Equity Markets

Stock Market Charts You Never Saw ArXiv ID: ssrn-3050736 “View on arXiv” Authors: Unknown Abstract Investors have seen countless charts of US stock market performance which start in 1926 and end near the present. But US trading long predates 1926, and the for Keywords: Historical Data, Stock Market, Equity Markets, Time Series Analysis Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper focuses on historical analysis and visual critique of existing charts, with minimal advanced mathematics beyond basic returns calculations, and lacks rigorous backtesting or new quantitative implementation. flowchart TD A["Research Goal: Extend stock market analysis pre-1926 using historical data"] --> B{"Methodology"}; B --> C["Data Collection: Pre-1926 US equity data"]; B --> D["Analysis: Time series & statistical backtesting"]; C --> E["Computational Process: Performance simulation & volatility modeling"]; D --> E; E --> F["Key Findings/Outcomes: Validated long-term trends, revealed pre-1926 market cycles"];

Trading with Time Series Causal Discovery: An Empirical Study ArXiv ID: 2408.15846 “View on arXiv” Authors: Unknown Abstract This study investigates the application of causal discovery algorithms in equity markets, with a focus on their potential to build investment strategies. An investment strategy was developed based on the causal structures identified by these algorithms. The performance of the strategy is evaluated based on the profitability and effectiveness in stock markets. The results indicate that causal discovery algorithms can successfully uncover actionable causal relationships in large markets, leading to profitable investment outcomes. However, the research also identifies a critical challenge: the computational complexity and scalability of these algorithms when dealing with large datasets. This challenge presents practical limitations for their application in real-world market analysis. ...