Monotonous 发表于 2025-3-28 15:16:44
Why customer driven manufacturings. Compared to the traditional arbitrage-based approach, this technique is useful when the underlying asset dynamics are unknown or when the pricing equations are too complicated to solve analytically. Comparing to other established data-driven option pricing techniques such as neural networks, impl后来 发表于 2025-3-28 20:19:13
http://reply.papertrans.cn/39/3825/382438/382438_42.png无表情 发表于 2025-3-29 00:42:52
https://doi.org/10.1007/3-540-31319-2 that is suitable for online trading. Our benchmark for . is the Tucker (1991) put-call-futures (.) parity condition for detecting arbitrage profits in the index options and futures markets. The latter presents two main problems, (.) The windows for profitable arbitrage opportunities exist for short可转变 发表于 2025-3-29 06:46:56
http://reply.papertrans.cn/39/3825/382438/382438_44.png阴险 发表于 2025-3-29 07:38:31
Evolutionary Decision Trees for Stock Index Options and Futures Arbitragesampling is used to train . to pick up the fundamental arbitrage patterns. The further novel aspect of . is a constraint satisfaction feature supplementing the fitness function that enables the user to train the .. by learning to satisfy a minimum and maximum set on the number of arbitrage opportuni对手 发表于 2025-3-29 14:17:52
the volume, which will enable readers without a strong programming background to gain hands-on experience in dealing with much of the technical material introduced in this work.978-1-4613-5262-4978-1-4615-0835-9问到了烧瓶 发表于 2025-3-29 19:20:47
https://doi.org/10.1007/978-3-658-43326-0ptimized using Genetic Algorithm (GA). Two training approaches—incremental and dynamic—are designed and studied. The system was evaluated with the stocks in NASDAQ market. Experimental results showed that the system can give reliable buy-sell signals and using the system to perform buy-sell can prodONYM 发表于 2025-3-29 22:15:36
https://doi.org/10.1007/3-540-31319-2sampling is used to train . to pick up the fundamental arbitrage patterns. The further novel aspect of . is a constraint satisfaction feature supplementing the fitness function that enables the user to train the .. by learning to satisfy a minimum and maximum set on the number of arbitrage opportuniAffection 发表于 2025-3-30 00:44:08
http://reply.papertrans.cn/39/3825/382438/382438_49.png刻苦读书 发表于 2025-3-30 07:25:01
http://reply.papertrans.cn/39/3825/382438/382438_50.png