Papers With Backtest: An Algorithmic Trading Journey

Papers With Backtest

Welcome to Papers With Backtest, where data means profit in the world of algorithmic trading. Each episode dives into backtests, real-life trading applications, and groundbreaking research that every aspiring quant should know. Tune in to stay ahead in the algo trading game. Our website: https://paperswithbacktest.com/ Hosted by Ausha. See ausha.co/privacy-policy for more information.

  1. How Investor Sentiment Influences Long-Term Stock Performance Trends

    -5 J

    How Investor Sentiment Influences Long-Term Stock Performance Trends

    Have you ever wondered how investor sentiment can influence stock performance overnight? In this enlightening episode of Papers With Backtest: An Algorithmic Trading Journey, the hosts dissect a groundbreaking research paper that uncovers the intricate relationship between overnight stock returns and firm-specific investor sentiment. This exploration reveals the hidden dynamics of after-hours trading and its potential to serve as a reliable sentiment indicator, making it a must-listen for algorithmic trading enthusiasts. Join us as we delve into the fascinating world of overnight returns, where the persistence of these returns is not just a statistical anomaly but a powerful signal for traders. The episode reveals that stocks exhibiting high overnight returns tend to maintain their momentum in the following weeks, raising critical questions about how individual investor sentiment shapes market behavior. We analyze the implications of this persistence and discuss how various firm characteristics—such as volatility and institutional ownership—can further refine our understanding of sentiment dynamics. As we navigate through the research findings, we also explore the intriguing concept of longer-term reversals in stock performance. Can stocks that soar overnight actually underperform in the long run? This episode challenges conventional wisdom and encourages algorithmic traders to rethink their strategies based on initial overnight returns. By considering these factors, you can enhance your trading approach and make more informed decisions in the fast-paced world of algorithmic trading. Throughout the episode, we emphasize the importance of leveraging overnight returns as a quantifiable measure of investor sentiment. This insight is particularly valuable for those looking to develop robust trading algorithms that can adapt to changing market conditions. Whether you're a seasoned trader or just starting your algorithmic trading journey, the knowledge shared in this episode is sure to elevate your understanding of market sentiment and its implications for stock performance. Don't miss this opportunity to gain a deeper understanding of how firm-specific factors and investor sentiment intertwine in the realm of overnight trading. Tune in to Papers With Backtest: An Algorithmic Trading Journey and empower your trading strategies with data-driven insights that could redefine your approach to the market. Hosted by Ausha. See ausha.co/privacy-policy for more information.

    13 min
  2. Unusual Trading Volume

    25 OCT.

    Unusual Trading Volume

    What if the key to unlocking profitable trading strategies lies in the volume of stocks traded rather than their price? In this episode of Papers With Backtest: An Algorithmic Trading Journey, we take a deep dive into the groundbreaking research paper "Abnormal Volume Effect in the Stock Market," revealing how unusual trading volume can serve as a powerful indicator of future price movements. Join our hosts as they dissect the intricate relationship between abnormal trading volume—defined as activity exceeding 2.33 standard deviations from the average over the previous 66 days—and its correlation with stock price fluctuations. Throughout this enlightening discussion, we uncover compelling evidence that during periods of abnormal volume, significant positive excess returns are often observed. This suggests that these spikes in trading activity may signal underlying information that has not yet made its way into the public domain. By synthesizing volume signals with price direction, traders can enhance their strategies, making informed decisions that could lead to substantial gains. But what does the data say about the effectiveness of these strategies? Our hosts share insightful backtesting results that reveal a nuanced landscape. While long positions based on significant price increases following abnormal volume exhibited promising profitability, short selling strategies faltered primarily due to transaction costs. This critical analysis emphasizes the necessity of factoring in trading costs when developing strategies that leverage volume signals. As we navigate this complex terrain, we stress that while unusual trading activity can provide valuable insights, it is not a guaranteed path to profits. The episode concludes with a call to action for traders to meticulously evaluate their methodologies, ensuring they strike a balance between volume signals and the realities of market costs. Tune in to Papers With Backtest for an expert examination of how the abnormal volume effect can transform your trading approach and lead you towards more informed, data-driven decisions. Don't miss out on this opportunity to elevate your trading strategies—join us as we explore the fascinating intersection of volume and price, and uncover the potential hidden within abnormal trading patterns. Hosted by Ausha. See ausha.co/privacy-policy for more information.

