Algorithmic stablecoins play a critical role in the Decentralized Finance (DeFi) ecosystem by aiming to maintain price stability without relying on traditional collateral reserves. However, these systems are prone to catastrophic failures known as death spirals, leading to irreversible price collapse and systemic instability. Despite increasing attention from both academia and regulators, existing approaches fall short in providing proactive prediction of such destabilizing events-an issue that poses serious risks to the sustainable development of the blockchain financial ecosystem. In this paper, we present SpiralSeer, a novel framework for fine-grained prediction of death spiral risks in algorithmic stablecoins. At its core, SpiralSeer introduces a stage-wise risk model that integrates on-chain user behaviors and off-chain market data, formalizing distinct states in risk evolution. We then develop a LightGBM-based risk detection model capable of detecting vulnerabilities in emerging stablecoins, and incorporates an additive feature attribution mechanism to reveal the most influential factors contributing to risk across different stages. Extensive evaluation demonstrates that SpiralSeer achieves a $27.8 \%$ improvement in precision over state-of-the-art baselines, and can flag early risk stages well before catastrophic decoupling events. By enabling early risk identification, SpiralSeer offers a practical foundation for building more resilient algorithmic stablecoin systems and enhancing risk transparency in DeFi.
This study aims to examine the relationship between Bitcoin trading volume and key technical indicators using data-mining techniques to better understand how trading activity influences momentum and volatility in blockchain markets. The methodology involves analyzing a historical dataset of Bitcoin’s daily trading records from 2018 to 2023, which includes the Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Simple and Exponential Moving Averages (SMA, EMA), and the Average True Range (ATR). Pearson correlation analysis was applied to identify linear associations between trading volume and these technical indicators. The results show significant positive correlations between trading volume and momentum or trend measures such as the 7-day RSI (r = 0.45, p < 0.05), SMA (r = 0.38, p < 0.05), EMA (r = 0.41, p < 0.05), and ATR (r = 0.48, p < 0.05), indicating that higher participation accompanies stronger market momentum and greater price variability. Conversely, the weak and non-significant correlation with MACD (r = –0.12, p = 0.15) suggests that volume has limited influence on lagging trend-reversal signals. The novelty of this study lies in integrating volume-based behavior into technical indicator analysis, extending the traditional volume–price–volatility framework to cryptocurrency markets and providing practical insights for momentum-driven trading strategies and volatility-aware risk management.
I Made Candra Girinata, Budi Styawan, Arwin Wahyu Saputra, M Aidil Arif · 5 authors
ABSTRAK Perkembangan aset kripto yang pesat, khususnya Ethereum, menuntut adanya model prediksi harga yang akurat untuk mendukung strategi investasi dan manajemen risiko. Penelitian ini bertujuan untuk menganalisis dan membandingkan kinerja dua algoritma machine learning ensemble, yaitu Random Forest (RF) dan XGBoost, dalam memprediksi harga harian Ethereum. Dataset historis ETH/USD sebanyak 3.423 observasi dari periode September 2016 hingga Juli 2025 diperoleh dari platform Bitfinex. Setelah melalui tahap pra-pemrosesan data dan rekayasa fitur temporal, dataset dibagi dengan rasio 80:20 untuk pelatihan dan pengujian. Model dievaluasi menggunakan metrik Root Mean Square Error (RMSE) dan Koefisien Determinasi (R²). Hasil eksperimen menunjukkan bahwa XGBoost secara signifikan mengungguli Random Forest, dengan nilai RMSE 134.63 dan R² 0.958. Sebagai perbandingan, Random Forest menghasilkan RMSE 208.45 dan R² 0.899. Temuan ini mengindikasikan bahwa mekanisme boosting pada XGBoost lebih efektif dalam menangkap kompleksitas dan volatilitas data pasar kripto. Kata kunci: Prediksi Harga, Ethereum, Machine Learning, XGBoost, Random Forest.
Cryptocurrency markets are difficult to model due to high volatility and multi-scale dynamics. This study investigates the directional predictability of crypto asset prices across multiple forecast horizons using Support Vector Machines (SVM). A daily Ethereum dataset (2018-2025), comprising candlesticks, technical indicators, and sentiment data, is used to predict upward or downward price movements from one to thirty days ahead. Model interpretability is achieved through SHAP, a popular XAI methodology, which quantifies feature contributions across various horizons. Results show that short-term forecasts approach random performance, while accuracy rises steadily with horizon length, peaking near 70% around the 24-day horizon. SHAP analysis reveals that short horizons rely on fast-reacting momentum indicators, whereas longer horizons emphasize slower, trend-following features. These findings highlight that medium-term price movements contain more structured information and demonstrate how explainable machine learning can uncover horizon-dependent dynamics in digital asset markets.
