Industry Spotlight – Real Time Intraday Liquidity Management
This piece from Finextra, in association with Montran, shines a light on an increasingly dynamic industry focus- the importance of intraday liquidity management- and the benefits to banks who tackle this head on.
Finextra interviewed Joost Bergen, Cash Dynamics, liquidity management specialist and industry speaker about a real time and technology-first approach to the challenge.
Read the associa…
Where do we stand with Brexit? | Trading Spotlight
In this webinar, professional trader Paul Wallace discusses the latest situation with Brexit and covers the following topics:
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Machine Learning Framework for Liquidity Risk Management (Cloud Next ’19)
Financial models have always been impacted by the lack of data (or many highly noisy data), by necessary mathematical simplifications such as normality or linearity assumptions and by a limited ability to use a wide set of features to better describe the problem and have greater predictive power. This is true in risk management, trading and portfolio construction, but even more so in liquidity models.
Liquidity is a multi-dimensional beast that economists, quants and statisticians have tried to understand for several decades. This type of problem has a very high dimensionality and highly non-linear patterns and very sparse data.
The nature of the problem lends itself to be faced with machine learning techniques.
We have therefore decided over the years to test some of these techniques in the calibration of models for liquidity.
In this research, leveraging GPUs and cloud, we focused on the estimation of market liquidity, in particular of the transaction cost.
In this research, we tested random forests and neural networks for the estimation of tradable volumes showing a significant increase in the out-of-sample performances. We are now extending the experiment to the entire transaction cost and not to a single component of it by testing deep learning and in particular deep reinforcement learning.
In the application of these more advanced and complex techniques, we are paying particular attention to the ongoing research on the interpretability (XAI), which is a necessary condition and not yet completely resolved for extensive use of Deep Learning in finance.
Liquidity Risk Mangement → http://bit.ly/2WQBYtk
Next ’19 ML & AI Sessions here → https://bit.ly/Next19MLandAI
Next ‘19 All Sessions playlist → https://bit.ly/Next19AllSessions
Subscribe to the GCP Channel → https://bit.ly/GCloudPlatform
Speaker(s): Stefano Pasquali
Session ID: MLAI232
product: Cloud – General; fullname: Stefano Pasquali; event: Google Cloud Next 2019;
Corporate Liquidity Management
Effective liquidity management requires the right liquidity, delivered at the right time and with the right reporting. Responsiveness of the bank to offer bespoke solutions, based on an understanding of the customer’s business model, specific markets in which the customer is in, regulations, tax considerations, unique operational and reporting requirements are critical attributes. Treasurers need access to liquidity across geographies and multi-bank solutions are an integral part of the liquidity management solution, whether domestic or cross-border. Oracle provides corporate treasurers with a unified real-time view of global liquidity structures and underlying multi currency accounts, balances & liquidity, enhancing returns through investment into high yield instruments and compensate Trapped Liquidity.
For more information, visit https://www.oracle.com/industries/financial-services/banking/solutions/corporate-banking.html
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