簡易檢索 / 詳目顯示

回結果列表
研究生： 賴廷嘉
Lai, Tin-chia
論文名稱： 預測模型運用於散裝海運二手船市場
Prediction Model for Dry Bulk Secondhand Ship Market
指導教授： 張瀞之
Chang, Ching-Chih
學位類別： 碩士
Master
系所名稱： 管理學院 - 交通管理科學系
Department of Transportation and Communication Management Science
論文出版年： 2009
畢業學年度： 97
語文別： 英文
論文頁數： 55
中文關鍵詞： 二手船 、散裝航運
外文關鍵詞： MAPE, PAT, X11, Secondhand Ship, Dry Bulk
相關次數： 點閱：86  下載：7 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 隨著近年來新興國家之迅速發展，在人口上升與頻繁工程建設下，帶動以原物料為主要運輸物之散裝航運市場迅速成長，導致原物料運輸需求增加，造成散裝船各市場皆處於劇烈波動之情況，尤其是散裝航運之二手船市場面臨二手船價格波動劇烈之高風險。所以若能有效掌控與預測未來二手船價格可能之波動情形，對於市場之參與者而言，可提供降低市場風險之重要資訊。
    由於散裝航運之二手船價格資料具有強烈之季節性與週期性之特性，所以在本文中將採用時間序列分析方法，透過季節調整與濾波分析方式將二手船價格中之週期因素分解出來，而在二手船市場中，有許多種不同船齡與不同大小之船型，其中以船的載重噸位DWT(Dead Weight Tonnage)區分為三種主要船型，由大到小依序為海舺型(Capesize)、巴拿馬極限型(Panamax)以及輕便極限型(Handymax)船，本文將對於三種船型船齡五年之月平均價格資料進行分析。
    在本論文之中，主要使用時間序列X-11季節調整方法與階段平均法PAT (Phase average trend)來分析散裝航運二手船市場的週期因素(Cyclical component)，為了更加了解與掌握週期之走勢與波動，在本文中提出一個預測模型用來預測二手船價格之週期行為與走勢。對於模型預測能力之驗證，採用平均絕對誤差百分比MAPE (mean absolute percentage error)判斷基準，MAPE對於本文所建立之預測模型之檢驗結果皆低於20%，因此，表示透過預測模型之建構，能夠有效了解且預測未來散裝航運市場二手船價格之週期變動，以降低市場參與者對於二手船價格劇烈波動下所承受之風險。

    The purpose of this research is to develop a novel model to forecast the cyclical movements for the monthly secondhand ship price for three different vessel sizes (Capesize, Panamax and Handymax) in the dry bulk market over the period of January 1996 to December 2007. Both X11 decomposition method and the Phase average trend (PAT) method are applied in the extraction of the cyclical components. The empirical results indicate that there are two whole cycles during the research period for Panamax and three whole cycles for Capesize and Handymax. This research builds some prediction models to approximate these whole cycles and to predict the full cyclical movement from the last trough point of this research for each vessel size. This study adopted the mean absolute percentage error (MAPE) validate the accuracy of these kinds of models. MAPE value in practical models is found to be varying from 0.004 to 0.17 in different cycles. All the value of MAPE less than 0.2, it suggests that the novel model gives a good prediction for the cyclical movements for secondhand ship price. The results of forecasting model for three different vessel sizes reveal that the next cyclical trough point beyond this research period is around late 2009. From this point, this study predict the trough of international economics could be on the mid or end of 2009, followed by a three years sideway, the next recovery stage could happen in 2012/13.

