Equity

A Comparative Study of Portfolio Optimization Methods for the Indian Stock Market ArXiv ID: 2310.14748 “View on arXiv” Authors: Unknown Abstract This chapter presents a comparative study of the three portfolio optimization methods, MVP, HRP, and HERC, on the Indian stock market, particularly focusing on the stocks chosen from 15 sectors listed on the National Stock Exchange of India. The top stocks of each cluster are identified based on their free-float market capitalization from the report of the NSE published on July 1, 2022 (NSE Website). For each sector, three portfolios are designed on stock prices from July 1, 2019, to June 30, 2022, following three portfolio optimization approaches. The portfolios are tested over the period from July 1, 2022, to June 30, 2023. For the evaluation of the performances of the portfolios, three metrics are used. These three metrics are cumulative returns, annual volatilities, and Sharpe ratios. For each sector, the portfolios that yield the highest cumulative return, the lowest volatility, and the maximum Sharpe Ratio over the training and the test periods are identified. ...

Co-Training Realized Volatility Prediction Model with Neural Distributional Transformation ArXiv ID: 2310.14536 “View on arXiv” Authors: Unknown Abstract This paper shows a novel machine learning model for realized volatility (RV) prediction using a normalizing flow, an invertible neural network. Since RV is known to be skewed and have a fat tail, previous methods transform RV into values that follow a latent distribution with an explicit shape and then apply a prediction model. However, knowing that shape is non-trivial, and the transformation result influences the prediction model. This paper proposes to jointly train the transformation and the prediction model. The training process follows a maximum-likelihood objective function that is derived from the assumption that the prediction residuals on the transformed RV time series are homogeneously Gaussian. The objective function is further approximated using an expectation-maximum algorithm. On a dataset of 100 stocks, our method significantly outperforms other methods using analytical or naive neural-network transformations. ...

Topological Portfolio Selection and Optimization ArXiv ID: 2310.14881 “View on arXiv” Authors: Unknown Abstract Modern portfolio optimization is centered around creating a low-risk portfolio with extensive asset diversification. Following the seminal work of Markowitz, optimal asset allocation can be computed using a constrained optimization model based on empirical covariance. However, covariance is typically estimated from historical lookback observations, and it is prone to noise and may inadequately represent future market behavior. As a remedy, information filtering networks from network science can be used to mitigate the noise in empirical covariance estimation, and therefore, can bring added value to the portfolio construction process. In this paper, we propose the use of the Statistically Robust Information Filtering Network (SR-IFN) which leverages the bootstrapping techniques to eliminate unnecessary edges during the network formation and enhances the network’s noise reduction capability further. We apply SR-IFN to index component stock pools in the US, UK, and China to assess its effectiveness. The SR-IFN network is partially disconnected with isolated nodes representing lesser-correlated assets, facilitating the selection of peripheral, diversified and higher-performing portfolios. Further optimization of performance can be achieved by inversely proportioning asset weights to their centrality based on the resultant network. ...

Do We Price Happiness? Evidence from Korean Stock Market ArXiv ID: 2308.10039 “View on arXiv” Authors: Unknown Abstract This study explores the potential of internet search volume data, specifically Google Trends, as an indicator for cross-sectional stock returns. Unlike previous studies, our research specifically investigates the search volume of the topic ‘happiness’ and its impact on stock returns in the aspect of risk pricing rather than as sentiment measurement. Empirical results indicate that this ‘happiness’ search exposure (HSE) can explain future returns, particularly for big and value firms. This suggests that HSE might be a reflection of a firm’s ability to produce goods or services that meet societal utility needs. Our findings have significant implications for institutional investors seeking to leverage HSE-based strategies for outperformance. Additionally, our research suggests that, when selected judiciously, some search topics on Google Trends can be related to risks that impact stock prices. ...

To the Moon: Analyzing Collective Trading Events on the Wings of Sentiment Analysis ArXiv ID: 2308.09968 “View on arXiv” Authors: Unknown Abstract This research investigates the growing trend of retail investors participating in certain stocks by organizing themselves on social media platforms, particularly Reddit. Previous studies have highlighted a notable association between Reddit activity and the volatility of affected stocks. This study seeks to expand the analysis to Twitter, which is among the most impactful social media platforms. To achieve this, we collected relevant tweets and analyzed their sentiment to explore the correlation between Twitter activity, sentiment, and stock volatility. The results reveal a significant relationship between Twitter activity and stock volatility but a weak link between tweet sentiment and stock performance. In general, Twitter activity and sentiment appear to play a less critical role in these events than Reddit activity. These findings offer new theoretical insights into the impact of social media platforms on stock market dynamics, and they may practically assist investors and regulators in comprehending these phenomena better. ...

