---
language:
- en
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
tags:
- finance
- earnings
- alpha
- fine-tuned
- lora
---

# Earnings Call Alpha Extractor

Fine-tuned Llama 3.1 8B for extracting quantitative signals from earnings call Q&A transcripts.

## What it does

Scores earnings call Q&A sections on two dimensions:
- **Confidence score** (1-10): How confident and direct is management?
- **Evasiveness score** (1-10): How evasive, hedging, or deflecting is management?

The mismatch between these scores and reported financial results (EPS surprise) forms an alpha signal with statistically significant IC.

## Training

- **Base model**: `meta-llama/Meta-Llama-3.1-8B-Instruct`
- **Fine-tuning**: LoRA on Together AI
- **Training data**: ~2500 earnings calls labeled by Llama 3.3 70B (Groq)
- **Dataset**: Rogersurf/earnings-call-transcripts (9k+ calls, 2023-2026)

## Backtest results

| Metric | Value |
|--------|-------|
| IC (5-day) | +0.076 |
| IC t-stat | +2.00 (significant) |
| Sharpe | 0.74 |
| N events | 700 |
| Period | 2023–2026 |

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model = AutoModelForCausalLM.from_pretrained(
    "kevinkakkad168/earnings-alpha-llm",
    torch_dtype=torch.float16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("kevinkakkad168/earnings-alpha-llm")

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
```

See the [project GitHub](https://github.com/your-username/earnings-alpha-extractor) for the full pipeline.

## References

- Mayew & Venkatachalam (2012): *The Power of Voice*
- Li (2008): *Annual report readability and earnings persistence*