ALL THINGS BIG AND SMALL 2.0

Introduction Uncertainty is an inherent property of financial markets due to its highly dynamic and ever-evolving nature. Therefore, forecast models of financial markets should not focus on pin-point accuracy, for it will be spurious accuracy.  Rather the output should be within a statistical confidence levels band. Bands can be based on quantiles of the Probability Distribution Function of the data.  Case study: Singapore’s 6-month T-Bill and SORA (Singapore Overnight Rate Average). The chart above shows the time series (daily yields) of Monetary Authority of Singapore (MAS) 6-month T-Bill (6MT), overlaid on the SORA.  SORA has a broad positive correlation with 6MT but has daily swings of significant volatility. This is natural because SORA’s daily volatility is a feature of an unsecured overnight funding benchmark: sensitive to immediate liquidity, calendar, and cross ‑ currency conditions of banks and other users while 6MT yield is a forward-looking, term ‑ averaged pr...

  What are Ensembles Ensemble methods in predictive modeling are like gathering the opinions of multiple experts to make a final decision. Instead of relying on just one model to predict future market trends or ETF performance, ensemble techniques combine several different models to improve accuracy and reduce the risk of errors. Using a group of models, instead of just one, Ensembles "vote" on what should be the final output, leading to more reliable and balanced predictions. In regression models, ensemble methods typically use averaging to arrive at a final prediction. The final prediction is the average of all the individual predictions from these models. In boosting techniques, models are built sequentially, each improving upon the errors of the previous ones, and their predictions are also averaged to yield a final result. By combining multiple models, ensemble methods smooth out any individual errors, leading to more accurate and reliable predictions in tasks such a...