Algorithmic sign prediction and covariate selection across eleven international stock markets
| Autor: | Markku Karhunen |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
business.industry General Engineering 02 engineering and technology Logistic regression Stock market index Computer Science Applications Efficient-market hypothesis 020901 industrial engineering & automation Artificial Intelligence Covariate 0202 electrical engineering electronic engineering information engineering Econometrics Economics 020201 artificial intelligence & image processing Asset management Trading strategy Predictability business health care economics and organizations Stock (geology) |
| Zdroj: | Expert Systems with Applications. 115:256-263 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2018.07.061 |
| Popis: | I investigate whether an expert system can be used for profitable long-term asset management. The trading strategy of the expert system needs to be based on market predictions. To this end, I generate binary predictions of the market returns by using statistical and machine-learning algorithms. The methods used include logistic regressions, regularized logistic regressions and similarity-based classification. I test the methods in a contemporary data set involving data from eleven developed markets. Both statistical and economic significance of the results are considered. As an ensemble, the results seem to indicate that there is some degree of mild predictability in the stock markets. Some of the results obtained are highly significant in the economic sense, featuring annualized excess returns of 3.1% (France), 2.9% (Netherlands) and 0.8% (United States). However, statistically significant results are seldom found. Consequently, the results do not completely invalidate the efficient-market hypothesis. |
| Databáze: | OpenAIRE |
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