金融科技應用之探討

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。

關鍵字

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

並列摘要

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.