Intelligent decision support: A fuzzy stock ranking system

This paper presents an intelligent decision support system for financial portfolio management. An adaptive business intelligence approach combines optimization, forecasting and adaptation with application specific financial information processing and quantitative investment paradigms. The methodology involves constructing a ranking of stocks by strength of a buy or sell recommendation which is inferred using an adapting forecasting model that considers a range of factors. These include company balance sheet information, market price and trading volume as well as the wider economy. The system adjusts its prediction model dynamically as market conditions change. An evolving fuzzy rule base mechanism encodes a model of relationships between model factors and a recommendation to buy, sell or hold securities.Adam Ghandar, Zbigniew Michalewicz and Ralf Zurbrueg

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants