跳到主要內容

臺灣博碩士論文加值系統

(100.28.0.143) 您好!臺灣時間:2024/07/18 08:19
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:蘇芮瑩
研究生(外文):SU, JUI-YING
論文名稱:探討經濟指標對金融科技投資影響:灰色關聯分析與神經網路方法的應用
論文名稱(外文):Exploring the Influence of Economic Indicators on Fintech Investment: A Grey Relational Analysis and Neural Network Approach
指導教授:陳若暉陳若暉引用關係
指導教授(外文):CHEN, JO-HUI
口試委員:承立平胡伯森
口試委員(外文):CHENG, LI-PINGSabbor Hussain
口試日期:2024-01-25
學位類別:碩士
校院名稱:中原大學
系所名稱:財務金融學系
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2024
畢業學年度:112
語文別:英文
論文頁數:62
中文關鍵詞:金融科技價格變動灰色關聯分析預測市場指數人工神經網絡指數型基金波動率
外文關鍵詞:FintechPrice MovementsGrey Relational AnalysisPredictionsMarket indicesArtificial Neural NetworkExchange-Traded Funds (ETFs)Volatility
相關次數:
  • 被引用被引用:0
  • 點閱點閱:106
  • 評分評分:
  • 下載下載:30
  • 收藏至我的研究室書目清單書目收藏:0
本研究在探討經濟指標與金融科技相關領域價格變動之間的關係,並利用灰色關聯分析(GRA)評量經濟指標之灰關聯等級,分別透過多層感知器(MLP)、支持向量機(SVM)、隨機森林(Random Forest)和極限梯度提升(XGBoost)模型來預測金融科技相關領域的指數型基金(ETFs)價格趨勢。透過使用GRA研究確定重要的經濟指標,並且基於這些指標,使用類神經網路(ANN)模型預測價格。數據集分為 10%、20% 和 33% 進行測試,以驗證所開發模型的性能。
研究結果顯示,選定的經濟指標與每日收盤價之間存在強烈的預測關係,而結合GRA和ANN的模型展示出高度的預測準確性。研究中調查的輸出變數為ETFs,包括人工智慧(AI)、區塊鏈、雲系統、金融科技、物聯網(IoT)和行動支付,而輸入變數則包括CRB指數、納斯達克指數、S&P 500銀行指數、費城半導體指數、VIX指數和交易量。
此研究結果發現隨機森林模型和極限梯度提升模型,在預測金融科技相關ETF價格趨勢方面,預測能力表現良好。在不同評估指標和成長率類別中,觀察到一致表現,驗證這些模型在預測ETF行為方面的有效性。
This study explores the relationship between economic indicators and price fluctuations in the FinTech-related field, utilizing Grey Relational Analysis (GRA) to measure the grey correlation grade of economic indicators. The Multilayer perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), and EXtreme Gradient Boosting (XGBoost) models are used respectively to predict the price trends of Exchange Traded Funds (ETFs) in the FinTech-related field. Through the use of GRA, the research identifies the significant economic indicators, while the ANN model predicts the prices based on these indicators. The dataset is divided 10%、20%, and 33% for testing, which validates the performance of the developed models. The findings suggest a strong predictive relationship between selected economic indicators and daily closing prices, with the combined GRA and ANN model showing high predictive accuracy.
The output variables investigated in the study include Artificial Intelligence (AI), blockchain, cloud systems, FinTech, Internet of Things (IoT), and mobile payments ETFs, while the input variables comprise the CRB Index, NASDAQ Index, S&P 500 Bank Index, Philadelphia Semiconductor Index, VIX Index, and trading volume.
This study underscores the robust predictive capacity of the Random Forest and XGBoost models in forecasting the price trends of FinTech-related ETFs. The consistent performance observed across various evaluation metrics and growth rate categories validates their efficacy in predicting ETF behavior.
