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LIBOR Fallback and Quantitative Finance [PDF]
Risks, 2019 With the expected discontinuation of the LIBOR publication, a robust fallback for related financial instruments is paramount. In recent months, several consultations have taken place on the subject.
Marc Pierre Henrard
doaj +4 more sources
Gradient boosting for quantitative finance
The Journal of Computational Finance, 2021 In this paper, we discuss how tree-based machine learning techniques can be used in the context of derivatives pricing. Gradient boosted regression trees are employed to learn the pricing map for a couple of classical, time-consuming problems in quantitative finance.
Davis, Jesse +3 more
openaire +3 more sources
Metals and Energy Finance, 2018 Quantitative Finance: An Object-Oriented Approach in C++ provides readers with a foundation in the key methods and models of quantitative finance. Keeping the material as self-contained as possible, the author introduces computational finance with a focus on practical implementation in C++.
Jian Geng, I. M. Navon, Xiao Chen
semanticscholar +3 more sources
Editorial for special issue on advances in Actuarial Science and quantitative finance [PDF]
Methodology and Computing in Applied Probability, 2022 Claude Lefèvre, , Runhuan Feng
exaly +2 more sources
FinRL: deep reinforcement learning framework to automate trading in quantitative finance [PDF]
International Conference on AI in Finance, 2021 Deep reinforcement learning (DRL) has been envisioned to have a competitive edge in quantitative finance. However, there is a steep development curve for quantitative traders to obtain an agent that automatically positions to win in the market, namely to
Xiao-Yang Liu +3 more
semanticscholar +1 more source
FinRL-podracer: high performance and scalable deep reinforcement learning for quantitative finance [PDF]
International Conference on AI in Finance, 2021 Machine learning techniques are playing more and more important roles in finance market investment. However, finance quantitative modeling with conventional supervised learning approaches has a number of limitations, including the difficulty in defining ...
Zechu Li +5 more
semanticscholar +1 more source
LSTM in Algorithmic Investment Strategies on BTC and S&P500 Index
Sensors, 2022 We use LSTM networks to forecast the value of the BTC and S&P500 index, using data from 2013 to the end of 2020, with the following frequencies: daily, 1 h, and 15 min data.
Jakub Michańków +2 more
doaj +1 more source
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance [PDF]
Social Science Research Network, 2020 As deep reinforcement learning (DRL) has been recognized as an effective approach in quantitative finance, getting hands-on experiences is attractive to beginners. However, to train a practical DRL trading agent that decides where to trade, at what price,
Xiao-Yang Liu +6 more
semanticscholar +1 more source
Applied Sciences, 2023 Forecasting the behavior of the stock market is a classic but difficult topic, one that has attracted the interest of both economists and computer scientists.
S. Sahu, A. Mokhade, N. Bokde
semanticscholar +1 more source