65 lines
1.7 KiB
Markdown
65 lines
1.7 KiB
Markdown
---
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language:
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- en
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license: llama3.1
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base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
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tags:
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- finance
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- earnings
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- alpha
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- fine-tuned
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- lora
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---
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# Earnings Call Alpha Extractor
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Fine-tuned Llama 3.1 8B for extracting quantitative signals from earnings call Q&A transcripts.
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## What it does
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Scores earnings call Q&A sections on two dimensions:
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- **Confidence score** (1-10): How confident and direct is management?
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- **Evasiveness score** (1-10): How evasive, hedging, or deflecting is management?
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The mismatch between these scores and reported financial results (EPS surprise) forms an alpha signal with statistically significant IC.
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## Training
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- **Base model**: `meta-llama/Meta-Llama-3.1-8B-Instruct`
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- **Fine-tuning**: LoRA on Together AI
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- **Training data**: ~2500 earnings calls labeled by Llama 3.3 70B (Groq)
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- **Dataset**: Rogersurf/earnings-call-transcripts (9k+ calls, 2023-2026)
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## Backtest results
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| Metric | Value |
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|--------|-------|
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| IC (5-day) | +0.076 |
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| IC t-stat | +2.00 (significant) |
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| Sharpe | 0.74 |
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| N events | 700 |
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| Period | 2023–2026 |
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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import torch
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model = AutoModelForCausalLM.from_pretrained(
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"kevinkakkad168/earnings-alpha-llm",
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torch_dtype=torch.float16,
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained("kevinkakkad168/earnings-alpha-llm")
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
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```
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See the [project GitHub](https://github.com/your-username/earnings-alpha-extractor) for the full pipeline.
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## References
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- Mayew & Venkatachalam (2012): *The Power of Voice*
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- Li (2008): *Annual report readability and earnings persistence*
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