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MERIT

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Text Classification
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Tags:
finance
earnings-calls
nlp
market-microstructure
sentiment-analysis
benchmark
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metadata 
language:
  - en
license: cc-by-4.0
tags:
  - finance
  - earnings-calls
  - nlp
  - market-microstructure
  - sentiment-analysis
  - benchmark
  - finbert
  - nbbo
pretty_name: 'MERIT: Microstructure and Earnings call Real-time Information Trajectories'
size_categories:
  - 100K<n<1M
task_categories:
  - text-classification

MERIT: Microstructure and Earnings call Real-time Information Trajectories

Overview

MERIT is a sentence-level benchmark that links earnings conference call language to sub-minute market microstructure responses. It repositions earnings call analysis from static, document-level summaries to micro-temporal language trajectories anchored to live market quote data.

Each observation pairs a unit of earnings call language—a presentation sentence or a Q&A exchange—with tick-level NBBO (National Best Bid and Offer) statistics measured in fixed windows immediately before and after that language unit is spoken.

The main benchmark covers 1,118 earnings call events (regular-session and after-hours only) from 167 S&P 500 companies spanning 2021–2023. An additional 747 pre-market events are excluded from the main benchmark due to differences in pre-open quote liquidity, and are stored in robustness/ for reference.

Motivation

Prior earnings-call datasets focus on summarization, argumentation mining, or subjectivity annotation, but provide no market grounding. Finance datasets that measure market reactions operate at daily or longer horizons. MERIT fills this gap with three design principles:

  1. Sentence-level anchoring — each utterance is temporally aligned to short-horizon quote responses, rather than aggregated at call or session level.
  2. Four representation regimes — document-level aggregate, instantaneous utterance, cumulative level, and cumulative trajectory — allowing gains from each source of signal to be separately identified.
  3. Temporal generalisation — a chronological train/test split (train: 2021–2022; test: 2023) prevents look-ahead bias and tests transfer to future market periods.

Dataset Statistics

Split EC Events Companies Regular-session ECs After-hours ECs
Train (2021–2022) 729 159 382 347
Test (2023) 389 151 192 197
Pre-market (excluded) 747 — — —
Total (main benchmark) 1,118 167

Companies shared across train and test splits: 143

Anchor counts (Presentation)

Train Test
Raw sentence anchors 88,650 50,891
Selected anchors, post=30s 24,101 13,896
Selected anchors, post=300s 5,346 2,968

Anchor counts (Q&A)

Train Test
Raw QA-pair anchors 9,699 5,600
Selected anchors, post=30s 8,600 5,050
Selected anchors, post=300s 3,895 2,311

Company Selection

MERIT is anchored to S&P 500 constituents. Three filtering steps were applied:

  1. Sector exclusion — Financials, Utilities, and Real Estate (GICS) are excluded due to systematic differences in disclosure conventions and trading environment.
  2. Quote coverage — only firm-quarter events with a matched NBBO file in the NYSE TAQ database are retained.
  3. Structural validity — only firms where transcript structure and quote coverage jointly support sentence-level alignment are included.

The retained firms span eight GICS sectors: Technology (44), Industrials (28), Healthcare (19), Consumer Cyclical (32), Consumer Defensive (14), Energy (16), Communication Services (7), Basic Materials (7).

Language Features

Presentation segment — FinBERT-tone

Each Presentation sentence is scored with FinBERT-tone, yielding:

  • finberttone_expected_value: scalar tone = P(Positive) − P(Negative) ∈ [−1, 1]
  • finberttone_cumulative_tone: running sum of expected value up to this sentence
  • finberttone_change_point: 1 if this sentence starts a new sentiment regime (PELT + AIC penalty = 2); 0 otherwise

Median Presentation length: 164 sentences per call (max: 626).

Q&A segment — SubjECTive-QA

Each analyst–executive exchange is scored along six dimensions using SubjECTive-QA (Pardawala et al., NeurIPS 2024): assertive, cautious, optimistic, specific, clear, relevant

Each dimension has three columns: {dim}_negative_score, {dim}_neutral_score, {dim}_positive_score.

Anchor timestamp: Q_Timestamp (question start — earliest moment the market can react).

Median Q&A depth: 14 pairs per call (max: 65). Of the 1,118 main benchmark events, 1,032 contain at least one Q&A pair with valid quote coverage.

Market Microstructure Targets

Three targets capture complementary dimensions of short-horizon quote dynamics, each expressed as the change from the pre-window mean to the post-window mean:

Target Description
∆BAS Change in relative bid–ask spread — trading frictions and liquidity costs
∆QRF Change in quote revision frequency (NBBO updates/min) — price discovery intensity
∆QVol Change in realised quote volatility (std of mid-price changes) — local price uncertainty

Targets are binarised as extreme-event indicators:

Y = 1  if |∆metric| > τ₇₅   (75th percentile of training-split delta distribution)
Y = 0  otherwise

This focuses evaluation on the upper tail of microstructure changes and avoids label noise from near-zero fluctuations. Balanced accuracy is the primary metric (chance level = 0.500).

Majority-class baselines at post=300s: ∆BAS 82.6%, ∆QRF 65.3%, ∆QVol 77.7%.

Window Design

For each anchor at time t, quote statistics are aggregated over:

|<── pre-window (fixed 30s) ──|── post-window (W) ──>|
t − 30s                       t                    t + W

Post-window widths W ∈ {30, 60, 120, 300} seconds → four files per anchor type.

Non-overlapping anchor protocol

To prevent mechanical autocorrelation, anchors are selected greedily so that no two retained anchors share any portion of their post-window. For horizon W, each retained anchor must start at least W seconds after the previously retained anchor.

