Predicting next-day “heat” for stocks from r/WallStreetBets

1. Predictive Task & Evaluation Plan

Predict large next-day moves for each ticker-day using WSB chatter + price/volume history, then rank tickers by a heat score.

Overview. We start with a time-series view for NVDA plotting price index and daily WSB attention (mentions, scores, unique authors), highlighting high-attention days and large next-day absolute returns. Hype days often coincide with volatility, but not every spike is followed by a big move.

2. Exploratory Analysis, Data Collection & Pre-processing

Reddit WSB posts/comments and Yahoo Finance prices are aligned into a daily (ticker, day) table used for EDA and all downstream models.

2.1 Data sources (context)

• Reddit (r/WallStreetBets): CSV of posts/comments with text, author, timestamp, score, and tags.
• Yahoo Finance: Daily OHLCV prices for a curated set of WSB tickers over 2023–2025.

Goal: align these into a daily (ticker, day) table that feeds both EDA and the prediction task.

Example raw WSB rows (truncated)

2.2 Cleaning and ticker construction

• Normalize datetime to dates; drop bots/mods and [deleted]/[removed] rows.
• Build raw_text from title + text, then clean_text by stripping URLs, collapsing whitespace, lowercasing.
• Extract candidate tickers via cashtags ($TSLA) and uppercase tokens (TSLA, NVDA); keep tokens with ≥500 mentions; filter obvious non-stocks; require valid Yahoo price history.
• Explode to one row per (WSB row, ticker): wsb_exploded.

2.3 Price alignment and feature table

Sample Yahoo Finance price rows (long format)

• From Yahoo prices: compute daily return, next_return, and big_move = 1[|next_return| ≥ 5%]; keep (datetime, ticker, close, volume, return, next_return, big_move).
• Aggregate WSB to (datetime, ticker):
  • Numeric: mention_count, score_sum, score_mean, post_fraction, unique_authors.
  • Text: concatenate clean_text → doc_text.
• Merge price + WSB numeric + text, add log_volume = log(1 + volume), drop rows with missing core features. Final modeling table: ~16k daily examples.

2.4 Key EDA findings (with plots)

• Return distribution: fat-tailed around 0; 5% sits in the tail → reasonable “big move” cutoff.
• WSB attention vs volatility: higher mention buckets have larger mean |next-day return| → attention aligns with higher volatility.
• Text content: word cloud dominated by tickers, options slang, and event words → confirms domain relevance.
• Ticker case studies (NVDA, TSLA): indexed price + high-attention marks + big-move bars show large moves often coincide with elevated WSB activity, motivating a probabilistic model.

2.5 Inputs to the model (summary)

• Inputs: WSB numeric features, price/volume + lags/volatility, VADER sentiment, and doc_text (for TF-IDF).
• Label: big_move ∈ {0,1} for ≥5% absolute next-day moves.

Daily returns distribution (all tickers)

Takeaway: Returns are centered near 0 with fat tails; the 5% VaR bands capture the big-move threshold used for labels, underscoring the class imbalance of rare ±5% days.

Average daily return by day of week

Takeaway: Modest positive drift early in the week and weaker performance on Thursdays; day-of-week is a minor effect compared to WSB attention spikes.

WSB attention vs. volatility

Takeaway: Higher mention buckets correlate with larger absolute next-day returns (volatility lift), motivating the inclusion of WSB volume features.

WSB title/phrase cloud

Takeaway: Dominant tokens are ticker symbols, options slang, and catalyst language (earnings, calls/puts), which the TF-IDF text features capture.

3. Modeling

Binary classification at the (ticker, day) level; the predicted probability is used as a heat score to rank tickers by risk of a large move.

3.1 Task formulation

• Unit: one row = one (ticker, date).
• Label: big_move = 1 if |next_return| ≥ 5% (next-day close-to-close); else 0.
• Output: \u005Chat{p} = P(big_move = 1 | x), the heat score; higher = more likely ≥5% absolute move tomorrow.

3.2 Features

• Price & volume (short-term dynamics): return, log_volume; lags ret_prev_1d/3d/5d; volatility vol_5d/vol_10d; volume anomaly vol_rel_5d.
• WSB numeric signals: mention_count, score_sum, score_mean, post_fraction, unique_authors.
• Sentiment: VADER sent_mean per ticker-day.
• Textual signal: doc_text (all WSB text per ticker-day) → TF-IDF (1–2 grams, capped vocab).

3.3 Baseline models (all logistic regression)

• Price-only logistic — features ['return', 'log_volume'], scaled.
• WSB-numeric-only logistic — features ['mention_count', 'score_sum'].
• Price + WSB numeric logistic — ['return', 'log_volume', 'mention_count', 'score_sum'].
• Text-only logistic — TF-IDF on doc_text (max_features 10k, ngram_range (1,2)).
• Text + basic numeric logistic — TF-IDF + scaled [return, log_volume, mention_count, score_sum] (sparse + dense hstack).

