دانلود رایگان مقاله انگلیسی پیش بینی الگوریتمی و انتخاب متغیر در 11 بازار بین المللی سهام - الزویر 2019

abstract

Iinvestigate 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.

5. Conclusions

This paper demonstrates how algorithmic methods and macroeconomic covariates can be used to construct an expert system to perform independent trading in the stock markets. Some of the results are qualitatively appealing, but statistically significant results are seldom found. Moreover, for a large number of statistical tests, positive results are bound to occur randomly. In Table 1, there are four significant associations, about as many as would be expected at random (4.95). In the trading test (Table 3), there are 39 mean-variance dominating portfolios, significantly less would be expected if the portfolios were equally likely to be mean-variance dominating or not (P = 0.02). However, if the trading costs are removed (Appendix), there are 61 dominating portfolios, more than would be expected at random (P = 0.008). Interesting results are found by using Lasso and Elastic Net in Netherlands. These results are both statistically and economically significant (Tables 1 and 3, respectively) and they are robust both towards the choice of statistical test and increasing the trading costs (Appendix). Moreover, very interesting results are found if the target of the analysis is shifted, i.e. if the algorithms are used to predict large downturns (more than 2% monthly) in place of sign return. In that case, statistically significant predictions can be generated in most markets and economically significant results can be obtained in all markets.