Issuers’ growing use of buybacks has drawn fresh scrutiny from the UK Financial Conduct Authority, which has concluded a multi-firm review of bank structuring, marketing and execution with no material concerns about outcomes or unmanaged conflicts. But an industry expert insists that the current system tilts advantages to the corporate bankers who perform the buybacks for issuers.
The FCA study covered 165 buybacks worth £40 billion executed by seven banks for FTSE 350 non-investment companies over 18 months; by comparison, FTSE 350 companies repurchased £78 billion over the same period. The FCA also notes buybacks have risen from 20% of shareholder distributions pre-COVID (2017–2019) to 42% in 2022–2024.
VWAP
The regulator distinguishes vanilla agency mandates from structured products, where fees are linked to “outperformance” versus a benchmark defined as the arithmetic average of daily VWAPs during the actual execution window. Benchmarks typically use continuous trading on the LSE and exclude auctions, reflecting Market Abuse Regulation (MAR) safe-harbour constraints. The FCA found structured buybacks represented 39% of transactions by number (45% by volume), that fee outcomes varied widely—including negative fees (rebates) in 30% of cases—and that, across four strategies, it found no statistically significant differences in average price outcomes.
This comforting message is disputed by ex-Goldman Sachs veteran Michael Seigne, founder of Candor Partners, which provides consulting services on buyback execution. Speaking to Global Trading, Seigne argues that the review “does not draw the right conclusions” and leaves “the goal open to misalignment, opacity, and avoidable cost.” He welcomes the work’s scope but says the findings reveal structural weaknesses that boards and regulators should not ignore.
“When the regulator’s own findings reveal how these products tilt the playing field, it’s right to ask who really benefits from the current design and whether issuers, their boards and investors are getting the whole picture,” Seigne says.
On volatility risk transfer, Seigne notes the FCA describes banks monetising volatility by varying participation and determining the buyback’s completion within contractual bounds, discretion that drives outperformance versus the benchmark. Seigne says that “a well-designed buyback execution strategy does not need to incorporate a volatility bet.” He contrasts structured buybacks with convertibles, arguing the latter are securities with prospectuses and listing safeguards, whereas “structured buybacks lack these protections.”
Seigne’s second concern is the design of variable “outperformance” fees. Because the fee is calculated after completion as the difference between the achieved price and the contracted benchmark, he argues it “functions economically as a retroactive change to purchase price,” pointing to the Companies Act 2006 requirement that shares be fully paid at purchase.
Third, Seigne challenges benchmark construction. The FCA acknowledges that the common benchmark is the average of daily VWAPs (equal shares per day). “If the purpose is to execute a value-based buyback, the suitable product is self-evidently the one with a benchmark that uses the correct maths,” Seigne says, framing it as a suitability question when banks both design the benchmark and control the calculation window.
Seigne also highlights constraints embedded in the UK market abuse regulation (MAR) safe harbour, which exclude certain liquidity sources and can extend durations. The FCA acknowledges features of the listing rules and MAR “could weaken the efficiency of an issuer’s buyback programme” and pledges to “consider this feedback” in future reviews.
“Safe harbours are intended to protect against market-abuse liability, not to lock in long-term inefficiencies in the cost of returning capital to shareholders,” Seigne says.
Finally, Seigne points to asymmetric economics and fee caps. “A fee cap is not automatic; they must be contractually negotiated,”. He argues banks can retain upside from outperformance while their downside is limited by structure.
The FCA concludes it saw no unmanaged conflicts and that enhanced issuer education and clearer option menus—fee caps, price/volume constraints, early-termination terms—would improve outcomes. Seigne agrees education is necessary but insists it is not sufficient: boards should interrogate volatility transfer, benchmark suitability, disclosure of post-trade fee effects and asymmetric payoffs before approving structured buybacks.
With just a handful of traders, Swedish pension fund AP2 leverages machine learning-based vendor technology to handle $25 billion annual equities turnover for its quant strategies
Working as a trader for a Swedish government pension fund might seem a recipe for lifelong career stability. But for Mathias Eriksson, senior trader equities and credits, trading at the country’s AP2 buffer fund, the situation is anything but. The government is in the midst of consolidating five buffer funds into three. While Eriksson can glean comfort from the fact that AP2 will survive, he can’t predict the final outcome.
If performance is a guide, Eriksson should have nothing to worry about. The Gothenburg-based US$48 billion fund reported a 8.2% net return in 2024, with an expense ratio of just 8 basis points. Of this, US$27 billion is invested in equities, which the fund manages using a quantitative dynamic asset allocation approach.
