Generation of realistic synthetic financial time-series
Financial markets have always been a point of interest for automated systems. Due to their
complex nature, financial algorithms and fintech frameworks require vast amounts of data to …
complex nature, financial algorithms and fintech frameworks require vast amounts of data to …
Modelling stock markets by multi-agent reinforcement learning
Quantitative finance has had a long tradition of a bottom-up approach to complex systems
inference via multi-agent systems (MAS). These statistical tools are based on modelling …
inference via multi-agent systems (MAS). These statistical tools are based on modelling …
Generating realistic stock market order streams
We propose an approach to generate realistic and high-fidelity stock market data based on
generative adversarial networks (GANs). Our Stock-GAN model employs a conditional …
generative adversarial networks (GANs). Our Stock-GAN model employs a conditional …
Welfare effects of market making in continuous double auctions
We investigate the effects of market making on market performance, focusing on allocative
efficiency as well as gains from trade accrued by background traders. We employ empirical …
efficiency as well as gains from trade accrued by background traders. We employ empirical …
Reinforcement learning in agent-based market simulation: Unveiling realistic stylized facts and behavior
Investors and regulators can greatly benefit from a realistic market simulator that enables
them to anticipate the consequences of their decisions in real markets. However, traditional …
them to anticipate the consequences of their decisions in real markets. However, traditional …
[PDF][PDF] Generating realistic stock market order streams.(2020)
We propose an approach to generate realistic and highfidelity stock market data based on
generative adversarial networks (GANs). Our Stock-GAN model employs a conditional …
generative adversarial networks (GANs). Our Stock-GAN model employs a conditional …
Analyzing Stock Market Dynamics: A Game-Theoretic Perspective
Trading in the stock market is quite risky due to the high level of fluctuations in the stock
prices, which are not merely governed by stock fundamentals, technical indicators, trend …
prices, which are not merely governed by stock fundamentals, technical indicators, trend …
Modeling Trading Strategies in Financial Markets with Data, Simulation, and Deep Reinforcement Learning
M Shearer - 2022 - deepblue.lib.umich.edu
Rapidly advancing algorithmic trading techniques and lagging financial market regulations
have led to opportunities for traders to use these advancements to their own advantage. This …
have led to opportunities for traders to use these advancements to their own advantage. This …
A Strategic Agent-Based Analysis of Economic and Technological Changes in Financial Networks
K Mayo - 2024 - deepblue.lib.umich.edu
Economic events and advancements in technology have drastically transformed the
financial system over the past 20 years. This includes the implementation of new policies …
financial system over the past 20 years. This includes the implementation of new policies …
Stock market microstructure inference via multi-agent reinforcement learning
Quantitative finance has had a long tradition of a bottom-up approach to complex systems
inference via multi-agent systems (MAS). These statistical tools are based on modelling …
inference via multi-agent systems (MAS). These statistical tools are based on modelling …