What is Kavout?
Kavout is an AI-powered stock ranking and screening platform that uses machine learning to rate thousands of US stocks on a scale from 1 to 9 — the proprietary "K Score." Founded in 2015 by a team of data scientists and finance professionals including former Google and Microsoft engineers, Kavout was one of the first fintech companies to apply deep learning to equity selection. The platform's core thesis is that human analysts cannot possibly process the vast amount of data — price history, fundamentals, alternatives data, sentiment signals — that influences stock performance for thousands of companies simultaneously, but machine learning models can. The K Score is a composite rating generated by AI models trained on decades of market data, incorporating hundreds of factors across multiple categories: value (P/E, P/B, P/S, EV/EBITDA, dividend yield), momentum (price momentum, earnings momentum, relative strength), quality (ROE, ROA, profit margins, debt-to-equity, earnings stability), growth (revenue growth, earnings growth, cash flow growth), sentiment (news sentiment, analyst revisions, social media signals), and technical factors (moving averages, RSI, volume patterns, volatility). The AI assigns each stock a score from 1 (strongest sell signal) to 9 (strongest buy signal), updated daily. Stocks rated 8-9 have historically outperformed the market by significant margins in Kavout's backtests, while stocks rated 1-2 have underperformed. The platform serves individual traders, financial advisors, and institutional investors — with tiered access ranging from basic screening to full API access for quantitative integration.
Key Features
K Score — AI Stock Rating System
The K Score is the centerpiece of Kavout's platform and the feature that distinguishes it from traditional stock screeners. Every trading day, Kavout's machine learning models process data on approximately 5,000 US-listed stocks and assign each a K Score from 1 to 9. The models are trained on 20+ years of market data and are continuously retrained as new data becomes available. The K Score is not a single model — it is an ensemble of multiple specialized models, each focused on different prediction horizons and investment styles: K Score (Momentum) — optimized for short-term performance (1-3 month horizon), weighing technical and momentum factors most heavily, suitable for active traders. K Score (Value) — optimized for longer-term value investing (6-12 month horizon), weighing fundamental factors most heavily, suitable for position traders and investors. K Score (Growth) — optimized for identifying companies with accelerating fundamentals, weighing growth and quality factors. K Score (Composite) — the overall rating, an ensemble of all sub-models, designed for general stock selection across styles. In Kavout's published backtests, portfolios of stocks rated K Score 8-9 have historically outperformed the S&P 500 by 8-12% annually (gross of fees and transaction costs), while stocks rated 1-2 have underperformed by similar margins. The score distribution across the market is roughly normal — approximately 10% of stocks get a 9, 10% get a 1, and the remaining 80% distribute across scores 2-8. The platform provides transparency into why a stock received its score: users can see the contribution of each factor category (momentum, value, quality, growth, sentiment, technical) to the overall rating, allowing them to understand the AI's reasoning rather than treating it as a black box.
AI-Powered Stock Screener
Kavout's stock screener combines traditional screening criteria with AI-powered ranking to help traders find stocks that match their strategy. Users can screen on: K Score (filter by score range — e.g., only show stocks with K Score 7+), traditional fundamental criteria (market cap, sector, P/E ratio, revenue growth, profit margins, debt levels, dividend yield), technical criteria (price vs. moving averages, RSI levels, volume patterns, volatility), and factor exposure (tilt toward momentum, value, quality, or growth — e.g., "show me high K Score stocks that are also undervalued on traditional metrics"). The screener returns ranked results — not just a list of stocks that meet the criteria, but a list ordered by the AI's conviction in each stock's potential. Users can save screens, set up alerts (notified when stocks enter or exit a screen), and export results. What differentiates Kavout's screener from traditional screeners (Finviz, TradingView, Stock Rover) is the AI ranking layer: a traditional screener shows you all stocks with a P/E below 15 and revenue growth above 20% — Kavout shows you which of those stocks the AI believes are most likely to outperform. For traders who use screening as a first step in their research process — narrowing thousands of stocks to a watchlist of 20-50 — the AI ranking adds a second layer of curation that traditional screeners lack.
How Traders Use Kavout in Practice
Kavout users employ the platform in several distinct ways, depending on their trading style. The K Score Filter Approach: The most common workflow — run a screen for stocks with K Score 7+, then apply your own fundamental and technical analysis to the resulting list. This combines the AI's breadth (scanning 5,000 stocks for signals you might miss) with your depth (applying domain expertise to the specific stocks the AI surfaces). A trader who specializes in technology stocks might screen for K Score 8+ tech companies and then do deep due diligence on the top 10 — using the AI to narrow the universe and their expertise to make the final selections. Systematic Portfolio Construction: Some quantitative-minded users build rules-based portfolios using the K Score as a primary factor. For example: buy the top 20 K Score 9 stocks, equal-weighted, rebalanced monthly — running this as a systematic strategy with minimal discretion. Kavout's backtesting tools allow users to test this approach before committing capital. Published backtests show that K Score 9 portfolios have historically generated significant alpha, though — as with all backtests — past performance does not guarantee future results. Factor Rotation Strategy: Advanced users employ Kavout's sub-models (Momentum, Value, Growth) strategically — tilting toward the Momentum model in trending markets, the Value model in mean-reverting markets, and the Growth model when growth stocks are in favor. This factor rotation approach requires market judgment but can enhance returns compared to using the Composite K Score alone. Contrarian Warning System: Some traders use Kavout inversely — when the AI gives a stock a very high K Score (8-9) but the trader's own analysis disagrees, it signals a potential crowded trade or AI blind spot worth investigating. Similarly, a low K Score (1-2) on a stock the trader likes may reveal risks the trader has overlooked. In this usage, Kavout functions as a second opinion — not dictating decisions but surfacing data points the trader should consider.
Kavout Pricing Details: Kavout offers three plans. The Basic plan ($49/month) provides K Scores, basic screening, and end-of-day data — sufficient for swing traders and position traders who make decisions after market close. The Pro plan ($99/month) adds real-time data, advanced screening, factor analysis, paper trading, and alerts — designed for active traders who need intraday signals and portfolio simulation. The Enterprise plan ($199/month) provides API access, custom model training, dedicated support, and institutional-grade analytics for quantitative traders and financial advisors managing client portfolios. All plans include US stock coverage (NYSE, NASDAQ, AMEX). Kavout does not offer a free tier, but provides a 14-day trial of the Pro plan. Annual billing provides a 15-20% discount on all plans.
Additional Features
Paper Trading & Portfolio Simulation
Kavout includes a paper trading module that allows users to test strategies using the K Score without risking real capital. Users can: build a virtual portfolio by selecting stocks based on K Score criteria — e.g., "Buy the top 10 K Score 9 stocks in the technology sector, equal-weighted, rebalanced monthly," simulate the portfolio's performance over historical periods to see how the strategy would have performed, run the portfolio in real-time with virtual capital — tracking daily performance, trade history, and key metrics (Sharpe ratio, drawdown, win rate), and compare performance against benchmarks (S&P 500, sector ETFs). For traders who want to build confidence in the K Score before committing real money — or who want to compare different K Score-based strategies (Momentum vs. Value tilt, top 10 vs. top 20, monthly vs. quarterly rebalancing) — paper trading provides a risk-free environment for strategy development and validation.
Factor Analysis & Model Transparency
Kavout provides detailed factor exposure analysis for every stock. Users can see a stock's score on each of the six factor categories that compose the K Score: Value, Momentum, Quality, Growth, Sentiment, and Technical. Each factor score ranges from 1-9, showing which factors are driving the overall K Score up or down. For example, a stock with a K Score of 8 might have a Value score of 3 (not cheap), a Momentum score of 9 (strong price and earnings momentum), a Quality score of 7 (solid fundamentals), a Growth score of 6 (moderate growth), a Sentiment score of 8 (positive analyst and news sentiment), and a Technical score of 7 (bullish technical setup). This transparency allows traders to: validate that the AI's reasoning aligns with their own assessment, identify disagreements (the AI is bullish on momentum but the trader sees the momentum as overextended), and screen for specific factor profiles — e.g., "Show me stocks with K Score 7+ that also score 7+ on Value" — combining AI ranking with traditional factor preferences. The factor transparency is what separates Kavout from black-box AI trading platforms — you can see why the AI rated a stock the way it did, allowing for informed human override when needed.
Kavout vs Traditional Stock Screeners
Traditional stock screeners (Finviz, Stock Rover, Yahoo Finance) are rule-based — the user defines criteria (P/E < 15, revenue growth > 20%, market cap > $1B) and the screener returns all stocks matching those criteria. The output is unsorted — every stock that meets the criteria appears equally. Kavout adds a second layer: AI ranking. For the same screening criteria, Kavout returns the list sorted by K Score — showing not just which stocks meet the criteria, but which ones the AI believes are most likely to outperform. This ranking layer transforms the screening workflow: instead of getting 200 stocks matching your criteria and trying to research all of them, you get 200 stocks ranked by AI conviction and can focus your research on the top 20-30. For traders who use screening as the first step in their research process, this AI ranking can save hours per week by directing attention to the highest-probability candidates first. However, Kavout is not a replacement for a traditional screener — it works best as a complement. A recommended workflow: use a traditional screener to define your universe based on your specific criteria, then export the results to Kavout (or use Kavout's built-in screener) to rank them by AI conviction. The combination of human-defined criteria and AI-driven ranking provides a more sophisticated filtering and prioritization system than either approach alone.
Pros & Cons
Pros
- K Score provides a simple, actionable AI signal: The 1-9 rating is intuitive — no need to interpret complex model outputs. A stock rated 8 or 9 is AI-recommended; a stock rated 1 or 2 is AI-discouraged. This simplicity enables traders to integrate AI into their process without a quantitative background.
- Factor transparency builds trust in the AI: Unlike black-box AI platforms that output a "buy" or "sell" without explanation, Kavout shows which factors drove the score. This transparency allows traders to validate the AI's reasoning against their own judgment.
- Ensemble approach reduces model risk: Using multiple specialized models (momentum, value, growth, composite) rather than a single model provides diversification of AI approaches — reducing the risk that one model's bias or blind spot leads to poor recommendations.
- Daily updates keep signals current: K Scores are recalculated every trading day based on the latest data, ensuring the AI's recommendations reflect current market conditions rather than stale information.
Cons
- Historical outperformance does not guarantee future results: Kavout's backtests show impressive historical performance, but markets change. The AI models that worked well in the bull market of 2010-2021 may behave differently in different market regimes.
- US stocks only — no international, options, or crypto: Kavout covers US-listed equities only. Traders interested in international markets, options strategies, or crypto assets will need other platforms.
- Pricing can escalate for active features: While the basic plan ($49/month) provides K Scores and screening, features like paper trading, real-time data, and API access require higher-tier plans ($99-$199/month). The value proposition diminishes at higher price points compared to platforms with richer feature sets.
FAQ
How should I use the K Score in my trading?
Most Kavout users treat the K Score as a screening and filtering layer — using it to identify candidates for further research, not as a standalone buy/sell signal. Common workflows: run a screen for K Score 7+ stocks in sectors you understand, then apply your own fundamental and technical analysis to the resulting list. This combines the AI's breadth (scanning 5,000 stocks for signals you might miss) with your depth (applying domain expertise to the specific stocks the AI surfaces). Some users build systematic portfolios: buy the top 10-20 K Score 9 stocks, equal-weighted, rebalanced monthly — treating the K Score as an alpha factor in a rules-based strategy.
Caveats & Limitations to Understand
While Kavout's K Score is a powerful tool, traders should understand its limitations. Model drift: Machine learning models are trained on historical data and assume the future will resemble the past. When market regimes change — e.g., from a low-interest-rate growth-stock bull market to a high-inflation value-stock market — the factors that historically predicted outperformance may change. Kavout retrains its models regularly, but model drift during regime transitions is a real risk. Factor crowding: When many traders use similar AI-driven strategies, the factors that the AI identifies can become crowded — too much capital chasing the same stocks, reducing or eliminating the alpha. K Score's value depends partly on it not being universally adopted. Survivorship bias in training: While Kavout takes steps to address survivorship bias, any model trained on historical data of companies that still exist necessarily excludes companies that went bankrupt or were delisted. This can make historical backtest performance look better than forward-looking real-world performance. AI is not a substitute for risk management: A K Score of 9 is not a guarantee — it is a statistical signal. Even the highest-confidence AI recommendations will be wrong some percentage of the time. Position sizing, stop losses, and portfolio diversification remain essential regardless of how strong the AI signal appears. These caveats do not diminish the K Score's value as a decision support tool — but they underscore that AI should augment trader judgment, not replace it.
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