,”will provide a specialized set of listing standards for companies looking to go public while making a commitment to long-term value creation, reported Bloomberg. Until LTSE is approved to be an exchange, companies can get an LTSE listing standard on IEX, whose exchange is aligned with the idea of protecting investors from short-term predatory strategies.

Does Venue Proliferation Matter?

While US equity market structure is already complex, each ATS and exchange operator feels there is room in the market for innovation. Luminex’s Clark points out that when he was a buy-side trader, there were 50 dark pools operating, but some have closed their ATSs. “So now there are sub-40, and so there’s been some consolidation in that arena,” said Clark.

According to Imperative’s Ginis, “The number of venues doesn’t really matter as much since they are all connected to each other. “Everyone builds just one book,” he said. If he makes a decision to buy or sell stock and an order is routed via smart order router to Nasdaq or Arca, “it goes down one pipe to wherever I want to go.”

At the same time, major exchanges are floating the idea of launching more matching engines to differentiate their offerings. As reported, Cboe Global Markets suggested it would light up medallions to increase its revenues if the regulators impose price controls on access fees and rebates. Also, Intercontinental Exchange (ICE), the owner of the NYSE, is expected to bring its fourth exchange, NYSE National, live in May, and ICE also agreed to acquire the Chicago Stock Exchange for a reported $70 million, reported S&P Global Market Intelligence.

Alluding to this trend, Clark said, “I know that exchanges have been buying up different medallions. That’s innovation,” he said. “If you want to take a small venue and give it a go, Why not? “I think innovation is what’s driving the market structure today. I don’t think it’s broken.”

SEC Steers Pilot on Equity Trading Fee and Rebates Amid Headwinds

May 8, 2018 by Ivy Schmerken Leave a Comment Filed Under: Thought Leadership Articles Tagged: asset managers, Brokers, Buy-side firms, Equity Trading Fee, Exchange rebates, fee caps, hedge funds, market makers, regulators, SEC, stock exchanges

Ivy Schmerken

By Ivy Schmerken

Diving into one of the more controversial issues in equity market structure, U.S. regulators are moving forward with the transaction-fee pilot that will force stock exchanges to lower the fee caps on rebates they pay to brokers and market makers.

“I think the SEC is about to embark upon a productive time for assessing market structure reforms,” said Brett Redfearn, Director of Trading & Markets in remarks at the Securities Industry Financial Markets Association (SIFMA) Equity Market Structure Conference, which was held on April 18.

At the same time, the SEC has decided to sunset the tick-size pilot, which its due to expire on Oct. 2^nd. An assessment by exchanges is due on July 3^rd.

Brett Redfearn, Director of Trading & Markets

But the SEC’s transaction-fee pilot has stirred up debate over whether the regulator should be involved in setting fee caps on rebates that stock exchanges pay to brokers and market makers.

The proposed pilot, which is open for public comment until May 13, is slated to run for two years with an automatic sunset after one year unless the Commission extends it.

The transaction-fee pilot was announced in March to address criticism over the maker-taker system of rebates, which reward brokers that route client orders to the exchanges with the highest rebates. The practice has been controversial as some say it distorts the order routing behavior of brokers.

Buy-side firms and other critics maintain that the rebates cause conflicts of interest by incentivizing brokers to route their orders to the highest rebate exchanges, and not necessarily to the exchange that will provide the best execution.

“Exchange rebates, maker-taker and inverse (i.e., taker-maker) pricing, as well as other incentives have become a growing concern for investors, ” wrote Southeastern Asset Management, with $18 billion in assets under management, in a comment letter sent to the SEC in April. The letter was signed by a group of 21 asset managers, including the likes of Franklin Templeton Investments, Janus Henderson Investors and hedge funds such as Greenlight Capital, Pershing Square, and Oak Tree Capital, showing a groundswell of support for the pilot.

Some panelists were skeptical of regulators setting market fee caps or rebates. They contend the government should not be involved.

Proponents of the maker-taker fee model contend it has increased liquidity and narrowed spreads for investors.

Chris Concannon, President and COO of Cboe Global Markets

A Cboe executive speaking at the SIFMA conference, said the SEC should not be banning rebates.

“I have a problem with banning rebates,” said Chris Concannon, president and COO of Cboe Global Markets, who called it “Draconian” and warned of its impact on investors. “The consensus is that if you adjust the rebate, it will widen spreads,” he said. “Impacting the rebate is taking money out of investors pockets,” he added.