    CONTENTS I LIST OF TABLES II LIST OF FIGURES III CHAPTER 1 Introduction 1 1.1 Research motivation and background 1 1.2 Research objective 2 1.3 Research scope 3 1.4 Flow chart 4 CHAPTER 2 Literature Review 6 2.1 Time series analysis 6 2.2 Detrending method 10 2.3 Model accuracy with MAPE 12 2.4 Summary 14 CHPATER 3 Methodology 15 3.1 Data collection 15 3.2 Time series 16 3.3 X11 time series decomposition 16 3.4 Phase average trend (PAT) 18 3.5 The novel prediction model 22 3.6 Mean absolute percentage error (MAPE) 26 3.7 Summary 27 CHPATER 4 Empirical Analysis 28 4.1 Data description and statistics 28 4.2 PAT analysis of dry bulk secondhand ship price 31 4.3 Numerical analysis for the cycle movements 34 4.3.1 Numerical analysis for the cycle movements in the Capesize market 34 4.3.2 Numerical analysis for the cycle movements in the Panamax market 38 4.3.3 Numerical analysis for the cycle movements in the Handymax market 40 4.4 Forecasting 43 4.5 Summary 46 CHPATER 5 Conclusion and Suggestion 48 References 53 Table 3-1 Typical MAPE Values and Interpretation………………………………………27 Table 4-1 Descriptive Statistics for Dry Bulk Secondhand Ship Price, from Jan-1996 to Dec-2007 30 Table 4-2 The Durations of Recovery and Recession of Capesize Secondhand Ship Price. 33 Table 4-3 P The Durations of Recovery and Recession of Panamax Secondhand Ship Price 33 Table 4-4 The Durations of Recovery and Recession of Handymax Secondhand Ship Price 34 Table 4-5 Performances of Predicted Models in different Vessel Sizes 47 Fig. 1-1 Research Flow Chart 5 Fig. 3-1 The Approximated Change Rate and Variation Trend of Secondhand Ship Price Relative to the Period Studied 23 Fig. 3-2 Comparison of Approximating Data Distribution of Different Deviation Values in the Dry Bulk Secondhand Ship Market. 24 Fig. 3-3 Comparison of Approximating Data Distribution of Different Month of Maximum Values in the Dry Bulk Secondhand Ship Market. 25 Fig. 3-4 Comparison of Approximating Data Distribution of Different Initial Values in the Dry Bulk Secondhand Ship Market. 25 Fig. 3-5 Comparison of Approximating Data Distribution of Different Peak of Variation in the Dry Bulk Secondhand Ship Market. 26 Fig. 4-1 Historical Secondhand Ship Prices for Capesize , Panamax and Handymax from 1996–2007. 30 Fig. 4-2 Seasonally Adjusted Series (STC), Trend (T) and Cycle (C) for Capesize Secondhand Ship Price from 1996 to 2007 31 Fig. 4-3 Seasonally Adjusted Series (STC), Trend (T) and Cycle (C) for Panamax Secondhand Ship Price from 1996 to 2007 31 Fig. 4-4 Seasonally Adjusted Series (STC), Trend (T) and Cycle (C) for Handymax Secondhand Ship Price from 1996 to 2007 32 Fig. 4-5 Prediction Model and Original Data of the First Cycle for Capesize Secondhand Ship Prices 36 Fig. 4-6 Prediction Model and Original Data of the Second Cycle for Capesize Secondhand Ship Prices 37 Fig. 4-7 Prediction Model and Original Data of the Third Cycle for Capesize Secondhand Ship Prices 37 Fig. 4-8 Prediction Model and Original Data of Three Whole Cycles for Capesize Secondhand Ship Prices (Whole Cycle Period). 38 Fig. 4-9 Prediction Model and Original Data on the First Cycle for Panamax Secondhand Ship Prices 39 Fig. 4-10 Prediction Model and Original Data of the Second Cycle for Panamax Secondhand Ship Prices 40 Fig. 4-11 Prediction Model and Original Data of Three Whole Cycles for Panamax Secondhand Ship Prices (Whole Cycle Period) 40 Fig. 4-12 Prediction Model and Original Data on the First Cycle for Handymax Secondhand Ship Prices 42 Fig. 4-13 Prediction Model and Original Data of the Second Cycle for Handymax Secondhand Ship Prices 42 Fig. 4-14 Prediction Model and Original Data on the Third Cycle for Handymax Secondhand Ship Prices 43 Fig. 4-15 Prediction Model and Original Data of Three Whole Cycles for Handymax Secondhand Ship Prices (Whole Cycle Period) 43 Fig. 4-16 Prediction Model and Original Data for Capesize Secondhand Ship Prices 45 Fig. 4-17 Prediction Model and Original Data for Panamax Secondhand Ship Prices 45 Fig. 4-18 Prediction Model and Original Data for Handymax Secondhand Ship Prices 46

    Alizadeh, A. H., & Nomikos, N. K. (2007). Investment timing and trading strategies in the sale and purchase market for ships. Transportation Research Part B, 41, 126-143.
