Kavout

Kavout applies machine learning to quantitative investment research — an application that institutional hedge funds have used internally for years but that retail and independent investors have historically been unable to access. The platform's K Score ranks stocks using ML models trained on 200+ factors including financial metrics, technical patterns, news sentiment, analyst revision momentum, and alternative data signals. For individual investors and independent research analysts who want quant-based investment signals without building their own ML infrastructure, Kavout provides institutional-grade quantitative analysis in an accessible platform.

K Score Quantitative Ranking

The K Score is Kavout's ML-generated ranking for each stock on a 1-9 scale, where 9 indicates the highest probability of outperformance based on the multi-factor model. The score updates daily as new data inputs (earnings, news, price action, fundamental revisions) are processed. Backtested analysis shows high-K-Score stocks have historically outperformed low-K-Score stocks, though past performance of quantitative factors doesn't guarantee future results. The score provides a starting point for investment research — a factor-model-generated view of which stocks are exhibiting the combination of characteristics that have historically indicated outperformance.

• K Score ranking — ML-generated daily stock ranking (1-9) based on 200+ factors including fundamentals, technicals, and sentiment.
• Factor analysis — breakdowns showing which specific factors are driving each stock's K Score for transparent signal attribution.
• News sentiment analysis — NLP-powered news and social media sentiment scoring integrated into stock analysis.
• Portfolio analysis — ML-based portfolio risk analysis, factor exposure measurement, and optimization suggestions.
• Stock screener — filter 7,000+ US equities by K Score, fundamental metrics, technical patterns, and factor exposures.

Alternative Data Integration

Kavout's K Score incorporates alternative data sources beyond traditional financial metrics — satellite imagery data for retail traffic analysis, credit card transaction trends for consumer spending signals, social media sentiment for brand and consumer perception shifts. These alternative data inputs are the types of signals that institutional hedge funds pay millions annually to access through specialized data providers. Kavout's aggregation of multiple alternative data signals into a single quantitative score makes this institutional-grade data analysis accessible at individual investor pricing — a democratization of quantitative investment research that was previously impossible outside of large asset managers.

Investment Research Workflow

Kavout integrates into a research workflow rather than replacing analyst judgment. The K Score identifies which stocks in a universe are exhibiting quantitatively favorable signals — a screen that narrows 7,000 equities to a manageable research list. The factor breakdown explains why a stock scores highly — is it fundamental momentum, sentiment improvement, or technical pattern signals? This attribution guides where to focus research effort. The platform's portfolio analysis tools measure factor exposure in existing holdings, identifying unintended concentration in factors that have recently underperformed.

Start at kavout.com.