StockPulse Self-Improvement Experiment — 2026-08-25 (Failure)
StockPulse AI Self-Improvement Experiment — Daily Analysis
Date: 2026-08-25
🧠 LLM Forecast Analysis
Forecast: Down / 6,600–6,700
Forecast summary: The previous day's combined foreign and institutional selling and large-cap weakness had pushed the KOSPI below 6,700, weakening the earlier bullish expectation. A sharp fall in top market-cap stocks such as Samsung Electronics added downside pressure, so the forecast expected the correction to continue at the open. Individual investors were still buying, but a reversal in foreign flows was identified as the key variable.
Actual KOSPI close: 6743
Actual direction: Up
Accuracy: 0.35/1.0
Analysis:
The forecast focused on the early selloff and incorrectly predicted the final closing direction (up). Institutional and individual bargain buying and a rebound in large caps produced an intraday recovery. The system underestimated how quickly market sentiment could recover after the previous day's selling.
Improvement:
The morning forecast should explicitly consider intraday volatility and rebound potential. It should analyze the possibility of a “weak-open, strong-close” pattern in which an early low is followed by a rebound, even after heavy selling on the previous day.
🤖 ML Model Forecast Analysis
Model: LGB + XGB + LSTM three-model ensemble (AUC=0.7755, Acc=0.7086)
Market bias: Down (-1.9%)
Bullish candidate: Entotek
Bearish candidates: Hana Financial Group, Shinhan Financial Group
Forecast: Down (-1.9%)
Evaluation: ❌ Failure
Accuracy: 0.0/1.0
Analysis:
The model expected the market to fall -1.9%, but the KOSPI rose +0.68%, so the direction was completely wrong. Despite heavy foreign net selling, institutional and individual buying and a rebound in selected sectors, including semiconductors, lifted the index. The model missed this weak-open, strong-close regime.
Improvement:
In addition to index direction, the model should include foreign and institutional flow data and weighted sector leaders as features. Volatility and momentum indicators should also be strengthened so the time-series model can learn V-shaped reversals after an intraday selloff.
This analysis was produced by AI (Qwen3.5-35B) as an evaluation of forecast performance. It is not investment advice; it is an AI performance measurement experiment.
📝 Applied Improvement Actions
🧠 LLM: Prompt improvement
The morning analysis prompt now includes: “Even after an early selloff, check institutional and individual buying and technical rebound signals in large caps. Evaluate the possibility of a weak-open, strong-close pattern or a V-shaped rebound. Make a combined judgment that considers intraday volatility and rebound capacity rather than only the direction.”
The change is applied from the next morning forecast.
🤖 ML: Feature engineering
New flow and sector features are added: foreign net buying/selling amount, institutional buying share, and the semiconductor sector index. Volatility indicators are also introduced.
The change is applied to the next data-collection pipeline.