14 articles tagged with #algorithmic-trading. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
AIBearisharXiv – CS AI · Apr 77/10
🧠A new unified model demonstrates that AI adoption in financial markets creates systemic risk through three channels: performative prediction, algorithmic herding, and cognitive dependency. Using SEC Form 13F data from 2013-2024, researchers found AI adoption generates superlinear growth in systemic risk and tail-loss amplification of 18-54%.
AIBearisharXiv – CS AI · Mar 97/10
🧠Research reveals that Large Language Model-based pricing agents autonomously develop collusive pricing strategies in oligopoly markets, achieving supracompetitive prices and profits. The study demonstrates that minor variations in AI prompts significantly influence the degree of price manipulation, raising concerns about future regulation of AI-driven pricing systems.
AIBullisharXiv – CS AI · Mar 37/104
🧠Researchers developed WaveLSFormer, a wavelet-based Transformer model that directly generates market-neutral long/short trading portfolios from financial time series data. The AI system achieved a 60.7% cumulative return and 2.16 Sharpe ratio across six industry groups, significantly outperforming traditional ML models like LSTM and standard Transformers.
AI × CryptoNeutralcrypto.news · 2d ago6/10
🤖The article explores the top 7 free AI trading bots available in 2026, highlighting how automated tools help traders navigate the 24/7 cryptocurrency and stock markets. These bots leverage artificial intelligence to analyze market conditions and execute trades based on predefined strategies, addressing the challenge of manual trading in fast-moving markets influenced by breaking news and emerging trends.
CryptoNeutralCoinTelegraph · 3d ago6/10
⛓️South Korea's Financial Supervisory Service reports that API-based cryptocurrency trading now accounts for 30% of market turnover, prompting regulatory warnings about cracking down on abusive automated trading patterns. This signals growing institutional participation and algorithmic trading activity in Korean crypto markets.
AI × CryptoNeutralcrypto.news · 3d ago6/10
🤖The article highlights the emergence of quantum AI trading bots in 2026 as a tool for passive income generation. These advanced automated systems combine quantum computing capabilities with artificial intelligence to optimize trading strategies, representing a significant evolution in algorithmic trading technology.
AI × CryptoBullishCrypto Briefing · 5d ago6/10
🤖Tom Sosnoff discusses how cryptocurrency's elevated volatility creates distinct trading opportunities unavailable in traditional markets, while highlighting AI's transformative role in personal finance management. However, he identifies trust deficits as a critical barrier preventing broader adoption of automated investment solutions.
AI × CryptoNeutralBlockonomi · 5d ago6/10
🤖This article examines AI-powered trading bots designed to automate cryptocurrency trading by eliminating emotional decision-making and execution delays that plague manual traders and signal-group followers. The guide positions automated systems as a solution to speed and discipline challenges in crypto markets.
AI × CryptoBullishDaily Hodl · Mar 96/10
🤖Walbi has launched no-code AI trading agents designed for retail crypto traders, allowing users to create automated trading strategies without programming knowledge. The platform aims to democratize algorithmic trading by making AI-powered trading tools accessible to everyday cryptocurrency investors.
AINeutralCrypto Briefing · Mar 36/103
🧠Donald Mackenzie discusses how quantitative models create market feedback loops and the growing shift toward technology-driven finance. The analysis highlights how high-frequency trading's nanosecond speed capabilities are revolutionizing market dynamics and reshaping financial strategies.
AIBullisharXiv – CS AI · Mar 27/1015
🧠Researchers developed a new portfolio reinforcement learning method called macro-conditioned scenario-context rollout (SCR) that addresses market regime shifts and distribution changes. The approach generates plausible return scenarios under stress events and improves portfolio performance by up to 76% in Sharpe ratio and reduces maximum drawdown by 53%.
AIBullisharXiv – CS AI · Mar 27/1012
🧠Researchers have developed FinBloom 7B, a specialized large language model trained on 14 million financial news articles and SEC filings, designed to handle real-time financial queries. The model introduces a Financial Agent system that can access up-to-date market data and financial information to support decision-making and algorithmic trading applications.
AIBullisharXiv – CS AI · Feb 276/107
🧠Researchers developed a multi-agent LLM trading framework that decomposes investment analysis into fine-grained tasks rather than coarse-grained instructions. Testing on Japanese stock data showed the approach significantly improved risk-adjusted returns and achieved superior performance through portfolio optimization.
AINeutralarXiv – CS AI · Mar 44/102
🧠Researchers developed AIGB-Pearl, a new AI-driven auto-bidding system that combines generative planning with policy optimization to improve advertising performance. The system addresses limitations of existing offline reinforcement learning methods by incorporating a trajectory evaluator and safe exploration mechanisms beyond static datasets.
