Aim: The purpose of the thesis is to give small private investors an insight the financial world of derivatives and to show that an investor does not need to consult with an advisor in order to make decisions about the investments. The aim was to show through a new product that a small investor can beat the market return. Method: The method used in the thesis is to collect data over a three year period for an option, a bull ETF and a treasury bill. The database DataStream was used to obtain statistics of the option and the Treasury bill and Nasdaq OMX Nordic was used for the Bull ETF. We calculated the expected return and variance of each in order to use in the portfolio. Having the information needed we then used a trial-and-error method to calculate the weight each component will be given, with the help of Excel and its Solver add-on. Result & Conclusion: The results were surprising in that over the three year period the product had a 100% increase, while the market only went up by 30%. The major reason for the products strong return was that the daily earnings were shifted everyday so that the weights remained constant throughout the life of the product. The issue with the product was that no transaction costs were included in the calculations, and as there would be at least one transaction per day the costs would be enormous for the given product. Suggestions for Further Research: As one of the limitations for the thesis was that no transactions cost were included, one idea for further research could be to calculate the transaction costs as well as seeing if there is a method to minimize them so that the product could be profitable. Contribution to the Field: To our knowledge we are the first to test theses three components in order to from a structured product. Through our method interested parties could do the same with other components or retest our product. We have showed through our method one way to create your own structured product.

Abstract In the recent years, there has been a wave of financial globalization and it caused the rapid expansion of financial markets, financial markets become more open, capital flows speeded greatly, and new financial product published in the market, increased the degree of mutual influence. In the 2007, the subprime mortgage of America caused the global financial crisis. Most invest banks such as Lehman Brother and brokers and lots of investors were hurt deeply by the financial crisis and the assets reduced rapidly as well. Due to the collapse of the financial system, lots of banks need to issue new financial product like Dual Currency Deposit (DCD) to customers instead of structure note. However, the new financial products have sold very well, it caused great concern to the government to restrict strictly to financial consultants and establish the classification for investors. The thesis use survey research and collect relevant reference to discuss the change of the investors¡¦ investment behavior and risk preference of the new financial products after the financial crisis. The questionnaire can be divided into four parts which are personal data¡Binvestor¡¦s risk attributes¡Bthe risk of new financial products and investment related information. In conclusion, we found the level of personal risk and commodity risk inconsistently. For the reason, the investment risk does affect the confidence of investors after the financial crisis. Furthermore, the financial institutions can target the groups of 40-49 year old ages as the future marketing. Most respondents believed that the investment environment between Taiwan and Hong Kong are different in financial policies¡Bfinancial officers knowledge. Consequently, we wish can provide Taiwan financial institutions for further reference for professional investors with specialized service and qualified financial consultants. Keyword: financial crisis, structured product, risk, investment behavior, survey research

In the study the first part is focused on anylysing the U.S. corporate bond market especially its imporance and progress. Second part is devoted structured products which are integrated in the corporate bond market in USA. Last part is focused on historical yields and especially credit spreads of corporate bonds and the factors which influence them.