Blockchain-based financial ecosystems generate unprecedented volumes of multi-temporal data streams requiring sophisticated analytical frameworks that leverage both on-chain transaction patterns and off-chain market microstructure dynamics. This study presents an empirical evaluation of a two-class confidence-threshold framework for cryptocurrency direction prediction, systematically integrating macro momentum indicators with microstructure dynamics through unified feature engineering. Building on established selective classification principles, the framework separates directional prediction from execution decisions through confidence-based thresholds, enabling explicit optimization of precision–recall trade-offs for decentralized financial applications. Unlike traditional three-class approaches that simultaneously learn direction and execution timing, our framework uses post-hoc confidence thresholds to separate these decisions. This enables systematic optimization of the accuracy-coverage trade-off for blockchain-integrated trading systems. We conduct comprehensive experiments across 11 major cryptocurrency pairs representing diverse blockchain protocols, evaluating prediction horizons from 10 to 600 min, deadband thresholds from 2 to 20 basis points, and confidence levels of 0.6 and 0.8. The experimental design employs rigorous temporal validation with symbol-wise splitting to prevent data leakage while maintaining realistic conditions for blockchain-integrated trading systems. High confidence regimes achieve peak profits of 167.64 basis points per trade with directional accuracies of 82–95% on executed trades, suggesting potential applicability for automated decentralized finance (DeFi) protocols and smart contract-based trading strategies on similar liquid cryptocurrency pairs. The systematic parameter optimization reveals fundamental trade-offs between trading frequency and signal quality in blockchain financial ecosystems, with high confidence strategies reducing median coverage while substantially improving per-trade profitability suitable for gas-optimized on-chain execution.
Blockchain technology, combined with smart contracts, serves as a revolutionary force in financial operations by enabling automated, transparent, and tamper-proof transaction execution. The conventional stock market infrastructure faces operational shortcomings due to multiple intermediaries, slow settlement times, and security weaknesses. The integration of deep learning models with blockchain-based smart contracts demonstrates emerging potential to enhance security while delivering precise and efficient financial ecosystem operations. The review investigates how artificial intelligence collaborates with distributed ledger technologies through adaptive deep temporal models as well as consensus optimization and secure contract execution on private Ethereum consortium blockchains. This study examines two advanced financial approaches by evaluating their performance as the Adaptive Deep Temporal Context Network (ADTCN) and the Dynamic Butterfly-Billiards Optimization Algorithm (DB-BOA). Important obstacles related to system expansion, together with system compatibility and model visibility, and on-time deployment, are examined. Future directions for creating intelligent, secure, efficient blockchain-based financial systems are established through the identification of present research gaps.
Abstract This study evaluates the predictive accuracy of traditional time series (TS) models versus machine learning (ML) methods in forecasting realized volatility across major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), and Ripple (XRP). Employing high-frequency data, we analyze cross-cryptocurrency volatility dynamics through two complementary approaches: volatility forecasting and connectedness analysis. Our findings reveal three key insights: (i) TS models, particularly the heterogeneous autoregressive (HAR) model, exhibit superior predictive performance over their ML counterparts, with the long short-term memory (LSTM) model providing competitive yet inconsistent results due to overfitting and short-term volatility challenges; (ii) including lagged realized volatility of large-cap coins improves predictive accuracy for mid-cap coins, especially XRP, whereas forecasts for large-cap coins remain stable, indicating more resilient volatility patterns; and (iii) volatility connectedness analysis reveals substantial spillover effects, particularly pronounced during market turmoil, with large-cap assets (BTC and ETH) acting as primary volatility transmitters and mid-cap assets (XRP and LTC) serving as volatility receivers. These results contribute to the understanding of volatility forecasting and risk management in cryptocurrency markets, offering implications for investors and policymakers in managing market risk and interdependencies in digital asset portfolios.
