Investment and academic research, operational systems, and quantitative methods across systematic investing, asset allocation, forecasting, financial data science, and AI/ML. Where available, projects link directly to papers, evidence, code, tools, documentation, and live interfaces.
A forecasting model can add portfolio value without winning the average prediction-error contest. This study evaluates Chronos-2 inside a matched cross-asset Forecast-to-Rank process and asks which portfolio decisions actually change, how integration changes the result, and whether those changes earn their keep.
Lower average forecast errors can coexist with limited statistical separation and performance concentrated in a relatively small number of months. The analysis evaluates average accuracy, formal inference, and temporal stability under a common FRED-MD forecasting design.
SlackQuant's multi-strategy portfolio operating system. PDS Core combines independently maintained strategy providers, PDS Adaptive adds a bounded hybrid risk-control layer that combines an RL signal with independent volatility confirmation, and Dynamic FX applies a separately managed investor-level currency overlay. The public dashboard shows the current operating state while machine-specific infrastructure remains internal.
Decision-Diversified Multi-Asset Portfolio Strategy
Public live →Forecast-Ranked Multi-Asset Portfolio Strategy
Public live →Benchmark-Aware Global Equity ETF Portfolio
Public live →Scenario-Based Portfolio Stress Decision Support
Public live →Practitioner-oriented research on systematic investing, asset allocation, portfolio decision design, robustness, and implementation.
Explore Investment Research →Academic and technical research across forecasting, financial data science, AI/ML, and empirical methodology.
Explore Academic Research →A practical library of statistical, econometric, portfolio, time-series, data-design, and model-evaluation methods used across SlackQuant research and systems. Each article connects intuition, formal definitions, implementation, common failure modes, and reproducible examples.
Explore Quantitative Methods →Quantitative Methods provides reusable explanations, implementation guidance, reproducible examples, and failure-mode checks for methods that recur across Investment Research, Academic Research, and Systems.
Counts reflect the current public catalog across Research, Systems, and Quantitative Methods. Repository links are deduplicated across research, deployment, and replication records.
SlackQuant Research brings investment research, academic research, operational systems, and a shared Quantitative Methods library into one public platform while keeping the role of each resource clear.