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All Journal Journal of Technology Informatics and Engineering International Journal of Graphic Design
Hailin Zhou
Applied Analytics, Columbia University, NY, USA
Arts Computer Science & IT

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News-Based Uncertainty and Macro-Market Fusion for VIX Direction Forecasting: Evidence from 2015-2024 FRED Panel Hailin Zhou; Kai Zhang
Journal of Technology Informatics and Engineering Vol. 4 No. 2 (2025): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i2.540

Abstract

This paper evaluates news-based uncertainty and macro-market fusion for one-trading-day VIX direction forecasting using a 2015-2024 daily FRED panel. The dependent variable equals one when the next trading-day VIX close exceeds the current close. The final panel contains 2,512 processed observations from 2015-02-04 to 2024-12-30, with 1,997 training observations from 2015-2022, 257 validation observations in 2023, and 258 holdout observations in 2024. The feature set combines VIX market-state variables, the daily newspaper-based Economic Policy Uncertainty index, the effective federal funds rate, 10-year and 2-year Treasury yields, lagged CPI inflation, lagged unemployment, and interaction terms. Expanding cross-validation on the 2015-2022 training sample gives the highest average ROC AUC to Fusion Random Forest (0.5620). The 2023 validation window selects a weighted fusion ensemble with ROC AUC 0.6005; its weights are fixed before the 2024 test. In the 2024 holdout window, VIX-only Logistic achieves the highest one-day ROC AUC (0.5869) and F1 (0.5448), while the weighted fusion ensemble reaches ROC AUC 0.5638 and F1 0.4786. Event-window diagnostics show that EPU shocks following calm VIX states have a next-day VIX-up rate of 0.5672, compared with 0.4445 on other trading days. The findings support a cautious interpretation: news-based uncertainty contains conditional information, but one-day practical forecasting reliability remains modest and VIX state variables remain the strongest 2024 holdout benchmark.
Evidence-Grounded Visual Explanation Design for E-commerce Recommendation Cards: Complementary and Substitute Relations, Image Availability, and Interface Hierarchy Hailin Zhou
International Journal of Graphic Design Vol. 4 No. 1 (2026): April | IJGD: International Journal of Graphic Design
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/ijgd.v4i1.3992

Abstract

E-commerce recommendation cards must explain why an item is related while maintaining a compact, consistent visual hierarchy. This study presents an evidence-grounded card framework and evaluates its retrieval inputs with the final TREC Product Recommendation 2025 topics, a 48,616-product corpus, 7,633 NIST judgments for 47 topics, and the SQID supplementary image-URL table. Fixed lexical methods generated separate top-10 complement and substitute rankings over the full corpus before assessor judgments were applied. Under the official scoring procedure, the relation-aware lexical method achieved an average nDCG@10 of 0.1840, with a complement nDCG@10 of 0.0562 and a substitute nDCG@10 of 0.3119. Its average difference from full-text TF–IDF was not statistically reliable (difference = 0.0111, 95% CI [-0.0090, 0.0321]). The complement-minus-substitute gap was -0.2557 (95% CI [-0.3151, -0.1937]), confirming that complementary recommendation remains the weaker relation. Requiring a confirmed supplementary image URL increased confirmed URL coverage among displayed items to 100% but reduced average nDCG@10 to 0.0138. The findings support relation-qualified reasons, confirmed-image and text-led fallback states, and conservative language for complementary suggestions. The framework specifies evidence and layout behavior; shopper responses and different verbalization methods require direct comparative evaluation.
A Constrained, Data-Driven Budgeting Framework Integrating Macro Demand Forecasting and Marketing Response Modeling Yifei Lu; Hailin Zhou; Yitian Zhang
Journal of Technology Informatics and Engineering Vol. 4 No. 3 (2025): DECEMBER | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i3.466

Abstract

Budgeting and financial planning & analysis (FP&A) increasingly require combining macroeconomic signals, channel-level marketing effectiveness, and hard accounting constraints into a single, auditable decision process. This paper proposes and empirically evaluates an end-to-end framework that (i) forecasts category-level demand from public macro data, (ii) learns diminishing-returns marketing response curves, and (iii) solves a constrained portfolio optimization problem to allocate marketing spend while satisfying SG&A and cash-flow guardrails consistent with real public-company statements. Using quarterly Personal Consumption Expenditures (PCE) components from FRED (durable goods, nondurable goods, and services) as a proxy for market demand, we compare seasonal naïve, SARIMAX, gradient boosting, and a multivariate VAR model in a rolling backtest (2018Q1-2025Q3). In parallel, we estimate marketing response from the Advertising dataset (TV, radio, and newspaper spend) via linear models, gradient boosting, and a Hill-function saturation model. We then calibrate financial constraints-gross margin, SG&A ratio, and operating cash-flow coverage-directly from Apple Inc.’s FY2025 Form 10-K filed with the SEC, and integrate all components into a Monte Carlo-evaluated budgeting optimizer. Results show that multivariate models improve total-demand accuracy (≈2.85% MAPE) and that nonlinear response curves indicate strong diminishing returns and negligible incremental value for newspaper spend. The constrained optimizer produces stable allocations that trade off expected operating profit and downside risk, and it highlights a practical insight: budgets that exactly meet a ratio-based cap under point forecasts may violate constraints under realistic demand uncertainty. The proposed workflow is fully reproducible from public data sources and provides a template for transparent, constraint-aware budgeting.
Co-Authors Kai Zhang Yifei Lu Yitian Zhang