Machine learning
Earnings call analysis 2.0 goes beyond good and bad words
Quants develop new ways to extract signals from media-savvy chief executives and their financial statements
Strengthening risk frameworks
Interest rates might be a challenge, but they are also an opportunity for insurers, Gus Ortega, head of operational risk management at Voya Financial, tells Risk.net
Shaping the future of risk and finance with analytics and integrated technology
This webinar explores how to enhance business planning activities, while accelerating regulatory demands with limited resources amid a need to derive greater value from the analytic lifecycle
Vulnerabilities arise in financial services as AI and machine learning use balloons
The scale at which financial services firms are adopting artificial intelligence and machine learning continues to grow, bringing with it new dimensions of risk and vulnerability. In a recent Risk.net webinar sponsored by TCS, experts discussed…
Goldman exec: rogue algos could spark ‘systemic’ crashes
Device proliferation and digital assets also altering risk environment, says Europe op risk head
Machine learning models: the new standard in capital markets
Zoi Fletcher speaks to Alexander Sokol, founder and executive chairman at CompatibL, about why he believes machine learning technology will be used to calculate risk measures across the industry going forward
Banks strive for machine learning at quantum speed
Embryonic work on quantum neural networks raises hope of faster, more accurate models
Quant of the year: Hans Buehler
Risk Awards 2022: Architect of deep hedging aims to supplant orthodox models with method based purely on data
Equity derivatives house of the year: JP Morgan
Risk Awards 2022: US dealer filled flow gaps with a little help from some robots
Derivatives house of the year: JP Morgan
Risk Awards 2022: Big bet on AI is delivering results
Podcast: UBS’s Gordon Lee on conditional expectations and XVAs
Top quant explains why XVA desks need a neighbour and a reverend
Dynamically controlled kernel estimation
An accurate data-driven and model-agnostic method to compute conditional expectations is presented
Review of 2021: Default, revolt, reform
Archegos, GameStop, the last days of Libor – markets just about coped in a bleak and disorderly year
Customer churn prediction for commercial banks using customer-value-weighted machine learning models
In this paper the authors propose a framework to address the issue of customer churn prediction, and they quantify customer values with the use of an improved customer value model.
Probabilistic machine learning for local volatility
In this paper, the authors propose to approach the calibration problem of local volatility with Bayesian statistics to infer a conditional distribution over functions given observed data.
Language barrier: quants slog to teach investing bots to read
Training models to interpret text can be dull; doing it badly can be costly
Moonshots and machines: can AI solve the problems of fincrime?
New technologies such as artificial intelligence (AI) and machine learning promise much in the battle against financial crime, but where are these solutions best deployed? A panel of anti-money laundering and analytics professionals convened for a Risk…
Degree of influence 2021: XVA marks the spot
Research into valuation adjustments is back on quants’ to-do list
Multi-horizon forecasting for limit order books
A multi-step path is forecast using deep learning and parallel computing
RBC applies ‘deep hedging’ to stress scenarios
Risk USA: machine learning model generates more realistic estimates of trading losses
Quants see promise in Bayesian machine learning
Risk USA: probability theory may hold key to creating ‘self-aware’ AI
Scalability could trump complexity in machine learning debate
Risk USA: banks “on the precipice” of adopting more complex models, says Goldman exec