Why? (1 - 60-70% of trades in the US equities market are done by automated AI-driven algorithms, including high frequency micro-second trading. These algorithms make their decisions not on economic or corporate fundamentals but on momentum, arbitrage opportunity, pattern recognition etc. (2 - Previously the USD was a safe-haven currency that investors flock to in periods of economic, political or military instability. But with the chaos that is now going on in the USA, the USD seems to be losing its safe-haven status. As for Gold, it is still a safe haven asset, and recently we have seen the Central Banks of China, Russia, Japan and even some of the EU countires load up on Gold while paring down the amount of US Treasuries they hold. (3- The USD is the lynchpin that holds the US stock market and the Treasuries market together and a falling USD will make any rise in stock or bond prices unsustainable. Would you invest in US stocks if your profit is ...
A big Thank You to EODHD.com for the data without which this post could not have been written. The Close of Silver (XAGUSD) as of Friday 24 Oct 2025 is 48.62.The 21 business days ahead forecast for spot investment grade Silver bullion is summarized thus: 10% chance of going below 48.32 50% chance of being 54.33 10% chance of being above 60.80 Methodology We prefer to apply probabilistic quantile forecasting due to the highly unstable situation as the MAGA Man keeps changing his policies almost daily, and even just the words he uses generates asymetric shocks to the model that renders straight forecasts useless. Model input variables: 10yr Treasury yield US Dollar Index Spot Gold price S&P500 US Unemployment Rate Methodology Methodology summary (plain-language bullets) Data we used Daily end-of-day prices from EODHD for Silver (XAGUSD) and Gold (XAUUSD). Macro/market context series (Dollar Index, 10-year Treasury yield, S&P 500, unemployment). We rei...
Knowing the market’s current risk state is foundational for portfolio decisions. Basic Markov chain models are a good starting point, but they fall short on three realities of financial data. Using as case study China's CSI300 Index, here’s how our model resolves each one and delivers a robust, reproducible Risk‑On/Risk‑Off signal. 1) Beyond “memoryless”: markets have persistence and aging Problem: Standard Markov chains assume memorylessness—the next state depends only on the current state, not on how long we’ve been there. Markets don’t behave that way; regimes “age” and persistence changes with time. Our solution: We use a hidden semi‑Markov model (HSMM) with explicit duration modeling. Each regime has a duration distribution (e.g., Negative Binomial), and transitions are allowed to depend on time spent in the state. This breaks the memoryless assumption, curbs flicker, and captures realistic regime persistence. 2) Heavy‑tailed, skewed returns are the norm—not Gaussian Problem: ...
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