本研究論述分為兩大部份，均是以實務上之動態避險並以DELTA NEUTRAL為分析上之比較基礎，第一個部分是以認購權證發行商角度出發，考量發行實論述務架構、避險成本、交易成本，進而說明發行商造市及避險之損益結構及其關鍵因子，並以商品實例，分別使用模擬的資料、歷史回溯資料、發行後真實資料，配合不同的避險策略方法以量化方法來分析發行商損益及其風險。 另一部份則探討結構型商品，結構型商品在銷售上即對行銷通路付出通路手續費支出，發行後勿需有如認購權證之次級市場造市交易問題，因此在發行商損益上觀察，交易報價即反映了預期利潤與風險的對價或承擔，並以商品實例，藉以歷史資料基礎來估計分析模型之參數風險，並說明理論定價其值本為分配而非一固定數之本質，再者又取『發行商稅後損益佔期初理論利潤之比例』來作為衡量避險（複製商品之報償（PAYOFF））效率之指標，進一步論述發行商作交價報價時需充份考慮其本身之實際避險能力（即複製選擇權之成本），以免高估本身獲利能力而低報商品報價。 本文另從券商風險管理角度而言，建議以上兩項業務均可以設定商品標的之評價波動率等參數之計算準則，來區隔交易員避險操作使用之避險波動率等參數，並以DELTA NEUTRAL及上述評價參數為基礎來求算應避險金額，以利與實際避險金額作比較，了解商品操作其超缺避情況是否有逾發行商內外規規定，以此來落實執行證券商商品操作風險管理。 本論文最重要結論是具體建議並提出發行商對所發行商品標的之篩選及避險策略等之系統性評估方法，並對商品發行前及發行後之評估所用之研究方法作詳細說明，對發行商而言其重點為了解此系統性評估方法，並利用電腦化在每次發行前作此研究，可作為標的是否適合發行之考量參考，及交易員之發行前指導及情境分析使用，以提昇操作上對市況變化之因應敏感度，發行後也可以交易員其操作實際損益來比較是否有優於本文所論述之程式性交易結果，以作為判別交易員表績效表現參考，並作為該商品在未來再發行時，其交易操作方法提昇之參考。

這幾年由於市場利率逐漸走低，大量資金尋求收益較高之商品，結果造成訴求高收益之各式結構型(條件式)商品如雨後春筍般地冒出，而銷售量也屢創新高，由於結構型商品之所以能夠讓投資者獲得「可能的」之高收益，主要原因是理財商品內都包含了各種選擇權，只要符合選擇權之執行條件，則產品就可以獲得高收益，但是未能符合選擇權之執行條件時，投資人資金往往就只能求得到期保本，而損失了一些利息收入。 另外也由於這些產品投資年期都不是一兩年而已，有些長達十年以上，往往一投資其資金就會被固定在那裡，若投資人沒有考量清楚或在理專強力促銷下購買，常常會發生投資人短期內就想解約贖回之狀況，解約時就像壽險商品一樣，大都解約價格已經折價了許多本金，這就是時有聽聞客戶與銀行之間投資糾紛的來由，這也促使主管機關－金管會陸續頒布相關管理規範來建立投資秩序與環境。 本研究內容擬提出一個投資產品之管理應用工具－「投資菜單」來解決以上爭議，「投資菜單」之於理財產品策略工具就好比「平衡計分卡」之於企業績效管理工具一樣，此工具可以幫助金控投資產品委員會檢視過去產品績效，進而決策未來上架產品方向；以期希望能夠從下而上一致性地、整體性地解決顧客端、理專端、公司營運管理端與主管機關四方所各自關心的重點，以期降低客戶交易糾紛、增加業務銷售速度以及提升財富管理業務整體形象。

Structured Product Labels (SPLs), the package inserts that accompany drugs governed by the Food and Drugs Administration (FDA), hold information about Adverse Drug Reactions (ADRs) that exists associated with drugs post-market. This information is valuable for actors working in the field of pharmacovigilance aiming to improve the safety of drugs. One such actor is Uppsala Monitoring Centre (UMC), a non-profit conducting pharmacovigilance research. In order to access the valuable information of the package inserts, UMC have constructed an SPL mining pipeline in order to mine SPLs for ADRs. This project aims to investigate new approaches to the solution to the Scan problem, the part of the pipeline responsible for extracting mentions of ADRs. The Scan problem is solved by approaching the problem as a Named Entity Recognition task, a subtask of Natural Language Processing. By using the transformer-based deep learning model BERT, with domain-specific pre-training, an F1-score of 0.8220 was achieved. Furthermore, the chosen model was used in an iteration of Active Learning in order to efficiently extend the available data pool with the most informative examples. Active Learning improved the F1-score to 0.8337. However, the Active Learning was benchmarked against a data set extended with random examples, showing similar improved scores, therefore this application of Active Learning could not be determined to be effective in this project.