A Framework for Making Smarter Decisions in Financial Markets

A framework for equity valuation that replaces point estimates with probability distributions. Cash flows are modeled on their natural positive support via log-increments, the economic constraint $r > g_{\text{term}}$ is enforced in sampling rather than assumed, and dependence between growth and discount rates is captured through a heavy-tailed copula. Nowcasting layers—robust anomaly detection and calibrated sentiment—update short-horizon priors without duplicating price information. The result is a valuation output that preserves uncertainty through to decision statistics: exceedance probabilities, expected mispricing, and tail risk measures that support risk-constrained portfolio allocation.

If this article has been sought, it’s reasonable to assume a vexing situation is afoot. You have been presented with a dataset—a term which here describes a vast and bewildering ledger of figures and facts—and have been assigned the lamentable task of prediction. You seek to discern what is to come from what has been, a truly precarious endeavor, and you have likely learned that relying on a single method of divination is a path to spectacular error ...

This design proposal presents a comprehensive framework for enhancing the Relative Strength Index (RSI) by incorporating macroeconomic liquidity conditions and market sentiment. The traditional RSI, while widely used, suffers from well-documented limitations including false signals during trending markets and fixed thresholds that fail to adapt to changing market conditions

A step-by-step mathematical explanation of typed mean-field-type games (MFTGs), reinforcement learning integration, risk measures, and forecasting under uncertainty for decentralized markets.

This whitepaper presents a decentralized negotiation framework for local energy and re- source markets and guides the reader from first principles to implement...