Contents
摘要............................................................I
Abstract.......................................................II
致謝...........................................................III
Contents.......................................................IV
List of figures.................................................V
Table of contents..............................................VI
1. Introduction.................................................1
1-1 Background and motivation...................................1
1-2 Purpose.....................................................3
1-3 Research Structure..........................................4
1-4 Structure of Process........................................5
2. Literature Review............................................6
2-1 Fintech.....................................................6
2-1-1 Fintech Evolution and Influence of Market Indices.........6
2-1-2 The Impact of Fintech on Banking and Financial Services...6
2-1-3 Regulatory Challenges and the Impacts of Stock Market.....7
2-2 ETF.........................................................7
2-2-1 The Influence and Evolution of ETFs.......................7
2-2-2 The Spillover Effect for ETFs.............................8
2-3 Artificial Neural Network for Prediction....................9
3. Data and Methodology........................................11
3-1 Data.......................................................11
3-2 Methodology................................................13
3-2-1 Grey Relational Analysis(GRA)............................14
3-2-2 Artificial Neural Network (ANN)..........................15
4. Empirical Results and Analysis..............................23
4-1 Grey relational analysis (GRA).............................23
4-2 ANN model for Fintech ETFs.................................23
5. Conclusion..................................................49
5-1 Main Results...............................................49
5-2 Research Contribution and implication......................49
5-3 Research limitation........................................50
5-4 Research Suggestions.......................................50
Reference......................................................51

List of figures
Figure 1 Private Investment in AI...............................2
Figure 2 Number of AI Publications by Field.....................3
Figure 3 The Structure of Process...............................5

Table of contents
Table 3-1 Research variables...................................11
Table 3-2 The confusion matrix and model accuracy metrics are as follows........................................................22
Table 4-1 Fintech ETFs and GRGs of the six determinants........26
Table 4-2a Assessing the predictive performance of neural network and machine learning for fintech ETFs with all variables...........27
Table 4-2a (continued).........................................27
Table 4-2b Assessing the predictive performance of neural network and machine learning for fintech ETFs with High GRG variables......28
Table 4-2b (continued).........................................28
Table 4-2c Assessing the predictive performance of neural network and machine learning for fintech ETFs with Low GRG variables.......29
Table 4-2c (continued).........................................29
Table 4-3-1a Analyzing ETF GRG results with MLP for ANN prediction with all variables......................................................30
Table 4-3-1b Analyzing ETF GRG results with MLP for ANN prediction with High GRG variables and Low GRG variables................................31
Table 4-3-2a Analyzing ETF GRG results with MLP for ANN prediction with all variables......................................................32
Table 4-3-2b Analyzing ETF GRG results with MLP for ANN prediction with High GRG variables and Low GRG variables................................33
Table 4-4-1a Analyzing ETF GRG results with SVM for ANN prediction ...............................................................34
Table 4-4-1b (continued).......................................35
Table 4-4-2a Analyzing ETF GRG results with SVM for ANN prediction ...............................................................36
Table 4-4-2b (continued).......................................37
Table 4-5-1a Analyzing ETF GRG results with Random Forest for ANN prediction.....................................................40
Table 4-5-1b (continued).......................................41
Table 4-5-2a Analyzing ETF GRG results with Random Forest for ANN prediction.....................................................42
Table 4-5-2b (continued).......................................43
Table 4-6-1a Analyzing ETF GRG results with XGBoost for ANN prediction ...............................................................44
Table 4-6-1b (continued).......................................45
Table 4-6-2a Analyzing ETF GRG results with XGBoost for ANN prediction ...............................................................46
Table 4-6-2b (continued).......................................47
Table 4-7 Testing the fintech market ETFs GRA results for ANN prediction.....................................................48
Table 4-7 (continued)..........................................48
Abidoye, Rotimi Boluwatife and Chan, Albert P.C. (2017). Modelling property values in Nigeria using artificial neural network. Journal of Property Research, 34(1), 36-53.
Acharya, Ram N., Gentle, Paul F., and Paudel, Krishna P. (2010). Examining the CRB index as a leading indicator for US inflation. Applied Economics Letters, 17(15), 1493-1496.
Agarwal, Nipun, and Farooque, Omar (2016). Alternate equity indexation for technology stocks: An application to the NASDAQ index. Economics, Management and Financial Markets, 11(1), 41-51.