Through the transaction-fee pilot recommended by the SEC’s Equity Market Structure Advisory Committee (ESMA) in July of 2016, “the SEC changed it and threw in a ban on rebates,” he said.

Concannon said his exchange has market makers that have “no conflict,” trade as principal, and are paid a rebate for making markets. “I find it unacceptable that we can’t enter into that relationship to support stocks.”

Banning rebates could result in exchanges coming up with other incentives for market makers such as market data discounts, he noted.

However, Brandes Investment Partners, with $31.2 billion of asset under management, supports the pilot with the no-rebate bucket. “It is critical that the pilot include a no-rebate bucket,” stated Brandes in its comment letter. Citing a study by IEX, Brandes wrote that public data shows the negative impact on investors from the maker-taker pricing model.

“Rebates result in long queues of orders to buy and sell on the largest exchanges, which can result in delays in execution of trades for long-term investors,” wrote Brandes.

Under the proposed pilot, regulators will collect data on whether lower fee caps for different groups of stocks will alter the order routing behavior for brokers. The pilot will cover all NMS stocks of any capitalization and include all maker-taker and taker-maker exchanges.

The access-pilot will force exchanges to reduce the fee cap that currently allows them to pay up to 30 cents per 100 shares. Instead, the pilot will cap the rebate and take fee at 15 and 5 mils in two different test groups while a third test group prohibits rebates and linked pricing. But it’s the no-rebate bucket, which has sparked the most debate.

The inclusion of the prohibition on rebates is a departure from what EMSAC recommended EMSAC wasn’t able to reach consensus on the no-rebate bucket or on inverted venues, according to Joe Saluzzi and Sal Arnuk, partners and co-heads of equity trading at Themis Trading in their comment letter.

In their view, stock exchanges employing maker-taker pricing sell speed in ways that maximize the insertion of high-speed intermediaries between natural buyers and sellers.

“This Transaction Fee Pilot provides an opportunity, specifically in Test Group 3, to test whether or not this excessive intermediation is good for investors, and we commend the Commission for undertaking it,” wrote Saluzzi and Arnuk. “We believe that forcing out some of the conflicts in order routing will improve market quality and lower investor costs.”

A prohibition on rebates will allow the SEC to explore a number of questions related to order routing behavior, execution quality and market quality, said Redfearn at the SIFMA event.

“We thought we would be missing an opportunity to fully evaluate exchange pricing levels without proposing this feature,” continued Redfearn.

While the 30-mil fee cap was decided in 2004 when Reg NMS was designed, the same fee cap governs the marketplace today. “We must all wonder whether this number was the right number for all time,” said Redfearn.

As for “where the pilot could take us,” Redfearn said that analysis of the proposed pilot data could discover there’s a “sweet spot” for certain stocks at different price levels and a “sweet spot” for others at a different level. This could lead to an outcome where regulations would propose tiers and fees at multiple levels for different types of stocks.

In contrast, with the no-rebate bucket, regulators would try to understand whether competitive market forces could actually cap the fees in the marketplace without a government-imposed cap, he said.

Along the way, he noted it might be necessary to revisit the order protection rule (OPR) within Reg NMS, which requires brokers to lift the best bid or offer across all the venues, which are deemed protected quotes. OPR has led to the proliferation of exchanges and different rebate/maker-taker pricing models, which many blame on fragmentation and the arms race with colocation, high-speed trading and proprietary market data feeds.

Yet, some leading firms have reservations about the pilot.

Doug Cifu, CEO of Virtu Financial

Doug Cifu, CEO of Virtu Financial, said rather than conduct a pilot, the problem can be fixed with increased disclosure. “What we need is better policing and disclosure around the principal agent relationship,” said Cifu, on April 10 at the Equity Market Structure Symposium, hosted by the STA Foundation and the University of Chicago’s Booth School of Business. Institutions should be able to go to their executing brokers and find out where they sent their orders and why, said Cifu.

As far as the rebates and maker-taker go, Cifu said the issues have been conflated with order-routing conflicts. “We’re paying for queue priority. We’re getting a rebate. That’s what the algorithm does. It’s not trading for rebates. It’s trying to get you the best execution possible. We pay every exchange out there. Go look at our public disclosures. It’s a question of narrowing spreads,” he said. As an executing broker, Virtu has a tool that can show clients in microseconds where they send an order and why they sent it there. Institutional investors should be able to demand this of their brokers, or they should get another broker, said Cifu.