“The main part of our equity management is based on a quant model”, he says. “We have a large developed market mandate, that is about $8 billion of value. And we have an emerging market mandate, and the value of that is about four and a half billion dollars. And we have two domestic mandates as well, based on fundamental research”.
“The quant team are rebalancing or running the model on a scheduled basis. They do that every week, and the total turnover for the quant team is about $25 billion a year. From a turnover perspective, it’s huge. And, given that our trading desk has only three people, or four, if you add the FX trader, we need to be very streamlined. We need to have a lot of straight-through processing (STP). We need to have as much technical help as possible in order to handle this amount of trading”.
However, Eriksson cautions that trade-offs are involved. “Being a governmental pension fund, there is always cost pressure”, he says. “If everything was STP and automated, we could probably save transaction costs with help from technologies and from models. But we still think that human beings add value to the process”.
Moreover, Eriksson points out that AP2’s traders are less important than the investment team when it comes to the fund’s total performance. “We use technology to save parts of basis points when we are trading. But what is even more important is the investment strategy. We are talking of percentage points if we choose the wrong strategy. So that process is more manual in that sense that more people are working on it.”
Optimising quant signals
As a 20-year veteran of AP2, Eriksson understands his colleagues’ strategy well. “We use traditional quant factors, like momentum, value, quality and size”, he says. “We try to optimise the best blend of factors or signals for each specific region, on a continuous basis”.
“We have a huge library of different signals, of different factors that the quant team can use, at different points in time”, Eriksson continues. “Some factors are better than others, so we continuously evaluate which factors or signals are optimal. And that can differ from region to region, and it differs over time. We also have a liquidity model for creating the trades, and also a kind of TCA model to create trades that are liquid”.
And that is just the alpha side, since AP2 also determines asset allocation using its own custom benchmark. “We build our own benchmarks internally, on the beta side, trying to have optimal benchmarks compared to a strategic benchmark”, Eriksson explains. “On the equity side we use the MSCI universe, all the stocks within MSCI standard indices. Then we run an optimisation to define the starting weights after each quarterly MSCI index rebalance and send these to MSCI, to calculate the custom index on a daily basis”.
“For the Swedish population, the benchmark is actually much more important for their pensions, than each bet, or the alpha. The beta is what adds value. Of course, our goal is to beat the benchmark, so we aim to earn alpha as well. But the contribution to the P&L of the alpha is much lower than the contribution of the of the beta or the benchmark index selection”.
Only after all these multiple stages are carried out, the AP2 trading desk can see an actual order, Eriksson explains. “The quant team generate a list of orders, a list of trades which is imported into our trading system, into our EMS. When the trade hits the EMS, the trading team owns the trade. We own the P&L, while the underlying positions are owned by the quant team or by the equity team. From a pure P&L perspective it’s up to the trading team to decide strategy, to decide where to trade, and so on.”
Using AI for broker allocation
Using only regulated markets (AP2 doesn’t do any OTC equity trading), Eriksson’s team is heavily dependent on its favoured vendor, Virtu.
“We use Virtu, both for EMS, and also for TCA”, Eriksson says. “We do that because it’s very convenient for us when we trade, all the trading information is already in the EMS, and then it’s automatically fed into the TCA.. Whenever we have traded, we can immediately do a TCA analysis of what we have done”.
“If we’re looking at a developed market trade, for example, it’s between maybe 300-500 names or unique equities per trade. We can’t go into detail with every single order. And we need to decide which broker to use for each trade.”.
Allocation of trades to brokers is another area where AP2 is a heavy user of technology, this time with UK-based vendor BTON, using machine learning. “They have trained their model on our historical data, as well as other clients’ data”, Eriksson explains. “When we are going to trade, we upload the orders into our EMS, and the BTON algo wheel is built into our EMS. So we choose a strategy for the trades and send that off to BTON’s service. They run the evaluation and suggest a broker that optimal for that individual order, for that individual stock. With just one click, we then send the trade to the identified broker with a preset strategy. So, we define the strategy, and when it’s sent off to the broker, all the parameters are already set.”
“The trading strategy defines parameters such as what algorithm is the best to use? How should they treat the trade? Should it be internalised as trading on their own risk book? Trading lit or trading dark?
Significantly, as a government pension fund, AP2 is not subject to EU MiFID 2 regulations so is free to adopt a fully bundled research model.
“We are still bundled, which means that in order to get research, we need to allocate orders to each individual broker”, Eriksson explains. “We conduct a broker evaluation once a year, when we look at both trading performance, and the quality of the research that we get. “The broker review is based on the review for all asset classes, including derivatives, fixed income, FX, and equities. So all teams are involved in the evaluation.”
