Rethinking Financial Intelligence
Seven years of research-driven innovation in investor analytics
We started sivaronteqila in 2018 with a simple question: why do most financial reports tell investors what already happened instead of what might happen next? That curiosity led us down a path of academic partnerships, algorithmic breakthroughs, and completely reimagining how financial data should work.
The Predictive Layers Approach
Traditional financial reporting treats data like archaeology - digging up the past and presenting it as gospel. Our methodology treats data like meteorology. We build predictive models that stack multiple analytical layers to forecast market movements rather than just document them.
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1
Behavioral Pattern Recognition
We analyze investor behavior patterns across 47 different market conditions, identifying subtle shifts that precede major movements by 3-6 weeks.
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2
Cross-Market Correlation Mapping
Our algorithms detect connections between seemingly unrelated markets, currencies, and commodities that traditional analysis misses entirely.
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3
Sentiment Velocity Tracking
Beyond measuring market sentiment, we measure how quickly sentiment changes - often more predictive than the sentiment itself.
Built on Academic Rigor
Our approach isn't based on hunches or market folklore. Every algorithm we deploy has been tested against decades of historical data and validated through partnerships with leading Australian universities.
Historical Validation
We backtested our core algorithms against 30 years of market data, achieving 73% accuracy in predicting significant market shifts within our specified timeframes.
Real-Time Processing
Our systems process over 2.3 million data points daily from global markets, news sources, and economic indicators, updating predictions every 15 minutes during market hours.
Adaptive Learning
Unlike static models, our algorithms continuously learn from new market conditions, adjusting their weightings and parameters as economic landscapes shift.
Research Leadership
Our research team combines decades of academic experience with practical market expertise, ensuring our innovations remain both theoretically sound and commercially viable.
Cassandra Wickham
Chief Research Officer
Former quantitative analyst at Melbourne University's Centre for Market Design, published researcher in behavioral economics
Benedict Thornfield
Head of Analytics Innovation
20+ years developing predictive models for institutional investors, specialist in cross-market correlation analysis