Physical Sciences and Mathematics Commons^™

Understanding Sentiment Through Context, Richard M.Crowley, M.H. Franco Wong

Research Collection School Of Accountancy

We examine whether empirical results using text-based sentiment of U.S. annual reports depend on the underlying context, within documents, from which sentiment is measured. We construct a clause-level measure of context, showing that sentiment is driven by many different contexts and that positive and negative sentiment are driven by different contexts. We then construct context-level sentiment measures and examine whether sentiment works as expected at the context-level across four prediction problems. Our results demonstrate that document-level sentiment exhibits significant noise in prediction and suggest that document-level aggregation of sentiment leads to missed empirical nuances. The contexts driving sentiment results vary …

Towards Automated Safety Vetting Of Smart Contracts In Decentralized Applications, Yue Duan, Xin Zhao, Yu Pan, Shucheng Li, Minghao Li, Fengyuan Xu, Mu Zhang

Research Collection School Of Computing and Information Systems

We propose VetSC, a novel UI-driven, program analysis guided model checking technique that can automatically extract contract semantics in DApps so as to enable targeted safety vetting. To facilitate model checking, we extract business model graphs from contract code that capture its intrinsic business and safety logic. To automatically determine what safety specifications to check, we retrieve textual semantics from DApp user interfaces. To exclude untrusted UI text, we also validate the UI-logic consistency and detect any discrepancies. We have implemented VetSC and applied it to 34 real-world DApps. Experiments have demonstrated that VetSC can accurately interpret smart contract code, …

Secure Hierarchical Deterministic Wallet Supporting Stealth Address, Xin Yin, Zhen Liu, Guomin Yang, Guoxing Chen, Haojin Zhu

Research Collection School Of Computing and Information Systems

Over the past decade, cryptocurrency has been undergoing a rapid development. Digital wallet, as the tool to store and manage the cryptographic keys, is the primary entrance for the public to access cryptocurrency assets. Hierarchical Deterministic Wallet (HDW), proposed in Bitcoin Improvement Proposal 32 (BIP32), has attracted much attention and been widely used in the community, due to its virtues such as easy backup/recovery, convenient cold-address management, and supporting trust-less audits and applications in hierarchical organizations. While HDW allows the wallet owner to generate and manage his keys conveniently, Stealth Address (SA) allows a payer to generate fresh address (i.e., …

An Analysis Of Weighted Least Squares Monte Carlo, Xiaotian Zhu

Electronic Thesis and Dissertation Repository

Since Longstaff and Schwartz [2001] brought the amazing Regression-based Monte Carlo (LSMC) method in pricing American options, it has received heated discussion. Based on the research done by Fabozzi et al. [2017] that applies the heteroscedasticity correction method to LSMC, we further extend the study by introducing the methods from Park [1966] and Harvey [1976]. Our work shows that for a single stock American Call option modelled by GBM with two exercise opportunities, WLSMC or IRLSMC provides better estimates in continuation value than LSMC. However, they do not lead to better exercise decisions and hence have little to no effect …

Neural-Progressive Hedging: Enforcing Constraints In Reinforcement Learning With Stochastic Programming, Supriyo Ghosh, Laura Wynter, Shiau Hong Lim, Duc Thien Nguyen

Research Collection School Of Computing and Information Systems

We propose a framework, called neural-progressive hedging (NP), that leverages stochastic programming during the online phase of executing a reinforcement learning (RL) policy. The goal is to ensure feasibility with respect to constraints and risk-based objectives such as conditional value-at-risk (CVaR) during the execution of the policy, using probabilistic models of the state transitions to guide policy adjustments. The framework is particularly amenable to the class of sequential resource allocation problems since feasibility with respect to typical resource constraints cannot be enforced in a scalable manner. The NP framework provides an alternative that adds modest overhead during the online phase. …

Representation Learning In Finance, Ajim Uddin

Dissertations

Finance studies often employ heterogeneous datasets from different sources with different structures and frequencies. Some data are noisy, sparse, and unbalanced with missing values; some are unstructured, containing text or networks. Traditional techniques often struggle to combine and effectively extract information from these datasets. This work explores representation learning as a proven machine learning technique in learning informative embedding from complex, noisy, and dynamic financial data. This dissertation proposes novel factorization algorithms and network modeling techniques to learn the local and global representation of data in two specific financial applications: analysts’ earnings forecasts and asset pricing.

