This is a summary of links recently featured on Quantocracy as of Saturday, 03/15/2025. To see our most recent links, visit the Quant Mashup. Read on readers!
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The Impact of the Inflation on the Performance of the US Dollar [Quantpedia]Inflation is one of the key macroeconomic forces shaping financial markets, influencing asset prices across the board. In our previous analysis, we examined how gold and Treasury prices react to changes in the inflation rate, uncovering patterns that suggested inflation dynamics also impact the US Dollar. In this follow-up, we shift our focus entirely to the dollar, analyzing how it responds to
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Finding the Nearest Valid Correlation Matrix with Higham s Algorithm [Sitmo Machine Learning]In quantitative finance, correlation matrices are essential for portfolio optimization, risk management, and asset allocation. However, real-world data often results in correlation matrices that are invalid due to various issues: Merging Non-Overlapping Datasets: If correlations are estimated separately for different periods or asset subsets and then stitched together, the resulting matrix may
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Macro trading signal optimization: basic statistical learning methods [Macro Synergy]A key task of macro strategy development is condensing candidate factors into a single positioning signal. Statistical learning offers methods for selecting factors, combining them to a return prediction, and classifying the market state. These methods efficiently incorporate diverse information sets and allow running realistic backtests. This post applies sequential statistical learning to
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Variance for Intuition, CVaR for Optimization [Anton Vorobets]While everyone understand that investment risk is characterized by large losses or drawdowns, mainstream finance and economics academics still continue to promote mean-variance analysis. Even Harry Markowitz understood that risk should be measured by the downside, but in 1950s the computational burden was unimaginably large. Estimating a low-dimensional covariance matrix was considered
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Weekly Research Insights [Quant Seeker]Many readers have asked for a richer discussion of useful and interesting papers, alongside the most recent research I cover in my weekly recap. So, Im testing a new Thursday post: Weekly Research Insights. In this format, Ill highlight a few noteworthy papers and discuss their key findings and practical takeaways. Let me know what you think. In This Post: Generating Alpha from Analysts
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Backtesting the Opening Range Breakout (ORB) Strategy using Polygon.io [Concretum Group]In this article, we will show you how to run, customize, and analyze a backtest for the Opening Range Breakout (ORB) strategy. Instead of explaining every line of code, well focus on how to execute the backtest, adjust key parameters, and interpret the results. By the end, youll be able to: ???? Run the backtest in Google Colab with minimal setup. ???? Modify strategy settings to test
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Weekly Recap [Quant Seeker]Behavioral Finance How Costly are Trading Heuristics? (Han, He, and Weagley) Retail investors often rely on simple decision-making shortcuts when picking stocks, but these habits can be costly. By analyzing decades of research and actual trading data, the paper finds that traders frequently use heuristics like chasing lottery-like stocks (betting on extreme past winners) or herding (following the
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Capturing Volatility Risk Premium Using Butterfly Option Strategies [Relative Value Arbitrage]The volatility risk premium is a well-researched topic in the literature. However, less attention has been given to specific techniques for capturing it. In this post, Ill highlight strategies for harvesting the volatility risk premium. Long-Term Strategies for Harvesting Volatility Risk Premium Reference [1] discusses long-term trading strategies for harvesting the volatility risk premium in