Behavioral finance has emerged as a critical framework for understanding market dynamics beyond traditional rational agent models. This research presents a comprehensive multimodal approach to behavioral finance analysis, integrating market data, macroeconomic indicators, news sentiment, cryptocurrency metrics, Web3 analytics, GitHub development activity, and social sentiment to test five advanced hypotheses regarding behavioral pattern identification and market anomaly detection. The study employs an ultra-comprehensive data pipeline processing 30,400 samples across seven distinct data sources, generating 91 engineered features representing behavioral biases, investment patterns, and market psychology. Advanced machine learning techniques including Principal Component Analysis, t-Distributed Stochastic Neighbor Embedding, Variational Autoencoders, K-Means, Hierarchical Clustering, DBSCAN, Isolation Forest, One-Class SVM, and Elliptic Envelope are applied to identify behavioral structures and detect anomalies. Statistical validation through chi-square tests, ANOVA, Granger causality analysis, and lagged correlation studies demonstrates that three of five hypotheses (60%) achieve statistical significance at p < 0.05. Key findings reveal that behavioral structures exist and correspond to canonical biases (chi-square = 3406.780, p < 0.001), cluster assignments maintain moderate stability across market regimes (Jaccard similarity = 0.300), and sentiment and macroeconomic factors exhibit 65 significant causal relationships with behavioral patterns. However, multimodal data integration does not uniformly improve clustering quality (Silhouette score decrease of 0.116), and cluster-conditioned anomaly detection fails to outperform global methods (F1-score decrease of 0.017). These findings contribute to behavioral finance theory while providing practical applications for investment management, fraud detection, and regulatory compliance.
Mohd Shahid Ali, Alam Ahmad, Mohd Atif, Monika Mittal · 5 authors
Decentralized exchanges (DEXs) are one of the keystones of decentralized finance (DeFi). Instead of booking the order under centralized system, you have straight peer-to-peer trades via Automated Market Makers (AMM). AMMs like Uniswap and Curve have actually been developed to reduce the friction of liquidity provisioning. Nevertheless, they still experience impermanent loss, deadweight loss, compartmentalization of market liquidity, in addition to suboptimal operation in volatile environments. This paper explains an RL-based algorithm that can regulate liquidity and flexible market-making in DEXs. RL agents has been trained to maximize capital allowance, liquidity rebalancing, and spread adjusting in live trading information from SushiSwap and Uniswap in addition to synthetically created cardiovascular test. DQN, PPO, and A3C are three RL algorithms that we have actually contrasted versus constant-product AMM standards. With risk-adjusted returns of as much as 1.6 vs. 0.9, an impermanent loss reduction of 15-20%, and test-set revenues of 12.5 -15.7% vs. 8, it seems that RL-poured method is considerably much better. The stability and scalability of RL models under different swimming pool dimensions and volatility regimes are further made certain by level of sensitivity analysis. Actually, PPO is the most effective in high-volatility circumstances, DQN assembles more quickly in moderate scenarios, and A3C offers a trade-off. Our results open up the design of flexible monetary AI systems and are right away appropriate to enhancing liquidity rewards, stability, and performance in DeFi. The result of the experiment shows that RL can be made use of to improve the rationality of liquidity administration in DEXs. The integration of administration systems right into multi-agent RL and the gas-efficient migration of these algorithms from off-chain to on-chain ought to be the primary tasks of future research study.
This paper examines factors that influence prices of most common five cryptocurrencies such as Bitcoin, Ethereum, Dash, Litecoin, and Monero over 2010-2018 using weekly data. The study employs ARDL technique and documents several findings. First, cryptomarket-related factors such as market beta, trading volume, and volatility appear to be significant determinant for all five cryptocurrencies both in short- and long-run. Second, attractiveness of cryptocurrencies also matters in terms of their price determination, but only in long-run. This indicates that formation (recognition) of the attractiveness of cryptocurrencies are subjected to time factor. In other words, it travels slowly within the market. Third, SP500 index seems to have weak positive long-run impact on Bitcoin, Ethereum, and Litcoin, while its sign turns to negative losing significance in short-run, except Bitcoin that generates an estimate of -0.20 at 10% significance level. Lastly, error-correction models for Bitcoin, Etherem, Dash, Litcoin, and Monero show that cointegrated series cannot drift too far apart, and converge to a long-run equilibrium at a speed of 23.68%, 12.76%, 10.20%, 22.91%, and 14.27% respectively.
This study compares classical, deep learning, and hybrid approaches for cryptocurrency price forecasting across Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), and Cardano (ADA) using daily data from 15 September 2022 to 15 September 2025. We implement an ARIMA baseline, a stacked LSTM network, and an inverse-error-weighted ARIMA- LSTM ensemble. Feature engineering includes trend, momentum, volatility, and volume indicators; models are evaluated with expanding walk-forward validation and multiple metrics (R2, RMSE, MAE, MAPE, sMAPE, MASE). Statistical significance is assessed via Diebold-Mariano tests. Results indicate that ARIMA consistently outperforms LSTM across all assets, with average performance of R2= 0.924 ± 0.051, MAPE = 2.19 ± 0.81%, and MASE = 0.95 ± 0.30, compared with LSTM’s R2= 0.527 ± 0.709, MAPE = 4.36 ± 0.26%, and MASE = 1.91 ± 1.25. The ensemble attains R2= 0.902 ± 0.084 and MAPE = 2.36 ± 0.58%, retaining ~97% of ARIMA’s explanatory power while reducing volatility relative to LSTM. Asset-specific analyses show strong ARIMA performance for ETH (R2= 0.972 ± 0.038) and BNB (R2= 0.964 ± 0.045), and LSTM failure on BTC (R2= -0.534 ± 0.214). Cross-asset dispersion in R2is markedly lower for ARIMA than for LSTM, indicating superior generalization. DM tests confirm ARIMA’s advantage over LSTM (p < 0.001) across assets, with ARIMA versus ensemble differences nonsignificant for BNB. Under rigorous out-of-sample evaluation, the parsimonious ARIMA model provides the most accurate and stable forecasts, while the ensemble offers a robust alternative when cross-asset stability is prioritized.
