DSpace community: 財務金融研究所

The Pricing and Analysis of SOFR-Linked Structured Note

title: The Pricing and Analysis of SOFR-Linked Structured Note abstract: 此文獻有鑑於現今對SOFR-linked derivative缺乏整套通順且簡單的訂價流程。故此文獻的重點在於將前人的智慧結晶加以應用並嘗試為SOFR-Linked derivative建立一定價的框架，供後人使用。其中，模型的選擇為J.Hull & White (1993) 提出的 one factor model，其優點不限於本身模型易於分析外，其還可利用J.Hull & White (1994)提出的數值方法使模型可以簡單快速為衍生性商品評價。;This article is based on the current lack of a smooth and simple pricing process for SOFR-linked derivatives. Therefore, the focus of this article lies in applying the crystallized wisdom of predecessors and attempting to establish a pricing framework for SOFR-linked derivatives for the benefit of future generations. The chosen model is the Hull and White one-factor model (J.Hull & White, 1993), whose advantages extend beyond its analytical simplicity. Additionally, it leverages the numerical method introduced by J.Hull & White (1994), enabling the model to efficiently and straightforwardly evaluate derivatives.

利用機器學習預測與評估專利維護分級機制-以國立中央大學為例;Predicting and Evaluating Patent Maintenance Classification Using Machine Learning: a Case Study of National Central University

title: 利用機器學習預測與評估專利維護分級機制-以國立中央大學為例;Predicting and Evaluating Patent Maintenance Classification Using Machine Learning: a Case Study of National Central University abstract: 一件專利從申請、獲證並持續維護，需花費很高的成本，獲證後的專利權不僅是發明人心力成果，也是大學珍貴的無形資產。然而專利獲證後若無授權，長期繳納維護費用，著實為大學的負擔，如何在資源有限下，優先維護價值較高較具商業淺力的專利，並提高管理決策的時效，為大學裡長遠課題，也是本研究的初心。以國立中央大學為研究個案，彙整學校專利資料並利用迴歸分析，篩選出影響專利價值的因素，將這些因素使用不同的預測模型，從中選擇表現較佳且穩定的梯度提升模型。透過模型將專利分成四個等級，並依分級結果，建議專利維護優先順序，並選出具商業淺力之專利進行推廣運用，使有限的經費獲得運用並發揮效益。實證結果顯示，透過機器學習模型可有效的將專利評分轉為價值評等，縮短判斷時效，期盼學術機構得以應用此模型降低維護成本，提升研發成果的實用性。;Maintaining a patent from application to grant and throughout its lifecycle incurs substantial costs. Once granted, a patent not only represents the inventor’s intellectual effort but also serves as a valuable intangible asset for universities. However, patents that remain unlicensed after being granted can become a financial burden due to recurring maintenance fees. Therefore, how to prioritize the maintenance of high-value patents with greater commercial potential under limited resources, while improving the timeliness of management decisions, has become a long-term challenge for universities and the driving motivation of this study.This research uses National Central University as a case study. By compiling the university’s patent data and applying regression analysis, we identify key factors influencing patent value. These factors are then incorporated into various predictive models, from which l Gradient Boosting is selected for its superior and stable performance. The model categorizes patents into four levels and, based on these classifications, provides recommendations for maintenance priority and highlights patents with commercialization potential for promotion and utilization. This approach aims to maximize the use of limited funding and enhance the overall impact of university-owned patents.Empirical results have shown that machine learning models can effectively evaluate patent value, significantly reducing decision-making time. It is hoped that academic institutions can adopt this model to lower maintenance costs and improve the practical application of research outcomes.

Strategic Deviation and Credit Rating

title: Strategic Deviation and Credit Rating abstract: 本研究主要探討策略差異化如何影響公司的信用評級。我們使用了 1983 年至 2017 年美國公司的數據和 18,385 個公司年度觀察結果，我們證明策略差異化透過資訊不對稱、代理問題和企業的文化價值觀對信用評等產生負面影響。我們利用 2SLS 和 PSM-DID 來解決內生性問題，2SLS中以地理接近度作為工具變量，而PSM-DID以沙賓法案（SOX）作為外生衝擊事件，並獲得更穩健的結果。此外，我們透過子樣本分析驗證，當企業財務狀況較弱且總體經濟風險較高的環境時，這一點更為明顯。透過了解策略差異化的影響，企業可以制定更有效的企業策略，減少資訊不對稱和代理問題，從而提高企業的信用評等。;This study predominantly discuss how strategy deviation affects a company′s credit rating. Using data on U.S. corporates from 1983 to 2017 and 18,385 firm-year observations, we demonstrate that strategy deviation negatively affects credit rating through information asymmetries, agency issues, and cultural values. We utilize both 2SLS and PSM-DID to solve the endogeneity problem, using geographic proximity as an instrumental variable and the Sarbanes‐Oxley Act (SOX) as an exogenous shock, and obtain more robust results. Furthermore, we validate through our subsample analysis is more noticeable when firms are in a weaker financial status and in an environment of high macroeconomic risk. With knowing the influence of strategy deviation, firms can make more efficient corporate strategies and decrease information asymmetry and agency problem, and thus raise the firms’ credit ratings. Moreover, we provide some valuable insights about strategy and credit rating to the decision makers, corporate managers, and credit rating agencies.

策略差異與公司創新;Strategic Deviance and Corporate Innovation

title: 策略差異與公司創新;Strategic Deviance and Corporate Innovation abstract: 在我們的研究中探討了2011年至2019年中策略差異(STD)對企業創新的影響。我們有兩個主要結果，第一，STD對企業的創新產生了正面影響。第二，通過中介分析，我們發現經理人管理能力、產業競爭性和持有現金在STD對企業的創新起到了部分影響。而為了解決內生性問題，我們使用了two-stage least squares (2SLS) 和 propensity score matching with difference-in-differences (PSM-DID)。我們做了三項穩健性測試，首先，我們使用替代方法衡量STD後仍然得到正顯著的結果。再者，我們發現，STD對創新的影響在美國東部更為明顯；第三，在不同行業中，STD對創新的影響有所不同。總結來說，我們的研究表明策略差異與企業創新呈現正相關，並建議企業應採用差異性策略以提升企業創新，從而使自身和整個行業受益。;In this study, we investigate the influence of strategic deviance (STD) on firms’ innovation from 2011 to 2019. Our primary results show that STD positively impacts a firm’s innovation. Additionally, through mediation analysis, we find that managerial ability, industry competition, and cash holding exert a partial mediation effect on the impact of STD on firms’ innovation. Our results remain robust after applying two-stage least squares (2SLS) and propensity score matching with difference-in-differences (PSM-DID) to mitigate endogeneity issues, and using alternative measurements of STD. We also observe that the impact of STD on innovation is more prominent in the eastern U.S. and varies across different industries. Our study implies that there is a positive correlation between STD and corporate innovation, suggesting that firms should adopt deviant strategies to enhance innovation, thereby benefiting both themselves and the entire industry.