AN INTELLIGENT FINANCIAL PREDICTION SYSTEM

Step 1

CollectGather data from stock prices, news, calendar events and psychology research

Step 2

AnalyseTransform raw data into sentiment, surprise, psychology and technical signals

Step 3

PredictCombine all signals into price forecasts with confidence ranges and explanations

Step 4

ValidateCheck predictions against actual prices and measure error rates and band hits

Step 5

LearnFeed outcomes back, retrain models and automatically retire underperformers

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Why FIN?

Fin has six pillars

1

Data OverloadThousands of news articles and social signals appear every day. No human can keep up. FIN can processes them all.

2

Psychology InsightsLoss aversion, herding, recency, confirmation, 12 cognitive biases in total drive every prediction. FIN measures them from articles data.

3

Statistical FoundationHistorical prices, trading volumes, and financial fundamentals are analyzed with statistical methods.

4

Machine LearningTrains on past predictions and outcomes to spot patterns that repeat.

5

Self-CorrectionAutomatically adjusts when consistent over- or under-predicting is detected.

6

AI & LLMLarge language models synthesize everything into clear, reasoned predictions you can analyse.

What Does FIN Do?

Gathers information

from stock prices, financial news, social media, corporate announcements, and psychology research

Analyzes everything

to understand market sentiment, detect surprises, and measure investor psychology

Makes predictions

about where a stock's price is heading

Checks its accuracy

after the market closes

Learns from mistakes

to get better over time

Explains Why

each prediction was made with detailed reasoning

All of this happens automatically every day. Your job is to explore the results, tune the system and understand the story behind each prediction.

💡

• Your predictions, your context — These are not general market forecasts
• Every prediction is personalized to your chosen tickers, news feeds, competitor definitions, LLM settings, and scheduling preferences
• You are in control. No two FIN instances are alike

💡

• Optionally, you can define competitor tickers
• Their price performance, valuation and news sentiment is factored in every prediction

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True Multi-Modal Signal Fusion

• Quantitative: OHLCV + RSI, ATR, SMA via market data
• Unstructured: RSS feeds + LLM-graded earnings surprises
• Behavioral: RAG over 12 cognitive biases from psychology research
• Relative: Peer ratios & competitor news impact analysis

Not just price + news — context-aware predictions that understand competitive dynamics.

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Built-in Self-Improvement Loop

• Feature snapshots capture all signals at prediction time
• Post-close accuracy measurement (MAE/MAPE, band hits)
• ML models learn to correct LLM biases over time
• Models versioned, drift-monitored, auto-deactivated

The system learns from its own mistakes — for each ticker and each horizon.

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Competitor Intelligence

• Configure peers per ticker from the dashboard
• Auto-pulls and analyzes competitor news & sentiment
• Enriches surprise detection with relative performance
• Distinguishes sector moves from company-specific alpha

Few tools — personal or professional — automate relative analysis like this.

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Horizon- & Confidence-Aware

• Tailored signal weighting per horizon (short/medium/long)
• Psychology biases weighted differently by time frame
• Confidence bands from volatility × historical accuracy
• Not arbitrary percentages — grounded in real error data

Predictions understand that different factors matter at different time scales.

Why Use FIN?

You want to know...	FIN tells you...
What will the stock be worth tomorrow / next week?	Predicted price with a confidence range
Why does the system think that?	Full reasoning for every prediction
What news is driving the sentiment?	Scored articles with relevance and bias
Are people acting irrationally?	Psychology bias scores (FOMO, herding, etc.)
Was the prediction accurate?	Track record with error percentages
Is the market surprised by something?	Surprise signal detection from news
How does my stock compare to its peers?	Competitor price, valuation, and news sentiment comparison
How's the system running?	Jobs, feeds, schedules overview

The Pipeline: How FIN Works

Every day, FIN runs a 5-stage cycle:

Collect→Analyze→Predict→Validate→Learn

Stage 1: Collect

FIN pulls in data from multiple sources:

• Stock prices — daily trading data (Open, High, Low, Close, Volume)
• News articles — from RSS feeds (Google News, Yahoo Finance, Reddit, etc.)
• Calendar events — earnings dates, dividend dates
• Financial statements — income, balance sheet, cash flow
• Psychology research — academic studies on 12 cognitive biases
• Competitor data — peer stock prices, financial metrics, and news sentiment for relative performance analysis

Stage 2: Analyze

Raw data is transformed into useful signals:

• Sentiment Analysis — every article is scored for how positive/negative it is about the company and the economy
• Surprise Detection — looks for unexpected events (earnings beats/misses, economic surprises)
• Psychology Scoring — measures 12 cognitive biases (FOMO, herding, loss aversion, etc.)
• Technical Indicators — RSI, moving averages, and volume trends from price data

Stage 3: Predict

The system combines all signals to produce predictions:

• Predictions for 1 day, 2 days, and ~10 days ahead
• Each prediction includes a confidence range (low–high expected price)
• The AI writes a detailed explanation for every prediction
• Competitor context is injected when competitors are configured — the model sees peer price performance, valuation comparisons, and competitor news sentiment to distinguish sector-wide moves from company-specific signals
• A MAPE (Mean Absolute Percentage Error) gating check prevents flaky predictions from being shown — if the system's recent MAPE exceeds a predefined threshold, predictions are withheld until accuracy recovers

Stage 4: Validate

After market close, FIN checks its work:

• How far off was the prediction? (error percentage)
• Did the actual price fall within the expected range? (band hit)
• Results are fed back into the learning system

Stage 5: Learn

The system improves over time:

• Every prediction and its outcome are saved
• When enough data accumulates, the system trains itself to spot patterns in its own errors
• The trained ML model is user-specific — it learns from your chosen tickers, RSS feeds, LLM settings, and scheduling preferences
• If performance drops, old models are retired automatically

Quick Start: Using the Dashboard

1

Select a Stock

Choose a ticker (stock symbol) from the top bar. The ticker controls which data you see across every page.

2

Choose Feeds

Select which RSS feeds to collect news from. Each feed brings in articles that are scored for sentiment and relevance.

3

Configure LLM Parameters

Set the AI model, temperature, and other parameters that control how predictions are generated.

4

Update Schedule if Needed

Adjust when the pipeline runs — collection, analysis, and prediction schedules are all configurable.

5

Track Results

Monitor predictions, accuracy, and system health from the dashboard. Check back after market close to see how the system performed.