The Quantopian Podcast

Artificial intelligence is reshaping the landscape of quantitative investment. In this video, we explore the shift from traditional quant models to AI-driven approaches, covering how deep learning and large language models (LLMs) are revolutionizing the way investors generate alpha, manage risk, and execute trades.We delve into how deep learning models—like convolutional neural networks, transformers, and graph neural networks—are being used to uncover complex patterns in financial data. You'll also see how reinforcement learning is being applied to optimize decision-making in dynamic market environments.The video also highlights the growing role of LLMs in finance. These models can process vast amounts of unstructured data, generate novel alpha signals, and even function as AI agents within the investment process. But while the potential is exciting, we also address the key limitations, including issues with interpretability, overfitting, market frictions, and the numerical reasoning gaps still present in current LLMs.Whether you're a quant, a data scientist, or just curious about how AI is changing the future of investing, this video offers a thoughtful and balanced look at one of the most transformative trends in finance today.Find the full research paper here: https://community.quantopian.com/c/community-forums/from-deep-learning-to-llms-a-survey-of-ai-in-quantitative-investmentFor more quant-focused content, join us at ⁠⁠⁠⁠https://community.quantopian.com⁠⁠⁠⁠. There, you can explore a wealth of resources, connect with fellow quants, engage in insightful discussions, and enhance your skills through our extensive range of online courses.Quant Radio is an AI-generated podcast, intended to help people develop their knowledge and skills in Quant finance. This podcast is not intended to provide investment advice.

In this video, we explore the design of a volatility trading system that blends two quantitative options strategies with a strong emphasis on risk management. The first strategy takes a long-short position in straddles, based on signals from the implied volatility term structure, aiming to exploit short-term dislocations. The second strategy involves selling out-of-the-money (OTM) puts, but only when the absorption ratio suggests stable market conditions—helping to avoid exposure during periods of systemic risk.The video also walks through how these strategies are combined using Equal Risk Contribution (ERC) to balance their risk inputs, and how a Constant Proportion Portfolio Insurance (CPPI) overlay helps protect the system from large drawdowns. Historical data and real-world events like the 2008 crisis and COVID crash are used to highlight both the strengths and limitations of each approach.If you’re interested in systematic trading, options strategies, or risk-adjusted portfolio design, this breakdown offers a clear, research-based perspective on how to use volatility both as a source of return and a signal for risk control.Find the full research paper here: https://community.quantopian.com/c/community-forums/volts-a-volatility-based-trading-system-to-forecast-stock-markets-trend-using-statistics-and-machine-learningFor more quant-focused content, join us at ⁠⁠⁠⁠https://community.quantopian.com⁠⁠⁠⁠. There, you can explore a wealth of resources, connect with fellow quants, engage in insightful discussions, and enhance your skills through our extensive range of online courses.Quant Radio is an AI-generated podcast, intended to help people develop their knowledge and skills in Quant finance. This podcast is not intended to provide investment advice.

Discover a fast, adaptive trend following strategy built specifically for NQ futures using the power of Kalman Filters. In this video, we explore how this innovative approach goes beyond traditional moving averages by filtering out market noise and dynamically tracking price trends. You’ll learn how the Quantitative Trend Indicator (QTI) is constructed using both fast and slow Kalman Filters to generate clear entry and exit signals.We walk through the exact trading rules, review detailed backtest results from 2017 to 2024, and examine how the strategy performed both before and after optimization. With high-frequency execution—up to 16 trades per day—the potential is eye-catching: strong annualized returns, reduced drawdowns, and a Sharpe ratio that outperforms the benchmark.But it’s not all upside. The video also dives into the real-world frictions that can erode theoretical performance, like execution speed, slippage, and transaction costs. We discuss those risks honestly and look at possible next steps for improving or adapting the system to other markets and timeframes.If you’re interested in algorithmic trading, quant strategies, or just want to understand how Kalman Filters can be applied to financial markets, this is one you won’t want to miss. Subscribe for more insights into advanced trading systems, performance analytics, and practical challenges in turning code into edge.Find the full research paper here: https://community.quantopian.com/c/community-forums/fast-trend-followingFor more quant-focused content, join us at ⁠⁠⁠⁠https://community.quantopian.com⁠⁠⁠⁠. There, you can explore a wealth of resources, connect with fellow quants, engage in insightful discussions, and enhance your skills through our extensive range of online courses.Quant Radio is an AI-generated podcast, intended to help people develop their knowledge and skills in Quant finance. This podcast is not intended to provide investment advice.#TrendFollowing #QuantTrading #KalmanFilter #NQFutures #Backtesting #AlgoTrading