    13 min
  3. Abnormal Trading Volume: Key Findings on Stock Returns

    18 OCT.

    Abnormal Trading Volume: Key Findings on Stock Returns

    What if the secret to unlocking the mysteries of stock market performance lies in understanding abnormal trading volume? In this enlightening episode of Papers With Backtest: An Algorithmic Trading Journey, our hosts delve deep into a groundbreaking research paper by Lee, Kim, and Kim from 2016 that scrutinizes the intricate relationship between abnormal trading volume and stock returns. This episode is a must-listen for traders and investors eager to enhance their understanding of market behavior and refine their trading strategies. Join us as we explore the core question: Can unusual trading activity be a reliable predictor of future stock performance? The hosts dissect the comprehensive methodology employed in the study, which analyzed a vast dataset of common stocks from the NYSE, Amex, and Nasdaq spanning an impressive timeframe from January 1968 to December 2015. This extensive analysis not only provides insights into historical trends but also equips listeners with the knowledge to navigate today's dynamic trading landscape. One of the key takeaways from this episode is the innovative approach of separating trading volume into two distinct components: expected trading turnover (E-turn) and unexpected trading turnover (U-turn). The findings are striking: E-turn negatively predicts stock returns, suggesting that higher expected trading often correlates with lower future returns. Conversely, U-turn demonstrates a positive correlation with future returns, indicating that unexpected trading activity may signal potential price increases. This nuanced understanding is crucial for traders seeking to make informed decisions based on volume data. Throughout the episode, we emphasize the significance of distinguishing between these two types of trading volume. Without this decomposition, raw volume can send mixed signals, leading to potentially misguided trading strategies. By honing in on the subtleties of trading volume, you can elevate your trading acumen and enhance your algorithmic trading strategies. Whether you’re a seasoned algorithmic trader or just starting your journey, this episode of Papers With Backtest will equip you with valuable insights and actionable knowledge. Tune in to discover how abnormal trading volume can reshape your approach to stock selection and risk management, and gain a competitive edge in the ever-evolving world of finance. Don’t miss out on this opportunity to deepen your understanding of market dynamics and refine your trading approach! Hosted by Ausha. See ausha.co/privacy-policy for more information.

    11 min
  4. Deep Learning vs. Traditional Methods: Enhancing Stock Return Forecasts in Japan's Financial Landscape

    11 OCT.

    Deep Learning vs. Traditional Methods: Enhancing Stock Return Forecasts in Japan's Financial Landscape

    Are you ready to unlock the secrets of stock market prediction using cutting-edge technology? In this episode of Papers With Backtest: An Algorithmic Trading Journey, we delve deep into the transformative paper "Deep Learning for Forecasting Stock Returns in the Cross-Section" by Abe and Nakayama, where the potential of deep learning techniques is put to the test in the realm of Japanese stock performance. This episode is a must-listen for algorithmic trading enthusiasts and data scientists alike, as we dissect the intricate methodologies that bridge finance and technology. Our discussion centers around a comprehensive dataset that encompasses constituents of the MSCI Japan Index, enriched by 25 standard financial factors tracked over a significant period from December 1990 to November 2016. We explore how these inputs serve as the backbone for predictive modeling, and how deep neural networks (DNNs) stack up against traditional machine learning methods like support vector regression (SVR) and random forests (RF). The insights gained from our analysis reveal that deeper neural networks generally outperform their shallower counterparts, providing a fascinating glimpse into the future of algorithmic trading. Throughout the episode, we scrutinize various neural network architectures and their effectiveness in enhancing predictive accuracy and achieving superior risk-adjusted returns in simulated trading strategies. The conversation takes a critical turn as we emphasize the often-overlooked impact of transaction costs in real-world applications, a crucial factor for any algorithmic trader aiming for profitability. As we navigate through the complexities of stock return forecasting, we also suggest intriguing avenues for future research, including the potential of recurrent neural networks and other advanced architectures that could revolutionize the field. Join us as we reflect on the robustness of deep learning advantages in stock prediction, and what this means for the future of finance and algorithmic trading. Whether you’re a seasoned trader or a curious newcomer, this episode is packed with insights that could reshape your understanding of market forecasting. Don’t miss the chance to elevate your trading strategies with the knowledge shared in this enlightening discussion on Papers With Backtest: An Algorithmic Trading Journey. Tune in now and discover how deep learning is not just a buzzword but a game-changer in the world of stock market predictions! Hosted by Ausha. See ausha.co/privacy-policy for more information.