Joe Gawronski, President and COO of Rosenblatt Securities

Pilot Fatigue

“I’m a bit lukewarm on it,” commented Joe Gawronski, president and COO of Rosenblatt Securities referring to the access fee pilot on a SIFMA panel about the impact of market structure on capital formation.

What’s more the tick pilot showed an increase in volume executed on inverted venues, with high take fees. One panelist noted that multiple studies show that brokers are paying 10 cents to get 3 cents of price improvement, calling it “a classic inefficiency in the system.”

Unintended Consequences

But the transaction-fee pilot could also result in some unintended consequences. As a result of the pilot lowering access fees into 15 and 5 mils, it’s believed that exchange rebate pricing models will become less differentiated. At the STA-Chicago Symposium, Concannon stated that Cboe might “light up” more medallions when he spoke.

Vlad Khandros, Managing Director, Market Structure & Liquidity Strategy at UBS

“The fact that we’re going to have more exchanges because of this proposal is clearly unintended,” commented Vlad Khandros, Managing Director, Market Structure & Liquidity Strategy at UBS on the SIFMA panel. “And the fact that this is already happening when is just a proposal still is unfortunate.” As a result, Khandros said that regulators might have to revisit the order-protection rule (OPR) along with the access fee pilot, which allows exchanges to have protected quotes, causing proliferation of venues and fragmentation.

Artificial Intelligence Gains Momentum: From Machine Learning to Deep Learning

March 1, 2018 by Ivy Schmerken Leave a Comment Filed Under: Thought Leadership Articles Tagged: A.I., algo, algorithmic trading, artificial intelligence, Deep Learning, financial services, Machine learning, regulators

Artificial intelligence has quickly evolved from science fiction to digital assistants such as Alexa and Siri learning about our daily lives. A.I. applications are interpreting MRIs and will soon be operating self-driving cars. In personal finance, many of us interact with chat boxes on bank web sites. And in the investing world, robo-advisors are managing portfolios for retail investors.

“But we haven’t seen the penetration of AI within institutional finance,” said Richard Johnson, vice president of market structure and technology at Greenwich Associates on a recent webinar about the evolution of A.I. and current levels of adoption on Wall Street.

Richard Johnson, vice president of market structure and technology at Greenwich Associates

In an audience poll taken during the Greenwich webinar, “Artificial Intelligence: The Coming Disruption on Wall Street, “37.5% of attendees said that artificial intelligence would have the biggest impact on research, 34.7% trading, 23.6% compliance and 4.2% cited sales.

Among the key benefits of artificial intelligence is that it can analyze large volumes of structured and unstructured data more quickly than humans do, which can boost their productivity.

Large banks, hedge funds and traditional asset managers have been hiring data scientists to develop machine-learning algorithms that scan billions of data points, news articles, blogs, social media posts, and images.

“Machine learning cannot only find alternative data but also measure the value of satellite images, predict how much oil is in the ground or how many cars are in a parking lot,” noted Johnson.

One of the world’s biggest hedge funds, Man Group, has used machine-learning techniques to find new strategies, “from start to finish,” noted Johnson. Man runs $43 billion of its $96 billion in total assets quantitatively, mainly through algorithms, operating 21.5 hours a day, from the open in Asia to the close in the U.S., reports CNBC.

But Man Group’s AHL quantitative investment unit is now allowing the models to learn from the data and trade completely on their own, reported CNBC. The firm’s Sandy Rattray, Man’s London-based chief investment officer, told CNBC that the strategy is earning higher returns than traditional quant methods.

Deep learning refers to machine-learning algorithms with many different layers of logic. These are artificial neural–networks which replicate how the brain works, explained Johnson.

Sandy Rattray, Man’s London-based chief investment officer

Many people are worried that artificial intelligence will automate tasks performed by humans, resulting in downsizing and job losses in banks and brokerage houses. But experts say that adoption of AI is inevitable. “AI is blending the attributes of humans with computers,” said Johnson.

While humans learn from trial and error, computers follow instructions that are programmed for them. “What’s changed is computers can learn from data, and data is a key part of the growth of AI,” said Johnson.

In research conducted last summer, Greenwich found that 18% of financial services firms had already implemented AI technology in their business, while 57% were exploring the technology and had plans to implement in the next 12 months. Overall, three-quarters of banks and financial services firms have plans to implement AI in the next 12 months, said Johnson.

One has no look no further than the compliance space, which needs to analyze large amounts of data to look for suspicious patterns.

