This paper examines the quantitative finance aspects of AMMs in decentralized finance.
problem Understanding the mathematical and financial underpinnings of AMMs.
method Review of existing literature and analysis of mathematical aspects.
result Interesting relationship between AMMs and derivatives pricing and hedging.
FinTech uses data science and AI to transform finance.
problem Transforming finance with data science and AI.
method DSAI techniques including complex system methods, quantitative methods, etc.
result DSAI enables smart FinTech for various financial sectors.
This paper explores how decentralized finance mitigates traditional finance's shortcomings.
problem Lack of transparency and moral hazard in centralized finance.
method Analysis of smart contracts and decentralized governance in DeFi.
result DeFi mitigates traditional finance's shortcomings through decentralized governance and smart contracts.
Cryptofinance generates yield through innovative decentralized finance.
problem Lack of intrinsic yield in crypto-assets.
method Analysis of yield-generating mechanisms in cryptofinance.
result Cryptofinance innovations enable yield generation through various decentralized and centralised mechanisms.
Graph learning categorizes DeFi services into similar functionalities.
problem Identifying similar financial services in decentralized finance protocols.
method Graph representation learning (GRL) to categorize smart contract blocks into clusters.
result Purity of clustering reaches .888 in the best-case scenario.
Improved AMM protocol supports diverse loan maturities in DeFi.
problem Challenges in designing AMMs for fixed-income lending with time-related complexities.
method Generalized BondMM protocol to support arbitrary maturities.
result BondMM-A protocol demonstrates superior performance in interest rate stability and financial robustness.
Compound examines decentralized lending users and their short loan durations.
problem Systemic risk in decentralized finance due to concentration and interconnection.
method Analysis of on-chain transaction data and smart contract programming.
result Many users borrow for yield farming, not for traditional lending.
This paper evaluates random forest models for predicting stock price trends.
problem Predicting stock price trends to assist investors in making informed decisions.
method Random forest models combined with artificial intelligence, using optimal parameters.
result Random forest models show better predictive performance and time efficiency.
New model assesses risks of staking and borrowing in smart contracts.
problem Security and efficiency of staking in smart contract platforms.
method Combines birth-death Pólya processes and credit derivatives models.
result Derivatives can reduce wealth concentration in staking networks.
The paper establishes axioms for AMMs to ensure fair pricing and fee structures.
problem Ensuring fair and efficient pricing in decentralized finance (DeFi) AMMs.
method Formulating axioms on utility functions to characterize swap sizes and pricing oracles.
result Most existing AMMs satisfy the proposed axioms, and a new AMM is proposed with desirable properties.
Survey of yield farming protocols in DeFi.
problem Understanding and evaluating yield farming mechanisms in DeFi.
method Analyzed smart contracts, performed simulations, reviewed literature.
result Characterized major yield aggregators and identified risks.
Compound Finance optimizes risk metrics for V3 protocol using Chainrisk simulations.
problem Optimizing systemic risks in Compound V3 protocol.
method Millions of Chainrisk simulations to evaluate VaR and LaR, providing parameter adjustments.
result Optimization framework enhances protocol stability.
Study analyzes smart contract adoption under bounded risk, showing stable adoption but fragile financial outcomes.
problem Understanding smart contract adoption in derivative markets under risk constraints.
method Structural theory linked with simulation and real-world validation.
result Adoption intensity is stable but profitability and service outcomes are sensitive to volatility.
Survey of RL in finance, tackling complex decision-making.
problem Complex financial decision-making problems with limited model assumptions.
method Value and policy-based RL algorithms, neural networks, deep RL.
result Improved financial decision-making with less model assumptions.
DeFi doesn't fully remove trust, showing run risk and personal character's importance.
problem The need for trust in DeFi despite its code-based approach.
method Natural experiment revealing identities of DeFi participants, including a criminal.
result DeFi doesn't fully remove trust, showing run risk and personal character's relevance.
Proposes a decentralized insurance protocol for DeFi.
problem Over-insurance and inefficiencies in DeFi collateral.
method Smart contract-based economic model without external dependencies.
result Solves over-insurance and capital inefficiencies.