    Balog, K., & Rijke, M. D. (2006). Decomposing Bloggers' Moods: Towards a Time Series Analysis of Moods in the Blogosphere. Paper presented at the Third annual Workshop on the Weblogging Ecosystem, Edinburgh, UK.
    Beenstock, M. (1985). A Theory of Ship Prices. Maritime Policy and Management, 12, 215-225.
    Beenstock, M., & Vergottis, A. (1989). An Econometric Model of the World Shipping Market for Dry Cargo, Freight and Shipping. Applied Economics, 21, 339-356.
    Beenstock, M., & Vergottis, A. (1992). The interdependence between the Dry Cargo and Tanker Markets. Logistics and Transportation Review, 29(1), 3-38.
    Bera, A. K., & Jarque C. M. (1981). Efficient tests for normality, heteroscedasticity and serial independence of regression residuals: Monte Carlo evidence. Economic Letters, 7, 313-308.
    Bikker, J.A., & Haan, L. de. (1988). Conjunctuur en conjunctuurprognose: een conjunctuurindicator voor Nederland. Kwartaalbericht, De Nederlandsche Bank, 3, 71–83.
    Boschan, C., & Ebanks, W. (1978). The Phase-Average Trend: A New Way of Measuring Economic Growth. Proceedings of the Business and Economic Statistics section, American Statistical Association, 332-335.
    Brunet, O. (2000). Calculation of Composite Leading Indicators: A Comparison of Two Different Methods. Paper Presented at the CIRET Conference, Paris, France.
    Bry, G., & Boschan, C. (1971). Cyclical analysis of time series: selected procedures and computer programs. NBER Technical Paper 20. Cambridge, MA: National Bureau of Economic Research.
    Canova, F. (1999). Does Detrending Matter for the Determination of the Reference Cycle and the Selection of Turning Points? The Economic Journal, 109(452), 126-150.
    Central Planning Bureau (1990). CPB-conjunctuurindicator. Hague: Central Planning Bureau.
    Central Planning Bureau (1993). Tussenrapportage 1993, Hague: Central Planning Bureau.
    Fader, P. S., Hardie, B. G. S., & Zeithammer, R. (2003). Forecasting New Product Trial in a Controlled Test Market Environment. Journal of Forecasting, 22(5), 391-410.
    Ghysels, E., Granger, C. W. J., & Siklos, P. L. (1996). Is Seasonal Adjustment a Linear or Nonlinear Data-Filtering Process? Journal of Business & Economic Statistics, 14(3), 374-386.
    Gómez, V., & Bengoechea, P. (2000). The quarterly national accounts trend–cycle filter versus model–based filters. Spanish Economic Review, 2(1), 29-48.
    Hardie, B. G. S., Fader, P. S., & Wisniewski, M. (1998). An Empirical Comparison of New Product Trial Forecasting Models. Journal of Forecasting, 17, 209-229.
    Huang, J., Chen, R., & Wang, G. (2007). An Empirical Comparison of Weibull and Its Extended Models for New Product Trial Calibration and Forecasting. Paper presented at the IEEE, International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2007), Shanghai, China.
    Jacobs, J., Salomons, R., & Sterken, E. (1997). The CCSO composite leading indicator of the Netherlands: construction, forecasts and comparison. (CCSO Series 31) Groningen, Netherlands. Retrieved June 23, 2008, from http://www.eco.rug.nl/ccso/ccsoseries/ccso31.pdf
    Jazayeri, S. M. T., & Yahyai, A. (2004). An analysis of seasonality of non-OPEC supply. Maritime Policy & Management, 31(3), 213-224.