“Spoofing is a big topic for regulators but there is really not a good definition of spoofing,” said Johnson.

The Commodity Futures Trading Commission (CFTC) has defined spoofing as the intent to mislead, but that is ambiguous.

One firm is using machine learning to analyze patterns in public spoofing cases, and then looks for those patterns in real time across 50 million public cases, said Johnson. The machine is programmed to scan the market and to alert the firm when it spots this kind of regulatory action.

A Perfect Storm for Artificial Intelligence

Although the term artificial intelligence was coined 50 years ago, it has met with limited success. However, over the past five years there’s been an explosive growth in data due to three factors. First, the Internet has given rise to billions of documents, blogs, and social media feeds like Twitter and images. Computers are able to scan data sources and identify patterns in data and calculate sentiment. Hardware advances in graphical processing units (GPUs) from Nvidia for PCs and streaming video gaming are available for number crunching as required by AI. Another driver is the growth of cloud computing from Amazon Web Services, Google and Microsoft, allowing small teams to access machine learning on demand.

Research Efficiencies

From a productivity standpoint, sell-side research is one area that is ripe for disruption, since analysts must generate many reports.

With about 4,000 publicly traded equities in the US stock market, most banks cover no more than 1,000 stocks mainly for productivity reasons, said Johnson.

Because there is a ton of structured data in finance from balance sheets, income statements, credit ratings, economic data and government data, computers can be trained to write simple quarterly results based on financial statements.

A junior analyst could write eight summary reports each day. But it would take 2,000-person days to write a quarterly research summary for each publicly traded company, estimates Johnson. That would equate to eight full-time employees dedicated to writing these quarterly research reports, he said.

“AI can generate 3,000 publicly traded reports in less than 10 minutes, whereas it would take 2,000-person days for human to do that, estimated Johnson. “So, for structured data, firms need AI for productivity reasons,” said Johnson.

Sales & Customer Service

While it’s common today to call up a bank and utilize online chat support for help with a transaction or problem, this is usually found in retail banking or wealth management. Chat could be deployed in the institutional finance support area to save time for the sales-trader. There are a number of simple questions that could be answered through chat, such as “what’s the price of Verizon five-year bonds; what is the average price of my order in stock XYZ; and did I receive any fills in this stock form dark pool A?” Today, the sales trader has to respond to an inquiry and call back with a response. “There’s no reason that we can’t train AI to do that type of stuff, which would free up the sales trader to focus on higher-valued activities,” said Johnson.

Artificial Intelligence and Trading

While banks have increased the efficiency of algorithmic trading, sell-side firms are working on ways to incorporate AI. According to estimates, 80% percent of the buy–side order flow is executed through electronic channels, and even if it goes to a sales trader, it often ends up in an algo. Market making is also electronic. “The next generation of algo trading is going to incorporate AI,” predicted Johnson. “They’re going to be better able to make their own decisions based on learning from data they have in the [internal] trading logs of millions of historical orders and figuring out the best way to execute new orders entered into the system,” said Johnson.

For example, JP Morgan & Chase Co. recently announced it has incorporated AI into their liquidity seeking algos and that they performed better than previous non-AI algorithms, said Johnson.

According to Business Insider in “JP Morgan takes AI use to the next level”, JPM has been testing its AI program, LOXM, since Q1 2017 in Europe, and planned to roll it out across its Asian and US operations in Q4 2017 after trials proved successful.

“LOXM was trained on billions of historic transactions to enable it to execute equities trades at maximum speed and at optimal prices, and to offload large equity stakes without causing market swings,” wrote Business Insider.

As machine learning and other AI technologies emerge on Wall Street, there is debate around AI’s impact on jobs. In one camp, there are fears of robots taking over the world, said Johnson. On the other side, people see AI as augmenting technology that is no different than others, said Johnson. He estimates that about 15% of jobs are at risk in financial services.

Banks have reduced headcount over the past decade, and that was before A.I., so that process is likely to continue, he said. It remains to be seen whether A.I. poses a threat to jobs, but it’s also possible that people will move into different roles.

However, AI has its limitations, such as in research where it is not good at forming longer-term investment theses. If a company hires new management and changes its strategy, a traditional portfolio manager may pick up on this growth opportunity, but an AI algorithm can’t do that, said Johnson. “There is still plenty of value out here for human PMs and analysts,” said Johnson

Alt Data on the March with Machine Learning

January 25, 2018 by Ivy Schmerken Leave a Comment Filed Under: Thought Leadership Articles Tagged: A.I., Alt Data, asset management, big data, geolocation, hedge funds, Machine learning, sentiment analysis

The explosion of alternative data sources, such as satellite images, sentiment analysis, and geolocation data, is having a profound impact on the field of quantitative investing.

