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Can Machine Learning Address Risk Parity Concerns?

Smart Data Collective

Here at Smart Data Collective, we have blogged extensively about the changes brought on by AI technology. One of the most important changes pertains to risk parity management. We are going to provide some insights on the benefits of using machine learning for risk parity analysis. What is risk parity?

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Our quest for robust time series forecasting at scale

The Unofficial Google Data Science Blog

Selection and aggregation of forecasts from an ensemble of models to produce a final forecast. We conclude with an example of our forecasting routine applied to publicly available Turkish Electricity data. Finally, the time series model may give more accurate forecasts than an explanatory or mixed model.

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Data Science, Past & Future

Domino Data Lab

data science’s emergence as an interdisciplinary field – from industry, not academia. why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”.

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FRTB: Will 2023 Finally be the Year?

Cloudera

The Fundamental Review of the Trading Book (FRTB), introduced by the Basel Committee on Banking Supervision (BCBS), will transform how banks measure risk. FRTB is designed to address some fundamental weaknesses that did not get addressed in the post-2008 financial crisis regulatory reforms. FRTB Demands a Streamlined Architecture.

Risk 56
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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. Protein classification with imbalanced data. Chawla et al.