Volume-Sentiment Quadrant Analysis: A Framework for Asset Classification

Published: January 12, 2025
Category: Research
Author: Stockseer Quantitative Research Team
Read Time: 12 minutes
Keywords: asset classification, information diffusion, idiosyncratic risk, market microstructure, behavioral finance

Abstract

In markets characterized by high asset breadth (>3,000 publicly traded equities), distinguishing actionable signals from stochastic noise presents a significant computational challenge. This paper introduces a two-dimensional classification framework that isolates information diffusion velocity (sentiment) from market attention intensity (article volume). The resulting quadrant system categorizes assets into distinct behavioral regimes: Convergent High-Activity, Latent Accumulation, and Distressed Distribution. Snapshot analysis reveals that quadrant membership correlates with distinct idiosyncratic risk characteristics, temporary beta expansion during attention events, and predictable lifecycle transitions. This framework provides a systematic taxonomy for asset behavior that functions independently of traditional GICS sector or market capitalization classifications.

I. The Signal-to-Noise Problem in High-Breadth Markets

Modern equity markets present a fundamental information processing asymmetry. While data generation is exponential, attention capacity is linear. With over 3,000 publicly traded securities exhibiting daily price movements, market participants face an overwhelming volume of potential signals. Traditional factors—sector, size, and price momentum—provide coarse filters but fail to capture the dynamic interplay between information flow (news) and market reaction (volume).

The Core Problem: Price movements possess varying degrees of information content. A 5% daily gain in a large-cap technology stock accompanied by significant media coverage represents a consensus repricing event. An identical price move in a small-cap asset with zero media coverage may indicate information asymmetry or illiquidity-driven volatility.

Research Objective: This paper develops a proprietary two-dimensional framework to reduce this dimensionality problem. By isolating the velocity of sentiment change from the magnitude of market attention, we create a taxonomy that categorizes idiosyncratic asset behavior independent of systemic market movements.

II. Theoretical Framework: Dual-Axis Classification

The proposed framework rests on two orthogonal dimensions that capture distinct aspects of market information dynamics.

Axis 1: Information Diffusion (Sentiment)

Definition: The aggregate directional bias of unstructured text data (news articles, regulatory filings, social discourse), quantified via Transformer-based Large Language Model (LLM) classification.

Measurement: Sentiment is normalized on a scale of 0 (Maximal Negative) to 100 (Maximal Positive). The operational threshold for "Positive" is set at 58%, representing approximately one standard deviation ($1\sigma$) above the neutral baseline in aggregate financial news datasets.

Theoretical Basis: Sentiment captures market perception independent of capital allocation. Positive sentiment without corresponding price appreciation implies a lag in information diffusion—a temporary inefficiency in the Efficient Market Hypothesis (EMH).

Axis 2: Market Attention (Article Volume)

Definition: The density of information flow surrounding an asset, measured by unique article/post count per rolling 24-hour period.

Measurement: The threshold for "High Attention" is 8+ articles per day. This threshold filters for assets with statistically significant institutional or media coverage, separating "discovered" assets from "undiscovered" ones.

Theoretical Basis: Article volume proxies for investor attention. High volume implies lower search costs for investors and higher probability of incorporation into price. Low volume implies high search costs and slower price discovery.

The Quadrant Taxonomy

The intersection of these axes creates four distinct behavioral regimes:

Quadrant I: Convergent High-Activity (High Volume, High Sentiment)
Quadrant II: Latent Accumulation (Low Volume, High Sentiment)
Quadrant III: Distressed Distribution (High Volume, Low Sentiment)
Quadrant IV: Low-Attention Negative (Low Volume, Low Sentiment) [Excluded from analysis due to low signal-to-noise ratio]

III. Quadrant I: Convergent High-Activity Assets

Definition and Characteristics

Assets in Quadrant I exhibit elevated information diffusion (>58% sentiment) and high market attention (8+ articles). This convergence represents a Consensus Repricing Event.

Microstructure Implications:

Liquidity Deepening: Order flow concentration attracts liquidity providers. Bid-ask spreads typically narrow by 15-25% relative to baseline as market maker inventory risk decreases due to high two-way flow.
Beta Expansion: Assets in this quadrant exhibit temporary correlation with momentum factors. The realized beta often expands ($β > 1.2$) regardless of the asset's historical beta, as systematic capital flows chase the activity.

Risk Profile:

Crowding Risk: Consensus trades exhibit lower risk-adjusted returns due to positioning saturation.
Reversal Vulnerability: When sentiment scores exceed 75% (extreme optimism) coincident with peak volume, the probability of mean reversion increases significantly.

Empirical Characteristics (Snapshot Analysis)

Table 1: Convergent High-Activity Asset Statistics (30-Day Window)

Metric	Mean	Median	Std Dev	Interpretation
Article Count	14.2	12.0	6.8	High institutional visibility
Sentiment Score	71.3%	69.0%	8.2%	Strong bullish consensus
Beta (Realized)	1.45	1.32	0.41	High systematic correlation
Sharpe Ratio	0.82	0.76	0.34	Risk-adjusted outperformance

IV. Quadrant II: Latent Accumulation Assets

Definition and Characteristics

Assets in Quadrant II exhibit positive sentiment (>58%) but limited market attention (<8 articles). This divergence represents Information Asymmetry.

Microstructure Implications:

Wide Spreads: Bid-ask spreads average 40-60% wider than Quadrant I assets.
Fragmented Execution: Institutional accumulation in this quadrant typically utilizes TWAP (Time-Weighted Average Price) or VWAP algos to mask intent, keeping volume below the detection threshold.
Alpha Potential: This quadrant exhibits the highest theoretical "Alpha" (excess return independent of market) because the information has not yet been fully priced in by the broader market.

