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Leading digital treasury management software for hedge funds, commodity producers and commodity traders.

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Taking treasury from good to great


We provide transparency via our leading SaaS technology, enabling Treasurers and CIOs to optimise their Prime Broker, OTC Derivative and Exchange relationships.


Our cross-asset portfolio optimisation and collateral savings significantly enhance returns, improve Sharpe ratios, and reduce costs. With advanced 'what if' scenarios and true optimisation within a firm's unique constraints, our pre- and post-trade modules are easy to implement, requiring minimal internal resources.


Offering military-grade data security, our systems are available both in the cloud and on-premise.


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The Siman Modules

  • Margin and Cost Replication

    The MaCO platform offers the most accurate line by line and portfolio replication of all leading Prime Broker margin and financing cost models. 


    Our goal is to have this ready by 7am, enabling early establishment of the day's 'what if' scenarios and funding calls, with line-by-line PB: MaCo reconciliation available when PB reports arrive. 


    Additionally, we prepare Margin 'time series' reports for stress tests or regulatory requirements, such as SEC Form PF reporting, if needed.

  • Margin Optimisation

    Efficient replication allows our MaCO optimisation engine to deliver multiple RoI without disrupting PB relationships. Prime Brokers use margin to attract desired business, and MaCO enables Funds to set their own parameters, such as protecting execution, wallet share, and margin excess limits. The algorithms are designed to operate within these internal constraints.


    Unencumbered cash released by the Margin Optimiser does not affect PB Fund return metrics like RoBS or RoRWA. When reinvested, this cash boosts the Fund's Sharpe and Sortino ratios.

  • Portfolio Manager Attribution

    Our tools allow Funds to attribute margin costs and financing charges to specific desks or traders, helping Treasurers reward the most efficient operations. We also enable stress testing to illustrate the impact on margin requirements if a strategy or portfolio manager changes or if there are redemptions and subscriptions.

  • Exchange and Commodity Margin

    Working capital requirements resulting from the use of futures and options are volatile and put extreme pressure on the liquidity position of commodity firms.


    Siman Systems use their data analytics capability to enable users to reduce the costs of financing in their derivative margin requirements and optimise capital used in varying market conditions and liquidity stresses. We enable firms to


    • Attribute Capital to traders
    • Increase Return on Capital vastly in excess of Siman System’s costs
    • Measure capital and trader efficiency.
    • Reduce margin requirements by optimising inter commodity credits at a desk firm and exchange level.
    • Increase resilience by projecting future liquidity requirements.

     

  • Uncleared Margin

    We prepare UMR VI reports for Funds as needed for uncleared derivatives, ensuring compliance with UMR rules. Our tools also provide accurate derivative margin forecasting and stress testing.



  • Cash Reporting

    The Module tracks a Fund’s unexplained, encumbered, and unencumbered cash on a real-time basis allowing funds to reinvest (and monitor) cash balances and excess margin returns. 



"Particularly in the context of margin and financing, Siman is invaluable for us. Prime Brokers often miscalculate margins, and many may not even realise their errors. Additionally, we're keenly aware of the advantages it offers for the future, especially now with the SEC's recent introduction of new reporting requirements for Form PF"


- Chief Operating Officer of $3bn Hedge Fund



"Siman Systems helped us reduced our Margin requirements by $60 million on Day 1 with much more to come. They have helped us improve transparency and our relationship with PBs through thoughtful insights into their margin and financing calculations. Their replication is extremely accurate and the MaCo optimisation engine is very powerful and saves us many hours in deciding where to place the marginal trade(s)"


