臺灣博碩士論文加值系統

在股票市場中，有一句既通俗且古老的格言，即是：「莫把雞蛋放在同一個籃子裡」，這說明了投資組合的重要性。隨著現代投資組合理論的發展以及資訊科技的進步，使得投資人可以在實務上，以「數量化方法」進行投資分析，建構出策略性投資組合或動態投資組合。因此，伴隨而產生的分析工具和方法，也就不斷地出爐，在這眾多的工具和方法裡，想必一定會有一些結果和經驗的產生，而我們何不針對股市這些結果與經驗的產生，做一完整收集與彙整，來發展出一套投資策略工具，有了這樣的策略工具若再搭配上長期累積的完整財經資料庫，就可以進一步結合為投資策略驗證平台。
各種模型的創新或是改良，主要的目的是為了更貼近的應用於現實生活，造福人群。然而，一項新模型建構妥當與否？資料的特徵性質與預測工具之配適度是預測工具應用是否得當的主要關鍵。動態廣義式變精度粗集合預測模型(DGVPRS-Model)是結合廣義式粗集合模型、變精度粗集合模型、類神經模糊與灰色系統等理論，適時的使用K-means分群工具與排序工具。雖然整個模型中的個別理論在公司評價衡量上已獲得成果，證明該模型架構的可行性，但是對於此研究結果的正確性與客觀性我們仍須本著不斷求證的精神進行多項驗證、討論與改進。

As we know, “Don’t put all the eggs in a basket” is a popular and old proverb in stock market. What it stresses is the importance of portfolio diversification. Along with the development of modern portfolio theories and the advancement of IT, investors can conduct investment analysis by quantity method and construct strategic or dynamic portfolio in their investment practice. Therefore, numerous analysis tools and methods appear continuously with such practice. And from them, it should be some results and experiences were generated. Why not collect and organize those results and experiences in stock market in a whole to develop a set of investment strategic tool? With combining this and an integral financial database accumulated for a long term, we can form an investment strategic testing platform further.
The main purpose of creation and improvement of each model is to let it fit the real world and benefit the public. However, whether a newly built model is appropriate or not? The key factor affecting if a prediction tool is used properly or not is the matching level of data attributes and the prediction tool. Dynamic generalized varied precision rough set model (DGVPRS-Model) combines features of models of generalized rough set, varied precision rough set, neuro fuzzy and grey systems theory, and uses K-means clustering tool and ordering tool are used properly. Although successes of some individual theories for company evaluation have been attained and the feasibility of the model has been proved, we still need to conduct various tests, discussions and improvements for the validity and objectivity of our study results based on a spirit of continuous proving.

Chapter 1 Introduction 1
1.1 Research Background 1
1.2 Research Motivation and Purposes 3
1.3 Research Objects and Limitations 5
1.4 The Structure of the Report 5
Chapter 2 Literature Reviews 7
Chapter 3 Research Methodologies 15
3.1 Variable Precision Rough Sets Model 15
3.1.1 Introduction of VPRS 15
3.1.2 An overview of VPRS 18
3.2 A Generalized Rough Sets Model 23
3.2.1 Introduction of GRS 23
3.2.2 Uncertain Information Systems (UIS) 24
3.2.3 Noise Tolerance in Uncertain Information Systems 25
3.2.4 Set Approximation in the GRS-Model 27
3.2.5 The Degree of Attribute Dependencies in the GRS-Model 29
3.2.6 Attribute Reduct in the GRS-Model 31
3.3 Adaptive Neuro-Fuzzy Inference Systems (ANFIS) 33
3.3.1 Introduction of ANFIS 33
3.3.2 ANFIS Architecture 33
3.3.3 Hybrid Learning Algorithm 38
3.4 Grey Systems theory 40
3.4.1 Grey Prediction (GP) 41
3.4.2 Grey Relational Analysis (GRA) 43
3.5 K-means Clustering 44
Chapter 4 Research Designs 47
4.1 3W of Investment Strategy 47
4.2 Construction of Investment Strategy Systems 48
4.2.1 Flow on constructing investment Strategy system 48
4.2.2 Illustration on investment strategy system 50
4.3 Stock Selection System 50
4.3.1 Building dynamic general varied precision rough set model 50
4.3.2 Framework on Stock Selection System 51
4.3.3 Operation Flow Illustration on Stock Selection System 52
4.4 Fund Distribution Strategy System 59
4.4.1 Fund Distribution Strategy 59
4.4.2 Flow on constructing fund distribution strategy system 60
4.4.3 Illustrating for Fund distribution strategy system operation flow 61
4.5 Programming of Investment Strategy Systems 66
Chapter 5 Analysis of Empirical Result 67
5.1 Illustration on data selection and attribute determination of empirical result 67
5.2 Illustration on flow and steps of empirical result 71
5.2.1 Flow of empirical result study 71
5.2.2 Step illustration on empirical result study 72
5.3 Empirical result of Taiwan stock market 95
Chapter 6 Conclusions 100
References 103
Appendix 113

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