Alexander, Gordon J., Edwards, Amy K., and Ferri, Michael G. (2000). The determinants of trading volume of high-yield corporate bonds. Journal of Financial Markets, 3(2), 177-204.
Alt, Rainer, Beck, Roman, and Smits, Martin T. (2018). FinTech and the transformation of the financial industry. Electronic Markets, 28, 235-243.
Anderson, Keith, Brooks, Chris, and Katsaris, Apostolos (2010). Speculative bubbles in the S&P 500: Was the tech bubble confined to the tech sector?. Journal of Empirical Finance, 17(3), 345-361.
Andreou, A., Efstratios, G., and Spirdon, L. (2002). Exchange-rate forecasting: A hybrid algorithm based on genetically optimized adaptive neural network. Computational Economics, 20(3), 191-210.
Antoniewicz, Rochelle Shelly, and Heinrichs, Jane (2014). Understanding exchange-traded funds: How ETFs work. ICI Research Perspective, 20(5), 1-39.
Arner, Douglas W., Barberis, Janos, and Buckley, Ross P. (2015). The evolution of FinTech: A new post-crisis paradigm. Georgetown Journal of International Law, 47(4), 1271-1319.
Arora, R and S Suman (2012). Comparative analysis of classification algorithms on different datasets using WEKA. International Journal of Computer Applications, 54(13), 21-25.
Baba, N (1989). A new approach for finding the global minimum of error function for neural networks. Neural Networks, 2(5), 367-373.
Borup, D., Christensen, B. J., Mühlbach, N. N., and Nielsen, M. S. (2020). Targeting predictors in random forest regression. Technical report, Department of Economics and Business Economics, Aarhus University.
Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. (1984). Classification and regression trees. Taylor and Francis.
Breitung, C. (2023). Automated stock picking using random forests. Journal of Empirical Finance, 72, 532-556.
Brown, David C., Davies, Shaun William, and Ringgenberg, Matthew C. (2021). ETF arbitrage, non-fundamental demand, and return predictability. Review of Finance, 25(4), 937-972.
Carmona, P., Dwekat, A., and Mardawi, Z. (2022). No more black boxes! explaining the predictions of a machine learning XGBoost classifier algorithm in business failure. Research in International Business and Finance, 61, 101649.
Chang, F. J. and Huang, H. L. (2003). Theory and practice of artificial neural network. Dong Hwa Publication, Taiwan.
Chang, W. C. (2000). The study of grey relational generating operation. Grey System Journal, 3(1), 53-62.
Chen, Jo-Hui (2011). The spillover and leverage effects of ethical exchange traded funds. Applied Economics Letter, 18 (10), 983-987.
Chen, Jo-Hui and Diaz, John Francis (2019). The spillover and leverage effects of equity exchange-traded notes (ETNs). Global Economy Journal, 19(3), 1950013.
Chen, Jo-Hui, Diaz, John Francis, and Huang, Yu Fang (2013). High Technology ETF forecasting: Application of grey relational analysis and artificial neural networks. Frontiers in Finance and Economics 10 (2), 129-155.
Chen, Jo-Hui and Do Thi, Van Trang (2018). Testing leverage and spillover effects in precious metal ETFs. Theoretical Economics Letters, 8(3), 197–212.
Chen, Jo-Hui and Edwards, Nicholas (2021). The spillover, risk and leverage effects of smart beta management ETF. Global Economy Journal, 21(03), 2150016.
Chen, Jo-Hui and Fang, Yen-Po (2008). Forecasting the performance of the Asian currency unit and the causes of contagion of the Asian financial crisis. Asia Pacific Management Review, 13(4), 693-712.
Chen, Jo-Hui and Huang, C. Y. (2010). An analysis of the spillover effects of exchange traded funds. Applied Economics, 42 (9), 1155-1168.
Chen, Jo-Hui and Hussain, Sabbor (2022). Jump dynamics and leverage effect: evidences from energy exchange traded fund (ETFs). Journal of Applied Finance and Banking, 12(6), 127-150.
