#smrgKİTABEVİ Dynamic Market Modeling With Heterogeneous Agents: Applications In Diverse Markets - 2024
Editör:
Kondisyon:
Yeni
Sunuş / Önsöz / Sonsöz / Giriş:
Basıldığı Matbaa:
Dizi Adı:
ISBN-10:
6256627352
Kargoya Teslim Süresi:
4&6
Hazırlayan:
Cilt:
Amerikan Cilt
Boyut:
16x24
Sayfa Sayısı:
102
Basım Yeri:
İstanbul
Baskı:
1
Basım Tarihi:
2024
Kapak Türü:
Karton Kapak
Kağıt Türü:
Enso
Dili:
İngilizce
Kategori:
0,00
1199229983
616904
https://www.simurgkitabevi.com/dynamic-market-modeling-with-heterogeneous-agents-applications-in-diverse-markets-2024
Dynamic Market Modeling With Heterogeneous Agents: Applications In Diverse Markets - 2024 #smrgKİTABEVİ
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This book offers an in-depth exploration of financial market behavior through agent-based modeling, uncovering the complexities of trader interactions and market dynamics. It presents two distinct models that delve into the intricate workings of stock markets. The first model replicates key price features observed in real markets, comparing the performance of various trading strategies—from basic noise traders to advanced, AI-driven approaches. By simulating market conditions, the model demonstrates the impact of trader intelligence on market performance and liquidity.
The second model builds on these insights by incorporating real market data to simulate a limit order market, allowing agents to interact with actual order flows. This realistic simulation examines the relationship between order size, price impact, market volatility, and bid-ask spreads across different market environments. It offers a comprehensive view of how stock prices move and the strategies that drive them.
The book contributes to the broader understanding of financial markets, providing valuable tools for both researchers and practitioners to model, test, and optimize trading strategies. Future directions include integrating more advanced agents and exploring reinforcement learning techniques in market simulations.
The second model builds on these insights by incorporating real market data to simulate a limit order market, allowing agents to interact with actual order flows. This realistic simulation examines the relationship between order size, price impact, market volatility, and bid-ask spreads across different market environments. It offers a comprehensive view of how stock prices move and the strategies that drive them.
The book contributes to the broader understanding of financial markets, providing valuable tools for both researchers and practitioners to model, test, and optimize trading strategies. Future directions include integrating more advanced agents and exploring reinforcement learning techniques in market simulations.
This book offers an in-depth exploration of financial market behavior through agent-based modeling, uncovering the complexities of trader interactions and market dynamics. It presents two distinct models that delve into the intricate workings of stock markets. The first model replicates key price features observed in real markets, comparing the performance of various trading strategies—from basic noise traders to advanced, AI-driven approaches. By simulating market conditions, the model demonstrates the impact of trader intelligence on market performance and liquidity.
The second model builds on these insights by incorporating real market data to simulate a limit order market, allowing agents to interact with actual order flows. This realistic simulation examines the relationship between order size, price impact, market volatility, and bid-ask spreads across different market environments. It offers a comprehensive view of how stock prices move and the strategies that drive them.
The book contributes to the broader understanding of financial markets, providing valuable tools for both researchers and practitioners to model, test, and optimize trading strategies. Future directions include integrating more advanced agents and exploring reinforcement learning techniques in market simulations.
The second model builds on these insights by incorporating real market data to simulate a limit order market, allowing agents to interact with actual order flows. This realistic simulation examines the relationship between order size, price impact, market volatility, and bid-ask spreads across different market environments. It offers a comprehensive view of how stock prices move and the strategies that drive them.
The book contributes to the broader understanding of financial markets, providing valuable tools for both researchers and practitioners to model, test, and optimize trading strategies. Future directions include integrating more advanced agents and exploring reinforcement learning techniques in market simulations.
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