BASIR: Budget-Assisted Sectoral Impact Ranking -- A Dataset for Sector Identification and Performance Prediction Using Language Models

Paper: arXiv 2504.13189

Abstract

Government fiscal policies, particularly annual union budgets, exert significant influence on financial markets. However, real-time analysis of budgetary impacts on sector-specific equity performance remains methodologically challenging and largely unexplored. This study proposes a framework to systematically identify and rank sectors poised to benefit from India’s Union Budget announcements. The framework addresses two core tasks: (1) multi-label classification of excerpts from budget transcripts into 81 predefined economic sectors, and (2) performance ranking of these sectors. Leveraging a comprehensive corpus of Indian Union Budget transcripts from 1947 to 2025, we introduce BASIR (Budget-Assisted Sectoral Impact Ranking), an annotated dataset mapping excerpts from budgetary transcripts to sectoral impacts. Our architecture incorporates fine-tuned embeddings for sector identification, coupled with language models that rank sectors based on their predicted performances. Our results demonstrate 0.605 F1-score in sector classification, and 0.997 NDCG score in predicting ranks of sectors based on post-budget performances. The methodology enables investors and policymakers to quantify fiscal policy impacts through structured, data-driven insights, addressing critical gaps in manual analysis. The annotated dataset has been released under CC-BY-NC-SA-4.0 license to advance computational economics research.

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper uses advanced NLP techniques and statistical metrics like F1-score and NDCG, but the math is mostly applied ML rather than dense theoretical derivations. It demonstrates high empirical rigor with a specific dataset (1,671 entries), concrete backtesting-ready metrics (F1=0.605, NDCG=0.997), and clear data sources, making it practical for implementation.

Research Flowchart

  flowchart TD
  A["Research Goal: Systematic<br>Fiscal Policy Impact Analysis"] --> B["Input: BASIR Dataset<br>Budget Transcripts 1947-2025"]
  B --> C["Core Methodology"]
  subgraph C ["Two-Tier Pipeline"]
      C1["Fine-tuned Embeddings<br>Multi-label Sector Classification"] --> C2["Language Models<br>Performance Ranking NDCG"]
  end
  C --> D["Key Outcomes"]
  subgraph D ["Quantified Results"]
      D1["Class. F1-Score: 0.605"]
      D2["Rank. NDCG: 0.997"]
      D3["Dataset Released<br>CC-BY-NC-SA-4.0"]
  end

Cited References (27)

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4. Scalable training of L1 -regularized log-linear models (2007)
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6. Yara Parser: A Fast and Accurate Dependency Parser (2015) — link
7. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning (2025) — link
8. Impact of budget and GDP announcements on Indian stock market (2019)

0 of 27 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.