Machine learning
Singapore banks tighten ML governance amid regulatory scrutiny
DBS, StanChart and Deutsche build model inventories and draw up standards around use cases
No silver bullet for AI explainability
No single approach to interpreting a neural network’s outputs is perfect, so it’s better to use them all
Wells Fargo uses machine learning for performance attribution
Clustering algo delivers speedier and more accurate explanations of portfolio returns
Man and machine need each other – Systematica CEO
“The errors made by humans and robots are different,” says Leda Braga
Scoring models for roboadvisory platforms: a network approach
In this paper, the authors show how to exploit the available data to build portfolios that better fit the risk profiles of investors. This is made possible, on the one hand, by constructing groups of homogeneous risk profiles based on user responses to…
Don’t invest in bad ESG companies, hedge funds told
Managers have seen a “sea change” in attitudes to sustainability
Podcast: Kondratyev and Schwarz on generating data
Market generator models may aid areas of finance where data is limited or sensitive
The market generator
A generative neural network is proposed to create synthetic datasets that mantain the statistical properties of the original dataset
Ripping up the old asset class labels
Outmoded classifications of securities may be concealing market risk. AI has a better idea
Bank disruptors: Crédit Ag taps AI to lure swaptions business
Machine learning model predicts client demand with high accuracy, giving traders an edge in pricing
Bank disruptors: BofA’s ‘citizen devs’ take on innovation mantle
Central data and technology group enables frontline ‘citizen developers’
Interpretability of neural networks: a credit card default model example
Recently developed techniques aimed at answering interpretability issues in neural networks are tested and applied to a retail banking case
Bank disruptors: how tech joint ventures help Nomura’s bottom line
Nomura is developing new software services to supplement trading profits
Bank disruptors: SocGen’s call to start-ups
Fintechs and ‘intrapreneurs’ are leading Societe Generale’s digital transformation
Haitong taps NLP to inform collateral coverage
Hong Kong broker scours news and blogs in bid for better corporate signals in China’s opaque markets
Bank disruptors: tread carefully and bend things
How BofA, SocGen, JP Morgan, Nomura, UBS and others are disrupting themselves
Bank disruptors: Barclays finds blockchain nirvana
USC could transform financial markets. But first, backers must prove it is secure
Ex-Credit Suisse quants embrace machine learning
Founders of XAI Asset Management grapple with unsupervised learning and the problems of explainability
Allocation models that know their unknowns
Quants say probabilistic programming beats machine learning in balancing strategies
Private equity investors see savings in AI
Unigestion, Schroders using machine learning to avoid ‘obvious losers’ among private equity firms
Review of 2019: shaken, not stirred
The market survived a cocktail of hits. But is a hangover on the way?
Quant funds look to AI to master correlations
Machine learning shows promise in grouping assets better, predicting regime shifts
Dark materials: how one academic is delving into data
David Hand shines a light on dark data and the dangers of distortion by absence
Degree of influence: Regulatory policies drive quantitative research
Counterparty risk and market risk hold centre stage, data science moves up, quantum computing debuts