Behavioral finance explores the psychological influences and cognitive biases that affect investor behavior and financial decision-making, including herding, the disposition effect, overconfidence, and others. Algorithmic trading is a method that uses computer programs to automatically execute buy and sell orders based on predefined mathematical models and trading strategies. With the continuous development of modern technology, the advent of the Web3 era, and the gradual evolution of artificial intelligence, algorithmic trading is becoming increasingly prevalent and garnering significant attention. While algorithmic trading is automated and may seem immune to human cognitive biases, the opposite is often true. This study aims to review the main findings of existing research from the perspective of the stock market, exploring the interactive relationship between behavioral finance and algorithmic trading and how cognitive biases such as herding and the disposition effect can influence algorithm performance. The results emphasize the importance of behavioral finance in both the research and practice of algorithmic trading, while also proposing the potential for using machine learning techniques to advance the field of behavioral finance. By integrating existing theories, this study contributes to a deeper understanding of the relationship between behavioral finance and algorithmic trading and offers new perspectives for its future development.
Vera Mita Nia, Hermanto Siregar, Roy Sembel, Nimmi Zulbainarni
This study explores how surprise shocks in Indonesia’s macroeconomic environment—specifically interest rates, inflation, and exchange rates—affect the returns and volatility of key financial assets, including gold, Bitcoin (BTC), stocks (JKSE), and government bonds. Utilizing the EGARCH(1,1) model, this research demonstrates that gold exhibits enduring resilience as a safe-haven during periods of rising inflation and interest rate fluctuations. In contrast, Bitcoin is marked by pronounced speculative dynamics, showing persistent, asymmetric, and extreme volatility, yet delivering attractive gains when market conditions are strong. The findings indicate that stocks and bonds are particularly susceptible to changes in macroeconomic variables, thereby illustrating the vulnerabilities typical of emerging markets. Through portfolio optimization employing the Mean-Variance approach, gold dominates the optimal asset allocation, while Bitcoin provides notable diversification benefits. The results of backtesting using the Kupiec and Basel Traffic Light procedures confirm that GARCH-family risk estimations are robust and meet international regulatory standards. Furthermore, analysis of the Sharpe ratio and cumulative returns reveals that Mean-Variance portfolios consistently outperform equally weighted alternatives by delivering higher risk-adjusted returns and lower overall volatility. By integrating advanced econometric methods with real-world macroeconomic shocks in an Indonesian context, this research offers practical insights for both investors and policymakers addressing asset allocation under uncertainty, while laying the groundwork for future work involving broader asset universes and sophisticated modeling techniques.
Large Language Models (LLMs) are increasingly deployed in high-stakes financial domains, yet they suffer from specific, reproducible hallucinations when performing arithmetic operations. Current mitigation strategies often treat the model as a black box. In this work, we propose a mechanistic approach to intrinsic hallucination detection. By applying Causal Tracing to the GPT-2 XL architecture on the ConvFinQA benchmark, we identify a dual-stage mechanism for arithmetic reasoning: a distributed computational scratchpad in middle layers (L12-L30) and a decisive aggregation circuit in late layers (specifically Layer 46). We verify this mechanism via an ablation study, demonstrating that suppressing Layer 46 reduces the model's confidence in hallucinatory outputs by 81.8%. Furthermore, we demonstrate that a linear probe trained on this layer generalizes to unseen financial topics with 98% accuracy, suggesting a universal geometry of arithmetic deception.
