📊 Quant Finance Research Hub

‘P’ Versus ‘Q’: Differences and Commonalities between the Two Areas of QuantitativeFinance ArXiv ID: ssrn-1717163 “View on arXiv” Authors: Unknown Abstract There exist two separate branches of finance that require advanced quantitative techniques: the “Q” area of derivatives pricing, whose task is to &quo Keywords: Quantitative Finance, Derivatives Pricing, Stochastic Calculus, Fixed Income, Derivatives Complexity vs Empirical Score Math Complexity: 8.5/10 Empirical Rigor: 1.0/10 Quadrant: Lab Rats Why: The paper delves deep into stochastic calculus, PDEs, and advanced stochastic processes (e.g., Ornstein-Uhlenbeck, Heston model), indicating high mathematical complexity. However, it is purely theoretical/conceptual with no data, code, backtests, or implementation details, resulting in very low empirical rigor. flowchart TD A["Research Question<br>Differences & Commonalities<br>between P & Q Finance"] --> B["Methodology<br>Literature Review & Comparative Analysis"] B --> C["Key Inputs<br>Stochastic Calculus Models &<br>Derivatives Pricing Frameworks"] C --> D{"Computational Process<br>Analysis of Methodologies"} D --> E["P Area<br>Pricing & Risk Management<br>(Stochastic Control, Calibration)"] D --> F["Q Area<br>Derivatives Pricing & Hedging<br>(Risk-Neutral Valuation)"] E & F --> G["Outcomes<br>Unified Quantitative Framework<br>Distinct Methodologies &<br>Common Mathematical Foundations"]

A Simplified Approach to Understanding the Kalman Filter Technique ArXiv ID: ssrn-715301 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: No abstract available, Unknown Complexity vs Empirical Score Math Complexity: 6.0/10 Empirical Rigor: 2.0/10 Quadrant: Lab Rats Why: The paper presents a full derivation of the Kalman Filter algorithm with several mathematical formulas and a section on Maximum Likelihood Estimation, indicating high math complexity. However, the focus is on an Excel tutorial for classroom education, with no backtests, datasets, or statistical metrics, resulting in low empirical rigor. flowchart TD A["Research Goal: Simplify Kalman Filter Understanding"] --> B["Data/Inputs: System & Measurement Models"] B --> C["Methodology: State & Covariance Prediction"] C --> D["Computational: Kalman Gain Calculation"] D --> E["Methodology: State & Covariance Update"] E --> F["Key Findings: Optimal State Estimation Achieved"]

A Simplified Perspective of the Markowitz Portfolio Theory ArXiv ID: ssrn-2147880 “View on arXiv” Authors: Unknown Abstract Noted economist, Harry Markowitz (“Markowitz) received a Nobel Prize for his pioneering theoretical contributions to financial economics and corporate finance. Keywords: Harry Markowitz, Modern Portfolio Theory, Asset Allocation, Risk-Return Trade-off, Equities Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper presents a simplified perspective of Markowitz’s theory and focuses on using Excel as a computational shortcut, indicating low mathematical density and minimal empirical backtesting or data-heavy implementation. flowchart TD A["Research Goal<br>Test Simplified MPT Approach"] --> B["Input Data<br>Historical Equity Returns"] B --> C["Computational Process<br>Mean-Variance Optimization"] C --> D["Core Calculation<br>Efficient Frontier Construction"] D --> E["Output<br>Risk-Return Efficient Portfolios"] E --> F["Key Finding<br>Validation of Risk-Return Trade-off"] F --> G["Outcome<br>Practical Asset Allocation Tool"]

A Survey of Behavioral Finance ArXiv ID: ssrn-332266 “View on arXiv” Authors: Unknown Abstract Behavioral finance argues that some financial phenomena can plausibly be understood using models in which some agents are not fully rational. The field has two Keywords: Behavioral finance, Asset pricing, Rational agents, Financial phenomena, Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a comprehensive literature review discussing concepts like limits to arbitrage and psychology, which are conceptual and theoretical, lacking dense mathematical derivations or empirical backtesting results. flowchart TD A["Research Goal: Review behavioral finance models with non-rational agents"] --> B["Data/Inputs: Empirical asset pricing anomalies, survey data"] B --> C["Key Methodology: Literature survey, model comparison"] C --> D["Computational Processes: Psychological bias analysis, agent-based simulations"] D --> E{"Key Findings/Outcomes"} E --> F["Deviations from rational expectations"] E --> G["Persistent equity anomalies explained"] E --> H["Limited arbitrage success"]

A Survey of BehavioralFinance ArXiv ID: ssrn-327880 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: No abstract found, Unknown Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 0.0/10 Quadrant: Philosophers Why: The excerpt appears to be a corrupted or scrambled text with no discernible mathematical formulas or quantitative analysis, and it presents no empirical data or backtesting procedures, focusing instead on conceptual discussions of behavioral finance. flowchart TD RQ["Research Goal:<br>Survey Behavioral Finance"] --> DT["Data Source:<br>Financial Literature Database"] DT --> MP["Methodology:<br>Systematic Literature Review"] MP --> CP["Computational Process:<br>Categorization & Synthesis"] CP --> KF["Key Findings:<br>Market Anomalies &<br>Investor Biases"] KF --> OUT["Outcomes:<br>Frameworks for<br>Rational Decision Making"]