Financial analysts’ earnings forecast …

Modeling Empirical Stock Market Behavior Using A Hybrid Agent-Based Dynamical Systems Model, Daniel A. Cline, Grant T. Aguinaldo, Christian Lemp

Northeast Journal of Complex Systems (NEJCS)

We describe the development and calibration of a hybrid agent-based dynamical systems model of the stock market that is capable of reproducing empirical market behavior. The model consists of two types of trader agents, fundamentalists and noise traders, as well as an opinion dynamic for the latter (optimistic vs. pessimistic). The trader agents switch types stochastically over time based on simple behavioral rules. A system of ordinary differential equations is used to model the stock price as a function of the states of the trader agents. We show that the model can reproduce key stylized facts (e.g., volatility clustering and …

A Machine Learning Approach To Stochastic Optimal Control, Pablo Ever Avalos

Open Access Theses & Dissertations

Merton's portfolio optimization problem is a well-renowned problem in financial mathematics which seeks to optimize the investment decision for an investor. In the simplest situation, the market consists of a risk-less asset (i.e. a bond) that pays back a relatively low interest rate, and a risky asset (i.e. a stock) that follows a geometric Brownian motion. The optimal allocation strategy of the investor's wealth is found by optimizing the expected utility along the stochastic evolution of the market. This thesis focuses on several different applications of this optimization problem. We look at pre-constructed analytical solutions and showcase the results. We …

Intraday Algorithmic Trading Using Momentum And Long Short-Term Memory Network Strategies, Andrew R. Whitinger Ii

Undergraduate Honors Theses

Intraday stock trading is an infamously difficult and risky strategy. Momentum and reversal strategies and long short-term memory (LSTM) neural networks have been shown to be effective for selecting stocks to buy and sell over time periods of multiple days. To explore whether these strategies can be effective for intraday trading, their implementations were simulated using intraday price data for stocks in the S&P 500 index, collected at 1-second intervals between February 11, 2021 and March 9, 2021 inclusive. The study tested 160 variations of momentum and reversal strategies for profitability in long, short, and market-neutral portfolios, totaling 480 portfolios. …

The Correlation Of Winning And Money-Baseball, Jacob Bowman

Scholars Day Conference

This presentation over my thesis examines the feasibility of using statistics to predict win values for major league baseball. Definite correlations were discovered between a Major League organization’s finances and on-field performance. Stated correlations are used to generate a predictive model that will predict on-field outcomes. Using regression analysis, such a model is construed, and successfully predicted win ratios for Major League Baseball organizations using only available past financial data.

Esg Investing: From Fad To Force, Erin Cullen

Senior Theses

ESG Investing is the application of environmental, social, and governance factors to identify material investment risks and growth opportunities. Though traditionally viewed as non-financial factors, this paper asserts that ESG factors are indeed financially material. This work first surveys the current state of ESG Investing, its shortcomings, and its success despite these inherent issues. Section II adds a new perspective to ESG research by examining the applications of ESG scores in portfolio management. The study conducts t-tests to answer whether the typical holdings of an ESG mutual fund are more sustainable than those of traditional funds. The paper then focuses …

Thoughts And Suggestions On Development Of Earth Sciences In China, Lihua Zhou, Xin Wang, Chengxiong Zhou, Quanyou Liu, Jianhua Si, Wang Zhang, Zhijun Jin

Bulletin of Chinese Academy of Sciences (Chinese Version)

Earth Sciences is a multi-disciplinary science, which takes the interaction of Earth's various spheres and their resources and environmental effects as the research object. As a comprehensive and systematic science that not only expands the frontier of human knowledge but also serves the social and economic development of human beings, Earth Sciences not only studies the past and reveals the evolution history of the Earth, but also faces the future and provides solutions for the sustainable development of human beings. Based on the analysis of the international development trends of Earth Sciences and the summary of the development status and …

Strategic Consideration And Recommendation On Development Of Chemistry In China, Yanlin Cheng, Shuxian Wu, Qing Dai, Chunying Chen, Yuliang Zhao

Bulletin of Chinese Academy of Sciences (Chinese Version)

Chemistry, as a central science linking all the other sciences, has played an influential role in promoting the development of the whole scientific and technological field and the prosperity of economy and society. Developed countries strongly support the development of chemistry and effectively promote the level of chemical research and chemical industry. Since the founding of the People's Republic of China, chemical research has been supported among the field of natural sciences, and its current level is at the forefront of the world. However, there are still problems in the field of chemistry research in China, such as insufficient major …

Characteristics And Funding Strategies Of Mathematical Research, Xiaoxi Xiao, Xiaoshan Gao, Yaxiang Yuan

Bulletin of Chinese Academy of Sciences (Chinese Version)

Strengthening mathematical research aiming at the national strategic needs and major frontier problems is conducive to promoting mathematics research to play a more important strategic role in national development and international competition. This study analyzes the basic characteristics of mathematical research, summarizes the international experiences in funding mathematical research, and then deeply analyzes the major problems of Chinese funding mechanisms for mathematical research, such as the lack of overall planning, the short of sustainable and stable support, and the urgent need to improve the training, selecting, and funding mechanism for high-level mathematical talents. It also puts forward some policy suggestions, …

High School Student Perspective: My Njit Stem For Success Internship Experience, Michael Mora

STEM Month

During the 2020-2021 school year, I was a senior at the Academy for Mathematics, Science, and Engineering (AMSE) in Rockaway, NJ. At AMSE, a STEM-focused four-year magnet high school program hosted at Morris Hills High School, participating in an extended internship senior year is a cornerstone of the learning process. Required to complete a STEM-related internship to graduate, Academy students are encouraged to seek out an internship they’re passionate about in a field of their choice. The internship, which must be conducted under the mentorship of an industry professional, must meet the New Jersey-approved standards for a work-based learning experience …