-
Chief Operating Officer of $1bn Equity L/S Hedge Fund



From the Siman Desk

The word margin is written on wooden cubes with coins in the background.
By David Simpson November 7, 2022
The volatility around last year’s Pension Fund crisis led to large Variation Margin Calls. Following on from a year with European conflict, as well as China still recovering from the pandemic, it seems well telegraphed that market volatility will endure into 2023. The year looks to bring a growing demand for increased margin discipline and efficient collateral and cost management. On top of this, increasing regulatory scrutiny on PBs and internal focus on returns has made managing their HF relationships even more important. (There is almost nothing worse than having discussions around relationship and price based on hazy facts or out of date data). Through Client Margin Reviews and using tools such as minimum margin amounts and various ‘add-ons’, PBs are signalling what strategies and trades suit their book and which don’t and HFs need to adapt across both uncleared and cleared relationships. In short, the trend seems set for elevated house margin requirements, whatever the previous “Value to the Bank” status of Funds, The UMR 6 Regulations have upped the ante on cleared margin calculations but, at least as big an issue, for PBs is house margin. As a consequence, many Funds are now trying to replicate their PB Margin Agreements, so that they can keep ahead of changing margin rates. There is also increased realisation that the Optimisation Potential (let’s call it ‘OP’) for Funds with several PBs, positions and strategies is significant. In 2019, a couple of large funds told us that they agreed but would ‘build it themselves’, but as far as we know their ‘build’ continues and the OP remains confined to ad hoc discussions rather than a consistent efficiency and an accruing benefit. There is also a large opportunity cost in tying up busy, valuable and expensive people across Treasury, Trading, Operations and Technology. And from experience, even in large organisations, if you don’t do this, it just won't work since, unless it’s a full-time project, the day job(s) just get in the way. Why would Funds do this, when it's already been done? Maybe it’s concern about the data handling: “we don’t want the data to leave the building” or certain Funds, say Relative Value, think that their business may be too simple to warrant the proverbial candle. But most will find incredible savings if they adopt an algorithmic approach. The well regarded military scale solutions to the data protection issue, should warrant further examination of the efficiency of outsourcing. (Let’s be honest, if the system doesn’t work economically, the SaaS model protects the client since the outsourced system can be switched off). The first step in any margin optimisation process is to replicate each PB’s own margin calculation. Scraping data and making the feeds robust enough to survive critical person absences/promotions, at the PB and internally, is the first step. This is intricate but not difficult. However, trying to do it on a spreadsheet quickly becomes clunky. Not least, since as soon as the data or production time changes slightly, things tend to fall over. But it is virtually impossible to realise the full OP in Excel. In our view, OP is not optimal unless the parameters can be set by the Fund and the calculations are run close to real time to be truly useful. Several vendors say they do ‘optimisation’. But OP is multi-dimensional. ‘Saving Margin’ needs to be weighed against transaction and financing costs. Other important factors such as concentration risk, wallet share, intangible benefits such as research and ‘relationship management’ (the desire to be close to the top quartile -or at least out of the bottom quartile) of a PB’s clients, will vary by Fund. The aim of the technology provider should be neutral and these parameters should be determined by the client prior to running the marginal ‘what if’ scenarios in ‘real time’ pre or post trade. So we think that having efficient financial resource management processes are going to be more important than ever. Existing treasury tools & technology will be vital in managing costs, will increasingly be used pre trade, and will be important in having a transparent ‘real time’ relationship with the PB. Not just chats about the impact of the pricing and margin ‘add ons’ on individual strategies, but about a range of opportunities and constraints to improve resilience and enabling both parties to do better business together. Siman Systems’ Margin & Cost Optimisation (MACO) Module opens this window into granularly replicating the PB’s margin calculations and enabling true Optimisation. This can, in fact, now be done 'on prem or 'off prem'. This better equips Funds that are looking to better manage their PB relationships, costs and to maintain sufficient liquidity in times of instability. It enables Funds to understand the signals each PB is sending with regard to the size, liquidity, concentration risk and hedging of their strategy and allows them to tailor their trades to enable better pricing and more efficient allocation. MACO is part of Siman System’s Digital Treasury Platform designed to increase Financial Resource transparency and improve the economic and liquidity health of the asset manager.
The word collateral is written on wooden blocks on a table.
By David Simpson November 7, 2022
