Business Commons^™

Fraud Pattern Detection For Nft Markets, Andrew Leppla, Jorge Olmos, Jaideep Lamba

SMU Data Science Review

Non-Fungible Tokens (NFTs) enable ownership and transfer of digital assets using blockchain technology. As a relatively new financial asset class, NFTs lack robust oversight and regulations. These conditions create an environment that is susceptible to fraudulent activity and market manipulation schemes. This study examines the buyer-seller network transactional data from some of the most popular NFT marketplaces (e.g., AtomicHub, OpenSea) to identify and predict fraudulent activity. To accomplish this goal multiple features such as price, volume, and network metrics were extracted from NFT transactional data. These were fed into a Multiple-Scale Convolutional Neural Network that predicts suspected fraudulent activity based …

Social Impacts Of Robotics On The Labor And Employment Market, Kelvin Espinal

Dissertations, Theses, and Capstone Projects

Robotics have been introduced into the workplace to perform tasks that human beings have traditionally fulfilled. Complementing or substituting human labor with robotics eliminates human involvement in functions attributable to hazardous environments, heavy lifting, toxic substances, and repetitive low-level tasks. On the other hand, they are meant to be more efficient and cost-effective, saving money, time, and labor. However, since the introduction of robotics in the workforce, societal opposition has been towards this branch of technology in fear of losing employment, wages, and purpose.

Previous studies have reported an overarching societal fear that adopting robotics in the workplace and industry …

Stock Trend Prediction Using Candlestick Charting And Ensemble Machine Learning Techniques With A Novelty Feature Engineering Scheme, Yaohu Lin, Shancun Liu, Haijun Yang, Harris Wu

Information Technology & Decision Sciences Faculty Publications

Stock market forecasting is a knotty challenging task due to the highly noisy, nonparametric, complex and chaotic nature of the stock price time series. With a simple eight-trigram feature engineering scheme of the inter-day candlestick patterns, we construct a novel ensemble machine learning framework for daily stock pattern prediction, combining traditional candlestick charting with the latest artificial intelligence methods. Several machine learning techniques, including deep learning methods, are applied to stock data to predict the direction of the closing price. This framework can give a suitable machine learning prediction method for each pattern based on the trained results. The investment …

Quantifying A Mining Investability Quotient To Mitigate Junior Mining Investment Risk, Jonathan Mickey Merguerian

Open Access Theses & Dissertations

Uncertainty and risk are inherent features of investing in mineral exploration ventures. Investors rely on qualitative and quantitative analysis to evaluate risk of capital. The distinction between risk and uncertainty pertaining to mineral exploration is that risk is an opportunity for loss and uncertainty can be described as the range of probabilities that some condition may occur (Rose, 1987). Stakeholders rely on a combination of investment conferences, risk analysis equations, press releases, financial reports, and investment research to determine if an investment potential. J. M. Cozzolini developed a formula for Risk Adjusted Value (RAV) of an exploration venture. The study …

Ssrn As An Initial Revolution In Academic Knowledge Aggregation And Dissemination, David Bray, Sascha Vitzthum, Benn Konsynski

Sascha Vitzthum

Within this paper we consider our results of using the Social Science Research Network (SSRN) over a period of 18 months to distribute our working papers to the research community. Our experiences have been quite positive, with SSRN serving as a platform both to inform our colleagues about our research as well as inform us about related research (through email and telephoned conversations of colleagues who discovered our paper on SSRN). We then discuss potential future directions for SSRN to consider, and how SSRN might well represent an initial revolution in 21st century academic knowledge aggregation and dissemination. Our paper …