    Kavussanos, M. G. (1996). Comparisons of volatility in the dry-cargo ship sector: Spot versus time charters, and smaller versus larger vessels. Journal of Transport Economics and Policy, 30(1), 67-82.
    Kavussanos, M. G. (1997). The dynamics of time-varying volatilities in different size second-hand ship prices of the dry-cargo sector. Applied Economics, 29, 433-443.
    Kavussanos, M. G., & Alizadeh, A. H. (2001). Seasonality patterns in dry bulk shipping spot and time charter freight rates. Transportation Research Part E, 37(6), 443-467.
    Kranendonk, H. & Jansen, C. (1997). Using Leading Indicators in a Model-based Forecast (Quarterly Review of CPB, Report 97/3). Retrieved June 24, 2008, from http://www.cpb.nl/nl/pub/cpbreeksen/cpbreport/1997_3/s2_2.pdf
    Kavussanos, M. G., & Visvikis, I. D. (2006). Shipping freight derivatives: a survey of recent evidence. Maritime Policy and Management, 33(3), 233-255.
    Ladiray, D., & Quennevulle, B. (2004) Implementation issues on shrinkage estimators for seasonal factors within the X-11 seasonal adjustment method. International Journal of Forecasting, 20, 557-560.
    Lahiri, K., Yao W., & Young, P. (2004). Cycles in the Transportation Sector and the Aggregate Economy. Transportation Research Record: Journal of the Transportation Research Board, 1864, 103-111.
    Lewis, C.D. (1982). Industrial and Business Forecasting Method. London: Butterworth Scientific Publishers.
    Massmann, M. & Mitchell, J. (2003). Reconsidering the evidence: are Eurozone business cycles converging? Journal of Business Cycle Measurement and Analysis, 1(3), 275-307.
    Nilsson, R. (1987). OECD Leading Indicators. OECD Economic Studies 9 (pp. 105-146). Paris, France. Retrieved June 23, 2008, from http://www.oecd.org/dataoecd/63/40/35252065.pdf
    Oosse, M. (2004). Evaluation of April 1, 2000 School District Population Estimates Based on the Synthetic Ratio Method (Population Division Working Paper No. 74). Washington, DC: Population Division. Retrieved June 23, 2008, from http://www.census.gov/population/www/documentation/twps0074/twps0074.html
    Organization for Economic Co-operation and Development (2001). Business cycles and cyclical indicators in China. Retrieved June 23, 2008, from http://www.oecd.org/dataoecd/62/21/1877339.pdf
    Peeters, A. (1998). Interim Employment and a Leading Indicator for the Belgian Labour Market. Center for Economic Studies - Discussion papers ces9823,Katholieke Universiteit Leuven, Centrum voor Economische Studiën.
    Syriopoulos, T., & Roumpis, E. (2006). Price and volume dynamics in seconf-hand dry bulk and tanker shipping markets. Maritime Policy and Management, 33(5), 497-518.
    Tsai, T-H., Lee, C-K., & Wei, C-H. (2003). An Artificial Neural Networks Approach to Forecast Short-term Railway Passenger Demand. Journal of the Eastern Asia Society for Transportation Studies, 5, 221-235.
    Tsai, T-H., Lee, C-K., & Wei, C-H. (2005). Design of Dynamic Neural Networks to Forecast Short-term Railway Passenger Demand. Journal of the Eastern Asia Society for Transportation Studies, 6, 1651-1666.
    Tsolakis, S. D., Cridland, C., & Haralambides, H. E. (2003). Econometric Modelling of Second-hand Ship Prices, Maritime Economics & Logistics, (5), 347-377.
    Watson, M. W. (1996). Is Seasonal Adjustment a Linear or Nonlinear Data-Filtering Process: Comment. Journal of Business & Economic Statistics, 14(3), 394-396.
    Zarnowitz, V., & Ozyildirim, A. (2006). Time series decomposition and measurement of business cycles, trends and growth cycles. Journal of Monetary Economics, 53, 1717-1739.

    下載圖示 校內：2011-01-13公開
    校外：2011-01-13公開
    QR CODE