Chen, Jo-Hui and Malinda, Maya (2016). The study of the long memory in volatility of renewable energy exchange-traded funds (ETFs). Journal of Economics, Business and Management, 4(4), 252-257.
Chen, Jo-Hui and Tung, Chia Shan (2019). The empirical study of volatility asymmetry for FinTech ETF. Journal of International and Global Economic Studies, 12(2), 19-41.
Chen, Rongda, Huang, Jiahao, Jin, Chenglu, Yang, Yili, and Chen, Bin (2023). Multidimensional attention to Fintech, trading behavior and stock returns. International Review of Economics and Finance, 83, 373-382.
Chen, T., and Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.
Chow, K. V., Jiang, W., Li, B., and Li, J. (2020). Decomposing the VIX: Implications for the predictability of stock returns. Financial Review, 55(4), 645–668.
Cortes, Corinna and Vapnik, Vladimir (1995). Support vector machine. Machine Learning, 20(3), 273-297.
D’Amato, V., D’Ecclesia, R., and Levantesi, S. (2021). ESG score prediction through random forest algorithm. Computational Management Science, 19(2), 347-373.
Daigler, Robert T., Hibbert, Ann Marie, and Pavlova, Ivelina (2014). Examining the return–volatility relation for foreign exchange: Evidence from the euro VIX. Journal of Futures Markets, 34(1), 74-92.
Deka, Paresh Chandra (2014). Support vector machine applications in the field of hydrology: A review. Applied Soft Computing, 19, 372-386.
Del Brio, Esther B., Mora-Valencia, Andrés, and Perote, Javier (2020). Risk quantification for commodity ETFs: Backtesting value-at-risk and expected shortfall. International Review of Financial Analysis, 70, 101163.
Deng, Julong (1989). Introduction to grey system theory. The Journal of grey system, 1(1), 1-24.
Deng, S., Huang, X., Zhu, Y., Su, Z., Fu, Z., and Shimada, T. (2022). Stock index direction forecasting using an explainable EXtreme gradient boosting and investor sentiments. The North American Journal of Economics and Finance, 64, 101848.
Devos, Erik, Hao, Wei, Prevost, Andrew K., and Wongchoti, Udomsak (2015). Stock return synchronicity and the market response to analyst recommendation revisions. Journal of Banking and Finance, 58, 376-389.
Ding, S., Cui, T., and Zhang, Y. (2022). Futures volatility forecasting based on big data analytics with an incorporating order imbalance effect. International Review of Financial Analysis, 83, 102255.
Doumpos, Michalis, Zopounidis, Constantin, Gounopoulos, Dimitrios, Platanakis, Emmanouil, and Zhang, Wenke (2022). Operational research and artificial intelligence methods in banking. European Journal of Operational Research, 306(1), 1-16.
Ehrhardt, Michael, C. and Tucker, Alan, L. (1990). Pricing CRB futures contracts. Journal of Financial Research, 13(1), 7-14.
Elman, Jeffrey L. (1990). Finding structure in time. Cognitive Science, 14(2), 179-211.
Elyasiani, Elyas, Gambarelli, Luca, and Muzzioli, Silvia (2021). The skewness index: Uncovering the relationship with volatility and market returns. Applied Economics, 53(31), 3619-3635.
Ewees, A. A., Elaziz, M. A., Alameer, Z., Ye, H., and Jianhua, Z. (2020). Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility. Resources Policy, 65, 101555.
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189-1232.
Fu, Chengbo, Huang, Qiping, and Tang, Hongfei (2022). Do ETFs affect ADRs and US domestic stocks differently?. Journal of International Financial Markets, Institutions and Money, 80, 101643.
Funahashi, Hideharu (2021). Artificial neural network for option pricing with and without asymptotic correction. Quantitative Finance, 21(4), 575-592.
Gardner, M. W. and Dorling S. R. (1998). Artificial neural networks (the multilayer perceptron). A review of applications in the atmospheric sciences. Atmospheric Environment, 32(14-15), 2627-2636.
Gökçen, Umut and Post, Thierry (2018). Trading volume, return variability and short-term momentum. The European Journal of Finance, 24(3), 231-249.
