25 Mar
General

Robust Rolling PCA (R2-PCA): Managing Times Series and Multiple Dimensions

Principal Component Analysis (PCA) is an important methodology to reduce and extract meaningful signals from large data-sets. Financial markets introduce time and non stationarity aspects, where applying standard PCA methods may not give stable results. Our robust rolling PCA (R2-PCA) accommodates the additional aspects and mitigates commonly found obstacles including eigenvector sign flipping, and managing multiple dimensions of the data-set. Since a learning-based model is only as powerful as the data it trains on, the more stable results of the R2-PCA (versus the Standard PCA) make it a better candidate for usage across AI-based applications.

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17 Mar
General

ChatGPT Mutual Fund suggestions — Good or Bad?

Not good - buyer beware as the results were inconsistent textually and from a performance perspective, where it suggested index trackers (without specifying Indices) and otherwise generally poor performing funds. This is not surprising as it is akin to asking an English major to solve a differential equation - pun intended! The math here is in ingesting existing published results and making them contextually available and not on training the models to accurately select the asset. As such, the basis of ChatGPT is the Large Language Models (LLM) that are trained on existing ‘outcomes’ that are solicited from...

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10 Mar
EI&RoT Framework

Portfolios – How much RoT is there?

You are pitched multiple ‘tailored’ portfolios that have seemingly very similar risk-return expectations. Beyond the regulatorily mandated disclosures, the distributors/allocators generally point to the historical performance of the portfolios and forecasted performance under scenarios. In Table 1 we take an example of five portfolios with near identical first order risk-return profiles - how would you select? Table 1 - Model Portfolios The baseline financial analysis of the portfolios relies on two baseline facets: (a) the performance measures being considered, and (b) the assumptions embedded in the simulations. Where,

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01 Mar
ML/DL Journey

US Large Cap Mutual Funds – Basic Historic Analysis: are you always wrong?

Pretty much - more than 75% of the time. Unfortunately, with reference to the SP500 TR USD you will only be selecting top performers maybe 25% of the time (depending on the measures and evaluation criteria). Irrespective, over the last decade the US Large Cap mutual fund market grew 4x despite average relative poor performance versus the Index - $8 Trillion chasing fool's gold? For the overall US Large Cap mutual fund market, assuming the SP500 TR USD as the index, there may be lesser value in looking at historical price data (and the derived performance measures) as selection criteria. As...

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