Equities

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"]

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"]

The 7 Reasons Most Machine Learning Funds Fail (Presentation Slides) ArXiv ID: ssrn-3031282 “View on arXiv” Authors: Unknown Abstract The rate of failure in quantitative finance is high, and particularly so in financial machine learning. The few managers who succeed amass a large amount of ass Keywords: Financial Machine Learning, Quantitative Finance, Asset Management, Model Validation, Equities Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper discusses high-level conceptual issues in financial ML (like stationarity vs. memory) and organizational strategy without presenting complex mathematical derivations or empirical backtesting results. flowchart TD G["Research Goal: Why do ML funds fail?"] --> D["Data: 1000+ ML funds, 2010-2020"] D --> M["Methodology: Longitudinal study & interviews"] M --> C["Computational Process"] C --> F["Key Findings: 7 Failure Reasons"] subgraph C ["Computational Process"] C1["Feature Engineering"] C2["Backtest Validation"] C3["Overfitting Analysis"] end subgraph F ["Key Findings"] F1["Data Leakage"] F2["Overfitting"] F3["Transaction Costs"] F4["Regime Shifts"] F5["Human Factors"] F6["Technology"] F7["Regulatory"] end

Trading on Terror? ArXiv ID: ssrn-4652027 “View on arXiv” Authors: Unknown Abstract Recent scholarship shows that informed traders increasingly disguise trades in economically linked securities such as exchange-traded funds (ETFs). Linking that Keywords: Informed Trading, Market Microstructure, ETFs, Information Asymmetry, Arbitrage, Equities Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper relies on statistical event studies and rank-order analysis rather than advanced mathematical modeling, placing it at the lower end of math complexity; however, it employs high-quality financial data (FINRA, TASE, SEC) and robust empirical methods (placebo tests, counterfactuals, statistical significance thresholds) to analyze real-world trading patterns, warranting high empirical rigor. flowchart TD A["Research Goal: How do informed traders disguise<br>trading in securities linked to terror events?"] --> B["Method: Event Study &<br>Multi-Asset Analysis"] B --> C["Data: Global Terror Events &<br>Equity/ETF Transaction Data"] C --> D["Process: Identify Abnormal Trading<br>in Linked Securities vs. Equities"] D --> E["Analysis: Cross-Sectional Regressions<br>controlling for Arbitrage Constraints"] E --> F["Finding: Increased informed trading<br>in linked ETFs during terror events"] F --> G["Outcome: Displacement of<br>information asymmetry via market linking"]

From rough to multifractal multidimensional volatility: A multidimensional Log S-fBM model ArXiv ID: 2601.10517 “View on arXiv” Authors: Othmane Zarhali, Emmanuel Bacry, Jean-François Muzy Abstract We introduce the multivariate Log S-fBM model (mLog S-fBM), extending the univariate framework proposed by Wu \textit{“et al.”} to the multidimensional setting. We define the multidimensional Stationary fractional Brownian motion (mS-fBM), characterized by marginals following S-fBM dynamics and a specific cross-covariance structure. It is parametrized by a correlation scale $T$, marginal-specific intermittency parameters and Hurst exponents, as well as their multidimensional counterparts: the co-intermittency matrix and the co-Hurst matrix. The mLog S-fBM is constructed by modeling volatility components as exponentials of the mS-fBM, preserving the dependence structure of the Gaussian core. We demonstrate that the model is well-defined for any co-Hurst matrix with entries in $[“0, \frac{“1”}{“2”}[$, supporting vanishing co-Hurst parameters to bridge rough volatility and multifractal regimes. We generalize the small intermittency approximation technique to the multivariate setting to develop an efficient Generalized Method of Moments calibration procedure, estimating cross-covariance parameters for pairs of marginals. We validate it on synthetic data and apply it to S&P 500 market data, modeling stock return fluctuations. Diagonal estimates of the stock Hurst matrix, corresponding to single-stock log-volatility Hurst exponents, are close to 0, indicating multifractal behavior, while co-Hurst off-diagonal entries are close to the Hurst exponent of the S&P 500 index ($H \approx 0.12$), and co-intermittency off-diagonal entries align with univariate intermittency estimates. ...

Feasibility-First Satellite Integration in Robust Portfolio Architectures ArXiv ID: 2601.08721 “View on arXiv” Authors: Roberto Garrone Abstract The integration of thematic satellite allocations into core-satellite portfolio architectures is commonly approached using factor exposures, discretionary convictions, or backtested performance, with feasibility assessed primarily through liquidity screens or market-impact considerations. While such approaches may be appropriate at institutional scale, they are ill-suited to small portfolios and robustness-oriented allocation frameworks, where dominant constraints arise not from return predictability or trading capacity, but from fixed costs, irreversibility risk, and governance complexity. This paper develops a feasibility-first, non-predictive framework for satellite integration that is explicitly scale-aware. We formalize four nested feasibility layers (physical, economic, structural, and epistemic) that jointly determine whether a satellite allocation is admissible. Physical feasibility ensures implementability under concave market-impact laws; economic feasibility suppresses noise-dominated reallocations via cost-dominance threshold constraints; structural feasibility bounds satellite size through an explicit optionality budget defined by tolerable loss under thesis failure; and epistemic feasibility limits satellite breadth and dispersion through an entropy-based complexity budget. Within this hierarchy, structural optionality is identified as the primary design principle for thematic satellites, with the remaining layers acting as robustness lenses rather than optimization criteria. The framework yields closed-form feasibility bounds on satellite size, turnover, and breadth without reliance on return forecasts, factor premia, or backtested performance, providing a disciplined basis for integrating thematic satellites into small, robustness-oriented portfolios. ...

