Open Access. Powered by Scholars. Published by Universities.®
Finance and Financial Management Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Keyword
-
- Blockchain (3)
- ICO (2)
- MITB student (2)
- ARIMA (1)
- Analyst forecast accuracy (1)
-
- BCBS 239 (1)
- Basel Committee on Banking Supervision (1)
- Big data/machine learning (1)
- Bitcoin (1)
- Byzantine Fault Tolerance (1)
- Collusion attacks (1)
- Cryptocurrency (1)
- Cybersecurity (1)
- Data Science (1)
- Data analysis (1)
- Data governance frameworks (1)
- Deep Learning (1)
- Deep Learning Reinforcement Pairs Trading (1)
- Distributed Consensus (1)
- Distributed Ledger Technology (1)
- Double-spending (1)
- Ensemble (1)
- Ethereum (1)
- Fair deposit (1)
- Financial Technology Standards (1)
- Financial risk meter (1)
- Initial Coin Offerings (1)
- Investor sentiment (1)
- Kernel regression (1)
- LASSO (1)
- Publication
- Publication Type
Articles 1 - 14 of 14
Full-Text Articles in Finance and Financial Management
An Ai Approach To Measuring Financial Risk, Lining Yu, Wolfgang Karl Hardle, Lukas Borke, Thijs Benschop
An Ai Approach To Measuring Financial Risk, Lining Yu, Wolfgang Karl Hardle, Lukas Borke, Thijs Benschop
Sim Kee Boon Institute for Financial Economics
AI artificial intelligence brings about new quantitative techniques to assess the state of an economy. Here, we describe a new measure for systemic risk: the Financial Risk Meter (FRM). This measure is based on the penalization parameter (λ" role="presentation" style="box-sizing: border-box; display: inline; font-style: normal; font-weight: normal; line-height: normal; font-size: 18px; text-indent: 0px; text-align: left; text-transform: none; letter-spacing: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">λλ) of a linear quantile lasso regression. The FRM is calculated by taking the average …
Optimal Design And Ownership Structures Of Innovative Retail Payment Systems, Zhiling Guo, Dan Ma
Optimal Design And Ownership Structures Of Innovative Retail Payment Systems, Zhiling Guo, Dan Ma
Research Collection School Of Computing and Information Systems
In response to the Fintech trend, an ongoing debate in the banking industry is how to design the new-generation interbank retail payment and settlement system. We propose a two-stage analytical model that takes into account the value-risk tradeoff in the new payment system design, as well as banks’ participation incentives and adoption timing decisions. We find that, as the system base value increases, banks tend to synchronize their investment and adoption decisions. When the system base value is low and banks are heterogeneous, bank association ownership maximizes social welfare. When both the system base value and bank heterogeneity are moderate, …
Deep Reinforcement Learning Pairs Trading, Andrew Brim
Deep Reinforcement Learning Pairs Trading, Andrew Brim
All Graduate Plan B and other Reports, Spring 1920 to Spring 2023
This research applies a deep reinforcement learning technique, Deep Q-network, to a stock market pairs trading strategy for profit. Artificial intelligent methods have long since been applied to optimize trading strategies. This work trains and tests a DQN to trade co-integrated stock market prices, in a pairs trading strategy. The results demonstrate the DQN is able to consistently produce positive returns when executing a pairs trading strategy.
Quantum Consensus, Jorden Seet, Paul Griffin
Quantum Consensus, Jorden Seet, Paul Griffin
Research Collection School Of Computing and Information Systems
In this paper, we propose a novel consensus mechanism utilizing the quantum properties of qubits. This move from classical computing to quantum computing is shown to theoretically enhance the scalability and speed of distributed consensus as well as improve security and be a potential solution for the problem of blockchain interoperability. Using this method may circumvent the common problem known as the Blockchain Trilemma, enhancing scalability and speed without sacrificing de-centralization or byzantine fault tolerance. Consensus speed and scalability is shown by removing the need for multicast responses and exploiting quantum properties to ensure that only a single multicast is …
Automated Theme Search In Ico Whitepapers, Chuanjie Fu, Andrew Koh, Paul Griffin
Automated Theme Search In Ico Whitepapers, Chuanjie Fu, Andrew Koh, Paul Griffin
Research Collection School Of Computing and Information Systems
The authors explore how topic modeling can be used to automate the categorization of initial coin offerings (ICOs) into different topics (e.g., finance, media, information, professional services, health and social, natural resources) based solely on the content within the whitepapers. This tool has been developed by fitting a latent Dirichlet allocation (LDA) model to the text extracted from the ICO whitepapers. After evaluating the automated categorization of whitepapers using statistical and human judgment methods, it is determined that there is enough evidence to conclude that the LDA model appropriately categorizes the ICO whitepapers. The results from a two-population proportion test …
Collusion Attacks And Fair Time-Locked Deposits For Fast-Payment Transactions In Bitcoin, Xingjie Yu, Shiwen Michael Thang, Yingjiu Li, Robert H. Deng
Collusion Attacks And Fair Time-Locked Deposits For Fast-Payment Transactions In Bitcoin, Xingjie Yu, Shiwen Michael Thang, Yingjiu Li, Robert H. Deng
Research Collection School Of Computing and Information Systems
In Bitcoin network, the distributed storage of multiple copies of the block chain opens up possibilities for doublespending, i.e., a payer issues two separate transactions to two different payees transferring the same coins. While Bitcoin has inherent security mechanism to prevent double-spending attacks, it requires a certain amount of time to detect the doublespending attacks after the transaction has been initiated. Therefore, it is impractical to protect the payees from suffering in double-spending attacks in fast payment scenarios where the time between the exchange of currency and goods or services is shorten to few seconds. Although we cannot prevent double-spending …
Picking Flowers In An Ico Garden, Fam Guo Teng, Paul R. Griffin, Andrew Koh
Picking Flowers In An Ico Garden, Fam Guo Teng, Paul R. Griffin, Andrew Koh