Göleç, Adem, Murat, Atilim, Tokat, Ekin, and Türkşen, Burhan İ. (2012). Forecasting model of Shanghai and CRB commodity indexes. Expert Systems with Applications, 39(10), 9275-9281.
Guo, Hongquan, Nguyen, Hoang, Vu, Diep Anh Vu., and Bui, Xuan Nam (2021). Forecasting mining capital cost for open-pit mining projects based on artificial neural network approach. Resources Policy, 74, 101474.
Haddad, Christian, and Lars, Hornuf (2019). The emergence of the global fintech market: Economic and technological determinants. Small Business Economics, 53, 81-105.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter, 11(1), 10-18.
Hassanniakalager, A., Sermpinis, G., Stasinakis, C., and Verousis, T. (2020). A conditional fuzzy inference approach in forecasting. European Journal of Operational Research, 283(1), 196–216.
Haykin, S. (2010). Neural Networks and Learning Machines, 3rd Edition. India: Pearson Education.
Hornik, K., Stinchcombe, M., and H. White (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2, 359-366.
Huang, Jing Zhi and Huang, Zhijian James (2020). Testing moving average trading strategies on ETFs. Journal of Empirical Finance, 57, 16-32.
Ichinose, Takafumi, Hirobayashi, Shigeki, Misawa, Tadanobu, and Yoshizawa, Toshio (2012). Forecast of stock market based on nonharmonic analysis used on NASDAQ since 1985. Applied Financial Economics, 22(3), 197-208.
Jagtiani, Julapa and John, Kose (2018). Fintech: The impact on consumers and regulatory responses. Journal of Economics and Business, 100, 1-6.
John, Kose and Li, Jingrui (2021). COVID-19, volatility dynamics, and sentiment trading. Journal of Banking and Finance, 133, 106162.
Karaca, Y. and Hayta, S. (2016). Application and comparison of ANN and SVM for diagnostic classification for cognitive functioning. Associated Mathematical Sciences, 10(64), 3187-3199.
Kim, Sung Suk (1998). Time-delay recurrent neural network for temporal correlations and prediction. Neurocomputing, 20(1-3), 253-263.
Kumar, Annop S. and Kamaiah, B. (2014). Wavelet based sample entropy analysis: A new method to test weak form market efficiency. Theoretical and Applied Economics, 21 (8), 19-26.
Kung, C. Y. and Wen, K. L. (2007). Applying grey relational analysis and grey decision-making to evaluate the relationship between company attributes and its fnancial performance-A case study of venture capital enterprises in Taiwan. Decision Support Systems, 43(3), 842-852.
Lam, Ka Chi, Yu, C. Y., and Lam, Chun Kit (2009). Support vector machine and entropy based decision support system for property valuation. Journal of Property Research, 26(3), 213-233.
Lee, W. Y., Jiang, C. X., and Indro, D. C. (2002). Stock market volatility, excess returns, and the role of investor sentiment. Journal of Banking and Finance, 26(12), 2277-2299.
Li, Fang‐Fang, Wang, Zhi Yu, and Qiu, Jun (2019). Long‐term streamflow forecasting using artificial neural network based on preprocessing technique. Journal of Forecasting, 38(3), 192-206.
Liebi, Luca J. (2020). The effect of ETFs on financial markets: A literature review. Financial Markets and Portfolio Management, 34(2), 165-178.
Lin Y., Liao Q., Lin Z., Tan B., and Yu Y. (2022). A novel hybrid model integrating modified ensemble empirical mode decomposition and LSTM neural network for multi-step precious metal prices prediction. Resources Policy, 78, 102884.
Lin, Ching Chung and Chiang, Min-Hsien (2005). Volatility effect of ETFs on the constituents of the underlying Taiwan 50 Index. Applied Financial Economics, 15(18), 1315-1322.
Lin, Daw Tung, Dayhoff, Judith E., and Ligomenides Panos A. (1992). Adaptive time-delay neural network for temporal correlation and prediction. In Intelligent Robots and Computer Vision XI, 1826, 170-181.
