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研究生:華濰儒
研究生(外文):Wei-Ju Hua
論文名稱:分析即服務:財務危機分析的客製化服務架構
論文名稱(外文):Analysis as a Service: An Architecture For a Customized Service Of Financial Distress Analysis
指導教授:鄭為民鄭為民引用關係
指導教授(外文):Wei-Min Jeng
口試委員:余銘忠鄭武德
口試委員(外文):Min-Chun YuWu-Der Jeng
口試日期:2013-06-27
學位類別:碩士
校院名稱:東吳大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:46
中文關鍵詞:Big DataSOAAaaSFDP
外文關鍵詞:Big DataSOAAaaSFDP
相關次數:
  • 被引用被引用:0
  • 點閱點閱:258
  • 評分評分:
  • 下載下載:11
  • 收藏至我的研究室書目清單書目收藏:1
財務危機的分析研究雖存有許多理論上高正確率的模型,然實務上卻受到樣本資料於不同時間、產業別、國家等因素影響,造成其正確率下降,造成必須重新調整變數或權重。關於FDP(Financial Distress Prediction)模型的研究多為模型的建立與驗證,較少著墨於模型與資訊科技領域的結合。另一方面,財務資料巨量化(Big Data)的特性,以及全球經濟的高度連動性,凸顯出自動化取得公開財務資料,以及處理跨語系資料以協助分析的重要程度。
鑒於上述理由,本研究以服務導向架構(Service Oriented Architecture, SOA)為基礎,將不同分析模型分別獨立為可調校的服務,建立分析即服務(AaaS)系統架構,並舉台灣及美國地區為例,以該架構實現了非金融產業上市公司的財務危機分析系統。著重在探討將FDP模型服務化、系統化於實作的可行性,而非模型本身的正確性。
本架構下系統結合FDP模型Z-score(Altman, 1968)、現金流量風險值(NERA, 2000),與營運活動現金流預測(Cheng and Hollie, 2008)三種分析模型,以不同的角度分析企業的財務狀況。系統依據一系列自動化步驟,獲取、分析、計算資料,最後整合各模型分析結果,輔助使用者從現金流量的角度理解財務危機預測結果。除了呈現分析結果,系統亦提供回饋機制,方便使用者客製化調校模型。

There are a lot of high accuracy models of FDP (Financial Distress Prediction) in financial field, but the accuracy falls down as a result of different time periods, industry categories and countries; variables and coefficients need to be adjusted for different sampled data. In past researches of FDP model, most of them focus on how to develop and verify models, instead of use and combine different models with information technique.
Beside that, there are other factors should be considered : the big data in finance and the relationship of economy in world wide. Showing the importance of automatically data gathering and analyzing; the consistence of accounting terms between different languages.
For the above reasons, this thesis elaborates how to design and construct a system, which bases on SOA(Service Oriented Architecture) and named AaaS(Analysis as a Service). This system translates analytical models to independent services and combines other services to accomplish the automatically analytical goal. It means a financial distress analytical service for non-financial company in Taiwan and USA; this thesis focus on servicizing FDP model and implementing system, instead of the accuracy of model itself.
The system contains three models: Z-score (Altman, 1968), cash-flow at risk(NERA, 2000), operating cash-flow prediction(Cheng and Hollie, 2008); it automatically gathers, processes, analysis's, calculates data, and presents different analytical results together to explain the financial distress situation by FDP and cash-flow. Not only presenting result, users also can feed back their experience to adjust the variable and coefficient in analytical models.

謝誌 I
摘要 II
Abstract III
目錄 IV
圖目錄 V
表目錄 VI
1 緒論 1
1.1 研究背景 1
1.2 研究動機 1
1.3 研究目的 2
1.4 研究範圍 2
1.5 研究限制 3
2 文獻探討 4
2.1 系統架構文獻回顧 4
2.2 財務危機預測模型 7
2.3 現金流預測模型 10
2.4 風險評估工具 11
3 研究方法 14
3.1 資料服務 15
3.2 分析模型服務 16
3.3 AaaS核心服務(AaaS Core Service) 19
4 系統實作 21
4.1 系統介面 21
4.2 資料服務實作 23
4.3 分析模型服務實作 26
4.4 AaaS核心服務實作 29
4.5 個案分析 30
4.6 可視化結果與快速財務診斷 32
4.7 實作限制 38
5 結論與未來展望 39
6 參考文獻 40
7 附錄一 42
8 附錄二 46

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[6]Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance, 23(4), 589-609.
[7]Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accounting Research, 167-179.
[8]Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of accounting research, 18(1), 109-131.
[9]羅淑娟, 林晶璟, & 陳義方. (2009). 應用邏吉斯迴歸技術探討財務危機預警變數與資料長度之適用性研究-以台灣上市電子產業為例. 臺北科技大學學報, 42(2), 83-106.
[10]Balcaen, S., & Ooghe, H. (2006). 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems. The British Accounting Review, 38(1), 63-93.
[11]Bellovary, J., Giacomino, D., & Akers, M. (2007). A review of bankruptcy prediction studies: 1930 to present. Journal of financial education.
[12]Lane, W. R., Looney, S. W., Wansley, J. W., 1986. An Application of the Cox Proportional Hazards Model to Bank Failure. Journal of Banking and Finance 10(4), 511-31.
[13]Coats, P. K., & Fant, L. F. (1993). Recognizing financial distress patterns using a neural network tool. Financial Management, 142-155.
[14]Altman,E.I., Marco, G., Varetto,F., 1994. Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (The Italian Experience), Journal of Banking and Finance 505-529
[15]Barth, M. E., Cram, D. P., & Nelson, K. K. (2001). Accruals and the prediction of future cash flows. The Accounting Review, 76(1), 27-58.
[16]Cheng, CS Agnes, and Dana Hollie. "Do core and non-core cash flows from operations persist differentially in predicting future cash flows?." Review of Quantitative Finance and Accounting 31.1 (2008): 29-53.
[17]Jorion, P., Value at Risk: The New Benchmark for Controlling Financial Risk, Irwin Professional Pub, 1996
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[21]XBRL Service, from: http://xbrldemo.twse.com.tw/index.aspx

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