---
title: 'MarketCalls Dataset: Finance & Marketing'
url: https://www.emergentmind.com/topics/marketcalls-dataset
type: topic
---

# MarketCalls Dataset: Finance & Marketing

The term “MarketCalls dataset” refers to two distinct, high-value resources in financial and behavioral modeling: (1) a publicly released, sentence-level corpus for numerical claim detection in finance ("MarketCalls v1.0") and (2) a proprietary Mandarin speech benchmark for purchase propensity classification in outbound marketing calls. Both datasets are foundational for research in their respective domains, enabling quantitative NLP, multi-modal fusion, and robust downstream modeling tasks. The following sections document their construction, schema, evaluation protocols, and access modalities, referencing the respective sources [2402.11728] and [2511.11006].

## 1. Dataset Overview and Variants

MarketCalls, as described in Shah et al. (2024) [2402.11728] and Liu et al. (2025) [2511.11006], denotes two principal datasets:

- **MarketCalls v1.0 (Finance, Public Release):** A CC BY 4.0 licensed corpus for numerical claim detection, assembled from analyst reports and NASDAQ-100 earnings-call transcripts.
- **MarketCalls (Mandarin Marketing Calls, Proprietary):** A Mandarin audio dataset encapsulating 877 outbound sales calls, each annotated with purchase propensity and conversational segments, available by request for academic research.

These datasets differ in modality (text vs. audio-text), domain (financial reporting vs. customer engagement), and accessibility (public vs. controlled access).

## 2. Data Sources and Collection Methodology

### MarketCalls v1.0 (Finance)

- **Analyst Reports:** Sourced from Zacks Equity Research under Nexis Uni, spanning 1,530 public firms (Q1 2017–Q4 2020), 87,536 documents, ~167 million tokens.
- **Earnings-Call Transcripts:** Collected from NASDAQ-100 investor-relations pages (Q1 2017–Q1 2023), 1,085 transcripts, ~11.6 million tokens.
- **Market Metadata:** Incorporates daily stock prices (Polygon.io), EPS forecasts (I/B/E/S), and sector codes (Compustat GSECTOR).

### MarketCalls (Mandarin, Marketing Audio)

- **Phone Calls:** 877 automated outbound calls in Mandarin, spanning three domain contexts: dental care (490), beauty/cosmetics (229), and paid courses (158).
- **Annotation:** Each call labeled by senior sales managers according to a five-tier purchase propensity taxonomy: Very Positive, Receptive, Impatient/Polite Negative, Explicit Refusal, and Not Relevant.

## 3. Preprocessing, Annotation Schemas, and File Organization

### Preprocessing—MarketCalls v1.0

- **Sentence Splitting:** Custom regex to resolve abbreviations and numeric contexts.
- **Numeric Filtering:** Retain sentences with digits plus purpose currency/percent symbols.
- **Financial-Term Filtering:** Apply an 8,200-term financial dictionary; drop sentences without dictionary hits.

| Corpus              | Raw Sentences | Numeric Sentences | Numeric+Financial | In-Claim Subset |
|---------------------|--------------|-------------------|-------------------|-----------------|
| Analyst Reports     | 8,583,093    | 2,857,567         | 2,364,977         | 336,252         |
| Earnings Calls      | 48,686       | 41,013            | 1,233             | 1,233           |

**Annotation Schema:**
- Definition: Numerical financial sentences must include a number, a currency/percent symbol, and a financial term.
- Labels: “In-claim” (speculative forecast) vs. “Out-of-claim” (established fact).
- Annotators: Two financial experts per sentence; disagreements resolved by a PhD-level adjudicator.
- Agreement: Analyst reports 99.21%, earnings calls 95.78%.

- **File Format:** JSON per sentence, fields include: doc_id, sector, date, sentence, numeric_flag, financial_flag, label.

