Machine learning
Deep learning calibration of option pricing models: some pitfalls and solutions
Addressing model calibration and the issue of no-arbitrage in a deep learning approach
Lighting up the black box: a must for investors?
Many contend you must be able to interpret machine learning in order to use it
At Numerai, real-world figures need not apply
AI hedge fund CEO sees the light in black-box technology
Factor strategies seesaw in coronavirus-hit markets
Quants struggle to second-guess ongoing effect of virus on investments
Top 10 op risks 2020: regulatory risk
New technology and reams of red tape make non-compliance fines more likely
Top 10 op risks 2020: talent risk
Firms struggle to reduce headcount and fill gaps without cutting corners
Treasurers turn to AI in bid for sharper forecasting
Wider automation could usher in future of ‘hands-free hedging’, but obstacles lurk in data standards and sharing
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