Firms should maintain a forward looking assessment of uncrystallised conduct risks. We want to support the industry with publication of our conduct risk horizon.
We aim to include risks on our conduct horizon which:
have not yet resulted in any significant regulatory public focus or investigations
industry has wide exposure to
if materialised, could result in significant poor outcomes to customers, markets or effective competition
We aim to refresh our list on a periodic basis.
Large scale government support programmes e.g. guaranteed loans, interest rates payments holidays, preferential terms and large scale open market central bank interventions create potential new conflicts of interests. Firms participating, directly or indirectly, in these initiatives, either as beneficiaries or intermediaries, should consider how they will manage potential conflicts.
These programmes may result in significant price movements and changes in the market structure. Typical market dynamics, price formation process and risk-reward considerations may be significantly skewed by these large scale interventions.
Increase use of artificial intelligence also increases inherent risk associated with the possibility that AI-based decision models will pursue profitability without regard for appropriate market conduct standards. The risk could materialise in a number of situations including but not limited to:
There is significant mismatch in liquidity between some ETFs and their underlying holdings. This relates particularly to some fixed income ETFs and ETFs investing in alternative assets such as property.
Investors' expect to be able to access ETFs liquidity on an intra-day basis. At times of large outflows ETFs will not be able to sell underlying assets. At times of large inflows, there may not be enough assets available which may result in price bubbles.
Impact on products sold to clients in a scenario of sudden interest rate increase.
Some firms have visibility of their client flows and can collect historical client flow data spanning several years. With increasing computing power and rising popularity of machine learning and artificial intelligence, firms may attempt to explore client flow data that they capture in order to model potential future client behaviour as well as wider market flows. There are several potential questions / concerns related to this practice:
It is unclear whether firms should be allowed to use historical client flow data in their simulations without client explicit permission?
Since client flow data is not available to the public, should it be always considered on a 'need to know' basis only in relation to trade execution?
Larger firms have access to larger pools of clients and have bigger private data sets. Could this give them unfair advantage over new entrants and limit competition in the market?
How clients will be impacted by transition to new interest rate benchmarks. This is particularly relevant to products bought before the transition was announced and which will need to be re-based to a new interest rate benchmark. There may be some valuation and cash flow impacts from changing the reference rate and it clients may be exposed to losses from that transition.