Mandatory CSR and Sustainability Reporting: Economic Analysis and Literature Review ArXiv ID: ssrn-3945116 “View on arXiv” Authors: Unknown Abstract This study collates potential economic effects of mandated disclosure and reporting standards for corporate social responsibility (CSR) and sustainability topic Keywords: Corporate Social Responsibility (CSR), Sustainability Reporting, Mandated Disclosure, ESG Metrics, Equity Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a qualitative literature review synthesizing economic theory on CSR reporting regulations without mathematical derivations or statistical backtesting. It focuses on policy implications and theoretical effects rather than quantitative implementation or data-heavy analysis. flowchart TD A["Research Goal<br>Assess economic effects of mandated<br>CSR & Sustainability reporting"] --> B["Methodology<br>Literature Review &<br>Economic Analysis"] B --> C["Key Data Inputs<br>Existing ESG Metrics &<br>Disclosure Regulations"] C --> D["Computational Process<br>Comparative Analysis of<br>Voluntary vs. Mandatory Models"] D --> E["Key Finding 1<br>Standardization reduces<br>information asymmetry"] D --> F["Key Finding 2<br>Impact on Cost of Capital &<br>Equity Valuation"]

Consumer Spending Responses to the COVID-19 Pandemic: An Assessment of Great Britain ArXiv ID: ssrn-3586723 “View on arXiv” Authors: Unknown Abstract Since the first death in China in early January 2020, the coronavirus (COVID-19) has spread across the globe and dominated the news headlines leading to fundame Keywords: COVID-19, Volatility, Market Turbulence, Risk Management, Crisis Economics, Equity Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 9.0/10 Quadrant: Street Traders Why: The paper uses advanced econometric methods (e.g., time-series regressions with fixed effects) but is fundamentally an empirical study relying on a massive proprietary transaction dataset (23 million transactions) to analyze real-world consumer behavior, with no code/backtests presented but heavy data and implementation details. flowchart TD A["Research Goal:<br>Assess UK consumer<br>spending volatility<br>amid COVID-19"] --> B["Data Source:<br>UK Finance Admin Data<br>(n = 70M accounts)"] B --> C["Methodology:<br>Panel Regression &<br>Time-Series Analysis"] C --> D["Computational Process:<br>Compare Pre/Post-<br>Pandemic Spending Trends"] D --> E["Key Finding 1:<br>Immediate spending<br>contraction (Mar 2020)"] D --> F["Key Finding 2:<br>Shift from services<br>to durable goods"] D --> G["Key Finding 3:<br>Volatility spiked;<br>uncertainty persisted"]

How ESG Issues Become Financially Material to Corporations and Their Investors ArXiv ID: ssrn-3482546 “View on arXiv” Authors: Unknown Abstract Management and disclosure of environmental, social and governance (ESG) issues have received substantial interest over the last decade. In this paper, we outlin Keywords: ESG, Sustainable Investing, Corporate Governance, Risk Management, Equity Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper presents a conceptual framework on the pathways of ESG issues becoming financially material, lacking advanced mathematical models or statistical derivations. Empirical evidence is referenced but not derived from original backtests or datasets, relying more on literature review and case studies. flowchart TD A["Research Goal: Determine<br>ESG Financial Materiality"] --> B["Key Methodology:<br>Multi-Industry Regression Analysis"] B --> C{"Data Inputs"} C --> C1["Financial Data:<br>Cost of Equity & ROA"] C --> C2["ESG Scores:<br>Environmental, Social, Governance"] C --> C3["Control Variables:<br>Size, Leverage, Growth"] D["Computational Process:<br>Time-Panel Regression"] --> E["Key Findings/Outcomes"] C1 --> D C2 --> D C3 --> D E --> E1["Sector-Specific Materiality:<br>Varies by Industry"] E --> E2["Strong Governance<br>Universally Reduces Risk"] E --> E3["Low ESG = Higher<br>Cost of Equity Capital"]

Mandatory CSR and Sustainability Reporting: Economic Analysis and Literature Review ArXiv ID: ssrn-3439179 “View on arXiv” Authors: Unknown Abstract This study collates potential economic effects of mandated disclosure and reporting standards for corporate social responsibility (CSR) and sustainability topic Keywords: Corporate Social Responsibility (CSR), Sustainability Reporting, Mandated Disclosure, ESG Metrics, Equity Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper is primarily a conceptual literature review and economic analysis of disclosure mandates, using standard economic theory and accounting concepts with minimal advanced mathematics. It lacks empirical testing, backtests, or quantitative data analysis, focusing instead on synthesizing existing research and discussing policy implications. flowchart TD A["Research Goal: Economic Effects of Mandatory CSR Reporting"] --> B{"Methodology: Event Study & Literature Review"} B --> C["Data: Stock Returns, ESG Metrics, Regulatory Events"] C --> D["Computation: Abnormal Returns & Regression Analysis"] D --> E{"Key Findings"} E --> F["Positive Market Reaction to Mandates"] E --> G["Reduced Information Asymmetry"] E --> H["Improvement in Equity Valuation"]

Mandatory CSR and Sustainability Reporting: Economic Analysis and Literature Review ArXiv ID: ssrn-3427748 “View on arXiv” Authors: Unknown Abstract This study collates potential economic effects of mandated disclosure and reporting standards for corporate social responsibility (CSR) and sustainability topic Keywords: Corporate Social Responsibility (CSR), Sustainability Reporting, Mandated Disclosure, ESG Metrics, Equity Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a literature review and economic analysis of mandated CSR reporting, relying on conceptual arguments and discussion of existing academic literature rather than new mathematical models or empirical backtesting. flowchart TD A["Research Goal: Economic Effects of Mandatory CSR/Sustainability Reporting"] --> B{"Methodology"} B --> C["Literature Review &<br>Economic Analysis"] C --> D["Computational Process:<br>Cost-Benefit & Market Impact Model"] D --> E["Key Findings/Outcomes"] E --> F["Complex trade-offs:<br>Standardization vs. Compliance Costs"] E --> G["Potential for improved<br>equity and market efficiency"]