Research Collection School Of Computing and Information Systems
The rise of Initial Coin Offerings (ICO) in recent times and their potential for investment opportunities have investors spending a lot of time researching ICOs or having to follow the crowd. This paper aims to explore four broad factors of ICOs: identity, credibility, investor sentiment, and price movement to develop a framework that is useful in determining ICO quality. Research is shown using data sources including public forums, chat groups, web sites, white papers as well as smart contract details. Finally, a system, based on the framework, is proposed that can be used to detect and regulate ICO activities and …
Risk Pooling, Supply Chain Hierarchy, And Analysts' Forecasts, Nan Hu, Jian-Yu Ke, Ling Liu, Yue Zhang
Risk Pooling, Supply Chain Hierarchy, And Analysts' Forecasts, Nan Hu, Jian-Yu Ke, Ling Liu, Yue Zhang
Research Collection School Of Computing and Information Systems
We investigate whether a firm's risk pooling affects its analysts' forecasts, specifically in terms of forecast accuracy and their use of public vs. private information, and how risk pooling interacts with a firm's position in the supply chain to affect analysts' forecasts. We use a social network analysis method to operationalize risk pooling and supply chain hierarchy, and find that risk pooling significantly reduces analysts' forecast errors and increases (decreases) their use of public (private) information. We also find that the positive (negative) relationships between risk pooling and analyst forecast accuracy and analysts' use of public (private) information are more …
Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater
Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater
SMU Data Science Review
The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide best …
Procure-To-Pay Software In The Digital Age: An Exploration And Analysis Of Efficiency Gains And Cybersecurity Risks In Modern Procurement Systems, Drew Lane
MPA/MPP/MPFM Capstone Projects
Procure-to-Pay (P2P) softwares are an integral part of the payment and procurement processing functions at large-scale governmental institutions. These softwares house all of the financial functions related to procurement, accounts payable, and often human resources, helping to facilitate and automate the process from initiation of a payment or purchase, to the actual disbursal of funds. Often, these softwares contain budgeting and financial reporting tools as part of the offering. As such an integral part of the financial process, these softwares obviously come at an immense cost from a set of reputable vendors. In the case of government, these vendors mainly …
Stock Returns And Investor Sentiment: Textual Analysis And Social Media, Zachary Mcgurk, Adam Nowak, Joshua C. Hall
Stock Returns And Investor Sentiment: Textual Analysis And Social Media, Zachary Mcgurk, Adam Nowak, Joshua C. Hall
Economics Faculty Working Papers Series
The behavioral finance literature has found that investor sentiment has predictive ability for equity returns. This differs from standard finance theory, which provides no role for investor sentiment. We examine the relationship between investor sentiment and stock returns by employing textual analysis on social media posts. We find that our investor sentiment measure has a positive and significant effect on abnormal stock returns. These findings are consistent across a number of different models and specifications, providing further evidence against non-behavioral theories.
Adapting Financial Technology Standards To Blockchain Platforms, Gabriel Bello
Adapting Financial Technology Standards To Blockchain Platforms, Gabriel Bello
Theses and Dissertations
Traditional payment systems have standards designed to keep transaction data secure, but blockchain systems are not in scope for such security standards. We compare the Payment Application Data Security Standard’s (PA-DSS) applicability towards transaction-supported blockchain platforms to test the standard’s applicability. By highlighting the differences in implementation on traditional and decentralized transaction platforms, we critique and adapt the standards to fit the decentralized model. In two case studies, we analyze the QTUM and Ethereum blockchain platforms’ industry compliance, as their payment platforms support transactions equivalent to that of applications governed by the PA-DSS. We determine QTUM’s and Ethereum’s capabilities to …
Incorporating Data Governance Frameworks In The Financial Industry, Tarlochan Singh Randhawa
Incorporating Data Governance Frameworks In The Financial Industry, Tarlochan Singh Randhawa
Walden Dissertations and Doctoral Studies
Data governance frameworks are critical to reducing operational costs and risks in the financial industry. Corporate data managers face challenges when implementing data governance frameworks. The purpose of this multiple case study was to explore the strategies that successful corporate data managers in some banks in the United States used to implement data governance frameworks to reduce operational costs and risks. The participants were 7 corporate data managers from 3 banks in North Carolina and New York. Servant leadership theory provided the conceptual framework for the study. Methodological triangulation involved assessment of nonconfidential bank documentation on the data governance framework, …
Regression Tree Construction For Reinforcement Learning Problems With A General Action Space, Anthony S. Bush Jr
Regression Tree Construction For Reinforcement Learning Problems With A General Action Space, Anthony S. Bush Jr
Electronic Theses and Dissertations
Part of the implementation of Reinforcement Learning is constructing a regression of values against states and actions and using that regression model to optimize over actions for a given state. One such common regression technique is that of a decision tree; or in the case of continuous input, a regression tree. In such a case, we fix the states and optimize over actions; however, standard regression trees do not easily optimize over a subset of the input variables\cite{Card1993}. The technique we propose in this thesis is a hybrid of regression trees and kernel regression. First, a regression tree splits over …