Analyzing The Impact Of Digital Payment On Efficiency And Productivity Of Commercial Banks: A Case Study In China, Haopeng Wang, Aldy Gunawan

Research Collection School Of Computing and Information Systems

Digital payment has become one of the most popular payment methods all around the world, especially in countries that witnessed the rapid development of internet. As a traditional financial institution, commercial banks have been impacted by newly developed payment technology since third payment platforms have attracted customers to use the digital payment for daily consumption, transferring, and even investment. This paper focuses on analyzing whether and how the commercial banks in China have been affected by digital payment by using empirical methods. Systematic Generalized Method of Moments (SYS-GMM) is used to test the relationship between the productivity of commercial banks …

Session 5: Equipment Finance Credit Risk Modeling - A Case Study In Creative Model Development & Nimble Data Engineering, Edward Krueger, Landon Thompson, Josh Moore

SDSU Data Science Symposium

This presentation will focus first on providing an overview of Channel and the Risk Analytics team that performed this case study. Given that context, we’ll then dive into our approach for building the modeling development data set, techniques and tools used to develop and implement the model into a production environment, and some of the challenges faced upon launch. Then, the presentation will pivot to the data engineering pipeline. During this portion, we will explore the application process and what happens to the data we collect. This will include how we extract & store the data along with how it …

Optimizing Pension Outcomes Using Target Volatility Investment Concept, Zefeng Bai

2022

The target volatility strategy is a very popular investment concept in financial marketplace. For my dissertation, I focus on studying the target volatility investment concept in application to pension accumulation as well as decumulation stages. Additionally, I extend a basic target volatility strategy by introducing trading boundaries to its asset allocation mechanism. My dissertation study follows a three-paper format.

In paper one, we propose a new pension strategy that aims at improving the protection of a long-term pension plan in volatile market conditions. Over a hypothetical twenty-year pension scheme, we show that our newly proposed strategy, which attaches a target …

Defining Smart Contract Defects On Ethereum, Jiachi Chen, Xin Xia, David Lo, John Grundy, Xiapu Luo, Ting Chen

Research Collection School Of Computing and Information Systems

Smart contracts are programs running on a blockchain. They are immutable to change, and hence can not be patched for bugs once deployed. Thus it is critical to ensure they are bug-free and well-designed before deployment. A Contract defect is an error, flaw or fault in a smart contract that causes it to produce an incorrect or unexpected result, or to behave in unintended ways. The detection of contract defects is a method to avoid potential bugs and improve the design of existing code. Since smart contracts contain numerous distinctive features, such as the gas system. decentralized, it is important …

Cybersecurity In Fintech Companies, Efstratios Zouros

Cybersecurity Undergraduate Research Showcase

Have you recently accessed your bank account online? Have you accessed any financial instrument through your computer or your mobile device? If you are reading this, chances are you have. Every time you utilize those services, you ultimately put your trust in the financial institutions that offer them. You trust that they can securely keep your private information, while also keeping your savings safe. Ultimately, there is a certain dependability and trust in financial institutions that have been present on earth before most of us.

Strategies Business Leaders Use To Mitigate Online Credit Card Fraud, Clarissa Rosario-Tavarez

Walden Dissertations and Doctoral Studies

Online credit card fraud targeting banks, customers, and businesses costs millions of U.S. dollars annually. Online business leaders face challenges securing and regulating the online payment processing environment. Grounded in the situational crime prevention theory, the purpose of this qualitative multiple case study was to explore strategies online business leaders use to mitigate the loss of revenue caused by online credit card fraud. The participants comprised five online business leaders of an organization in the Southwest of the United States, who implemented strategies that successfully mitigated revenue losses due to online credit card fraud. The data were collected from semistructured …

Understanding The Effectivity And Increased Reliance Of Credit Risk Machine Learning Models In Banking, Grishma Baruah

Cybersecurity Undergraduate Research Showcase

Credit risk analysis and making accurate investment and lending decisions has been a challenge for the financial industry for many years, as can be seen with the 2008 financial crisis. However, with the rise of machine learning models and predictive analytics, there has been a shift to increased reliance on technology for determining credit risk. This transition to machine learning comes with both advantages, such as potentially eliminating human error and assumptions from lending decisions, and disadvantages, such as time constraints, data usage inabilities, and lack of understanding nuances in machine learning models. In this paper, I look at four …

Outsourcing Climate Change, Rui Dai, Rui Duan, Hao Liang, Lilian Ng

Research Collection Lee Kong Chian School Of Business

This paper examines whether and how firms combat climate change. Our study provides robust evidence that firms outsource part of their carbon emissions to foreign suppliers and shows how internal and external stakeholders significantly shape firms' environmental policies. Furthermore, firms tend to seek a foreign supplier and decrease their emission abatement efforts as pressure to reduce domestic emissions intensifies. These firms are also less incentivized to develop green technologies. Finally, we find that outsourcing emissions has real and economic consequences, with investors demanding a higher carbon premium for their exposures to carbon risks associated with increased outsourced emissions.