Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162086
Title: Newbuilding ship price forecasting by parsimonious intelligent model search engine
Authors: Gao, Ruobin
Liu, Jiahui
Zhou, Qin
Duru, Okan
Yuen, Kum Fai
Keywords: Engineering::Maritime studies
Issue Date: 2022
Source: Gao, R., Liu, J., Zhou, Q., Duru, O. & Yuen, K. F. (2022). Newbuilding ship price forecasting by parsimonious intelligent model search engine. Expert Systems With Applications, 201, 117119-. https://dx.doi.org/10.1016/j.eswa.2022.117119
Journal: Expert Systems with Applications
Abstract: Asset prices play a significant role in the financial survival and profitability of ship-owning firms. In a highly volatile shipping market, prices of newbuilding ships must be predicted to detect security shortfalls as well as opportunities for temporal arbitration (gaining on high–low pricing). Accordingly, this paper proposes an improved version of the intelligent model search engine (IMSE) by asynchronous time lag selection. The parsimonious IMSE algorithm comprises the essential components such as input and training data size selection by a grid search procedure. In the initial IMSE algorithm, time-lag (memory size) selection is designed such that a serial cluster of memory groups is assigned synchronously for all inputs. By relaxing of lag structures selection, the proposed algorithm estimates unique lead–lag relations for the input of the intended problem set. An extensive benchmark study with several baseline models and the persistence forecast (Naïve I) is performed to observe the out-of-sample accuracy of the proposed approach. The empirical results indicate that second-hand ship prices, scrap values, and orderbook (no. of orders) have predictive features and are selected by the search engine for two ship sizes. Different lag structures are estimated for each input with asynchronous time-lag selection improvement.
URI: https://hdl.handle.net/10356/162086
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2022.117119
Rights: © 2022 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Journal Articles

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