    10 min
  5. Combining Trading Signals

    4 OCT.

    Combining Trading Signals

    Are you relying on a single trading signal to navigate the complexities of the foreign exchange market? If so, you might be missing out on the potential for enhanced profitability and reduced risk. In this engaging episode of Papers With Backtest: An Algorithmic Trading Journey, we dive deep into a groundbreaking 2019 research paper by Sonam Srivastava and colleagues, which unveils a multi-strategy approach to trading FX futures that could transform your trading game. Join our hosts as they dissect the intricacies of combining various trading signals—including momentum, mean reversion, and carry trades—demonstrating how a diversified toolkit can significantly outperform reliance on a single indicator. This episode is packed with insights into the structured methodology employed in the paper, covering everything from instrument selection to signal creation and risk budgeting strategies. You'll gain a comprehensive understanding of how to craft a robust trading strategy that stands the test of market volatility. Throughout the discussion, we meticulously analyze the performance of individual strategies, spotlighting standout performers like the long-term yield difference strategy while also addressing those that fell short. This thorough examination not only highlights the importance of strategy evaluation but also emphasizes the critical need for adaptability in algorithmic trading. The hosts reveal that the key to success lies in the synergy of multiple strategies, leading to significantly enhanced risk-adjusted returns. As we explore different combination methods for these strategies, you'll discover how a diversified approach can mitigate risks and maximize returns, making a compelling case for traders to abandon the quest for a single optimal signal. Instead, you'll learn why building a robust toolkit of diverse indicators is essential for navigating the unpredictable waters of the FX market. Concluding with a discussion on the importance of understanding market dynamics, our hosts underscore the potential for further research in this area, encouraging listeners to remain curious and innovative in their trading endeavors. Whether you are an experienced trader or just starting your journey, this episode of Papers With Backtest offers invaluable insights that can elevate your trading strategy to new heights. Tune in and equip yourself with the knowledge to thrive in the ever-evolving landscape of algorithmic trading! Hosted by Ausha. See ausha.co/privacy-policy for more information.

    10 min
  6. Inventory Management: Backtesting Optimal Quoting Strategies from Guillain's Influential Market Making Paper

    27 SEPT.

    Inventory Management: Backtesting Optimal Quoting Strategies from Guillain's Influential Market Making Paper

    How can market makers navigate the treacherous waters of inventory risk while still capitalizing on the bid-ask spread? In this riveting episode of Papers With Backtest: An Algorithmic Trading Journey, we dissect the pivotal 2012 paper by Guillain, Lahaye, and Fernandez Tapia, which sheds light on the complexities of managing inventory in the fast-paced world of market making. The hosts dive deep into the nuances of inventory risk, emphasizing that the quest for profit can quickly turn perilous if price movements go against market makers' positions. The conversation centers around the innovative stochastic control approach employed by the authors to model price fluctuations and order flow—an essential framework for any trader looking to refine their strategies. Understanding risk preferences is not merely academic; it is a cornerstone of effective trading strategies that can mean the difference between success and failure. Our hosts unravel the mathematical intricacies involved in deriving optimal quoting strategies, including the formidable Hamilton-Jacobi-Bellman equations, which form the backbone of this sophisticated analysis. But theory alone isn’t enough. We take you through the rigorous backtesting of these models using real-world tick data, revealing astonishing insights: the model-based strategy significantly outperformed naive trading approaches, showcasing the power of actively managing quotes in response to inventory levels and prevailing market conditions. Yet, as we celebrate these successes, we also issue a cautionary note: the real world is fraught with challenges, including ever-changing market dynamics that can complicate implementation. Continuous refinement of the model is not just advisable; it is essential. Join us as we explore the intersection of theory and practice in algorithmic trading, equipping you with the knowledge to enhance your own trading strategies. Whether you're a seasoned trader or an academic looking to bridge the gap between theory and real-world application, this episode of Papers With Backtest is packed with insights that are both profound and actionable. Tune in to discover how understanding inventory risk can redefine your approach to market making and trading. Hosted by Ausha. See ausha.co/privacy-policy for more information.