Dataset Structure 

data/
├── ec_calendar_sp500_with_sector_and_industry.csv    EC event calendar
│                                                     (1,118 main benchmark events;
│                                                      747 pre-market excluded)
├── benchmark/                        Full benchmark (all main-session ECs, pre split)
│   ├── benchmark_pre_{30,60,120,300}s.csv
│   ├── benchmark_qa_{30,60,120,300}s.csv
│   ├── benchmark_labeled/            Benchmark with microstructure labels
│   │   ├── benchmark_pre_{30,60,120,300}s.csv
│   │   └── benchmark_qa_{30,60,120,300}s.csv
│   ├── benchmark_coverage.csv        Per-EC anchor counts (valid / excluded)
│   └── excluded_anchors_{30,60,120,300}s.csv   Anchors excluded (n_ticks_post < 3)
└── benchmark_split/
    ├── train/                        Train set: 2021–2022
    │   ├── benchmark_pre_{W}s.csv
    │   └── benchmark_qa_{W}s.csv
    ├── test/                         Test set: 2023
    │   ├── benchmark_pre_{W}s.csv
    │   └── benchmark_qa_{W}s.csv
    └── robustness/                   Pre-market ECs (excluded from main benchmark)
        ├── pre_market_pre_{W}s.csv
        └── pre_market_qa_{W}s.csv

File naming 

benchmark_{anchor_type}_{post_window}s.csv
  anchor_type  :  pre  (presentation sentence)
               :  qa   (Q&A pair)
  post_window  :  30 | 60 | 120 | 300  (seconds)

EC Calendar (ec_calendar_sp500_with_sector_and_industry.csv)

This file is the master event index for the benchmark. It is the single source of truth for EC start times, session labels, and company metadata.

Column Description
tic Ticker symbol
name Company name
sector GICS sector
industry GICS industry
year Earnings call year
quarter Quarter (Q1–Q4)
timestamp_start_utc EC start time in UTC (ISO 8601)
timestamp_start_et EC start time in US/Eastern (ISO 8601)

The ec_session column in all benchmark files is derived deterministically from timestamp_start_et in this calendar.

Key Columns

Identity columns (all files)

Column Description
tic Ticker symbol
year Earnings call year
quarter Quarter (Q1–Q4)
anchor_type pre or qa
anchor_id section_id (pre) or QA_Index (qa)
post_window_sec Post-window width in seconds
timestamp_anchor Seconds from EC start to this anchor
ec_session regular / after_hours / pre_market / non_trading

Quote features (pre- and post-window)

Each appears with both _pre and _post suffix:

Column Description
bid_ask_spread_mean Mean relative bid–ask spread
bid_ask_spread_std Std of bid–ask spread
obi_mean Mean order book imbalance
qrf_mean Mean quote revision frequency (ticks/min)
quote_volatility_mean Mean quote volatility
n_ticks Number of quote ticks in the window

Session Distribution

Session distribution computed over all main benchmark events (1,118) plus pre-market events (747):

Session ET Hours Description
regular 09:30–15:59 Main trading session
after_hours 16:00–19:59 Post-close session
pre_market 04:00–09:29 Pre-open session (excluded from main benchmark)

Main benchmark (train/, test/): regular + after_hours sessions only.

Excluded from main benchmark: pre_market sessions. NBBO data is systematically sparse before market open, making post-window targets less reliable as primary evaluation targets. Pre-market events are stored in robustness/ for reference.

Filtering

Anchors with fewer than 3 quote ticks in the post-window are excluded from the main benchmark. Excluded anchors are recorded in excluded_anchors_{W}s.csv for transparency.

Tick-level quality filters applied upstream:

  • Crossed-NBBO ticks (bid > ask) discarded (~1.2% of ticks)
  • Extreme-spread ticks (relative spread > 0.50) discarded (~1.3% of ticks)

Baseline Results (Summary)

Regime Presentation BAcc (W=30s) Q&A BAcc (W=30s)
Document-level aggregate 0.500 —
Instantaneous utterance (Inst.) 0.502 0.523
Cumulative level (CL) 0.508 0.552
Cumulative trajectory (CT) 0.520 0.560

Chance level = 0.500. Full results across all targets (∆BAS, ∆QRF, ∆QVol) and horizons (30s–300s) are reported in the accompanying paper.

Data Release Scope

Component Released Notes
Sentiment panel (FinBERT-tone + SubjECTive-QA scores) ✅ This repository
Benchmark split (pre-aggregated features) ✅ This repository
Raw earnings call transcripts ❌ Commercial licensing restrictions prohibit redistribution
Raw NYSE TAQ tick streams ❌ Commercial licensing restrictions prohibit redistribution

Reproducibility

The benchmark is deterministically reproducible given:

  • The same EC calendar (start times in ET)
  • The same tick-level quote panel
  • --min_ticks_post 3 (default filtering threshold)

Session labels depend only on EC start time (ET timezone).

License

Released under Creative Commons Attribution 4.0 (CC BY 4.0). Pipeline code and baselines are released separately under the MIT License.

Citation

The accompanying paper is currently under review. Citation information will be added upon publication.

References

  • Pardawala et al. (2024). SubjECTive-QA: Measuring subjectivity in earnings call transcripts' QA through six-dimensional feature analysis. Advances in Neural Information Processing Systems, 37.
  • Huang, Wang, & Yang (2023). FinBERT: A large language model for extracting information from financial text. Contemporary Accounting Research, 40(2), 806–841.
  • Beaver (1968). The information content of annual earnings announcements. Journal of Accounting Research, 6, 67–92.
  • Lee, Mucklow, & Ready (1993). Spreads, depths, and the impact of earnings information: An intraday analysis. Review of Financial Studies, 6(2), 345–374.
  • Killick, Fearnhead, & Eckley (2012). Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association, 107(500), 1590–1598.