Each numeric baseline uses:

pipe = Pipeline([
    ("scaler", StandardScaler()),
    ("clf", LogisticRegression(
        penalty="l2",
        C=1.0,
        class_weight="balanced",
        max_iter=1000,
        solver="lbfgs",
    )),
])
pipe.fit(X_train, y_train)

3.4 Final model and tuning

• Inputs: doc_text + all numeric columns (price/volume history, volatility, volume anomalies, WSB activity, sentiment).
• Architecture: ColumnTransformer with TF-IDF branch + scaled numeric branch → LogisticRegression(max_iter=1000).
• CV: train+val combined, chronological; TimeSeriesSplit(n_splits=5).
• Grid search: TF-IDF max_features ∈ {5000,10000}, ngram_range ∈ {(1,1),(1,2)}, min_df=5, max_df=0.7; Logistic C ∈ {0.1,1.0,10.0}, class_weight ∈ {'balanced', None}. Maximize F1; freeze best pipeline and evaluate once on test.

3.5 Advantages and limitations

• Pros: aligned with course content; fast on ~16k examples; produces probability heat scores for ranking; handles sparse TF-IDF + dense numeric.
• Handling imbalance/dynamics: class weights; lagged returns/volatility/volume anomalies; attention signals (mentions/authors/sentiment).
• Limits: linear boundary, no explicit text–price interactions, no temporal sequence model.

4. Evaluation

Metrics, baselines, and diagnostic plots that justify the final model choice.

4.1 Protocol

• Split: Time-based 70% train / 15% val / 15% test (no shuffling).
• Outputs: Model returns P(big_move=1) = heat score.
• Threshold: One decision threshold picked on validation F1, then fixed; test is used once.
• Metrics (test): main = F1 (positives ≈ 10–12%); also precision, recall, ROC-AUC, accuracy; diagnostics via confusion matrix + ROC curves.

4.2 Baselines vs. final model

• Trivial: always 0 (high accuracy, F1≈0); always 1 (recall=1, terrible precision/accuracy).
• Course-style logistics: price-only (return, log_volume); WSB numeric-only (mention_count, score_sum); price + WSB numeric (small lift); TF-IDF text-only (big recall/ROC gain); TF-IDF text + basic numeric (best untuned).
• Final model: TF-IDF + all numeric (price lags, volatility, volume anomaly, WSB numeric, sentiment), tuned by GridSearchCV (TimeSeriesSplit, scoring=F1). Best F1 and ROC-AUC on test with similar accuracy to simpler baselines.

All results summarized in a test metrics table with columns model, accuracy, auc, recall, f1, precision, sorted by F1.

4.3 Plots & main conclusions

• Confusion matrix (final model): built from y_test_grid vs. (y_prob_test_best ≥ τ) (F1-tuned τ); shows more true positives than false positives while keeping many true negatives.
• ROC: price-only vs. final: final curve dominates price-only over most FPR, confirming better ranking of “hot” ticker-days.

Accuracy / metrics table (test set)

Takeaway: Final text+numeric model leads on F1/ROC while keeping accuracy similar to simpler baselines; text-only already provides a big lift over numeric-only features.

Confusion matrix — tuned final model (test)

Takeaway: More true positives than false positives while keeping many true negatives, consistent with a threshold tuned for F1 on an imbalanced label.

ROC: price-only vs. final model

Takeaway: The tuned text+numeric model dominates the price-only baseline across operating points, showing better ranking of hot tickers.

5. Related Work & Discussion

WallStreetBets sentiment and markets

Huang & Shum Nolan (2024) build daily stock- and forum-level sentiment indices from 2.2M WSB posts using a BERT classifier, then link “attention herding” portfolios to abnormal returns and Robinhood flows. They find that positive WSB sentiment and concentrated attention predict strong abnormal returns without short-term reversal, and that influencers amplify these effects. Our work is smaller-scale and simpler (logistic regression + TF-IDF), but similar in spirit: we treat WSB as an attention/sentiment signal and test whether it helps flag large moves out of sample.

Descriptive WSB EDA

The Kaggle “r/WallStreetBets EDA & Sentiment Analysis” notebook focuses on basic cleaning, ticker mention counts, and VADER sentiment over time. It informed our preprocessing choices (ticker regexes, bot removal, sentiment features) but stops at exploration. We extend it by aligning Reddit with OHLCV prices and framing a supervised next-day big-move prediction task instead of just describing patterns.

Single-stock information transfer (GME)

Recent studies on GameStop use wavelet/transfer-entropy analyses to show how WSB topics and sentiment transmit information specifically to GME returns, with Reddit activity leading volatility and text features outperforming simple counts. Our results are consistent: richer Reddit features (TF-IDF text + sentiment + counts) beat pure price-only baselines, but we evaluate them in a cross-sectional, multi-ticker, next-day classification/ranking setup.

How we differ

Prior work often runs event studies or portfolio regressions to show that WSB attention/sentiment correlates with abnormal returns and flows, or zooms in on a few meme stocks. Our contribution is to package WSB + price data into a ticker-day panel and evaluate an explicit predictive model (probability of ≥5% move) with a clean train/val/test split. The takeaway aligns with the literature: WSB attention and sentiment carry information about volatility and extreme moves, and even relatively simple models can exploit that signal out of sample.