Residual value forecasting using asymmetric cost functions

被引:10
|
作者
Dress, Korbinian [1 ]
Lessmann, Stefan [1 ]
von Mettenheim, Hans-Joerg [2 ]
机构
[1] Humboldt Univ, Sch Business & Econ, Unter Linden 6, D-10099 Berlin, Germany
[2] Leibniz Univ Hannover, Inst Informat Syst, Hannover, Germany
关键词
Asymmetric cost of error; Quantile regression; Neural networks; Ensemble learning; Automotive industry; NEURAL-NETWORKS; QUANTILE REGRESSION; PROSPECT-THEORY; DURABLE GOODS; INFORMATION; PREDICTION; ACCURACY; FIRM;
D O I
10.1016/j.ijforecast.2018.01.008
中图分类号
F [经济];
学科分类号
02 ;
摘要
Leasing is a popular channel for marketing new cars. However, the pricing of leases is complicated because the leasing rate must embody an expectation of the car's residual value after contract expiration. This paper develops resale price forecasting models in order to aid pricing decisions. One feature of the leasing business is that different forecast errors entail different costs. The primary objective of this paper is to identify effective ways of addressing cost asymmetry. Specifically, this paper contributes to the literature by (i) consolidating prior work in forecasting on asymmetric functions of the cost of errors; (ii) systematically evaluating previous approaches and comparing them to a new approach; and (iii) demonstrating that forecasting using asymmetric cost of error functions improves the quality of decision support in car leasing. For example, if the costs of overestimating resale prices are twice those of underestimating them, incorporating cost asymmetry into forecast model development reduces costs by about 8%. (C) 2018 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:551 / 565
页数:15
相关论文
共 50 条
  • [1] Residual value forecasting using asymmetric cost functions (vol 34, pg 551, 2018)
    Dress, Korbinian
    [J]. INTERNATIONAL JOURNAL OF FORECASTING, 2021, 37 (03) : 1314 - 1314
  • [2] Solar Radiation Forecasting under Asymmetric Cost Functions
    Fatemi, Seyyed A.
    Kuh, Anthony
    [J]. PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2014, : 1727 - 1732
  • [3] Online Solar Radiation Forecasting under Asymmetric Cost Functions
    Fatemi, Seyyed A.
    Kuh, Anthony
    Fripp, Matthias
    [J]. 2014 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA), 2014,
  • [4] Asymmetric Loss Functions for Forecasting in Criminal Justice Settings
    Berk, Richard
    [J]. JOURNAL OF QUANTITATIVE CRIMINOLOGY, 2011, 27 (01) : 107 - 123
  • [5] Asymmetric Loss Functions for Forecasting in Criminal Justice Settings
    Richard Berk
    [J]. Journal of Quantitative Criminology, 2011, 27 : 107 - 123
  • [6] Reduced hypoglycemia risk in insulin bolus therapy using asymmetric cost functions
    Kirchsteiger, Harald
    del Re, Luigi
    [J]. ASCC: 2009 7TH ASIAN CONTROL CONFERENCE, VOLS 1-3, 2009, : 751 - 756
  • [7] Interference Prediction in Partially Loaded Cellular Networks Using Asymmetric Cost Functions
    Parthasarathy, Sudharsan
    Pulliyakode, Saishankar Katri
    Kalyani, Sheetal
    Ganti, Radha Krishna
    [J]. IEEE COMMUNICATIONS LETTERS, 2018, 22 (06) : 1288 - 1291
  • [8] Book value, residual earnings, and equilibrium firm value with asymmetric information
    Kwon Y.K.
    [J]. Review of Accounting Studies, 2001, 6 (4) : 387 - 395
  • [9] Forecasting Value at Risk and Expected Shortfall Using a Semiparametric Approach Based on the Asymmetric Laplace Distribution
    Taylor, James W.
    [J]. JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 2019, 37 (01) : 121 - 133
  • [10] A Deep Multi-task Approach for Residual Value Forecasting
    Rashed, Ahmed
    Jawed, Shayan
    Rehberg, Jens
    Grabocka, Josif
    Schmidt-Thieme, Lars
    Hintsches, Andre
    [J]. MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2019, PT III, 2020, 11908 : 467 - 482