Delta force: option pricing with differential machine learning

2021 ◽  
Author(s):  
Magnus Grønnegaard Frandsen ◽  
Tobias Cramer Pedersen ◽  
Rolf Poulsen
Keyword(s):  
Machine Learning ◽  
Option Pricing

scholarly journals Special Issue “Interplay between Financial and Actuarial Mathematics”

Risks ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 139
Author(s):  
Corina Constantinescu ◽  
Julia Eisenberg
Keyword(s):  
Machine Learning ◽  
Control Theory ◽  
Option Pricing ◽  
Interest Rates ◽  
Financial Mathematics ◽  
Special Issue ◽  
Actuarial Mathematics ◽  
Mortality Models ◽  
Low Interest Rates

The Special Issue aims to highlight the interaction between actuarial and financial mathematics, which, due to the recent low interest rates and implications of COVID-19, requires an interlace between actuarial and financial methods, along with control theory, machine learning, mortality models, option pricing, hedging, unit-linked contracts and drawdown analysis, among others [...]

Predicting the stock price of frontier markets using machine learning and modified Black–Scholes Option pricing model

2020 ◽  
Vol 555 ◽  
pp. 124444 ◽  
Author(s):  
Reaz Chowdhury ◽  
M.R.C. Mahdy ◽  
Tanisha Nourin Alam ◽  
Golam Dastegir Al Quaderi ◽  
M. Arifur Rahman
Keyword(s):  
Machine Learning ◽  
Option Pricing ◽  
Stock Price ◽  
Pricing Model ◽  
Black Scholes ◽  
Option Pricing Model

Option pricing using Machine Learning

2021 ◽  
Vol 163 ◽  
pp. 113799
Author(s):  
Codruț-Florin Ivașcu
Keyword(s):  
Machine Learning ◽  
Option Pricing

Option Pricing With Machine Learning

2019 ◽  
Author(s):  
Daniel Alexandre Bloch
Keyword(s):  
Machine Learning ◽  
Option Pricing

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Machine Learning for Speaker Recognition

2020 ◽  
Author(s):  
Man-Wai Mak ◽  
Jen-Tzung Chien
Keyword(s):  
Machine Learning ◽  
Speaker Recognition

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining
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