Home News The chain reaction that led to a significant volatility in Transaction Costs

The chain reaction that led to a significant volatility in Transaction Costs

by gbaf

By Ulf Herbig, Head of Product at Kneip.

The 2020 market volatility has led to higher spreads and higher transaction volumes. Because of the logic of the slippage methodology and how costs are calculated at individual transaction level, the aggregated transaction costs volatility has significantly gone up and it’s not without consequences.

The initial phase of the global pandemic brought high levels of anxiety amongst investors which translated into market volatility levels being multiple times higher than usual. Even in the months of July, August and September 2020, market volatility remained 50-100% higher than the phase before the pandemic.

For operations and businesses, it means that due to the higher volume of market data you consume from your data provider, you must pay even more attention to the quality of that data, because when the spreads get higher, inaccurate data can have a significant impact on the transaction cost number disclosed to the market.

Increased volatility + increased spreads = increased volatility in transaction costs

Higher fluctuation of prices on the market that typically go along with higher spreads (the difference between bid and ask). Alongside the high market volatility, there has indeed been a significant increase of spreads for major asset classes – January 20 vs. June 20 Equities (39%), Bonds (38%) and Money Market instruments (22%). *Source: AFG (Association Française de la Gestion Financière).

There has been a clear transaction volume increase from March -June 2019 compared to the same period in 2020 by 43% AVG and 19% weighted AVG. If you combine the significant increase in spreads and the significant increase in the number of transactions, the unsurprising result is that the percentage volatility of transaction costs that are disclosed to the market has significantly increased, too.

In that market environment, fund manufacturers are likely to face a 30-40% increase in transaction volume, supporting a significant increase in transaction costs disclosures.

In that context, EMTs are interesting to look at because they contain information used by every distributor in the market to promote the product – including the target market definition and the ex ante / ex post costs. Looking at a sample of 4,000 ISINS published in the market in Q2 2020, compared to their published value to Q2 2019, you can see a percentage change of nearly 80% in the transaction cost.

For a manufacturer, the higher your costs, the more disadvantaged you are at selling your funds successfully, even though there isn’t a hard disclosure requirement to update the number to the market on a monthly basis. For MiFID you need to do this at least on a quarterly basis for the ex-ante cost and on an annual basis for the ex post cost. So you must decide wisely if you should update costs to the market in the interim.

Monitoring thresholds should be updated dynamically, so individual transaction cost results can be validated automatically

The market conditions leading to the increase in transaction volumes and figures disclosed, imply that the thresholds that you define as an organisation (to be able to automatically validate individual transaction cost results) can no longer be maintained at a static level. Where they have been kept at a constant level, not adapted to the current market conditions, it’s noticeable that more trades than usual are likely being flagged as exceptions when using static thresholds. In consequence you need to spend time investigating them, even though there would be no reason to do that if you were to apply a dynamic threshold that is for example, linked to a volatility index.

Automation becomes key because if you are suddenly faced with a 30-40% increase in transaction volume, you could be spending a huge amount of time managing transaction costs in a manual way. The more you trade, the more you must calculate and the more market data you will have to log. And depending on how your contract is designed with your transaction costs and market data provider(s), you might also see a significant increase in the cost of market data needed.

For fund manufacturers, it is therefore vital to have a market accommodating solution that dynamically update thresholds so that they automatically validate transactions. Look for solutions that can automatically validate 90-95% of all transactions meaning so if you have a million transactions, you will only look at 100,000 transactions (ideally fewer!). And if you have static thresholds which no longer apply in this turbulent market, you may suddenly look at 20-30% of exceptions. This translates into 200-300,000 trades that you have to look at for no reason. It is therefore not just about your thresholds at once, so it’s critical that you adapt your threshold to the market conditions for example, by linking it to a volatility index.

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