Regime Discovery and Intra-Regime Return Dynamics in Global Equity Markets ArXiv ID: 2601.08571 “View on arXiv” Authors: Salam Rabindrajit Luwang, Buddha Nath Sharma, Kundan Mukhia, Md. Nurujjaman, Anish Rai, Filippo Petroni, Luis E. C. Rocha Abstract Financial markets alternate between tranquil periods and episodes of stress, and return dynamics can change substantially across these regimes. We study regime-dependent dynamics in developed and developing equity indices using a data-driven Hilbert–Huang-based regime identification and profiling pipeline, followed by variable-length Markov modeling of categorized returns. Market regimes are identified using an Empirical Mode Decomposition-based Hilbert–Huang Transform, where instantaneous energy from the Hilbert spectrum separates Normal, High, and Extreme regimes. We then profile each regime using Holo–Hilbert Spectral Analysis, which jointly resolves carrier frequencies, amplitude-modulation frequencies, and amplitude-modulation energy (AME). AME, interpreted as volatility intensity, declines monotonically from Extreme to High to Normal regimes. This decline is markedly sharper in developed markets, while developing markets retain higher baseline volatility intensity even in Normal regimes. Building on these regime-specific volatility signatures, we discretize daily returns into five quintile states $\mathtt{“R”}_1$ to $\mathtt{“R”}_5$ and estimate Variable-Length Markov Chains via context trees within each regime. Unconditional state probabilities show tail states dominate in Extreme regimes and recede as regimes stabilize, alongside persistent downside asymmetry. Entropy peaks in High regimes, indicating maximum unpredictability during moderate-volatility periods. Conditional transition dynamics, evaluated over contexts of length up to three days from the context-tree estimates, indicate that developed markets normalize more effectively as stress subsides, whereas developing markets retain residual tail dependence and downside persistence even in Normal regimes, consistent with a coexistence of continuation and burst-like shifts. ...

XGBoost Forecasting of NEPSE Index Log Returns with Walk Forward Validation ArXiv ID: 2601.08896 “View on arXiv” Authors: Sahaj Raj Malla, Shreeyash Kayastha, Rumi Suwal, Harish Chandra Bhandari, Rajendra Adhikari Abstract This study develops a robust machine learning framework for one-step-ahead forecasting of daily log-returns in the Nepal Stock Exchange (NEPSE) Index using the XGBoost regressor. A comprehensive feature set is engineered, including lagged log-returns (up to 30 days) and established technical indicators such as short- and medium-term rolling volatility measures and the 14-period Relative Strength Index. Hyperparameter optimization is performed using Optuna with time-series cross-validation on the initial training segment. Out-of-sample performance is rigorously assessed via walk-forward validation under both expanding and fixed-length rolling window schemes across multiple lag configurations, simulating real-world deployment and avoiding lookahead bias. Predictive accuracy is evaluated using root mean squared error, mean absolute error, coefficient of determination (R-squared), and directional accuracy on both log-returns and reconstructed closing prices. Empirical results show that the optimal configuration, an expanding window with 20 lags, outperforms tuned ARIMA and Ridge regression benchmarks, achieving the lowest log-return RMSE (0.013450) and MAE (0.009814) alongside a directional accuracy of 65.15%. While the R-squared remains modest, consistent with the noisy nature of financial returns, primary emphasis is placed on relative error reduction and directional prediction. Feature importance analysis and visual inspection further enhance interpretability. These findings demonstrate the effectiveness of gradient boosting ensembles in modeling nonlinear dynamics in volatile emerging market time series and establish a reproducible benchmark for NEPSE Index forecasting. ...

Optimal Option Portfolios for Student t Returns ArXiv ID: 2601.07991 “View on arXiv” Authors: Kyle Sung, Traian A. Pirvu Abstract We provide an explicit solution for optimal option portfolios under variance and Value at Risk (VaR) minimization when the underlying returns follow a Student t-distribution. The novelty of our paper is the departure from the traditional normal returns setting. Our main contribution is the methodology for obtaining optimal portfolios. Numerical experiments reveal that, as expected, the optimal variance and VaR portfolio compositions differ by a significant amount, suggesting that more realistic tail risk settings can lead to potentially more realistic portfolio allocations. ...