Financial AI

In the world of financial AI, where speed meets complexity, most systems are either too slow to adapt or too brittle to interpret the nuanced messiness of real-world finance. Enter FinKario, a new system that combines event-enhanced financial knowledge graphs with a graph-aware retrieval strategy — and outperforms both specialized financial LLMs and institutional strategies in real-world backtests. The Retail Investor’s Dilemma While retail traders drown in information overload, professional research reports contain rich insights — but they’re long, unstructured, and hard to parse. Most LLM-based tools don’t fully exploit these reports. They either extract static attributes (e.g., stock ticker, sector, valuation) or respond to isolated queries without contextual awareness. ...

The cryptocurrency market is infamous for its volatility, fragmented data, and narrative-driven swings. While traditional deep learning systems crunch historical charts in search of patterns, they often do so blindly—ignoring the social, regulatory, and macroeconomic tides that move crypto prices. Enter MountainLion, a bold new multi-agent system that doesn’t just react to market signals—it reasons, reflects, and explains. Built on a foundation of specialized large language model (LLM) agents, MountainLion offers an interpretable, adaptive, and genuinely multimodal approach to financial trading. ...

What happens when your AI co-pilot thinks it’s the pilot? In financial decision-making, autonomy isn’t always a virtue. A striking new study titled “Your AI, Not Your View” reveals that even the most advanced Large Language Models (LLMs) may quietly sabotage your investment strategy — not by hallucinating facts, but by overriding your intent with stubborn preferences baked into their training. Hidden Hands Behind the Recommendations The paper introduces a systematic framework to identify and measure confirmation bias in LLMs used for investment analysis. Instead of just summarizing news or spitting out buy/sell signals, the study asks: what if the model already has a favorite? More specifically: ...

The idea of merging language models and financial algorithms isn’t new — but HARLF takes it a step further by embedding them in a hierarchical reinforcement learning (HRL) framework that actually delivers. With a stunning 26% annualized ROI and a Sharpe ratio of 1.2, this isn’t just another LLM-meets-finance paper. It’s a blueprint for how sentiment and structure can be synergistically harnessed. From FinBERT to Fortune: Integrating Text with Tickers Most financial LLM pipelines stop at score generation: classify sentiment and call it a signal. But HARLF builds a full sentiment pipeline using FinBERT, generating monthly sentiment scores from scraped Google News articles for each of 14 assets. These scores aren’t just inputs — they form a complete observation vector that includes: ...

How do we judge whether an AI is thinking like a human—or at least like a financial analyst? A new benchmark, ConDiFi, offers a compelling answer: test not just whether an LLM gets the right answer, but whether it can explore possible ones. That’s because true financial intelligence lies not only in converging on precise conclusions but in diverging into speculative futures. Most benchmarks test convergent thinking: answer selection, chain-of-thought, or multi-hop reasoning. But strategic fields like finance also demand divergent thinking—creative, open-ended scenario modeling that considers fat-tail risks and policy surprises. ConDiFi (short for Convergent-Divergent for Finance) is the first serious attempt to capture both dimensions in one domain-specific benchmark. ...

In financial engineering, pricing exotic options often boils down to estimating one number: the expected payoff under a risk-neutral measure. But what if we’re asking the wrong question? That’s the provocative premise of a recent study by Ahmet Umur Özsoy, who reimagines option pricing as a distributional learning problem, not merely a statistical expectation problem. By combining insights from Distributional Reinforcement Learning (DistRL) with classical option theory, the paper offers a fresh solution to an old problem: how do we properly account for tail risk and payoff uncertainty in path-dependent derivatives like Asian options? ...

What if you could simulate thousands of realistic futures for the market, all conditioned on what’s happening today—and then train an investment strategy on those futures? That’s the central idea behind a bold new approach to portfolio optimization that blends score-based diffusion models with reinforcement learning, and it’s showing results that beat classic benchmarks like the S&P 500 and traditional Markowitz portfolios. ...

In the noisy world of algorithmic trading, volatility is often treated as something to manage or hedge against. But what if it could be a signal generator? Ivan Letteri’s recent paper proposes a novel trading framework that does just that: it treats mid-range volatility not as a nuisance, but as the key to unlocking directional causality between assets. From Volatility to Causality: The 4-Step Pipeline This is not your standard volatility arbitrage. The author introduces a four-stage pipeline that transforms volatility clusters into trading signals: ...

In technical analysis, few concepts are as foundational as support levels — those invisible lines where prices tend to stop falling, bounce back, and spark new rallies. For decades, traders have relied on hand-drawn trendlines, Fibonacci ratios, and moving averages to guess where those turning points might be. But what if the real market structure is too complex, too dynamic, and too subtle for static rules? Enter DeepSupp, a new deep learning architecture that doesn’t guess support zones — it discovers them. By analyzing evolving market correlations through attention mechanisms and clustering latent embeddings, DeepSupp offers a glimpse into a future where support level detection is less of an art, and more of a science. ...

Can a model trained to think like a day trader ever truly understand long-term market moves? Most financial AI systems today seem stuck in the equivalent of high-frequency tunnel vision — obsessed with predicting tomorrow’s returns and blind to the richer patterns that shape actual investment outcomes. A new paper, NGAT: A Node-level Graph Attention Network for Long-term Stock Prediction, proposes a more grounded solution. It redefines the task itself, the architecture behind the prediction, and how we should even build the graphs powering these systems. ...