Analyzing torrents of unstructured data requires sophisticated tools and technology, and this leads to opportunities as well as challenges.

Alongside the boom in alternative data, there is increasing demand for data scientists and machine-learning professionals who can work with petabytes of unstructured data sets. In January, Point72’s Aperio unit advertised for a “machine learning-data scientist” on AlternativeData.org, a public website that covers the industry.

And on Dec. 12, The Financial Times reported that AQR, a $208 billion hedge fund manager run by Clifford Asness, planned to explore big data. Despite previous doubts, AQR reportedly plans to experiment with machine learning “to parse through novel data sets such as satellite pictures of shadows cast by oil wells and tankers,” Asness told the FT.

Why is the growth in alternative data fueling the demand for data scientists with machine learning skills?

Mansi Singhal, cofounder at qplum

“It’s hard to make money on simple trades or macro-trades on basic relationships. So, everybody on the asset management side is chasing how to get that next edge,” said Mansi Singhal, cofounder at qplum, who spoke at TABB Forum’s Fintech Festival in November.

Based in Jersey City, NJ, qplum is a registered investment advisor that operates an online wealth management platform offering A.I. and machine learning-based portfolios of ETFs in stocks, bonds and real estate. The firm uses big data, algorithmic executions and risk parameters to run an automated end-to-end process. “Instead of asking people to write signals or strategies, the edge is to use a machine learning framework on it to extract features from data,” said Singhal in a follow-up interview.

Amidst the buzz surrounding alternative data, there are still concerns about the amount of resources, time and energy it takes to collect, process and reformat the data for use in algorithms and machine learning tools.

“Clearly, it’s not that simple. There are a lot of challenges in terms of collecting that data, coding that idea, and running the correct models,” said Patrick Pinschmidt, partner at Middlegame Ventures in Washington, D.C.

To extract value from alternative data, it requires an investment in infrastructure and talent, said qplum’s Singhal on the panel.

Data Scientists & Infrastructure

While traditional hedge funds will spend a lot of money on relationships and consultants, perhaps to obtain shipping or trucking data, qplum chose a different route. Firms can choose whether they want to spend money to get that data from a relationship or invest resources in developing their own data pipeline, continued Singhal. Investing in relationships or consultants is an ongoing expense, whereas once a data pipeline is built it can be utilized over and over again, she emphasized.

However, qplum’s Singhal said there has not been a better time to get one’s hands direct in the data space. For example, computational power is faster and infinite. In terms of data sources, “It’s like being a kid in a candy store,” she said.

While the term ‘alternative data’ is often associated with sentiment analysis from Twitter, Singhal said this is “very noisy data and hard to build sustainable models from.” Traditional asset managers “can start with stable, cleaner data,” she said.

Rather than pay consultants for data, qplum is downloading data from the Federal Reserve Economic Data – FRED – for free. FRED, which is provided by the Federal Reserve of St. Louis, offers access to 507,000 US and international time series from 87 sources. Anyone can scan the database for economic indicators, such as GDP, unemployment, and the consumer price index.

Though the FRED data is free, engineers and application programming interfaces (APIs) are needed to process that data, said Singhal. “To me the biggest challenge is processing — being able to build that pipeline. The actual code to derive the alpha part is so much smaller than the whole wrapper around it which is focused on cleaning and processing, reconciliation and post-trade processes.”

But she added, “It’s not easy to set up, unfortunately.”

Alt Data Ecosystem

However, firms no longer have to do all the work on their own. An entire ecosystem of alternative data providers, domain experts, data platform providers and visualization tools has evolved.

According to Alternativedata.org, there are 213 alternative data providers, and the alt data industry is expected to be worth $350 million in 2020.

On the sell side, there are many use cases, as well.

Nikhil Singhvi, global head of market and client connectivity at Credit Suisse

At TABB’s A.I. /alt data panel, Nikhil Singhvi, global head of market and client connectivity at Credit Suisse, said use cases on the sell side include client acquisition, research, trading operations and compliance. “Each of these uses cases has disparate data sets, and are at different levels of maturity,” he said.

One of the challenges is that models developed from alternative data would need more testing before releasing them to clients, said Singhvi.