Agent-to-agent finance aims to manage payments and trust for AI agents.
problem Managing financial interactions between autonomous AI agents.
method Develops agent-to-agent finance concept and explores blockchain solutions.
result Agent-to-agent finance can address coordination frictions in financial markets.
Study shows cryptocurrency market impact on DeFi returns stronger than other drivers.
problem Understanding drivers of DeFi returns and their relative importance.
method Investigated four drivers: cryptocurrency market exposure, network effect, investor attention, and valuation ratio. Designed a new market index, DeFiX.
result Cryptocurrency market impact on DeFi returns is stronger than other drivers and provides superior explanatory power.
Study finds Aave token network has core-periphery structure, with high decentralization predicting better returns.
problem Understanding the actual decentralization in DeFi token transactions on the Ethereum blockchain.
method Applied social network analysis to measure decentralization in Aave token transactions.
result A more decentralized Aave token network predicts higher returns and lower volatility.
Blockchain protocol improves traditional mutual funds with performance fees and investor protection.
problem Operational issues and performance fees in traditional mutual funds.
method Developed a blockchain protocol that integrates features of mutual funds and hedge funds.
result Blockchain can simplify performance fee calculations and protect investors.
This study compares decentralized banks and finds some lack decentralization.
problem Decentralized banks do not fully decentralize transactions as expected.
method Network analysis of transaction data from four banks using core-periphery features.
result MakerDao and Compound are more decentralized than Aave and Liquity.
Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL opens up many new applications in domains such as healthcare, roboti…
Systematic review finds reinforcement learning enhances financial tech performance.
problem Improving financial tech performance through reinforcement learning.
method Systematic review using PRISMA technique.
result RL-based strategies outperform other algorithms in financial tech.
Proposes a probabilistic framework for smart contract risk quantification.
problem Quantifying financial risk of smart contract cyber attacks and failures.
method Probabilistic graph-theoretical framework using bond percolation models.
result Analytical results and numerical examples for aggregate loss distribution.
Paper proposes auction method for smart derivatives to avoid disputes.
problem Disputes over derivative liquidation processes in smart contracts.
method Defines an auction type resolution for smart derivatives.
result Proposes a beneficial method for smart derivatives participants.
Recently, a wide range of smart devices are deployed in a variety of environments to improve the quality of human life. One of the important IoT-based applications is smart homes for healthcare, especially for elders. IoT-based smart homes enable elders' health to be properly monitored and taken care of. However, elder…
The paper analyzes risks and revenue dynamics of a liquid restaking protocol in decentralized finance.
problem Interconnected risks and revenue dynamics of a liquid restaking protocol in decentralized finance.
method Empirical analysis using OLS regression, Granger-causality, and random forest feature importance tests.
result Revenue is primarily driven by value locked in the ecosystem, yield of liquid restaking token, and multi-blockchain expansion.
The paper categorizes four types of scale-up: smart, dumb, forced, and fumbled.
problem Growing ventures in size and maintaining efficiency.
method Identifying modularity and speed as key factors, categorizing four types of scale-up.
result Modularity and speed are crucial for successful scale-up.
Auto.gov uses RL to automate DeFi governance, improving security and profitability.
problem Manual DeFi governance is prone to human bias and financial risks.
method Auto.gov employs a deep Q-network reinforcement learning strategy for semi-automated parameter adjustments. result Auto.gov outperforms traditional governance methods by at least 14% in terms of protocol profitability. Improved security of smart contracts by classifying them into four categories.
problem Detecting and classifying vulnerabilities in smart contracts efficiently.
method Used AWD-LSTM for multi-class classification, addressing class imbalance.
result Achieved a weighted average Fbeta score of 90.0%.
Future buildings will offer new convenience, comfort, and efficiency possibilities to their residents. Changes will occur to the way people live as technology involves into people's lives and information processing is fully integrated into their daily living activities and objects. The future expectation of smart build…
Study develops smart contract framework for procurement under demand variability.
problem Operational and economic implications of smart contract adoption under moderate uncertainty.
method Multi-supplier model with endogenized adoption costs, supplier readiness, and inventory penalties; analytical and numerical results.
result Partial adoption strategies support moderate demand variability, while excessive digital investment reduces profitability.
