TY - GEN
T1 - Strategic Capital Investment Analytics
T2 - 20th International Conference on Computational Science and Its Applications, ICCSA 2020
AU - Abdollahian, Mark
AU - Chang, Yi Ling
AU - Lee, Yuan Yuan
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - In this paper, we present an agent-based model (ABM) of multi-dimensional transportation choices for individuals and firms given anticipated aggregate traveler demand patterns. Conventional finance, economic and policy evaluation techniques have already been widely adopted to more evidenced based decision-making process with the aim to understand the financial, economic and social impacts on transportation choices. Prior scholars have examined common practices used to measure profitability for investment appraisal including internal rate of return (IRR), net present value (NPV) and risk analysis approaches, incorporating the concepts of time value of money and uncertainty to assess potential financial gains with different transportation projects. However, using conventional capital budget planning or static scenario analysis alone cannot capture significant, interactive and nonlinear project, demand and market uncertainties. Here we build an agent-based model on the current California High-Speed Rail (HSR) to provide insights into firm investment decisions from a computational finance perspective, given the coupling of individual choices, aggregate social demand, and government policy and tax incentives. Given individual level choice and behavioral aspects, we combine financial accounting and economic theory to identify more precise marginal revenue streams and project profitability over time to help mitigate both project and potential, system market risk.
AB - In this paper, we present an agent-based model (ABM) of multi-dimensional transportation choices for individuals and firms given anticipated aggregate traveler demand patterns. Conventional finance, economic and policy evaluation techniques have already been widely adopted to more evidenced based decision-making process with the aim to understand the financial, economic and social impacts on transportation choices. Prior scholars have examined common practices used to measure profitability for investment appraisal including internal rate of return (IRR), net present value (NPV) and risk analysis approaches, incorporating the concepts of time value of money and uncertainty to assess potential financial gains with different transportation projects. However, using conventional capital budget planning or static scenario analysis alone cannot capture significant, interactive and nonlinear project, demand and market uncertainties. Here we build an agent-based model on the current California High-Speed Rail (HSR) to provide insights into firm investment decisions from a computational finance perspective, given the coupling of individual choices, aggregate social demand, and government policy and tax incentives. Given individual level choice and behavioral aspects, we combine financial accounting and economic theory to identify more precise marginal revenue streams and project profitability over time to help mitigate both project and potential, system market risk.
KW - Agent based modeling
KW - Complex adaptive systems
KW - Computational finance
KW - Risk mitigation
KW - Social learning
KW - Transportation projects
UR - https://www.scopus.com/pages/publications/85093103159
U2 - 10.1007/978-3-030-58802-1_10
DO - 10.1007/978-3-030-58802-1_10
M3 - Conference contribution
AN - SCOPUS:85093103159
SN - 9783030588014
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 133
EP - 147
BT - Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings
A2 - Gervasi, Osvaldo
A2 - Murgante, Beniamino
A2 - Misra, Sanjay
A2 - Garau, Chiara
A2 - Blecic, Ivan
A2 - Taniar, David
A2 - Apduhan, Bernady O.
A2 - Rocha, Ana Maria A.C.
A2 - Tarantino, Eufemia
A2 - Torre, Carmelo Maria
A2 - Karaca, Yeliz
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 1 July 2020 through 4 July 2020
ER -