This Master's thesis investigates the application of machine learning methods to cryptocurrency market prediction and the development of hybrid trading strategies that combine predictive signals with decentralized finance yield components. The study addresses how machine learning models can predict directional shifts in cryptocurrency markets and whether integrating DeFi yield elements can improve risk-adjusted portfolio returns compared to traditional buy-and-hold approaches. The empirical investigation examined multiple machine learning architectures for binary directional forecasting of Bitcoin price movements. Models were trained on data spanning January 2018 to August 2024 using walk-forward validation. LightGBMRegressor achieved 53 % directional accuracy, while Random Forest reached 52 % accuracy. Other tested models, including LSTM networks and MLP, performed within the 51-56 % accuracy range. These results indicate that while machine learning methods demonstrate potential for market direction prediction when combined with properly formatted datasets and appropriate technical indicators, achieving high prediction accuracy remains challenging. A composed trading strategy was developed that integrated LSTM predictions with real-world DeFi yield rates from liquidity pools. The strategy utilized actual yield data to provide realistic performance assessment. Despite modest directional prediction accuracy of 53 %, the hybrid approach reduced drawdown by 50 % compared to the benchmark buy-and-hold strategy. The DeFi yield component compensated for imperfect directional signals, demonstrating that yield-enhanced strategies can achieve adequate risk-adjusted returns even without superior prediction accuracy. The study also examined structural differences between decentralized and traditional financial systems. DeFi offers global accessibility, programmable infrastructure, and fast settlement, but faces challenges including security vulnerabilities and regulatory uncertainty. However, the primary contribution lies in demonstrating that hybrid strategies combining machine learning signals with DeFi yield mechanisms represent a viable approach to portfolio management, when effective risk management is implemented.
S Thillainayagi, Paolo Pavan, S Shashank, L V Preetham · 5 authors
Bitcoin is known for its high volatility and speculative trading behavior.Predicting Bitcoin prices is valuable for investors, traders, and financial analysts.The study uses historical price data, technical indicators, and/or sentiment analysis.Machine learning and statistical models like ARIMA, Linear Regression, and LSTM are applied.Deep learning models, especially LSTM, show better accuracy in capturing time-series patterns
In this project, our goal is to employ machinelearning algorithms and big data analysis for forecasting and examining cryptocurrency prices. Cryptocurrencies are digital assets created to function as a means of exchange, utilizing cryptography to ensure transaction security and govern the generation of additional currency units. A key characteristic of cryptocurrencies is their decentralized nature, as they are not issued by any central authority, theoretically making them resistant to government interference or manipulation. Decentralized cryptocurrencies are generated collectively by the entire cryptocurrency system at a predefined rate established during the system’s creation, which is publicly accessible. Most cryptocurrencies utilize blockchain technology in their operations. A blockchain is an ever-expanding ledger of records, referred to as blocks, that are interconnected and safeguarded through cryptographic methods. Each block typically contains a hash pointer linking it to a prior block, a timestamp, and transaction data. By design, blockchains are highly resistant to data tampering. Blockchains serve as open, distributed ledgers capable of efficiently and verifiably recording transactions between two parties in a permanent manner.
This paper investigates the role of artificial intelligence (AI) stocks and AI cryptocurrencies in portfolio diversification, reflecting on the rising interest in technology-oriented assets. While much research has focused on the diversification, hedging, and safe-haven properties of digital assets, such as Bitcoin and Ethereum, this study focuses on whether AI cryptocurrencies and AI stocks provide untapped diversification potential. Using mean-variance, risk parity, and higher-order moments approaches, we construct portfolios that combine AI stocks, AI cryptocurrencies, and traditional assets under various optimization frameworks. The findings reveal that the mean-variance framework is more conservative in allocating to AI cryptocurrencies, while the higher-order moments approach accommodates for greater flexibility. Seemingly, investors may benefit from expanding their asset pool to incorporate AI stocks and AI cryptocurrencies. Across most portfolio settings, gold and commodities dominate allocations, followed by AI stocks, with AI cryptocurrencies receiving only marginal weights owing to their high volatility. However, allocations to AI cryptocurrencies increase as investor risk tolerance increases, thereby highlighting their potential for risk-seeking portfolios. Overall, the results indicate that AI stocks and AI cryptocurrencies can enhance portfolio diversification and improve risk-return outcomes. These results offer valuable insights for investors seeking to optimize their portfolios, through exposure to emerging technology-driven assets while balancing traditional risk considerations. • The study explores the diversification potential of AI Stocks and AI Cryptocurrencies to a traditional portfolio. • Dominated by NVIDIA and Tesla, AI stocks perform better than AI cryptocurrencies. • AI cryptocurrencies offer limited diversification benefits while significantly increasing portfolio risk. • Unlike AI stocks, AI cryptocurrencies are not dominated by a single player in portfolio diversification. • Allocation to AI cryptocurrencies is highly sensitive to investor risk aversion, particularly driven by their explosive market behaviour.