A new method streamlines digital payment programming using smart contracts.
problem High costs and security challenges in programming smart contracts for digital payments.
method Transforming digital currencies into token streams and using configurable templates to generate specialized smart contracts.
result Reduces payment programming costs and enhances security, self-enforcement, adaptability, and controllability.
Flashot visualizes Flash Loan attacks in DeFi systems.
problem Lack of standard tools to study Flash Loan attacks in DeFi.
method Proposes Flashot, a prototype to transparently illustrate asset flows.
result Demonstrates the effectiveness of Flashot in studying Flash Loan attacks.
In this paper, we propose a game theoretical adversarial intervention detection mechanism for reliable smart road signs. A future trend in intelligent transportation systems is ``smart road signs" that incorporate smart codes (e.g., visible at infrared) on their surface to provide more detailed information to smart veh…
SMART is an open source web application designed to help data scientists and research teams efficiently build labeled training data sets for supervised machine learning tasks. SMART provides users with an intuitive interface for creating labeled data sets, supports active learning to help reduce the required amount of …
Paper assesses the market value of sharing privacy-protected smart meter data.
problem Value of sharing privacy-protected smart meter data between consumers and load serving entities.
method Discounted differential privacy model, ANN-based load forecasting, optimal procurement problem.
result Significant value in sharing smart meter data while retaining individual consumer privacy.
The 1/3 Financial Rule helps prevent household bankruptcy through balanced spending, savings, and debt repayment.
problem Reducing household bankruptcy risk through effective financial planning.
method Mathematical modeling, game theory, behavioral finance, and technological analysis.
result The 1/3 Financial Rule emerges as a robust solution for supporting household financial stability.
Smart Close-out Netting aims to automate close-out netting processes.
problem Inefficiencies in close-out netting processes for financial institutions.
method Standardisation and automation of legal and regulatory processes using a data-driven framework and controlled natural language.
result Standardisation and automation can improve close-out netting processes for prudentially regulated financial institutions.
Model predicts missing boarding stops in smart card data.
problem Missing boarding stop information in public transport datasets.
method Supervised machine learning with ordinal classification.
result Proposed method significantly outperforms existing imputation methods.
Smart reply systems have been developed for various messaging platforms. In this paper, we introduce Uber's smart reply system: one-click-chat (OCC), which is a key enhanced feature on top of the Uber in-app chat system. It enables driver-partners to quickly respond to rider messages using smart replies. The smart repl…
Paper addresses class imbalance in disk SMART dataset using GANs and genetic algorithms.
problem Class imbalance in disk SMART dataset.
method Data synthesised by multivariate GANs mixed with genetic algorithms.
result Higher disk fault classification prediction accuracy.
Smart contracts are a digital technology with potential but also flaws.
problem Understanding the potential and limitations of smart contracts.
method Exploratory study combining statistics, IT, and law.
result Smart contracts have both idealistic promises and practical challenges.
Deep learning enhances smart fish farming through automated feature extraction.
problem Challenges in obtaining labeled data limit DL applications in aquaculture.
method Application of DL techniques in aquaculture, including live fish identification, species classification, and feeding decision-making.
result Deep learning's ability to automatically extract features is a significant contribution.
SMART combines decision trees and MARS for better regression modeling.
problem High variance in decision trees for continuous relationships, poor performance in MARS for discontinuities.
method SMART uses a decision tree to identify subsets with distinct continuous relationships, then applies MARS to fit these relationships independently.
result SMART improves regression performance over state-of-the-art methods in capturing discontinuities and continuous relationships.
One of the main issues affecting the Italian NHS is the healthcare deficit: according to current agreements between the Italian State and its Regions, public funding of regional NHS is now limited to the amount of regional deficit and is subject to previous assessment of strict adherence to constraint on regional healt…
We propose a new model of minority game with so-called smart agents such that the standard deviation and the total loss in this model reach the theoretical minimum values in the limit of long time. The smart agents use trail and error method to make a choice but bring global optimization to the system, which suggests t…
In this note we describe the application of existing smart contract technologies with the aim to construct a new digital representation of a financial derivative contract. We compare several existing DLT based technologies. We provide a detailed description of two separate prototypes which are able to be executed on a …