After that, we set a broker list, and the commission allocation for each individual broker. This is fed into BTON as well. And BTON’s tool has a memory – it knows whom we have traded with in the past, and keeps track of the commission allocation over time. At the beginning of the year, it doesn’t matter much if we allocate 100% of the trades to one broker, but the closer we get to year-end, the more important it is that we end up with a commission allocation close to our target.”
Stable execution
According to Eriksson, the new approach is popular with the sell-side. “Historically, we traded one week with one broker, the next week with another broker, trying to evenly spread out the commission allocation. We have six brokers on the broker list, so they got one week of orders every six weeks. Now, we have a closer connection with all the brokers. They get approximately the same number of orders, on a yearly basis, as they had before. But now they get orders every week. The brokers think that this setup is much better because we have a closer connection.”
This is particularly important when volatility increases, Eriksson continues. “It’s a fairer evaluation of the brokers as well. If it was a very volatile day when you got the trade, then the P&L might look bad compared to your competitors, even though it was just the volatility that created that. But now it’s the same volatility for all brokers that we are trading with every time. So it’s a fair comparison, in that sense.”
Mathias Eriksson.
“The spreads have come up a little bit this year, or pretty much so over a period of time. But despite that, we can see a more stable execution, even though the spreads are wider and given the situation in the world, the volatility has been higher as well. But we can still see a more stable execution – in other words, the variation of the P&L, is lower than we have seen in the past. That is a result of the BTON strategy and algo wheel.”
So far, AP2 has applied BTON to its development market portfolio but emerging markets are a work in progress. “The problem with emerging markets is that we don’t have as much data as we have for developed markets”, Eriksson explains. “We do not only evaluate the brokers, we evaluate BTON as well. Before adding it to emerging markets, we would like to do the evaluation to see that the algo wheel is working”.
“As for using non-traditional brokers, the scope for that is limited by AP2’s bundling policy”, Eriksson says. “We use execution only brokers as well. But that is used for a smaller portion of our execution. That’s because when we trade with them, we pay commission to a broker who we don’t get any research from.
Mathias Eriksson, in conversation with Virtu’s Rob Boardman at TradeTech 2025.
Meanwhile, AP2’s quant investing strategy is being broadened into derivatives. “We did a pretty big strategic change in March 2024 where we added a number of new mandates”, Eriksson notes. “Historically, almost all our mandates have been cash mandates so we haven’t been using derivatives to very large extent. But in this strategic overview, we added a number of derivatives-only mandates that take into consideration the size of the underlying cash holdings for collateral purposes. Now we can use that muscle on the derivative side, and are building up new mandates with derivatives exposure.”
Gurdeep Bumbra has joined Mirabaud Asset Management as a senior portfolio manager within the global equities franchise.
In the London-based role, Bumbra will lead the global focus equity strategy.
Andrew Lake, chief investment officer, commented, “His appointment reflects our focus on strengthening our core capabilities where we can add meaningful value for clients, particularly through concentrated, forward-looking strategies.”
As of year-end 2024, Mirabaud Group holds CHF 32.3 billion (US$40 billion) in assets under management.
Bumbra has more than two decades of industry experience and joins Mirabaud from Tourbilon Investment Management, where he has been a managing director for the past year.
The majority of Bumbra’s career has been spent at Pictet Asset Management as a senior investment manager for international equities.
Brown has more than 20 years of experience, most recently serving as head of emerging cash equities execution at Citi. Prior to this, he was head of emerging equities trading at Morgan Stanley.
Throughout his career, Brown has been an equities trader at Morgan Stanley’s South African company (co-owned with Rand Merchant Bank), Bank of America and Credit Suisse, where he focused on emerging markets.
James Bastick has also joined Panmure Liberum as a director and equity trader. He is based in London.
Bastick has almost a decade of industry experience and joins the firm from Singer Capital Markets, where he has been a UK equities market maker since the start of the year. Prior to this, he was a UK equities dealer at Winterflood Securities.
With more than 20 years of experience Foley joins KCx from alternative trading system Provable Markets, where he has been managing director of sales since 2022. Prior to this, he was head of sales and an electronic sales trader at Proof Trading and part of the business development team at IEX.
More than a decade of his career was spent at Liquidnet, where he held roles including Japan country manager, head of US equity desktop trading, and global sales manager.
Clients purchased US$4.3 billion in single stock US equities at Bank of America last week, the firm said, marking a two-year record.
The week also represented the group’s tenth largest inflows since 2008, BofA reported
The majority of this activity went into large caps, BofA said, particularly looking at tech stocks (US$3.14 billion in inflows). This was led by institutional clients, who were buying for the second week in a row and reached the highest levels of net buy since September 2024.
