A Data-Driven Financialization Framework for Basketball Star Cards: Multi-Factor Price Prediction and Quantitative Trading Strategy via Performance Analytics

Abstract

Massive capital have flooded into basketball star card industry over recent years, but the whole market still rely on outdated, unsystematic pricing standards that cannot match its fast financialization speed. Most collector and investor judge card value by personal experience, forum rumors and scattered historical transaction records, those methods lack standardized quantitative risk evaluation and price prediction ability. This paper develop a Dynamic Multi-Factor Asset Pricing (DMFA) model based on classic Arbitrage Pricing Theory (APT), and combine Exponentially Weighted Moving Averages (EWMA) time-series algorithm to capture time-varying player performance momentum effects. Three core factor modules are constructed inside the model: fixed intrinsic card characteristics, time-decay weighted game statistical indicators, and cross-market systemic risk measured by crypto asset beta coefficient. Three representative card samples with different market lifecycle are selected for empirical test: LeBron James veteran blue-chip PSA10 card, Shai Gilgeous-Alexander limited-edition peak superstar RPA card, and Victor Wembanyama high-liquidity rookie silver card. The regression test results show asset volatility, price sensitivity to athletic performance have obvious non-linear relations with athletes’ career development stage, rookie cards always bear far higher overall market risk compared with veteran collectibles. On the basis of DMFA model’s factor output results, this paper also build a complete algorithmic quantitative trading framework. The system can separately calculate idiosyncratic abnormal return alpha and systemic market risk beta, provide clear operation signals for portfolio construction and dynamic position adjustment for this emerging alternative asset category.