For decades, the prevailing wisdom in finance has told us that higher risk equals higher reward. But what if that model is missing the most powerful driver of asset prices—human emotion? In this thought-provoking episode of Quant Radio, we explore the groundbreaking ideas of Robert D. Arnott and Edward F. McQuarrie, who argue that fear—not risk—is the real force shaping the markets. Drawing on historical data and behavioral insights, they challenge traditional models like CAPM and introduce their "Deranged Asset Pricing Model" (DAPM), which places investor psychology, especially fear of loss and fear of missing out (FOMO), at the heart of market movements. From meme stocks to bond yields, and even long-term equity underperformance, this episode offers a fresh, emotionally intelligent lens on why markets behave the way they do. Whether you're an investor, economist, or just curious about the inner workings of the financial world, this discussion will change the way you think about risk—and fear.Find the full research paper here: https://community.quantopian.com/c/community-forums/fear-not-risk-explains-asset-pricing-quantpediaFor more quant-focused content, join us at ⁠⁠⁠⁠https://community.quantopian.com⁠⁠⁠⁠. There, you can explore a wealth of resources, connect with fellow quants, engage in insightful discussions, and enhance your skills through our extensive range of online courses.Quant Radio is an AI-generated podcast, intended to help people develop their knowledge and skills in Quant finance. This podcast is not intended to provide investment advice.

In this video, we examine fascinating new research that uses machine learning to uncover hidden connections between global stock markets and US equities. The study reveals how artificial intelligence can detect predictive signals from foreign markets that influence US stocks - including companies with no obvious international exposure.The research team analyzed an enormous dataset spanning 47 foreign markets, employing advanced machine learning techniques like Lasso regression, Random Forests, Gradient Boosting, and Neural Networks. These models processed over 13,000 potential signals from both market-level and individual stock returns to identify meaningful patterns.One of the most surprising findings was the predictive power of signals from unexpected markets like Qatar, challenging conventional wisdom about which foreign markets matter most. The study also uncovered intriguing dynamics around information diffusion, showing that foreign signals tend to be more predictive when they receive less US media coverage, and that the full impact of global information on US stocks can take 5-8 weeks to materialize.While the best-performing models generated impressive hypothetical returns of 14.2% annualized, the research highlights significant practical challenges. High trading costs from frequent portfolio adjustments, the inherent "black box" nature of complex machine learning models, and the evolving efficiency of global markets all present hurdles for real-world implementation.The discussion concludes by considering the broader implications of these findings for market efficiency and the future of AI in finance. As machine learning tools become more sophisticated, will they eliminate these informational edges or simply uncover new layers of market complexity?

In this video, we dive deep into the surprising relationship between market volatility, opportunity sets, and short-term trading strategies like statistical arbitrage and mean reversion. Using groundbreaking research from Extract Alpha, we explore:Why higher VIX levels (market volatility) often boost the performance of reversal and factor momentum strategies.Why return dispersion — not just volatility — may be the real driver of trading opportunities.How measuring the cross-sectional standard deviation of stock returns reveals more reliable trading opportunities than simply watching the VIX.The difference between being "long volatility" vs "long opportunity" and what it means for quant traders and market neutral portfolios.The major pitfalls traders must watch out for, including transaction costs and strategy selection under different market conditions.If you're a day trader, quantitative strategist, or anyone interested in short-term alpha generation, this episode is packed with actionable insights on how to navigate volatile markets and exploit market inefficiencies.Find the full research paper here: https://community.quantopian.com/c/community-forums/volatility-opportunity-and-reversal-strategiesFor more quant-focused content, join us at ⁠⁠⁠⁠https://community.quantopian.com⁠⁠⁠⁠. There, you can explore a wealth of resources, connect with fellow quants, engage in insightful discussions, and enhance your skills through our extensive range of online courses.Quant Radio is an AI-generated podcast, intended to help people develop their knowledge and skills in Quant finance. This podcast is not intended to provide investment advice.

What if the stock market could help predict corporate bond returns? In this episode of Quant Radio, we explore groundbreaking research on “X Trend,” a strategy that leverages stock market technicals—like moving averages and trading volume—to forecast bond performance. Using machine learning to sift through quadrillions of model variations, the study shows that these equity trends have powerful, persistent predictive power for bonds, delivering strong returns even after accounting for trading costs. We unpack how this strategy works, why it might be effective, and what it could mean for investors looking to bridge the gap between equities and fixed income.Find the full research paper here: https://community.quantopian.com/c/community-forums/cross-asset-trend-spillover-a-novel-factor-for-corporate-bond-returnsFor more quant-focused content, join us at ⁠⁠⁠⁠https://community.quantopian.com⁠⁠⁠⁠. There, you can explore a wealth of resources, connect with fellow quants, engage in insightful discussions, and enhance your skills through our extensive range of online courses.Quant Radio is an AI-generated podcast, intended to help people develop their knowledge and skills in Quant finance. This podcast is not intended to provide investment advice.