    10 min
  7. Exploring the Ramadan Effect

    20 SEPT.

    Exploring the Ramadan Effect

    What if we told you that during the Muslim holy month of Ramadan, stock returns in 14 predominantly Muslim countries soar to nearly nine times greater than the rest of the year? Welcome to another enlightening episode of Papers With Backtest: An Algorithmic Trading Journey, where we dissect the groundbreaking research paper titled 'Piety and Profit: Stock Market Anomaly During the Muslim Holy Month' by Bielkowski, Edabari, and Wisniewski. This episode is not just about numbers; it’s about uncovering the intriguing intersection of culture, religion, and market dynamics. Join our hosts as they delve into the astonishing findings of this research, revealing that the average annualized stock return during Ramadan is a staggering 38.09%, while it plummets to a mere 4.32% in other months. We explore the implications of this Ramadan effect, a phenomenon that challenges the conventional wisdom of rational markets. Through rigorous event study analysis and cumulative abnormal returns, the authors provide compelling evidence of this anomaly, and we break down their methodology to understand how they confirmed its validity through various robustness checks. But the discussion doesn’t stop at analysis. We venture into actionable insights, contemplating potential trading strategies that savvy investors could employ. Imagine buying stocks before Ramadan and selling them shortly after—could this be a game-changer for your portfolio? Our hosts share their perspectives on how cultural and religious factors can sway market behavior, pushing the boundaries of traditional financial theory. As we navigate through this episode, we invite you to rethink your approach to algorithmic trading and consider the broader implications of market anomalies influenced by societal norms. This is a must-listen for anyone serious about understanding the intricate layers of trading psychology, market efficiency, and the unexpected variables that can lead to profitable outcomes. Tune in to Papers With Backtest: An Algorithmic Trading Journey for a deep dive into how faith and finance intertwine, revealing opportunities that lie beyond the charts and numbers. Whether you're a seasoned trader or an academic enthusiast, this episode will challenge your assumptions and inspire you to look at market trends through a new lens. Discover how the Ramadan effect could reshape your trading strategies and enhance your understanding of market anomalies. Don’t miss out on this captivating exploration of the stock market’s hidden rhythms, where piety meets profit! Hosted by Ausha. See ausha.co/privacy-policy for more information.

    9 min
  8. Exploring the 52-Week High Effect

    13 SEPT.

    Exploring the 52-Week High Effect

    Have you ever wondered why stocks that are near their 52-week highs tend to outperform those that are not? In this enlightening episode of the Papers With Backtest: An Algorithmic Trading Journey podcast, we dive deep into the intriguing 52-week high effect, a phenomenon first introduced by George and Wang in 2004. This episode unpacks the implications of this effect and its relevance in today’s trading landscape, providing insights that every algorithmic trader should consider. Join our expert hosts as they revisit a pivotal 2011 research paper by Hong, Jordan, and Liu, which meticulously investigates whether the 52-week high effect is driven by inherent risk factors or the often-overlooked nuances of investor behavior. The findings are compelling: by focusing on industry-level data rather than individual stock analysis, traders can unlock a more profitable strategy. Our discussion reveals that a backtest conducted from 1963 to 2009 showed an impressive average monthly return of 0. 60% for the industry-based approach, significantly outperforming the 0. 43% return from individual stock strategies. Throughout the episode, we emphasize the robustness of the industry strategy's performance, even after adjusting for various risk factors. This suggests that behavioral biases, particularly the anchoring effect, play a pivotal role in trading decisions. By understanding these biases, traders can refine their strategies to better align with market realities and investor psychology. As we unpack the implications of the 52-week high effect, we provide practical takeaways for traders eager to enhance their algorithmic trading strategies. We discuss the importance of focusing on industry trends, the psychological factors influencing investor decisions, and how these elements can be integrated into a comprehensive trading strategy. Whether you are a seasoned trader or just starting your algorithmic trading journey, this episode is packed with valuable insights that can help you navigate the complexities of the market. Don't miss out on this opportunity to deepen your understanding of the 52-week high effect and its potential to reshape your trading approach. Tune in to Papers With Backtest: An Algorithmic Trading Journey and equip yourself with the knowledge to elevate your trading game. Discover how to leverage industry dynamics and investor psychology to enhance your trading success! Hosted by Ausha. See ausha.co/privacy-policy for more information.

    14 min
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À propos

Welcome to Papers With Backtest, where data means profit in the world of algorithmic trading. Each episode dives into backtests, real-life trading applications, and groundbreaking research that every aspiring quant should know. Tune in to stay ahead in the algo trading game. Our website: https://paperswithbacktest.com/ Hosted by Ausha. See ausha.co/privacy-policy for more information.

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