In the old days, quants sat next to proprietary traders. “You could come up with a model and work with the prop trader to be able to really test that strategy or test that concept,” said Singhvi. Now, the prop traders are no longer there, [as a result of the Dodd Frank Volcker Rule], and this is a challenge, he said.

While there is a lot of work being done to use the alternative data sets on Wall Street, Credit Suisse’s Singhvi said he doesn’t think it’s going to override everything else that you see from the accounting statements. “I don’t see that happening anytime soon,” he said. “If it’s another data point, then we look at it and build in other data elements around it and improve the quality of research,” he said.

Even if usage of alternative data by Wall Street is in its early stages, panelists suggested that a fundamental transformation of quant methods is occurring. While people on the sell side have been doing quantitative analytics for a long time, Singhvi said that alternative data is “changing their thinking from a statistic-based quant to AI and machine learning. It’s a big change,” he said.

Seeking Clarity on MiFID II Inducement Trading Rules

January 24, 2018 by Ivy Schmerken Leave a Comment Filed Under: Thought Leadership Articles Tagged: analytics, Brokers, FCA, MiFID II, trading platforms, Trading Rules

With MIFID II’s rules on inducements now a reality, buy-side firms are paying close attention to the costs associated with their front-office trading platforms and analytics.

The EU regulation, which took effect on Jan. 3, could draw scrutiny to execution services and tools that are sponsored by brokers, such as order management and execution management systems as well as trade analytics.

“In order to align with MiFID II’s inducement regulations, the buy side has an obligation to understand what a third party is paying for and what is being paid for on their behalf,” said Spencer Mindlin, analyst at Aite Group on a recent webinar.

While MiFID II has been focusing on unbundling research payments from executions, there is a lot of ambiguity around the industry practice of brokers paying for vendor-sponsored buy-side client connectivity (i.e., FIX lines) and how that is going to align with MiFID II inducement regulations, said Mindlin on the webinar.

For example, some buy-side firms may be receiving an EMS free-of-charge from their sell-side firms, which are sponsoring the third-party systems by paying the vendors a fee for connectivity. Thus, brokers could be utilizing part of their trading commissions to compensate third-party vendors, on their behalf.

Under MiFID II, an investment firm providing portfolio management or investment advice to clients cannot accept anything free from the sell-side if it could be considered an inducement to trade.

“All in all, MiFID II will require a higher level of compliance from the buy-side than it is used to, especially concerning investment research and the topic of inducement,” according to RegTech in “The new MiFID world: research from the buy-side perspective.”

Article 24 (8) of MiFID II prohibits investment managers accepting “fees, commissions or any monetary or non-monetary benefits paid or provided by any third party,” summarized RegTech. “In principle, this reform will still allow investment firms to receive research from third parties, but greatly limits its scope in order for the action to not contravene inducement rules.”

Though an EMS is not specifically mentioned in MiFID II, it’s possible that the receipt of an EMS on a free-of-charge basis could be considered “a non-monetary benefit” under MiFID II.

FCA Offers Clarification on MiFID II & Inducements

A policy statement from the UK’s Financial Conduct Authority released in July 2017, sheds some light on these MiFID II implementation issues.

“We take the view that certain activities can be considered as inherent to the provision of execution services and received by the underlying client in return for execution costs and charges,” stated the FCA’s PS17/14.

For instance, the FCA guidance suggests that an investment firm could accept transaction reporting from a broker as part of the execution service being provided to clients. This is acceptable provided that “a reporting system does not influence best execution, and it’s offered as a standard term of business by the broker to everyone,” notes the UK regulator. [The FCA would frown if the service were selectively offered for free to certain clients and not to others.]

A key distinction is made between a broker providing a service, such as working a large order or structuring a series of derivatives trades, which is part of the execution service itself, as compared to providing a separate benefit.

“However, order transmission systems used by a broker (such as FIX networks), do not appear to be provided to either the firm or its clients as a distinct benefit,” according to the FCA.

The FCA interpretation does not apply to services that are not related to the execution of an order and its proper settlement and reporting.

“Provision of third-party analytic tools, order management systems, or RPA [Research Payment Account] administration services to a MiFID investment firm, are not inextricably linked to an execution service,” stated the FCA.

According to Mindlin, though there is a lack of clarity in how the MiFID II inducement rules apply to OMSs and EMSs, asset managers have an obligation to understand how much their systems cost and who is paying for it.

“They also need to know what they don’t know is being paid for to comply with MiFID II, said the analyst.