Looking for a high-probability intraday strategy for ES and NQ futures? In this video, we break down a powerful momentum-based trading system designed specifically for the S&P 500 E-mini (ES) and NASDAQ-100 E-mini (NQ) futures markets. This strategy is rooted in academic research and further refined by Quantitativo to capture short-term price breakouts while managing risk through smart trade design.At the core of the system is the concept of a “Noise Area”—a volatility-based range that helps filter out market noise and highlight significant breakouts. You'll learn how the strategy uses this zone to identify trade entries, along with well-defined exit rules using trailing stops, VWAP, and daily session closeouts. We also dive into dynamic position sizing based on volatility to keep risk exposure consistent, even in choppy conditions.Backtested results show strong potential: up to a 24.3% annual return on NQ futures with a Sharpe ratio of 1.67. Combined with ES futures and a long-only component, the portfolio achieved a 22.4% return with reduced drawdowns. These results make it a compelling strategy to consider for intraday and futures traders looking to boost performance while managing risk.We also cover the risks—slippage, flat periods, leverage, and the limitations of backtesting—so you get a balanced view of how this approach might fit your trading toolkit. Whether you're a day trader, futures trader, or exploring quantitative trading strategies, this video gives you a concise yet in-depth look at a research-driven trading edge.Find the full research paper here: https://community.quantopian.com/c/community-forums/intraday-momentum-for-es-and-nq-quantitativoFor more quant-focused content, join us at ⁠⁠⁠⁠https://community.quantopian.com⁠⁠⁠⁠. There, you can explore a wealth of resources, connect with fellow quants, engage in insightful discussions, and enhance your skills through our extensive range of online courses.Quant Radio is an AI-generated podcast, intended to help people develop their knowledge and skills in Quant finance. This podcast is not intended to provide investment advice.

Join us for a deep dive into one of the most overlooked yet powerful forces in investing: profitability. In this episode, we unpack research that positions profitability not just as another factor, but potentially the key to understanding a range of popular investment styles—quality, defensive, and value. We explore how this single concept might simplify the way we view the "factor zoo," cutting through complexity and marketing noise. Discover why profitability could be the master key to constructing smarter, more efficient portfolios, and how it connects seemingly disparate strategies under one unifying framework. If you're looking to refine your investment approach or just make sense of all the buzzwords in the finance world, this one's for you.Find the full research paper here: https://community.quantopian.com/c/community-forums/profitability-retrospective-what-have-we-learnedFor more quant-focused content, join us at ⁠⁠⁠⁠https://community.quantopian.com⁠⁠⁠⁠. There, you can explore a wealth of resources, connect with fellow quants, engage in insightful discussions, and enhance your skills through our extensive range of online courses.Quant Radio is an AI-generated podcast, intended to help people develop their knowledge and skills in Quant finance. This podcast is not intended to provide investment advice.

Are we placing too much trust in stock market indices like the S&P 500 and the Dow Jones when trying to predict the economy's future? In this video, we dive into surprising new research that questions the reliability of these major indices—especially when the economy might be heading for trouble. While these indices are often treated as crystal balls, offering clues about recessions and recoveries, the reality might be far more complicated.Drawing on a concept called Log-Supermodularity, the research we explore suggests that focusing on the very largest or best-performing stocks—what’s known as maximal selection—can introduce a bias that makes these indices less effective as leading indicators. This bias seems to kick in particularly during periods of economic uncertainty or decline, precisely when we rely on them most for early warnings.Through decades of historical market data, from 1976 to 2023, the study shows that these widely followed indices can underperform as predictors during downturns. One possible culprit? Herd behavior. In turbulent times, investors may flock to familiar large-cap stocks out of comfort or momentum, rather than fundamentals—distorting the true signal these indices are supposed to provide.So what should we be looking at instead? The findings suggest that in adverse conditions, a more random or diversified selection of stocks might give a clearer signal—or better yet, that complementary data outside the stock market, like bond yield spreads or the VIX, might be more useful for forecasting recessions.Whether you're an economist, investor, policy maker, or just curious about how financial signals really work, this episode offers a deep and thought-provoking look at the limitations of the most visible parts of the stock market. If the most watched metrics may mislead us during bad times, where else should we be focusing our attention?Find the full research paper here: https://community.quantopian.com/c/community-forums/biased-signals-rethinking-stock-market-indices-as-leading-economic-indicatorsFor more quant-focused content, join us at ⁠⁠⁠⁠https://community.quantopian.com⁠⁠⁠⁠. There, you can explore a wealth of resources, connect with fellow quants, engage in insightful discussions, and enhance your skills through our extensive range of online courses.Quant Radio is an AI-generated podcast, intended to help people develop their knowledge and skills in Quant finance. This podcast is not intended to provide investment advice.