臺灣人文及社會科學引文索引資料庫系統

:::

詳目顯示

回上一頁
第 1 筆 / 總合 1 筆   第一頁 上一頁 下一頁 最後一頁
/1頁
題名：金融科技應用之探討
作者：黃以達
作者(外文)：Yi-Ta Huang
校院名稱：國立臺灣大學
系所名稱：財務金融學研究所
指導教授：石百達
學位類別：博士
出版日期：2020
主題關鍵詞：金融科技；網路風險分散平台；新風險指標；動能策略；指數股票型基金；FinTech；online risk-transferring platforms；riskiness；momentum strategies；exchange traded fund
原始連結：連回原系統網址new window
相關次數：
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:0
本篇論文旨在探討金融科技的兩種應用層面－網路風險分散平台以及理財機器人的選股策略，故本文寫作方式將分作兩大部分。其中，第一部分主題為，以經濟模型分析網路風險分散平台之競爭力；第二部份主題為，以持有動能贏家策略優化ETF為標的之理財機器人。
由於金融科技(FinTech)的迅速發展，使得市場上已經出現沒有保險公司參與，純粹透過網路運作的網路風險分散平台，由於其提供之功能與傳統保險有其異同之處。故我們在第一部分的研究中，試圖以消費者的效用角度，藉由經濟模型比較網路風險分散平台與傳統保險兩者對於消費者的吸引力。研究主要有二個發現，第一、在人數規模夠大時，網路風險分散平台功能類似收取極低附加費用的保險公司。第二、由於臨界規模量(critical number of users)對於風險發生率的敏感度不高，因此推論網路風險分散平台可補足保險市場中所缺乏的各類高風險保單。綜合以上兩點我們推論，網路風險分散平台確實具有極高發展潛力。
選股策略可謂是投資學中的樞紐問題，其中以資產價格作為主要分析因子的方法流派，因數據可靠性高且成本低廉，深受基金經理人的重視。此外，國內銀行近幾年所推出的理財機器人，常以這類型的選股策略作為其核心，並以一籃子基金輔以自動化的模式提供消費者，且投資策略上鮮少涉及放空。故我們在第二部分的研究中，以美國上市的指數股票型基金作為研究選股的對象，並以持有動能贏家做為投資策略，試圖優化Da, Gurun and Warachka (2014)在動能策略法中所提出的ID指標的表現。我們發現，藉由Aumann and Serrano(2008)考慮整個報酬率分配下所得出的新風險指標，取代Da, Gurun and Warachka (2014)僅考慮相對漲跌次數百分比的ID指標，更能夠找出股價走勢中具平緩現象的標的，呈現出溫水煮青蛙的效果。實證結果顯示，不論從績效面或是風險面，我們的方法都優化了Da, Gurun and Warachka (2014)所提出的ID指標的結果。除此之外，若更進一步依據平緩指標只挑選出五支標的作為投資組合時，我們在績效指標的數值表現上還能有所進步。
This dissertation is to investigate two kinds of applications of FinTech: online risk-transferring platforms and Robo Advisor’s strategy of stock picking. Our research will thus be categorized into two topics. The first one is to analyze the competitiveness of online risk-transferring platforms with the help of economic models while the second topic is to dig into Robot Advisor’s holding momentum strategy which is in a way of longing ETF without shorting it.
With the rapid development of FinTech, online risk-transferring platforms without insurance companies participating arise in the market. The mechanism of these platforms is different from that of traditional insurance companies. Therefore, in the first part of our research, we try to compare the attractiveness of online risk-transferring platforms and traditional insurance mechanisms from perspectives of consumer’s utility. There are two main findings in our research. First, when the scale is large enough, the function of online risk-transferring platforms is similar to insurance companies with low loadings. Second, the critical number of users is not sensitive to the incidence risk, which infers online risk-transferring platforms can cover the shortage of every kind of high-risk insurance policy. In conclusion, online risk-transferring platforms indeed have extremely high development potentials.
One of key issues in investment is how to pick stocks wisely. Among all feasible approaches, fund managers specifically lay emphasis on the theory with the main focus on asset prices due to high data reliability as well as low cost. Moreover, in this approach, Robo Advisor recently launched by domestic banks frequently offer consumers a basket of funds automatically without involvement in shorting ETF. As a result, in our second part of our research, we will take ETF as our target in an attempt to optimize the performance of ID indicators which are once proposed in Da, Gurun and Warachka’s momentum strategy. Instead of using riskiness that Aumann and Serrano(2008) came up with under the overall distribution of returns, Da, Gurun and Warachka (2014)’s ID indicators which refers to the relative percentage of fluctuations in the stock market, can better pick stock targets with a mild trend in the long run. As empirical results suggest, this strategy we proposed optimizes Da, Gurun and Warachka (2014)’s ID indicators much better no matter from the aspects of performance or risks. In addition, even if we pick only five stocks to be included in our investment portfolio according to our indicators, we can still make progress on the performance of our portfolio.