As a result, MiFID II is triggering a focus on total cost of ownership or TCO, according to the Aite Group webinar and report “Total Cost of Ownership and the OMS/EMS Pivot Point.”

Fund managers are also incurring additional costs to comply with MiFID II on best execution and transaction reporting, and this is causing firms to re-evaluate their investment technology platforms. As a result, there is a lot of interest in what TCO is, said Mindlin.

Based on research conducted in late 2016 with asset managers and again in 2017’s Q2 and Q3 with executives at global hedge funds, Aite asked firms about both direct and indirect costs of their OMS and EMS platforms.

According to the report, hidden and indirect fees are difficult to quantify and even some managers who are familiar with the fees are unaware of them.

Indirect costs included network and broker connectivity fees, once again referring to FIX networks. “These connectivity fees were initially a negligible cost for the broker doing business, especially relative to the commission it would generate,” said Mindlin.

However, buy-side respondents told Aite that fees have not been adjusted downward alongside reductions in trading volume and declines in broker revenues.

In addition, there is ambiguity around [what] brokers pay for in terms of vendor-sponsored activity, said Mindlin.

Some of the larger hedge funds were critical of their vendors for obscuring the totality of the revenue earned rom their accounts.

It remains to be seen how the EU inducement rules will impact payment methods for trading technology. And if asset managers need to pay a separate charge for their FIX networks, then this could lead them to consolidate the number of brokers that they execute through.

Regulations such as MiFID II are clearly raising the bar on the buy side needing to understand costs, said Mindlin, adding that TCO is going to drive that conversation.

Wrestling with OMS and EMS Decisions

December 20, 2017 by Ivy Schmerken Leave a Comment Filed Under: Thought Leadership Articles Tagged: AUM, Celent, EMS, FlexTrade, OEMS, OMS, TCA

Many asset managers are wrestling with the decision on whether to keep the order management system (OMS) and execution management system (EMS) as two separate best-of-breed systems, or switch to a single unified platform known as the OEMS.

Buy-side firms have been demanding tighter integration between their order management and execution management systems for years. Some are looking to consolidate their trading platforms to drive down costs. Others want a single platform to reduce the complexity of trading across multiple asset classes and geographies.

Some critics contend that the OEMS has not lived up to the hype of creating a synchronized blotter and a complete audit trail in both systems. They argue that it’s been difficult to pull data from the OMS into the EMS or visa versa.

“The debate on whether the OMS and EMS should be merged or integrated continues to rage,” wrote James Wolstenholme, senior analyst at Celent in a report published Oct. 25, 2017.

James Wolstenholme

As the debate rages, the interaction between portfolio managers and traders is becoming more critical to earning incremental returns, stated the report. With assets shifting from active to passive investments, portfolio managers are relying on advice from quantitative tools – like pre-trade transaction cost analysis – to earn precious basis points.

There are 15,000 financial advisers in the United States, ranging from $5 trillion in AuM all the way down to $5 billion, and then there’s hundreds under $10 billion and then family offices with $100 million in AuM that need a complete investment life cycle, said Wolstenholme in an interview.

For many asset managers, the choice between integrating a best-of-breed OMS and EMS, or migrating to an OEMS, hinges on multiple factors and is by no means black or white.

“The best-of-breed EMS and OMS tends to go with the separation of asset classes and geographies,” said Wolstenholme.

In the case of an asset manager trading a majority of credit bonds and associated derivatives, the firm won’t specifically need a high-speed EMS, according to the report. In addition, the same firm investing in commodities, real estate, direct investment, or private lending, might use a specialized second OMS.

“One size doesn’t fit all, and so now as you move down the different AuM levels, some firms are going to want best-of-breed and are going to go across many multi-assets and geographies. Therefore, one behemoth system is not the answer,” said Wolstenholme.

If a hedge fund is investing in specific, highly liquid asset classes, and dealing in high volumes, then “the idea of an integrated OEMS is a better architectural method to follow,” said Wolstenholme. For example, a hedge fund that tends to concentrate in North American equities and is doing a high-frequency trading strategy or market neutral strategy, “that efficiency is gained within the combined OEMS,” he asserted,

Different Segments and Workflows

“Generally speaking, you can break the decision down into two client types — the institutional management space and the hedge fund space,” said Aaron Levine, vice president of FlexONE OEMS Solutions at FlexTrade.

Hedge funds tend to have different workflows than institutional asset managers. “Specifically, the interaction between PM and Trader can be seen as one from the systems perspective.”