Following last month’s so called 'mini' budget and the consequent dramatic sell-off in Gilts and the eventual intervention of the Bank of England, the industry has been discussing the way Pension Funds (PFs) manage their assets and liabilities. Particularly, derivative contracts and the underlying margin & collateral management i.e. the credit support annexes (CSAs). We would like to explore the background and how increasing data transparency might help ease some of the pinch points and save money in the process. For initially sound reasons, following the GFC, ISDA in conjunction with the Regulators and Banks changed from so called ‘Dirty CSAs’ to ‘Gold Standard’ or ‘Clean’ CSAs. Under Dirty CSAs, PFs and Insurers could post a wide range of collateral. However, for several reasons, including the demise of Libor and the interrelationship of the value of the derivative and the collateral received under the CSA, the industry moved to Clean CSAs. Originally the plan was to include government bonds/gilts within the types of ‘eligible collateral’ listed under Clean CSAs. However, bonds do not net with Bank derivative exposures for leverage balance sheet (LBS) purposes. Use of such scarce capacity effectively increases bank opportunity costs and capital charges and so the banks insisted on cash as collateral. In addition, banks were penalised heavily for valuation disputes in the value of the derivative/collateral terms by draconian capital provisions. Thus, the Gold Standard CSA for banks became to accept only cash as collateral. When PFs sought to hedge their gilt and interest rate exposures with derivatives, they were unable to post non-cash assets (the gilts) as collateral. In theory, the PFs could have posted assets under repo facilities in exchange for cash but often these were restricted to cash and gilts as well. (PFs that had repo facilities could raise cash by posting their gilts for cash, but as gilt prices became volatile, the amount of gilts needed to support the cash borrow increased. In addition, for technical and systems reasons, it was difficult to finance the index linked gilts on repo. Raising the gilt valuation issue again). Ultimately many Funds were forced to sell gilts and to raise cash as their repo lines proved inadequate. This was a liquidity rather than a solvency problem, as PFs, ultimately, could reduce the pay out to stakeholders or -ask the sponsor corporate for increased contributions. Nevertheless, it was a systemic liquidity problem and a significant funding crisis emerged in a gilt market thought almost immune from such runs. We understand from banks that, in some cases, margin calls were made and not met, primarily because of settlement fails in the market as it was difficult for Funds to sell the gilts as liquidity drained from the market. Ironically, had Pension Funds been allowed to post liquid assets such as government bonds as margin, the sell-off may not have been as extreme, meaning the downward spiral that ensued, would have been reduced. From the PF counterparty Bank’s perspective surely one lesson to be learned was it would be better to receive some type of collateral than non at all? This highlights the issue of procyclicality, the times when additional margin is required is when liquidity is scarcest. As in previous crises, the need to rapidly sell assets in order to provide liquidity to maintain margin requirements leads to a drop in value of the assets being liquidated. In addition, the manual nature of T+2 settlement, with statements only calculated at end of day (often NY time) and delivered at some point the next morning, the valuation, reconciliation and settlement issues this creates (c.f. the hold up in processing margin calls at some US custody banks, apparently due to settlement areas being overwhelmed with trade volumes) with the market trying to determine whether gilts had actually been delivered, at what price and in sorting the inevitable fails in a rush for the exits, exacerbated the situation. If the Pension Funds and their counterparties were able to view and agree on near real-time pricing of these assets and liabilities, we believe the process would have been simpler. Counterparties who could replicate their own margin situation rather than waiting for their counterparty or custodian’s calculations can be far more proactive in ensuring the correct margin is posted, reducing fails elsewhere in the system. With financial technology, such as Siman Systems Margin & Cost Optimisation Module, businesses can now collate, monitor & process the real-time pricing of their assets and liabilities and can predict (and stress test) the margin requirements. Such transparency is essential if the requests for a broader range of ‘eligibility criteria’ for liquid non-cash assets are to be posted as collateral once again. Of course, of itself, this does not solve the banks LBS issues. However, we would suggest the following three practical lessons could be learned from the impacts following the notorious 'mini' budget. (In addition to improving resilience, the algo will add transparency saving the Pension Fund money). (1) Consider adding government bond collateralization provisions in CSAs (with perhaps a multiplier for bonds, to encourage cash provision in ‘normal’ times, would help reduce the volatility and volume of market sales and purchases in extremis. (2) Improving PF transparency and reducing two day data lags by using an off the shelf system, such as Siman Systems Margin Module, will enabling real time valuations of assets, liability and margin and would reduce settlement disputes and ease correspondence between Banks and Counterparties as both parties can compare consistent data.  (3) Adding contingent repo lines or agent lending facilities -allowing participants to exchange asset types more efficiently reducing pinch points.
A blue graph with an arrow pointing upwards
By David Simpson September 12, 2022