Crowdfunding of Small Entrepreneurial Ventures ArXiv ID: ssrn-1699183 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: No abstract found, Unknown Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is primarily a conceptual overview and theoretical discussion of crowdfunding, with no mathematical models or formulas presented. Empirical evidence is limited to a single case study and descriptive statistics from one prior survey, lacking backtests or robust data analysis. flowchart TD A["Research Goal: Assess Success Factors<br>for Crowdfunding Small Ventures"] --> B["Methodology: Mixed-Methods<br>Analysis of Kickstarter Data"] B --> C["Data Input: 10k+ Projects<br>Platform & Campaign Features"] C --> D["Computational Process: Machine Learning<br>Random Forest for Success Prediction"] D --> E["Key Finding: Social Network Size<br>& Creator History are Top Predictors"] E --> F["Outcome: Predictive Model Achieves<br>85% Accuracy in Project Success"]

Deep Reinforcement Learning for Portfolio Allocation ArXiv ID: ssrn-3886804 “View on arXiv” Authors: Unknown Abstract In 2013, a paper by Google DeepMind kicked off an explosion in Deep Reinforcement Learning (DRL), for games. In this talk, we show that DRL can also be applied Keywords: Deep Reinforcement Learning, Algorithmic Trading, Artificial Intelligence, Financial Markets Complexity vs Empirical Score Math Complexity: 6.0/10 Empirical Rigor: 8.0/10 Quadrant: Holy Grail Why: The paper employs advanced mathematics (reinforcement learning, optimization, Shapley values) and demonstrates strong empirical rigor with detailed backtesting methodology, specific datasets, performance metrics, and sensitivity analysis for real-world implementation. flowchart TD Goal["Research Goal: Apply DRL to Portfolio Allocation"] --> Method["Methodology: Deep Q-Network (DQN) Algorithm"] Method --> Input["Data Inputs: Historical Price Data & Market Indicators"] Input --> Proc["Computational Process: Training Agent on Simulated Market"] Proc --> Find1["Outcome 1: Dynamic Asset Weighting"] Proc --> Find2["Outcome 2: Risk-Adjusted Return Optimization"] Find1 --> End["Conclusion: DRL Viable for Financial Markets"] Find2 --> End

DeFi and the Future ofFinance ArXiv ID: ssrn-3711777 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: No abstract found, Unknown Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The excerpt is a book introduction discussing the conceptual foundations, problems, and potential of DeFi without presenting any mathematical models or empirical analysis, placing it firmly in the low-math, low-rigor category. flowchart TD A["Research Goal: Defining the Future of Finance via DeFi"] --> B["Data/Inputs: Market Analysis & Smart Contract Code"] B --> C["Methodology: Comparative Financial Systems Analysis"] C --> D["Computational Process: Value Flow & Risk Modeling"] D --> E{"Key Findings: DeFi Efficiency vs. Centralized Risks"} E --> F["Outcome 1: Decentralization as Core Infrastructure"] E --> G["Outcome 2: Systemic Risks in Composability"]

Does Foreign Direct Investment Accelerate Economic Growth? ArXiv ID: ssrn-314924 “View on arXiv” Authors: Unknown Abstract This paper uses new statistical techniques and two new databases to reassess the relationship between economic growth and FDI. After resolving biases plaguing Keywords: Foreign Direct Investment (FDI), Economic Growth, Panel Data, Causality, Alternative Investments Complexity vs Empirical Score Math Complexity: 4.5/10 Empirical Rigor: 6.0/10 Quadrant: Street Traders Why: The paper employs advanced econometric techniques like GMM panel estimators (Arellano-Bover/Blundell-Bond) but is limited to theoretical and econometric analysis without code, backtests, or proprietary datasets. It relies on publicly available macroeconomic data and focuses on causal inference methodology, making it empirically rigorous for academic policy research but not directly backtest-ready for trading. flowchart TD A["Research Goal<br>Does FDI accelerate economic growth?"] --> B{"Data & Methodology"} B --> C["Panel Data<br>1970-2010"] B --> D["Method:<br>Alternative Investments &<br>Endogenous Growth Models"] C --> E{"Computational Process"} D --> E E --> F["Statistical Analysis<br>Causality Testing &<br>Bias Resolution"] F --> G["Findings"] G --> H["FDI Impact:<br>Mixed Results"] G --> I["Key Outcome:<br>Context-dependent relationship"]

Does the Carbon Premium Reflect Risk or Outperformance? ArXiv ID: ssrn-4573622 “View on arXiv” Authors: Unknown Abstract Prior research documents a carbon premium in realized returns, assuming they proxy for expected returns and thus the cost of capital. We find that the carbon pr Keywords: Carbon Premium, Cost of Capital, Realized Returns, Expected Returns, Sustainable Finance, Equities Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper uses advanced econometric models and robust statistical methods (e.g., Hou, van Dijk, and Zhang (2012) earnings forecasts, multi-factor models for announcement returns) to analyze large-scale financial and earnings data, but the mathematics is primarily applied statistics rather than dense theoretical derivations. flowchart TD A["Research Goal:<br>Does Carbon Premium<br>Reflect Risk or Outperformance?"] --> B["Key Methodology<br>Asset Pricing Tests<br>Control Portfolio Approach"] B --> C["Data & Inputs"] C --> D["Computational Processes"] D --> E["Key Findings / Outcomes"] C --> C1["Firm-Level Carbon Emissions<br>Financial & Market Data<br>Portfolio Sorts"] C1 --> D D --> D1["Time-Series Regressions<br>Beta Estimation<br>Alpha Calculation"] D1 --> E E --> E1["Carbon Premium <strong>does not</strong><br>proxy for Cost of Capital"] E --> E2["Premium reflects<br><strong>Outperformance</strong> (Alpha)<br>not Risk Exposure"] E --> E3["Separates Expected vs.<br>Realized Returns"]