政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/148536

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/148536

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政大機構典藏 > 國際金融學院 > 國際金融碩士學位學程 > 學位論文 > Item 140.119/148536

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/148536

題名:	證券商量化生態圈與自動化全委:以永豐金證券為例 Quantitative Trading Ecosystem and Automation Discretionary Investment of Securities Firms: A Case Study of SinoPac Securities
作者:	江承翰 Chiang, Chen-Han
貢獻者:	韓傳祥江彌修 Han Chuan-Hsiang Chiang Mi-Hsiu 江承翰 Chiang, Chen-Han
關鍵詞:	FinTech 量化交易交易策略機器人理財模型開發量化交易生態圈 robo-advisors FinTech Portfolio trading ecosystem mathematical models quantitative trading
日期:	2023
上傳時間:	2023-12-01 13:53:31 (UTC+8)
摘要:	全球資本市場經歷了幾次巨大轉型，這些轉變部分是由快速進步的科技所推動的。尤其是機器人理財，近期被金融界吹捧為可以最大限度地降低人力成本，並避免利益衝突以及讓龐大的退休投資族群受益的投資工具。雖然被一些使用者貼上了噱頭和演算法過於簡化的標籤，但不可否認的，科技技術的革命仍以重大方式改變了投資產品和服務的市場。隨著金融科技的普及和臺灣股市交易規則的變化（逐筆撮合和盤中零股交易），市場上湧現出一批勇於學習程式設計並開發自己交易策略的量化交易投資者。交易量不斷增長，已經形成一定規模。他們主要使用XQ全球贏家和永豐金證券自家研發的Python API等程式設計交易平臺，這些平臺備受歡迎。各種市場參與者積極參與運營，例如在2021年7月，"AI幫你顧"團隊推出了"豐XQ殿堂"的訂閱服務。投資者可以根據他們的操作邏輯學習並創建自動化交易策略，有經驗的交易員可以顯著提高交易效率，而不懂程式設計的新手也可以學習和應用高級的股票篩選、盤中監控和交易回測等技巧，以滿足不同的需求。量化交易生態圈是一個由各種參與者組成的生態系統，旨在利用數學模型、統計分析和電腦演算法來進行金融交易。在這個生態圈中，各個參與者通過合作和競爭共同推動著市場的發展和演進。隨著技術的不斷發展，量化交易在金融市場中的影響越來越大，成為了現代金融領域的重要一環。在這個生態系統中，監管機構和非政府組織在強調發揮全球金融生態系統的重要性方面具有重要作用，隨著交易市場行業向更加普及化的未來邁進，這個生態系統可以通過穩定的方式實現集體金融意識和增長。 The global capital markets have undergone several major transformations, many of which have been driven in part by rapid technological advancements. In particular, robo-advisors have been hailed in the financial industry as tools that can significantly reduce labor costs, avoid conflicts of interest, and benefit a large population of retail investors. However, some users have labeled them as gimmicks and criticized the oversimplification of algorithms. Nevertheless, it is undeniable that the revolution in technology has significantly changed the market for investment products and services. As the financial market environment becomes increasingly uncertain, facing current volatility factors, we aim to cultivate the participation of retail investors and describe the proactive role played by institutional investors throughout the ecosystem. In recent years, the spread of FinTech and changes in the trading system of the Taiwan stock market (transaction-by-transaction matching and intraday odd-lot trading) have led to the emergence of a group of quantitative trading investors who are willing to learn programming and develop their own trading strategies. The quant trading ecosystem is a complex system comprising various participants that aims to utilize mathematical models, statistical analysis, and computer algorithms for financial trading. These components are intertwined and collectively constitute the quant trading ecosystem. In this ecosystem, various participants work together through cooperation and competition to drive the development and evolution of the market. With the continuous development of technology, quantitative trading has an increasingly significant impact on financial markets, becoming an essential part of the modern financial landscape.
