This is a summary of links featured on Quantocracy on Friday, 10/13/2023. To see our most recent links, visit the Quant Mashup. Read on readers!
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Trend-Following Filters Part 7 [Alpha Architect]Financial time series that are structured as data sampled at a uniform time interval, e.g., hourly, daily, weekly, or monthly, are called discrete-time time series and referred to, from a digital signal processing (DSP) perspective, as being in the time domain. Technical market analysts generally study financial time series in the time domain. For example, a price chart displays time series
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AutoRegressive Fractionally Integrated Moving Average (ARFIMA) model [Quant Insti]Usually, in algorithmic trading, we talk about AutoRegressive Integrated Moving Average (ARIMA) models. Theyre very popular in the industry. You might remember that the d component in the model can be 0, 1, 2, etc. What if 'd' could take fractional values? Well learn about such models i.e. AutoRegressive Fractionally Integrated Moving Average (ARFIMA) here. Lets dive in
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Research Review | 13 October 2023 | Market Volatility [Capital Spectator]An ETF-Based Measure of Stock Price Fragility Renato Lazo-Paz (University of Ottawa) July 2023 A growing literature employs equity mutual fund flows to measure a stocks exposure to non-fundamental demand risk stock price fragility. However, this approach may be biased by confounding fundamental information, potentially leading to underestimating risk exposure. We propose an alternative