Madhavan, Ananth and Sobczyk, Aleksander (2016). Price dynamics and liquidity of exchange-traded funds. Journal of Investment Management, 14(2), 1-17.
McKenzie, Michael D. and Faff, Robert W. (2003). The determinants of conditional autocorrelation in stock returns. Journal of Financial Research, 26(2), 259-274.
Minsky, M. L. and Papert S. (1969). Perceptrons: An Introduction to Computational Geometry. Cambridge: MIT Press.
Mountrakis, Giorgos, Jungho Im, and Ogole, Caesar (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247-259.
Mumtaz, Muhammad, Z. and Smith, Zachary A. (2020). Empirical examination of the role of Fintech in monetary policy. Pacific Economic Review, 25(5), 620-640.
Murinde, Victor, Rizopoulos, Efthymios, and Zachariadis, Markos (2022). The impact of the FinTech revolution on the future of banking: Opportunities and risks. International Review of Financial Analysis, 81, 102103.
Nguyen, H., Bui, X.-N. (2018). Predicting blast-induced air overpressure: A robust artificial intelligence system based on artificial neural networks and random forest. Natural Resources Research, 28 (3), 893-907.
Nyitrai, T., and Virag, M. (2019). The effects of handling outliers on the performance of bankruptcy prediction models. Socio-Economic Planning Sciences, 67, 34-42.
Orru, Graziella, Pettersson-Yeo, William, Marquand, Andre F., Sartori, Giuseppe, and Mechelli, Andrea (2012). Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review. Neuroscience and Biobehavioral Reviews, 36(4), 1140-1152.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). Learning internal representations by error propagation. In parallel distributed processing: Explorations in the microstructure of cognition, 1st Edition, DE Rumelhart and JL McClelland (eds.). Cambridge, MA: MIT Press.
Saha, Kunal, Madhavan, Vinodh, and Chandrashekhar, G. R. (2022). Effect of COVID-19 on ETF and index efficiency: Evidence from an entropy-based analysis. Journal of Economics and Finance, 46(2), 347-359.
Schapire R. E. (1999). A brief introduction to boosting. Proceedings of the 16th International Joint Conference on Artificial intelligence, 2, 1401-1406.
Silva, I. N., Spati, D. H., Flauzino, R. A., Liboni, L.H.B., and Alves, S. F. R. (2017). Artificial Neural Networks: A Practical Course. Switzerland: Springer.
Sirikulviriya, N. and Sinthupinyo, S. (2011). Integration of rules from a random forest. International Conference on Information and Electronics Engineering, 6, 194-198.
Tong, Chen, Huang, Zhuo, and Wang, Tianyi (2022). Do VIX futures contribute to the valuation of VIX options?. Journal of Futures Markets, 42(9), 1644-1664.
Tseng, Ping Lun, and Guo, Wen Chung (2022). Fintech, credit market competition, and bank asset quality. Journal of Financial Services Research, 61(3), 285-318.
Uddin, M.S., Chi, G., Al Janabi, M.A.M., and Habib, T. (2020). Leveraging random forest in micro-enterprises credit risk modelling for accuracy and interpretability. International Journal of Finance and Economics, 27(3), 3713-3729.
Vapnik, Vladimir, Golowich, Steven, and Smola, Alex (1997). Support vector method for function approximation, regression estimation and signal processing. Advances in Neural Information Processing Systems, 9, 281-287.
Vasilescu, Bogdan (2009). A robust method for object classification based on Back-Propagation Neural Networks. Petroleum-Gas University of Ploiesti Bulletin. Technical Series, 61(3), 371-76.
Wang, Jue, Wang, Zhen, Li, Xiang, and Zhou, Hao (2022). Artificial bee colony-based combination approach to forecasting agricultural commodity prices. International Journal of Forecasting, 38(1), 21-34.
Zhang, Rongju., Langrené, Nicolas, Tian, Yu, Zhu, Zili, Klebaner, Fima, and Hamza, Kais (2019). Dynamic portfolio optimization with liquidity cost and market impact: A simulation-and-regression approach. Quantitative Finance, 19(3), 519-532.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