### Preprocessing and Segmentation—MarketCalls (Audio)

- **Audio Processing:** Mono WAV, 8 kHz, 16-bit PCM; segmentation into salesperson/customer “rounds” (max 40 s per segment).
- **Feature Extraction:**
  - Audio: Wav2Vec conv frontend, HuBERT transformer encoder ($L_a=999$ frames$\times d_a=768$).
  - Text: iFlytek ASR transcript, RoBERTa encoder ($L_t=199$, $d_t=768$).
- **Data Augmentation:**
  - Audio: Gaussian noise, speed perturbation, SpecAugment masking.
  - Text: Homophone substitutions (10% probability per character).

| Split      | Calls  | Segments | Audio-Augmented | Text-Augmented |
|------------|--------|----------|-----------------|---------------|
| Train      | 701    | 2,293    | 7,041           | 4,586         |
| Validation | 88     | 321      | —               | —             |
| Test       | 88     | 328      | —               | —             |

## 4. Labeling Taxonomies and Classification Tasks

### MarketCalls v1.0 (Finance)
- **Claim Labels:** “In-claim” (speculative forecast, e.g., "Revenue is expected to reach $5 billion"), “Out-of-claim” (established fact, e.g., "Revenue was $4.39 billion").
- **Annotation:**
  - Sector-balanced sampling: 2 analyst reports per sector per year; 2 earnings calls per year.

### MarketCalls (Mandarin Marketing Calls)
- **Purchase Propensity Classes:**
  - Very Positive (“A”)
  - Neutral/Receptive (“B”)
  - Impatient/Polite Negative (“C”)
  - Explicit Refusal (“D”)
  - Not Relevant (“E”) — not present in final release.

| Label Class | Calls | Proportion (%) |
|-------------|-------|----------------|
| A           | 137   | 15.6           |
| B           | 271   | 30.9           |
| C           | 379   | 43.2           |
| D           | 90    | 10.3           |
| E           | 0     | —              |

## 5. Evaluation Protocols and Metrics

### MarketCalls v1.0 (Finance)
- **Weak-Supervision Aggregation Function:**
  - $K$ labeling functions $\lambda_j$ with values in $\{-1, 0, 1, 2\}$.
  - Logic: If any $\lambda_j(x) = -1$, label is “out_of_claim”; else if $\max_j \lambda_j(x) = 2$, label is “in_claim”; else, majority vote on non-abstain labels.
- **Optimism Measure (per doc $i$):**
  $$
  \mathrm{Optimism}_i = 100 \times \frac{\mathrm{Pos.InClaim}_i - \mathrm{Neg.InClaim}_i}{\mathrm{TotalSentences}_i}
  $$
  (Sentiment computed via fine-tuned FinBERT.)

- **Metrics:**
  - Precision $P = \mathrm{TP}/(\mathrm{TP}+\mathrm{FP})$
  - Recall $R = \mathrm{TP}/(\mathrm{TP}+\mathrm{FN})$
  - Macro F$_1 = 2PR/(P+R)$
  - Accuracy $= (\mathrm{TP}+\mathrm{TN})/(\mathrm{TP}+\mathrm{TN}+\mathrm{FP}+\mathrm{FN})$

### MarketCalls (Marketing Audio)
- **Classification Tasks:**
  - $k$-way accuracy (2, 3, 4, 5 classes): ACC$_k = \frac{1}{N} \sum_{i=1}^N \mathbf{1}(\hat y_i = y_i)$
  - Additional metrics in related benchmarks: precision, recall, F$_1$, mean absolute error, Pearson correlation, unweighted average recall (UAR).

## 6. Access, Licensing, and Reproducibility

- **MarketCalls v1.0:** Publicly released under CC BY 4.0, with code, filtered JSON/CSV, and reproducibility scripts to be made available on GitHub/Hugging Face as per [2402.11728].
- **MarketCalls (Mandarin Audio):** Access by request for non-commercial research; files include mono-WAV audio, UTF-8 transcripts, and segment-level metadata. Licensing details confirmed by South China Normal University, contact: hongyuliu@…, ruijiewan@… [2511.11006]. Accompanying codebase is available at https://github.com/david188888/MSMT-FN.

## 7. Benchmarking and Downstream Modeling

- **MarketCalls v1.0:** Used to benchmark claim detection models and sentiment-based predictive indicators for market returns.
- **MarketCalls (Marketing Audio):** Empirical evaluations in MSMT-FN [2511.11006] compare state-of-the-art multimodal models:
  - MSMT-FN achieves ACC$_3 = 63.83$%, ACC$_4 = 61.70$%, ACC$_5 = 60.28$%—substantially outperforming the MMML baseline in fine-grained classification.
  - MSMT-FN employs multi-segment fusion and multi-task learning across all granularities of customer intent.

In summary, the MarketCalls datasets represent domain-specific benchmarks designed for robust modeling of financial reporting claims and real-world marketing dialogues. They provide structured splits, rigorous annotation, and evaluation protocols suited to both NLP and multi-modal audio-text research settings.

Source: https://www.emergentmind.com/topics/marketcalls-dataset