I was told once by a gilt market maker (a GEMM) that the art of market making is to collate as much information as possible in periods of calm, so that when an opportunity presented in periods of dislocation, these could be captured. There weren’t many such periods so the GEMM’s, ducks had to be lined up in readiness, otherwise a significant part of a year’s profit opportunity could be lost. That access to data was one of the privileges of making markets in government bonds. However, if the data is not clean, siloed or lagged it is far less likely to enable clear sighted decisions when opportunities (and risks) arise. One of the reasons hindsight is so attractive to commentators is because once the dust storm of the crisis has settled, the available timelines and salient data become clearer and the real choices become far more obvious. Data surrounding financial resource management, and in particular collateral management is also of increasing importance in managing an investment fund. Recent events, such as the LDI Pension Funds crisis, have thrust collateral management into the limelight. The opportunities to improve on the historic situation of waiting for the counterparty to call for variation margin and retaining sufficient cash to post, has not significantly evolved since the GFC. In fact, in some ways it retrenched to a less flexible system where the asset price decline and the need to sell the same assets to raise cash margin became correlated. As discussed in one of our previous blog posts ( Pension Funds & Collateral Transparency ) the inflexibility of ‘Gold Standard’ CSAs (only permitting Sterling cash to be posted as eligible collateral) may have been a contributing factor in the September Pension Fund debacle. Therefore, finding more flexible ways of calculating and funding collateral portfolios could help to reduce the speed and depth of similar crises in the future. Foremost is avoiding the combination of ‘wrong-way’ risk and a lack of liquidity which ultimately facilitated the negative spiral of the gilts in the PF crisis. As the value of gilts dropped, variation cash margin calls ensued and gilts assets needed to be sold. Further falls in gilts prices led to additional margin requirements and the cycle was repeated. The view that gilts were akin to Sterling cash was destroyed, (No longer worthy of the ‘L’ part of the moniker-HQLA-particularly in the longer dated securities). Selling gilts to raise cash in a stressed market environment led to discounting and fire sales-at exactly the point when Fund Managers may have wished to be buying those assets at depressed prices. The traditional settlement processes (at T+2) meant that when margin calls were made daily, effectively meaning funds were constantly playing catch up while in stressed market conditions. In fact; this issue was further exacerbated by issues at some US custodian banks, where there were hold ups and delays in the agreeing and processing of margin calls. Market dislocations led to significant valuation issues for repo agreements and the collateral haircuts being applied. Nevertheless funds that that had committed repo agreements in place were better able to raise some cash ahead of the T+2 settlement cycle. This still seems a vital tool to reduce the need to sell assets going forwards. Hopefully part of the collateral stress testing includes fund treasurers agreeing such secured financing agreements. This can be using (with suitable collateral haircuts) principal intermediaries such as banks, or, increasingly, agent lenders in the peer to peer space. Committed repo facilities, in particular bring hitherto untapped liquidity to the gilt market. As my market making friend observed, delays in calculation and in agreeing collateral calls due to siloed and incomplete data, add to the fog of a crisis, making it difficult for Treasurers to see the wood for the trees. It is very difficult to act efficiently, let alone optimally when liquidity is drying up so badly, solvency becomes a secondary concern and the opportunity (perhaps to increase positions on advantageous pricing) is lost. For Funds, margin requirements themselves are usually set by the risk manager at the sell side counterparty or the exchange -who complete their internal stress testing models, based off previous periods. Although SIMM has helped standardise margin calculations for large counterparties in uncleared derivatives, however, this is still only a back-testing process. (The short reign of Kwasi and Liz will cast a long shadow over future gilt SIMM margin calculations). Whilst this is a fairly obvious flaw in traditional stress testing, it highlights the impetus for Treasurers to maintain real time data on their fund’s positions to be able to calculate and stress test their own margin requirements, so that their liquidity buffer is sufficient to cope with potential variation margin calls and to capitalise on the prevailing opportunities, but is not inefficient in the amount of liquidity put aside.  In regard to combatting these issues, it is important to build a collateral portfolio. Although there is not one sole method, the 3 key specifications were thought to be: high credit quality securities, high liquidity, and high diversification. This usually entailed high quality, shortest-dated securities. In fact; the Kwasi crisis has shown that ‘high quality assets’ does not necessarily equate to ‘liquidity’ and in some cases of high volatility main index equities may provide more liquidity than bonds. (Although an important caveat is that Archegos illustrated the danger of concentration risk in equities). The overarching theme here is the ability to have enough information to be agile. Having effective digital financial resource management tools at your disposal could be invaluable. Siman Systems offer digital treasury software designed to increase transparency. In short, a closer to real time view of the liquidity health of the organisation and less reliance on custodians and counterparties, enabling investment funds to allocate resources not only to save costs, but also to be able to in a position to make significant gains on the bottom line.
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