ALL THINGS BIG AND SMALL 2.0

  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...

A Common Sense Valuation Comparison of Global Equity Markets * and the relevance for quantitative (algorithmic) Mean Reversion startegies # click on image to see bigger Table Table 1: Worksheet Table 2: Earnings-based Table 3: Dividends-based ·          Each country’s financial markets have their own characteristics and peculiarities so comparing them with a global average of Price/Earnings, Price/Book etc  is meaningful. ·          Because of the loose money policy (Quantitative Easing) by the major economies (Eurozone, USA, Japan, even China in some ways) in the recent years, leading to low interest rates, there is a tendency for financial asset bubbles to arise, caused by excess funds chasing yields. ·          The interplay between the Bond market and Equity market is an often underrated  factor in determining the directi...

I am still reading the three books above, but have already begun to wonder at the marvel that is Nature. Like all scientific endeavors, Artificial Intelligence is constantly evolving. But I can see that the direction for the future is towards 'softer' systems that emulate the biological neural systems. Less of the machine-based algorithms that are faster but more brittle; towards systems which closer emulate Nature. Nature's ways are sometimes strange. Many redundancies (e.g. junk DNA), meandering and seemingly aimless moves. But eventually a more robust solution. The workings of AI's current Genetic Algorithms that are used for Optimization are more of a Mendelian process of selective breeding rather than natural evolution. Nature's evolution is an open process. There is no predetermined goal. Each generation adapts to whatever its environment throws up, and the fitness of a generation is only specific to fitness for the environment of that moment. Genetic Algor...

Blackbox models of complex situations are tempting. Without the need to specify parameters, and not limited by choice of variables, you can throw in everything including the kitchen sink, and see how the model performs. But in real life, it is not that simple. With every variable that you throw in, you are increasing the complexity of the model exponentially as well as the computational workload. Although there are algorithms for dimension reduction as well as for assessing the significance of an input towards model accuracy, nothing beats a good old human being with expertise in the subject domain, for initial choice of input variables. Let's do an academic exercise for the building of a model that (1) hopefully gives an objective, 'true' valuation of a house. (2) predict the future house price. We will attempt to build our model using a Neural Network, or a hybrid Neural with Genetic Algorithms for the last mile of calculation, to avoid local optima. Any human expert in r...