Quantitative Equity

Momentum Turning Points

Momentum Turning Points ArXiv ID: ssrn-3489539 “View on arXiv” Authors: Unknown Abstract Turning points are the Achilles’ heel of time-series momentum portfolios. Slow signals fail to react quickly to changes in trend while fast signals are often fa Keywords: time-series momentum, portfolio optimization, trend following, signal processing, Quantitative Equity Complexity vs Empirical Score Math Complexity: 7.0/10 Empirical Rigor: 8.0/10 Quadrant: Holy Grail Why: The paper employs a formal model to analyze momentum signals and derive analytical results, indicating moderate-to-high mathematical complexity, while its empirical analysis uses 50+ years of U.S. and international stock market data, conditional statistics, and out-of-sample evaluation, demonstrating strong backtest-ready rigor. flowchart TD A["Research Goal: Optimize Time-Series Momentum to Mitigate Turning Point Vulnerabilities"] --> B["Data & Inputs"] B --> C["Methodology: Signal Processing Framework"] B --> D["Asset Class: Global Futures Period: 1985-2020"] B --> E["Signal Construction: Fast vs Slow Moving Averages"] C --> F["Process: Change-Point Detection Bayesian Online Changepoint Detection"] C --> G["Process: Regime Switching Adaptive Momentum Weights"] F --> H["Outcome: Reduced Drawdowns at Trend Reversals"] G --> H H --> I["Key Findings: 1) Signal momentum and volatility are negatively correlated 2) Fast signals capture trend starts; Slow signals reduce noise 3) Adaptive regime-switching outperforms static portfolios by 4-6% annual return"]

Deep Value

Deep Value ArXiv ID: ssrn-3122327 “View on arXiv” Authors: Unknown Abstract We define “deep value” as episodes where the valuation spread between cheap and expensive securities is wide relative to its history. Examining deep v Keywords: Deep Value, Value Investing, Valuation Spreads, Asset Pricing Anomalies, Quantitative Equity, Equity Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper uses straightforward descriptive statistics and historical analysis of valuation spreads, with minimal advanced mathematics, but appears heavily reliant on real market data and backtesting scenarios for its conclusions. flowchart TD A["Research Goal: Identify & model "Deep Value" episodes widest valuation spreads relative to history"] --> B["Data & Inputs"] B --> B1["Panel of US Stocks"] B --> B2["Valuation Metrics e.g., B/M, E/P"] B --> B3["Historical Time Series for spread distribution"] B --> B4["Market Cap & Returns"] B --> C["Key Methodology: Deep Value Definition"] C --> C1["Compute cross-sectional valuation spread e.g., Value - Growth spread"] C --> C2["Define Deep Value episodes periods where spread > 90th percentile of history"] C --> D["Computational Process: Portfolio Construction"] D --> D1["Sort stocks into Value quantiles"] D --> D2["Go long Cheapest (Deep Value) decile Short Expensive decile"] D --> D3["Calculate factor returns & alphas controlling for momentum/quality"] D --> E["Key Findings & Outcomes"] E --> E1["Deep Value spreads are cyclical & persistent predicting long-term returns"] E --> E2["Value factor returns significantly higher during Deep Value episodes"] E --> E3["Returns decay over short horizons but rebound over 3-5 years"] E --> E4["Out-of-sample performance robust across regions and time"]

Deep Value

Deep Value ArXiv ID: ssrn-3076181 “View on arXiv” Authors: Unknown Abstract We define “deep value” as episodes where the valuation spread between cheap and expensive securities is wide relative to its history. Examining deep value acros Keywords: Deep Value, Value Investing, Valuation Spreads, Asset Pricing Anomalies, Quantitative Equity, Equity Complexity vs Empirical Score Math Complexity: 5.0/10 Empirical Rigor: 6.5/10 Quadrant: Street Traders Why: The paper uses standard financial mathematics and Gordon’s growth model but is grounded in extensive empirical analysis across multiple asset classes with detailed data construction (522 value strategies, 3000 deep value episodes), backtesting, and statistical testing of competing theories. flowchart TD A["Research Goal: Define and analyze 'Deep Value' episodes"] --> B["Data Input: Historical valuation spreads (e.g., Price-to-Book, Price-to-Earnings)"] B --> C["Computational Process: Calculate z-scores of valuation spreads over time"] C --> D["Key Methodology: Identify 'Deep Value' regimes when spread > threshold"] D --> E["Outcome: Deep Value portfolios (Buy cheap, sell expensive)"] E --> F["Key Finding: Value spreads widen during crises, offering premium when reverting"]