Institutional investors had been sellers since May.
Financials (US$883 million), discretionary (US$590 million) and staples (US$443 million) were also popular categories, with stocks bought in seven of the 11 categories tracked by the bank. Industrials (-US$809 million), healthcare (-US$345 million), communication services (-US$140 million) and real estate (-US$30 million) saw the largest outflows over the week.
Alongside institutional investors, retail, corporate and hedge fund clients were also all buyers for a second week running. Private clients have been the most consistent buyers recently, the bank noted, buying for 33 of the last 35 weeks and on a six-week inflows streak.
Although single tech stocks were riding high, passive investment in the sector is waning – with tech exchange-traded funds (ETFs) reporting the largest outflows last week. Consumer staples led the race, and also hold the largest four-week average for inflows.
Blair has more than 25 years of industry experience and joins the firm from boutique investment banking firm Liquid Venture Partners, where he has been director of equity capital markets for over a decade.
Earlier in his career, Blair was senior vice president of sales trading at Penserra Securities and an equity and sales trader at ThinkEquity.
A third of US buy-side equity traders are using broker-provided analyses for transaction cost analysis (TCA), according to a Coalition Greenwich report – up from 20% in April 2024.
These analyses tend to be in the form of one-off or scheduled reports.
The majority (88%) of traders are outsourcing their TCA to third-party vendors, the report found – up from 80% of survey participants in April 2024. This strategy captures data from across asset managers, which respondents said allowed for an unbiased view of trading performance. Convenience, alongside low infrastructure and personal investments, were also favoured benefits.
Report author Jesse Forster, market structure and technology advisor at Coalition Greenwich, notes that established firms are preferred over new players, as clients aim to minimise the risk of introducing new technology.
“Traders tend to stick with trusted vendors rather than exploring new options,” he adds.
Just 18% of those polled were using a proprietary TCA system and only 10% said they used a proprietary/vendor hybrid system. Those opting for an in-house approach tended to be larger asset managers requiring more tailored TCA strategies, Coalition Greenwich explained. These platforms can also be integrated into existing systems, with firms able to maintain control over their data and analytics.
The report finds that almost half of US buy-side equity traders believe that TCA data is an important or very important tool when evaluating brokers.
Just 5% of traders stated that they did not incorporate TCA data into their broker evaluations – although all desks polled confirmed that they had used TCA for equity trading at some point over the last year. Close to 80% had done so on a quarterly basis.
This marks an increase from Coalition Greenwich’s April 2024 report, which noted that under 90% were conducting TCA for equity trading.
Post-trade analysis – the original purpose of TCA – is its most important feature used in equity trading, according to 90% of respondents. A further 53% cited oversight and reporting, highlighting the industry’s growing focus on compliance and regulatory requirements, while 38% stated that pre-trade modelling is a priority.
Despite its popularity, Forster observes, “Some expressed skepticism about the usefulness of pre-trade models, viewing them as primarily a means of justifying trading decisions, particularly in cases of underperformance.”
Citi has appointed Jignesh Patel as APAC head of prime finance as it continues to build out its presence in the region.
The announcement follows comments from Japan, Asia North, Australia (JANA) head of markets Paul Smith earlier this year that the company planned to increase its prime business headcount in the region by 5-10%.
Citi performed well in Q2 2025, with the markets division reporting US$5.9 billion in revenues – including a record quarter for equities, with a reported US$1.6 billion in revenues. Within this, the company says, prime balances were up 27%.
Based in Hong Kong, Patel reports to APAC head of equity trading Robert Stewart and Sebastien Mailleux and Tim Tomalin-Reeves, global heads of prime services.
In the role, Patel is responsible for the overall growth of Citi’s prime finance service in the region. He also oversees prime product stock borrow and lending, synthetic prime brokerage, funding trading, and in-business risk.
Patel joins Citi from Millenium, where he was Asia treasurer overseeing portfolio financing, liquidity management, cash and collateral management and counterparty risk. The bulk of his career has been spent at Goldman Sachs, where he co-managed the Asia prime business. He was with the company for close to 30 years.
City Different Investments: Fed uncertainty, mixed data signals, and record muni supply.
As fiscal, monetary, and economic growth forces drive fixed income activity, Trader TV speaks to Sweta Singh at City Different Investments about how trading desks are navigating the current market uncertainty and mixed data signals. Singh discusses the Fed’s interest rate trajectory, tariffs data, and the record-high municipal bond issuance for June and July. Amid these conditions, she shares where she sees value in the US treasury curve and looks at tactical opportunities for traders to capitalize on this summer.
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