參考文獻:	研究計劃 World Economic Forum, August, 2022. The Future of Capital Markets: Democratization of Retail Investing. Insight Report. 期刊 An, B., S. Sun and R. Wang (2022). "Deep reinforcement learning for quantitative trading: Challenges and opportunities." IEEE Intelligent Systems 37(2): 23-26. Aronson, D. (2011). Evidence-based technical analysis: applying the scientific method and statistical inference to trading signals, John Wiley & Sons. Barber, B. M., Odean, T. (2000). "Trading is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors." Journal of Finance, 55(2), 773-806. Barber, B. M., Lee, Y. T., Liu, Y. J., & Odean, T. (2009). "Just How Much Do Individual Investors Lose by Trading?" Review of Financial Studies, 22(2), 609-632. Bessembinder, H. and K. Chan (1995). "The profitability of technical trading rules in the Asian stock markets." Pacific-basin finance journal 3(2-3): 257-284. Boxer, H. (2014). Profitable day and swing trading,+ website: Using price/volume surges and pattern recognition to catch big moves in the stock market, John Wiley & Sons. Chen, N. F., Roll, R., & Ross, S. A. (1986). Economic Forces and the Stock Market. Journal of Business, 59(3), 383-403. Diaz, D., B. Theodoulidis and P. Sampaio (2011). "Analysis of stock market manipulations using knowledge discovery techniques applied to intraday trade prices." Expert Systems with Applications 38(10): 12757-12771. Gao, X., M. Qiu and Z. He (2021). Big data analysis with momentum strategy on data-driven trading. 2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), IEEE. Hirshleifer, D. (2015). "Behavioral finance." Annual Review of Financial Economics 7: 133-159. Grinblatt, M., Keloharju, M. (2009). "What Makes Investors Trade?" Journal of Finance, 64(5), 2289-2328. Leangarun, T., P. Tangamchit and S. Thajchayapong (2016). "Stock price manipulation detection based on mathematical models." International journal of trade, economics and finance 7(3): 81-88. Lee, J., J. Lee and A. Prékopa (2021). "Price-Bands: A Technical Tool for Stock Trading." Fintech with Artificial Intelligence, Big Data, and Blockchain: 221-246. Liu, P. (2023). Quantitative Trading: An Introduction. Quantitative Trading Strategies Using Python: Technical Analysis, Statistical Testing, and Machine Learning. P. Liu. Berkeley, CA, Apress: 1-33. Maroni, G., S. Formentin and F. Previdi (2019). A robust design strategy for stock trading via feedback control. 2019 18th European control conference (ECC), IEEE. Martínez, G. S. (2016). "Biased Roulette Wheel: A Quantitative Trading Strategy Approach." arXiv preprint arXiv:1609.09601. Moldovan, D., M. Moca and S. Nitchi (2011). "A stock trading algorithm model proposal, based on technical indicators signals." Informatica Economica 15(1): 183. Obizhaeva, A. A. and J. Wang (2013). "Optimal trading strategy and supply/demand dynamics." Journal of Financial markets 16(1): 1-32. Pardo, R. (2011). The evaluation and optimization of trading strategies, John Wiley & Sons. Pramudya, R. and S. Ichsani (2020). "Efficiency of technical analysis for the stock trading." International Journal of Finance & Banking Studies 9(1): 58-67. Siegel, J. J. (2021). Stocks for the long run: The definitive guide to financial market returns & long-term investment strategies, McGraw-Hill Education. Statman, M. (2014). "Behavioral finance: Finance with normal people." Borsa Istanbul Review 14(2): 65-73. Yang, H., X.-Y. Liu, S. Zhong and A. Walid (2020). Deep reinforcement learning for automated stock trading: An ensemble strategy. Proceedings of the first ACM international conference on AI in finance. Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.
描述:	碩士國立政治大學國際金融碩士學位學程 111ZB1045
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0111ZB1045
資料類型:	thesis
顯示於類別:	[國際金融碩士學位學程] 學位論文

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