2022/07/21 by Leonardo Kanashiro Felizardo, Felizardo, Leonardo Kanashiro, Élia Yathie Matsumoto +4 · 4 citations
Computer Science · Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Artificial Intelligence (cs.AI) #Artificial intelligence #Auction Theory and Applications #Bellman equation #Capital Investment and Risk Analysis #Computational Finance (q-fin.CP) #Computer science #FOS: Computer and information sciences #FOS: Economics and business #Finance #Mathematical optimization #Mathematics #Monte Carlo method #Optimal stopping #Reinforcement learning #Stochastic game #Stochastic processes and financial applications #cs.AI #q-fin.CP
paper · pdf · doi:10.48550/arxiv.2208.00765
published in arXiv (Cornell University) (Cornell University) · 8 pages, 6 figures, WCCI2022 IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE
arxiv created 2022/07/21 · openalex publication_date 2022/07/21 · arxiv updated 2022/08/02 · openalex created_date 2022/08/03 · openalex updated_date 2026/08/08
The optimal stopping problem is a category of decision problems with a specific constrained configuration. It is relevant to various real-world applications such as finance and management. To solve the optimal stopping problem, state-of-the-art algorithms in dynamic programming, such as the least-squares Monte Carlo (LSMC), are employed. This type of algorithm relies on path simulations using only the last price of the underlying asset as a state representation. Also, the LSMC was thinking for option valuation where risk-neutral probabilities can be employed to account for uncertainty. However, the general optimal stopping problem goals may not fit the requirements of the LSMC showing auto-correlated prices. We employ a data-driven method that uses Monte Carlo simulation to train and test artificial neural networks (ANN) to solve the optimal stopping problem. Using ANN to solve decision problems is not entirely new. We propose a different architecture that uses convolutional neural networks (CNN) to deal with the dimensionality problem that arises when we transform the whole history of prices into a Markovian state. We present experiments that indicate that our proposed architecture improves results over the previous implementations under specific simulated time series function sets. Lastly, we employ our proposed method to compare the optimal exercise of the financial options problem with the LSMC algorithm. Our experiments show that our method can capture more accurate exercise opportunities when compared to the LSMC. We have outstandingly higher (above 974% improvement) expected payoff from these exercise policies under the many Monte Carlo simulations that used the real-world return database on the out-of-sample (test) data.