The OEMS avoids the “swivel chair” concept where the PM is going into the OMS to create an order, running allocations, order marking, checking locates, and compliance, and then sending it over to an EMS and executing it there. “If the same person is doing both, they want all the functionality in one system,” said Levine.

“An OEMS equates to an order-entry workflow that allows you to do everything in one shot,” he said. A portfolio manager who never wants to leave the OMS, can see their positions, real time P&L, monitor exposures and slice and dice the portfolio in the OEMS. The trader can create orders from the OEMS, run compliance, stage the orders, and then route them to execution venues or actively manage the orders through algorithms.

Workflow & Order-Origination Are Keys to Decision

“Not everyone needs speed. It’s about order origination,” said Levine referring to the decision. In both cases, a PM originates the order, but how an order is communicated to the trader and input into the system is quite different. At many hedge funds, the PM still sends orders via emails, chats or via voice to tell the trader what to buy from a single stock perspective, said Levine.

Larger asset managers and plenty of hedge funds have traditional workflows, where multiple PMs are sending orders to a centralized trading desk, with a Chinese wall in between, he said. “They have two segregated workflows, two segregated users and two systems. In that scenario, they don’t just use one system,” said Levine.

Avoiding Bottlenecks in the OEMS

To arrive at a decision appropriate to each firm’s needs, Wolstenholme said it’s crucial to perform a gap analysis, which includes size of AuM, assets traded, investment mandate and liquidity.

“The idea of combining a very complex organization of multi-asset trading into just one system is a little too idealistic,” asserted the analyst. “In the real world, firms definitely have to segment and layer their different applications,” he advised.

Painting a complex picture of all the layers of applications that exist in an asset management firm, the report explores whether asset managers’ OMS/EMS and portfolio management system layers should be merged, and examines what is the best architecture for asset managers to adopt.

“If an asset manager moves up to a specialized EMS for routing capabilities, and is putting TCA on top of that for a specific market, I would not combine that with a full order management system that is dealing with fixed income, FX, private equity, and direct investments,” he said.

“There is going to be position data and derived data that comes off the atomic data, and that is going to become too specialized and too heavy a weight to store in a single object- oriented type of model,” he cautioned.

Data Replication: Possible Problem?

“Ultimately, we are talking about transactions and positions,” said Wolstenholme. For example, there are specialty applications such as TCA and performance attribution, funding and collateral management that produce derived data.

“If someone is doing TCA on top of the EMS, they’re going to need all of the fill information in real time because it might dynamically change the way they’re trading in terms of liquidity and transaction costs,” said Wolstenholme.

But these specialty applications can lead to duplication of data, which is okay as long as they are kept in synch, said Wolstenholme.

As long as the data is kept ‘in synch’ through a service-oriented architecture (SOA) and multiple instances of data, then maintaining the best-of-breed OMS and EMS approach is going work, said Wolstenholme.

Keeping Data in Synch

“File transfers with drop copies of executions and execution fills [via FIX) were the main means of communications between these separate applications,” according to Celent’s report.

This has been problematic, agreed Levine. “FIX is an orders/trades-based protocol which does not encompass all of the OMS/EMS needs such as compliance, allocations, positions, etc.,” he said.

Today the OMS/EMS or the unified OEMS can be kept in synch through APIs, said Levine. For example, FlexTrade uses a gRPC API framework for the OEMS integration that includes all aspects of the order life cycle; order marking, allocation logic, compliance, locates, and routing. “It’s the connectivity between the two systems that creates an OEMS,” said Levine.

Future Trends

Looking ahead, demand for a more consolidated OEMS is expected to keep growing, as firms see this as a way to lower costs rather than operating disparate platforms.

However, many traditional asset managers and some large hedge funds prefer to stay with an integrated best-of-breed OMS/EMS model. “We’re living in a best-of-breed world,” said Levine, noting that both arguments are valid depending on the client type.

According to Levine, having both a best-of-breed EMS that caters to the asset management community and a high powered OEMS specific to the hedge fund community offers clients the ideal choice as the evolution of the OMS/EMS continues.

“Obviously there is no silver bullet,” said Celent’s Wolstenholme. It’s all an incredibly detailed integration, and it depends on gap analysis of what the asset management firm is doing today, what are their investments today, and where they want to grow. And then it’s being able to match the right architecture and vendor systems to do that,” he said.

Sometimes, it might be a combined OEMS, or it could be a separate OMS and EMS, but it certainly isn’t one answer for all.”