This is a summary of links recently featured on Quantocracy as of Tuesday, 12/03/2024. To see our most recent links, visit the Quant Mashup. Read on readers!
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Day 27: Enhancement [OSM]On Day 26, we extended the comparative error analysis to the original, 12-by-12 strategy and showed how results were similar to the unadjusted strategy relative to the adjusted one. The main observation that emerged was that the adjusted strategy performed better than the others due to identifying most of the big moves when it was correct and not missing the big moves when it was not. This was
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Hurst Exponent Applications: From Regime Analysis to Arbitrage [Relative Value Arbitrage]One of my favourite ways to characterize the market regime is by using the Hurst exponent. However, its applications are not limited to identifying market regimes. There are innovative ways to utilize it. In this post, I will discuss two approaches to applying the Hurst exponent. Using the Hurst Exponent to Time the Market The Hurst exponent can be employed to directly time the market. Reference
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Day 26: Adjusted vs. Original [OSM]The last five days! On Day 25, we compared the peformance of the adjusted vs. unadjusted strategy for different prediction scenarios: true and false positives and negatives. For true positives and false negatives, the adjusted strategy performed better than the unadjusted. For true negatives and false positives, the unadjusted strategy performed better. Today, we run the same comparisons with the
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Time-Varying Drivers of Stock Prices [Alpha Architect]This paper examines the time-varying roles of subjective expectations in driving stock price and return variations. Specifically, it focuses on how subjective cash flow expectations (CF) and discount rate expectations (DR) contribute to stock price fluctuations across different economic conditions, with a special emphasis on periods of financial uncertainty and economic crises. Time-Varying