Risk Profile:

Liquidity Risk: Determining true market depth is difficult; large exits result in significant slippage.
Catalyst Dependency: Transition to Quadrant I requires an exogenous event (earnings, upgrade). Without a catalyst, the asset may stagnate (the "Value Trap" scenario).

Information Diffusion Dynamics

Lifecycle Hypothesis: In a standard positive repricing lifecycle, assets originate in Quadrant II (Fundamental improvement detected by few) $\rightarrow$ Transition to Quadrant I (Broad discovery) $\rightarrow$ Fade to Quadrant IV.

V. Quadrant III: Distressed Distribution Assets

Definition and Characteristics

Assets in Quadrant III exhibit negative sentiment (<58%) with high market attention (8+ articles). This represents Capitulation or Forced Liquidation.

Microstructure Implications:

Liquidity Withdrawal: Market makers widen spreads to protect against adverse selection from informed sellers.
Order Imbalance: Sell-side pressure overwhelms buy-side depth. Order book analysis often reveals a "hollow bid."

Risk Profile:

Falling Knife: Statistical analysis suggests that buying immediately upon entry into Quadrant III has a negative expected value.
Contrarian Signal: The optimal entry point is structurally identified when Volume remains high but Sentiment stabilizes (first derivative of sentiment becomes non-negative).

VI. Empirical Classification Methodology

Data Pipeline

The classification engine processes a real-time stream of unstructured data:

Ingestion: ~2,500 daily articles/filings across 1,000 tickers.
Processing: Transformer-based NLP model scores text segments for sentiment polarity and confidence.
Aggregation: Rolling 24-hour window, refreshed every 5 minutes.

Classification Logic (Pseudocode)

def determine_quadrant(ticker_data: AssetData) -> Quadrant:
    """
    Classifies asset regime based on normalized volume and sentiment vectors.
    """
    # Threshold Constants (Empirically Derived)
    SENTIMENT_THRESHOLD = 58.0  # 1σ above neutral
    ATTENTION_THRESHOLD = 8     # Significant coverage count
    MIN_LIQUIDITY = 1_000_000   # Min daily share volume

    if ticker_data.avg_volume < MIN_LIQUIDITY:
        return Quadrant.UNCLASSIFIED

    is_high_attention = ticker_data.article_count >= ATTENTION_THRESHOLD
    is_positive_sentiment = ticker_data.sentiment_score >= SENTIMENT_THRESHOLD

    if is_high_attention and is_positive_sentiment:
        return Quadrant.CONVERGENT_HIGH_ACTIVITY
    elif not is_high_attention and is_positive_sentiment:
        return Quadrant.LATENT_ACCUMULATION
    elif is_high_attention and not is_positive_sentiment:
        return Quadrant.DISTRESSED_DISTRIBUTION
    else:
        return Quadrant.LOW_SIGNAL

VII. Isolating Idiosyncratic vs. Systemic Risk

Traditional factor models decompose return as $R = \beta R_m + \alpha$. Quadrant analysis offers an alternative decomposition based on Information Flow.

Table 2: Risk Decomposition by Quadrant (30-Day Analysis)

Quadrant	Avg Beta	Alpha Potential	R² to SPY	Interpretation
Q1: Convergent	1.23	Moderate	0.42	Amplified Market Exposure
Q2: Latent	0.87	High	0.18	Pure Idiosyncratic Play
Q3: Distressed	1.41	Negative	0.51	High Systematic Downside

Note: Data derived from Stockseer aggregate market analysis, Oct 2024 - Jan 2025.

Analysis: Quadrant II (Latent) offers the lowest correlation to the broader market ($R^2 = 0.18$), making it the primary hunting ground for market-neutral alpha strategies. Quadrant I functions effectively as a "High Beta" proxy.

VIII. Temporal Dynamics and Quadrant Transitions

A key utility of this framework is predicting State Transitions. Assets rarely jump randomly; they follow flow-based paths.

Table 3: Quadrant Transition Matrix (Probability of Next State)

From \ To	Q1 (Convergent)	Q2 (Latent)	Q3 (Distressed)	Q4 (Low Signal)
Q1 (Convergent)	62%	8%	12%	18%
Q2 (Latent)	28%	55%	3%	14%
Q3 (Distressed)	5%	2%	48%	45%

Key Insight: The "Breakout" probability (Q2 $\to$ Q1) is 28%, significantly higher than the probability of a Latent asset collapsing into Distress (3%). This asymmetry validates the "Latent Accumulation" thesis.

IX. Conclusion

The Volume-Sentiment Quadrant Analysis provides a systematic taxonomy for equity market behavior based on information dynamics. By decoupling information velocity (sentiment) from market attention (volume), researchers and practitioners can isolate idiosyncratic asset behavior from systemic noise.

Summary Findings:

Beta is Dynamic: Assets in Quadrant I experience temporary beta expansion, requiring dynamic hedging adjustments.
Alpha is Latent: The highest information asymmetry exists in Quadrant II, where sentiment is high but diffusion (volume) is low.
Transitions are Signals: The migration of an asset from Quadrant II to Quadrant I acts as a robust leading indicator for price discovery.

Future research will focus on the predictive power of Quadrant Dwell Time—analyzing whether assets that remain in Quadrant II for longer periods exhibit more violent repricing upon eventual discovery.

References

Fama, E. F. (1970). "Efficient Capital Markets: A Review of Theory and Empirical Work." Journal of Finance.
Tetlock, P. C. (2007). "Giving Content to Investor Sentiment: The Role of Media in the Stock Market." Journal of Finance.
Barber, B. M., & Odean, T. (2008). "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors." Review of Financial Studies.
Stockseer Research. (2025). "Aggregate Sentiment Factor Analysis: Q4 2024 Data Review." Internal Research Note.