The cost and complexity of developing in-house quantitative models and technologies in today’s market is prohibitively expensive for smaller participants.
StratBench bridges this gap by providing an accessible outsourced quant team that delivers the expertise, innovation and tools necessary for success.
Quantitative Modeling & Research
Portfolio Optimization Models:
Development of multi-factor optimization frameworks to reduce risks within portfolio construction
Employment of advanced techniques such as mean-variance and robust optimization
Asset Pricing Models:
Custom pricing tools for derivatives, fixed income and alternative assets
Pricing methods including stochastic processes, Monte Carlo simulations and jump-diffusion
Factor Research:
Identification and validation of alpha-generating factors across asset classes
Development of cross-asset, factor-based systematic strategies
Risk Modeling:
Identification and analysis of extreme market conditions through stress testing using comprehensive models for Value-at-Risk (VaR), Conditional VaR and Expected Shortfall
Machine Learning Applications:
Clustering for securities screening and behavior analysis
Predictive modeling for returns incorporating supervised and reinforcement learning
Technology & Automation
Trading System Development:
Algorithmic trading systems for execution, market making or arbitrage
Tailored execution frameworks for latency reduction
Order and Execution Management Systems (OEMS):
Custom solutions for trade execution and workflow management
Seamless trading through integration of API-based platforms
Custom Risk Solutions:
Client-specific dashboards and tools for portfolio analytics and scenario modeling
Real-time monitoring
Data Pipeline Automation:
Automated data input, cleansing and processing pipelines that provides
ongoing updates on macroeconomic indicators and market pricing
Advanced Quantitative Research
Back testing Frameworks:
Robust, user-friendly tools for testing systematic strategies that incorporate transaction costs, slippage and liquidity constraints
Simulation Models:
Monte Carlo and agent-based simulations for stress testing investment strategies
Bootstrapping techniques for model validation
Statistical Arbitrage:
Development of pairs trading frameworks using co-integration and machine learning
Cross-sectional momentum and mean reversion strategies
Volatility Modeling:
GARCH or stochastic volatility models for derivatives and risk management
Implied volatility surfaces for options pricing and strategy development
Consulting for Your Quant Business
Personalized Multi-factor Portfolio Optimization Models:
Incorporates unique investor details like risk tolerance, desired return and investment constraints
Bespoke Asset Pricing Frameworks:
For derivatives, fixed income and alternative investments
Advanced Factor Research:
For alpha generation & cross-asset strategies
Comprehensive Risk Modeling:
Including Value-at-Risk (VaR), stress testing & scenario analysis
Machine Learning:
For predictive modeling and clustering
Case Studies
Family Office
Client Challenge:
A family office routinely missed opportunities and struggled with poor portfolio performance due to an inefficient securities screening process.
Our Solution:
QuantBench streamlined the client’s screening process using an advanced clustering methodology with in-house machine learning models to analyze and classify securities behavior.
Outcome
A significantly more effective securities screening process
Improved portfolio allocation efficiency
Hedge Fund
Client Challenge:
A small hedge fund struggled with delays and errors because of manual trade execution processes that hurt their operational efficiency.
Our Solution:
We developed a tailored trading framework for the fund’s unique strategies, automating execution workflows and seamlessly integrating with their existing systems.
Outcome
Superior execution speed and accuracy
Reduced operational risks and manual errors
