Stock Market

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:<br>Extend stock market analysis<br>pre-1926 using historical data"] --> B{"Methodology"}; B --> C["Data Collection:<br>Pre-1926 US equity data"]; B --> D["Analysis:<br>Time series & statistical<br>backtesting"]; C --> E["Computational Process:<br>Performance simulation<br>& volatility modeling"]; D --> E; E --> F["Key Findings/Outcomes:<br>Validated long-term trends,<br>revealed pre-1926 market cycles"];

A Decadal Analysis of the Lead-Lag Effect in the NYSE ArXiv ID: 2312.10084 “View on arXiv” Authors: Unknown Abstract As is widely known, the stock market is a complex system in which a multitude of factors influence the performance of individual stocks and the market as a whole. One method for comprehending – and potentially predicting – stock market behavior is through network analysis, which can offer insights into the relationships between stocks and the overall market structure. In this paper, we seek to address the question: Can network analysis of the stock market, specifically in observation of the lead-lag effect, provide valuable insights for investors and market analysts? ...

Stockformer: A Price-Volume Factor Stock Selection Model Based on Wavelet Transform and Multi-Task Self-Attention Networks ArXiv ID: 2401.06139 “View on arXiv” Authors: Unknown Abstract As the Chinese stock market continues to evolve and its market structure grows increasingly complex, traditional quantitative trading methods are facing escalating challenges. Particularly, due to policy uncertainty and the frequent market fluctuations triggered by sudden economic events, existing models often struggle to accurately predict market dynamics. To address these challenges, this paper introduces Stockformer, a price-volume factor stock selection model that integrates wavelet transformation and a multitask self-attention network, aimed at enhancing responsiveness and predictive accuracy regarding market instabilities. Through discrete wavelet transform, Stockformer decomposes stock returns into high and low frequencies, meticulously capturing long-term market trends and short-term fluctuations, including abrupt events. Moreover, the model incorporates a Dual-Frequency Spatiotemporal Encoder and graph embedding techniques to effectively capture complex temporal and spatial relationships among stocks. Employing a multitask learning strategy, it simultaneously predicts stock returns and directional trends. Experimental results show that Stockformer outperforms existing advanced methods on multiple real stock market datasets. In strategy backtesting, Stockformer consistently demonstrates exceptional stability and reliability across market conditions-whether rising, falling, or fluctuating-particularly maintaining high performance during downturns or volatile periods, indicating a high adaptability to market fluctuations. To foster innovation and collaboration in the financial analysis sector, the Stockformer model’s code has been open-sourced and is available on the GitHub repository: https://github.com/Eric991005/Multitask-Stockformer. ...

Evaluation of Reinforcement Learning Techniques for Trading on a Diverse Portfolio ArXiv ID: 2309.03202 “View on arXiv” Authors: Unknown Abstract This work seeks to answer key research questions regarding the viability of reinforcement learning over the S&P 500 index. The on-policy techniques of Value Iteration (VI) and State-action-reward-state-action (SARSA) are implemented along with the off-policy technique of Q-Learning. The models are trained and tested on a dataset comprising multiple years of stock market data from 2000-2023. The analysis presents the results and findings from training and testing the models using two different time periods: one including the COVID-19 pandemic years and one excluding them. The results indicate that including market data from the COVID-19 period in the training dataset leads to superior performance compared to the baseline strategies. During testing, the on-policy approaches (VI and SARSA) outperform Q-learning, highlighting the influence of bias-variance tradeoff and the generalization capabilities of simpler policies. However, it is noted that the performance of Q-learning may vary depending on the stability of future market conditions. Future work is suggested, including experiments with updated Q-learning policies during testing and trading diverse individual stocks. Additionally, the exploration of alternative economic indicators for training the models is proposed. ...