| dc.contributor.advisor | 蔡瑞煌 | zh_TW |
| dc.contributor.advisor | Tsai, Rai-Hwan | en_US |
| dc.contributor.author (Authors) | 林義評 | zh_TW |
| dc.contributor.author (Authors) | Lin, Yi-Ping | en_US |
| dc.creator (作者) | 林義評 | zh_TW |
| dc.creator (作者) | Lin, Yi-Ping | en_US |
| dc.date (日期) | 1997 | en_US |
| dc.date.accessioned | 27-Apr-2016 11:13:04 (UTC+8) | - |
| dc.date.available | 27-Apr-2016 11:13:04 (UTC+8) | - |
| dc.date.issued (上傳時間) | 27-Apr-2016 11:13:04 (UTC+8) | - |
| dc.identifier (Other Identifiers) | B2002001945 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/86217 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 資訊管理學系 | zh_TW |
| dc.description (描述) | 85356002 | zh_TW |
| dc.description.abstract (摘要) | 本篇論文旨在分析神經網路學習績效，並提出一套學習演算法，結合倒傳遞網路(BP)與理解神經網路(RN)，命名為RNBP，這套學習演算法將與傳統的BP做比較，以兩個不同的財務金融領域的應用，一個是選擇權上Black-Scholes訂價模式的模擬，一個是金融交換上利率的預測。主要績效的評估準則是以學習的效率與模擬、預測的準確度為依據。 | zh_TW |
| dc.description.abstract (摘要) | The study attempts to analyze the learning performance of neural networks in applications, and propose a new learning procedure for the layered feedforward neural network systems, named KNBP, which binds RN and BP learning algorithms. Two artificial neural networks, BP and KNBP, here are both applied to two financial fields, the simulation of Black-Scholes pricing model for the call options and the midrates forecasting in financial swaps. The explicit performance comparison between the two artificial neural network systems is mainly based on two criteria, which are learning efficiency and forecasting effectiveness. | en_US |
| dc.description.tableofcontents | CHAPTER 1 INTRODUCTION-----1 CHAPTER 2 LITERATURE REVIEW-----3 2.1. BACK PROPAGATION NEURAL NETWORKS-----4 2.2. REASONING NEURAL NETWORKS-----7 2.3. KNBP-AN ALTERNATIVE LEARNING PROCEDURE-----11 2.4. APPLICATIONS OF ANNS TO THE FINANCIAL FIELDS-----12 2.5. THE OPTION-----14 2.5.1. Black-Scholes pricing model-----14 2.5.2. Partial derivatives of Black-Scholes formula-----15 2.6. THE SWAP-----16 2.6.1. What is swap - definition-----16 2.6.2. Derivation of swap midrates from Eurodollar futures-----18 2.7. SENSITIVITY ANALYSIS WITHNEURAL NETWORKS-----20 2.8. A MODIFIED METHODOLOGY OF SENSITIVITY ANALYSIS-----24 2.9. THE DEAD REGION-----28 CHAPTER 3 EXPERIMENT DESIGNS AND METHODOLOGY-----30 3.1. EXPERIMENT DESIGNS AND PERFORMANCE CRITERIA-----30 3.2. THE PRICING MODEL OF CALL OPTIONS-----32 3.3. THE FORECASTING OF SWAP MIDRATES-----35 3.3.1. Moving forecasting-----35 3.3.2. Checking the data-----39 CHAPTER 4 PERFORMANCE AND ANALYSIS-----41 4.1. SIMULATION PERFORMANCE OF BP AND RNBP IN BLACK-SCHOLES FORMULA-----41 4.1.1. Simulation performance-----41 4.1.2. Sensitivity analysis-----47 4.1.3. The dead region analysis-----51 4.2. FORECASTING RESULTS OF BP AND RNBP IN SWAP MIDRATES-----54 4.2.1. Forecast performance-----54 4.2.2. A further discussion of RNBP-----59 4.2.3. Summary in swap rates forecasting-----63 4.2.4. Sensitivity analysis-----64 CHAPTER 5 SUMMARY-----67 5.1. DISCUSSIONS FROM THE SIMULATIONS AND FORECASTS-----67 5.3. CONTRIBUTIONS AND FUTURE WORK-----68 REFERENCE-----70 APPENDIX A-----74 APPENDIX B-----75 APPENDIX C-----78 Figure Index FIGURE 2.1. MULTI-LAYERED PECEPTRON NETWORK STRUCTURE-----4 FIGURE 2.2. THE SOFTEN LEARNING PROCEDURE-----8 FIGURE 2.3. RNBP LEARNING PROCEDURE-----11 FIGURE 2.4. THE GENERIC SWAP STRUCTURE-----18 FIGURE 2.5. THE SENSITIVITY CURVE-----26 FIGURE 3.1. IN-THE-MONEY CALLPRICES INTRAINING PATTERNS-----34 FIGURE 3.2. OUT-OF-THE-MONEY