Sentiment Analysis in the Turkish Stock Market (BIST): Generating Signals from Financial News with Qwen and Llama

Sentiment as a Forecast Variable

Sentiment analysis in equities is easy to oversell and hard to do well. The useful version treats sentiment as a conditional forecast variable: a noisy signal that may explain cross-sectional returns, volatility, or volume once aligned to the correct event timestamp and trading horizon.

In the Turkish market, open-weight LLM ecosystems have matured significantly. Qwen has publicly released Qwen3-family weights, and Meta promotes Llama 4-family models and Llama Stack distributions for self-hosted workflows. That makes a local, Turkish-language financial NLP stack increasingly practical.

The central problem is label design. For a news item arriving at time \(t\), a common target is the forward return over horizon \(h\):

A practical sentiment score can be defined as:

Local Inference Prototype

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "Qwen/Qwen3-8B"  # replace with local checkpoint
tokenizer  = AutoTokenizer.from_pretrained(model_name)
model      = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=3)

texts = [
    "Şirket, beklentilerin üzerinde net kar açıkladı ve yeni yatırım planı duyurdu.",
    "Faiz kararı sonrası banka hisselerinde satış baskısı artıyor."
]

inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits
    probs  = torch.softmax(logits, dim=-1)

print(probs)  # [bearish, neutral, bullish]

Critical Pitfalls

• Leakage: if you label a news article with end-of-day return even though it arrived after the close, your backtest is contaminated
• Non-stationarity: macro regime changes, regulation, inflation cycles, and sector narratives can all alter the meaning of the same words over time
• Turkish morphology: off-the-shelf English finance sentiment models often misread Turkish context, especially in KAP-style disclosures

The edge comes not from "using an LLM," but from building a time-aware, Turkish-native, market-aligned inference pipeline. The alpha comes from labeling discipline, entity resolution, and proper out-of-sample testing.

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