王志誠 (2017)，「金融創新夢想實驗之新里程：「金融科技創新實驗條例」草案之評釋」，台灣法學雜誌，第321期，頁67-73。
荊濤 (2003)，「保險的社會成本及其控制對策」，國際商務，第9期，頁42-45。
張冠群 (2017)，「自金融監理原則與金融消費者保護觀點論金融科技監理沙盒制度－兼評行政院版「金融科技發展創新實驗條例草案」」，月旦法學雜誌，第266期，頁5-34。
張鵬 (2006)，「網絡保險相關法律問題的探討，上海保險，第5卷，頁35-37。
彭金隆、黃以達、李世偉與黃文慧(2018)，「保險科技是否威脅傳統保險業?－網路風險分散平台之比較分析」，管理評論，第37期 ，頁1-17。
彭金隆與臧正運 (2017)，「我國金融科技創新實驗落地機制之檢視與構建」，月旦法學雜誌，第266期，頁35-51。
Aumann, R. J., & Serrano, R. (2008). An economic index of riskiness. Journal of Political Economy, 116, 810-836.
Banham, R. (2017). Investing in the InsurTech toolbox. Risk Management, 64, 12-14.
Bernstein, P. L. (1993). Capital ideas: the improbable origins of modern Wall Street. Simon and Schuster.
Best, M. J., & Grauer, R. R. (1991). On the sensitivity of mean-variance-efficient portfolios to changes in asset means: some analytical and computational results. The Review of Financial Studies, 4, 315-342.
Black, F., & Litterman, R. (1992). Global portfolio optimization. Financial Analysts Journal, 48, 28-43.
Braun, Alexander, and Florian Schreiber (2017). The current InsurTech landscape: Business models and disruptive potential. University of St. Gallen: Institute of Insurance Economics.
Casella, George, and Roger L. Berger (2002), Statistical Inference, 2nd ed. Canada: Duxbury Press.
Da, Z., Gurun, U. G., & Warachka, M. (2014). Frog in the pan: Continuous information and momentum. The Review of Financial Studies, 27, 2171-2218.
Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. Journal of Financial Economics, 122, 221-247.
De Bondt, W. F., & Thaler, R. (1985). Does the stock market overreact?. Journal of Finance, 40, 793-805.
De Bondt, W. F., & Thaler, R. H. (1987). Further evidence on investor overreaction and stock market seasonality. Journal of Finance, 42, 557-581.
Farinelli, S., Ferreira, M., Rossello, D., Thoeny, M., & Tibiletti, L. (2008). Beyond Sharpe ratio: Optimal asset allocation using different performance ratios. Journal of Banking & Finance, 32, 2057-2063.
Green, R. C., & Hollifield, B. (1992). When will mean‐variance efficient portfolios be well diversified?. Journal of Finance, 47, 1785-1809.
Jegadeesh, N. (1990). Evidence of predictable behavior of security returns. Journal of Finance, 45, 881-898.
Kadan, O., & Liu, F. (2014). Performance evaluation with high moments and disaster risk. Journal of Financial Economics, 113, 131-155.
Lehmann, B. N. (1990). Fads, martingales, and market efficiency. The Quarterly Journal of Economics, 105, 1-28.
Menkhoff, L., Sarno, L., Schmeling, M., & Schrimpf, A. (2012). Currency momentum strategies. Journal of Financial Economics, 106, 660-684.
Schäl, M. (2004). On discrete-time dynamic programming in insurance: Exponential utility and minimizing the ruin probability, Scandinavian Actuarial Journal, 3, 189-210.
Schnytzer, A., & Westreich, S. (2013). A global index of riskiness. Economics Letters, 118, 493-496.
Smith, V. L. (1968). Optimal insurance coverage. Journal of Political Economy, 76, 68-77.
Svetlana, V. (2016). InsurTech: Challeges and development perspectives. International Journal of Innovative Technologies in Economy, 3, 39-42.
Szpiro, G. G. (1985). Optimal insurance coverage. The Journal of Risk and Insurance, 52, 704-710.
 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
QR Code
QRCODE

臺灣人文及社會科學引文索引資料庫

目前上線人數：413（臺灣：409，外國：4） ／ 本年度總使用人次：2625314 ／ 訪客人次（自102年09月）：68421884
國家圖書館著作權聲明 Copyright © 2013 All rights reserved.
本館地址: 100201臺北市中山南路20號． 本館地圖及交通資訊．
總機：(02)23619132．最佳瀏覽解析度為1024x768以上