CALLPRICES IN TRAINING PATTERNS-----34 FIGURE 3.3. SWAP MIDRATES FROM AUGUST 1993 TO MAY 1994-----36 FIGURE 3.4. THE METHOD OF MOVING SIMULATION-----38 FIGURE 3.5. THE FORECASTING RESULTS OF APPLYING AR(5) MODEL TO THE DATA OF E1 AND E3-----40 FIGURE 4.1. CONVERGING ERRORS IN EARLY LEARNING ITERATIONS-----45 FIGURE 4.2. (A),(B),(C),(D) AND (E) SENSITIVITY VALUES OF THE FIVE VARIABLES-----50 FIGURE 4.3. FREQUENCY DISTRIBUTION IN IN-THE-MONEY SIMULATIONS-----52 FIGURE 4.4. FREQUENCY DISTRIBUTION IN IN-THE-MONEY SIMULATIONS-----52 FIGURE 4.5. THE FORECASTING RESULTS OF BP AND RNBP IN E1-----54 FIGURE 4.6. THE FORECASTING RESULTS OF BP AND KNBP IN E2-----55 FIGURE 4.7. THE FORECASTING RESULTS OF BP AND KNBP IN E3-----55 FIGURE 4.8. THE SUMMARY OF THE AMOUNTS OF HIDDEN NODES RECRUITED DURING THE LEARNING PROCESSES OF RN IN E3. THE LAST DATA POINT ON EACH LINE CORRESPONDS TO THEAMOUNT OF HIDDEN NODES RECRUITED AFTER THE PROCESSING OF THE LAST REASONING MECHANISM (REFERS TO FIGURE 2.2)-----61 FIGURE 4.9. AVERAGE SENSITIVITIES IN E1, E2 AND E3 RESULTED FROM BP AND RNBP-----66 Table Index TABLE 2.1. THE DEFINITION OF THE USED NOTATIONS-----4 TABLE 2.2. BP`S LEASNING ALGORITHM-----7 TABLE 3.1. TRAINING DATA Of RN-----32 TABLE 3.2. RANGES OF INPUT VARIABLES OF TRAINING NETWORKS-----34 TABLE 3.3. SUMMARY OF THE FORECASTING BYAR(5) MODEL IN E1 AND E3-----40 TABLE 4.1. IN-THE-MONEY SIMULATION RESULTS OF BP AND RNBP-----42 TABLE 4.2. OUT-OF-THE-MONEY SIMULATION RESULTS OF BP AND RNBP-----43 TABLE 4.3. STOPPING CRITERION WITH ERROR LEVEL = 0.02 (IN-THE-MONEY)-----45 TABLE 4.4. STOPPING CRITERION WITH ERROR LEVEL = 0.02 (OUT-OF-THE-MONEY)-----45 TABLE 4.5. THE DIFFERENCES OF SIMULATION DESIGNS FORM OTHER PREVIOUS STUDIES-----47 TABLE 4.6. SENSITIVITY ANALYSIS (IN-THE-MONEY)-----48 TABLE 4.7. SENSITIVITY AMALYSIS (OUT-OF-THE-MONEY)-----49 TABLE 4.8. SUMMARY OF THE FORECASTING BY BP AMD RNBP IN E1-----57 TABLE 4.9. SUMMARY OF THE FORECASTING BY BP AND RNBP IN E2-----57 TABLE 4.10. SUMMARY OF THE FORECASTING BY BP AND RNBP IN E3-----57 TABLE 4.11. THE SUMMARY OF THE EFFECTIVENESS-----58 TABLE 4.12. THE SUMMARY OF THE EFFICIENCY (TDENOTES THE AMOUNT OF LEARNING ITERATIONS)-----59 TABLE 4.13. A FURTHER ANALYSIS OF THE SIMULATIONS WITH KNBP. N IS THE AMOUNT OF RECRUITED HIDDEN NODES. F DISPLAYS THE FREQUENCY OF OCCURRENCES. T REPRESENTS THE (AVERAGE) AMOUNT OF THE LEARNING ITERATIONS. R IS THE MEAN RELATIVE ERROR. C DISPLAYS THE RATE OF PREDICTING CORRECTLY THE DIRECTION OF CHANCE. SUBSCRIPT I DENOTES THE SIMULATION OF E1-----62 TABLE 4.14. THE CORRECT HIT RATE OF BP AND RNBP-----63 TABLE 4.15. SENSITIVITY ANALYSIS IN SWAP MIDRATES FORECASTING-----65 | zh_TW |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#B2002001945 | en_US |
| dc.subject (關鍵詞) | 倒傳遞網路 | zh_TW |
| dc.subject (關鍵詞) | 裡解神經網路 | zh_TW |
| dc.subject (關鍵詞) | Black-Scholes 定價模式 | zh_TW |
| dc.subject (關鍵詞) | 金融交換 | zh_TW |
| dc.subject (關鍵詞) | 敏感度分析 | zh_TW |
| dc.subject (關鍵詞) | 滯留區分析 | zh_TW |
| dc.subject (關鍵詞) | BP | en_US |
| dc.subject (關鍵詞) | RN | en_US |
| dc.subject (關鍵詞) | Black-Scholes pricing model | en_US |
| dc.subject (關鍵詞) | Financial swaps | en_US |
| dc.subject (關鍵詞) | Sensitivity analysis | en_US |
| dc.subject (關鍵詞) | Dead region analysis | en_US |
| dc.title (題名) | 應用神經網路於金融交換與Black-Scholes定價模式之探討與其意義分析 | zh_TW |
| dc.title (題名) | A study and analysis of applying neural networks to the financial swapa and the Black-Scholes pricing model | en_US |
| dc.type (資料類型) | thesis | en_US |
