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Technical articles from the ARIA Analyst team on how deterministic AI actually scores investments, where Monte Carlo simulation helps and where it breaks, why walk-forward backtesting is the only honest validation, and the math behind the rest of modern quantitative finance.
How the EU AI Act actually applies to AI investment platforms — what is high-risk under Annex III, what stays under MiFID II, and what operators must document.
San Francisco, May 25, 2026. The two largest companies in the S&P 500 by market capitalization are once again trading places at the top, and the gap between them is now small enough that a single earnings cycle can flip the ranking....
On May 22, JPMorgan lifted its price target on Nvidia (NVDA) following the company's fiscal Q1 print, which delivered $44.1 billion in revenue against a consensus near $43.3 billion and guided the July quarter to roughly $45 billion...
Two queries, same ticker, 5 minutes apart. If the score changes meaningfully, you are reading sampled output, not analysis. Educational research from an analysis tool.
Mean-variance optimization concentrates portfolios in three stocks and one currency. Black-Litterman fixes the inputs, not the optimizer, and the result is a portfolio you would actually hold.
When XGBoost says "73% probability," that number is not really a probability. It is an unnormalized score that happens to live between 0 and 1. Isotonic regression turns it into a real probability, here is how.
Markets are not stationary. A model that ignores regime structure trades a single strategy in all conditions and gets handed back its bones in the wrong one. HMMs are the cleanest mathematical answer.
Standard k-fold cross-validation works beautifully for cats vs. dogs and breaks badly for stocks vs. bonds. The problem is overlapping labels, and the fix has a name.
Two random walks are random walks. Two cointegrated random walks form a spread that mean-reverts. That spread is the basis of every statistical arbitrage strategy that has ever made money.
The Kelly criterion is mathematically optimal for maximizing long-run wealth growth under known probabilities. Equities violate that assumption in several specific ways. This article explains which ones and why they matter.
Value at Risk is the most widely-used risk metric in finance and one of the most widely-misunderstood. Here is what it actually measures, where it breaks, and what to use instead.
Trying to use the same strategy across all market conditions is the surest way to underperform in two of them. Regime detection is the routing layer that fixes this, and there is more than one way to do it.
Two LLMs walk into a bar, one is long, one is short, and the third one judges them. This is not a joke. It is one of the most useful techniques in AI-assisted investing, when implemented correctly.
In a 60/40 portfolio, stocks contribute roughly 90% of the risk despite being 60% of the capital. Risk parity is the allocation framework built around that observation. This article explains what it tries to fix and where it breaks.
Naive backtesting overfits. Walk-forward, PurgedKFold, Deflated Sharpe, and PBO are the four corrections that separate a real backtest from theater.
Rolling-window regression is the duct tape of quant finance. Kalman filters are the proper solution, and they handle slow drift in parameters with mathematical elegance.
If your stock score changes between Tuesday morning and Tuesday afternoon with the same inputs, you do not have a model. You have a chat. Here is why that distinction is load-bearing for serious investing.
Realized volatility today is the best predictor of realized volatility tomorrow. GARCH formalizes this insight into a useful model, and the GARCH(1,1) variant still beats most ML approaches at one-day-ahead forecasting.
Sharpe is the default. Sortino is often better. Calmar is the unsung hero. Picking the wrong risk-adjusted return metric can systematically misjudge strategies, here is how to choose.
Monte Carlo is the closest thing finance has to a crystal ball, except the ball is cloudy on purpose. Done right, it tells you the distribution of outcomes; done wrong, it gives you false confidence in a single number.
Deep learning was supposed to eat finance. It did not. For tabular financial features at the daily frequency, gradient-boosted trees outperform LSTMs reliably and cost a fraction of the training time.
Black-Scholes is wrong in known, exploitable ways. AI models price options more accurately by fitting the volatility surface that classical models pretend does not exist.
Most "AI stock analysis" is a language model rewording sell-side research. Deterministic scoring is a different animal, same inputs, same outputs, every time. Here is how it works.
Model choice matters less than people think. Feature engineering matters more than people think. A well-engineered feature set with a vanilla model beats a poorly-engineered feature set with deep learning every time.
If your backtest excludes the stocks that went to zero, you have just rewritten history in your favor. The effect on apparent returns is large and consistent across decades, and the fix is to use the right database.
Survivorship bias is famous. Look-ahead bias is more insidious, it sneaks in through data preprocessing, restated financials, and feature normalization. The fixes are unglamorous and essential.
Crypto cycles are not equity cycles. Bitcoin halvings, on-chain capitulation, and global liquidity drive different regime structure than VIX-and-yield-curve equity models. Here is what works.
News sentiment is a small but persistent signal. The trick is using it correctly: finance-specific NLP models, careful source weighting, and short half-life decay. Done wrong, it adds noise; done right, it adds 0.05-0.15 Sharpe.
AI does not replace fundamental analysts wholesale. It replaces the boring parts of their job (data aggregation, transcript reading) and augments the interesting parts (judgment, scenario building). The result: better analysts, not fewer.
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