با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
Algorithmic sign prediction and covariate selection across eleven international stock markets
پیش بینی علائم الگوریتمی و انتخاب متغیرها در یازده بورس بین المللی سهام-2019
I investigate whether an expert system can be used for profitable long-term asset management. The trad- ing 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 re- sults do not completely invalidate the efficient-market hypothesis.
Keywords: Stock market indices | S&P 500 | Sign prediction | Efficient-market hypothesis | Regularized regression | Similarity-based classification
Sign prediction in social networks based on tendency rate of equivalent micro-structures
پیش بینی علامت در شبکه های اجتماعی بر اساس نرخ گرایش برابری ساختارهای ریز -2017
Online social networks have significantly changed the way people shape their everyday communications. Signed networks are a class of social networks in which relations can be positive or negative. These net works emerge in areas where there is interplay between opposite attitudes such as trust and distrust. Recent studies have shown that sign of relationships is predictable using data already present in the network. In this work, we study the sign prediction problem in networks with both positive and neg ative links and investigate the application of network tendency in the prediction task. Accordingly, we develop a simple algorithm that can infer unknown relation types with high performance. We conduct experiments on three real-world signed networks: Epinions, Slashdot and Wikipedia. Experimental results indicate that the proposed approach outperforms the state of the art methods in terms of both overall accuracy and true negative rate. Furthermore, significantly low computational complexity of the proposed algorithm allows applying it to large-scale datasets.
Keywords: Distrust | Equivalence relation | Probabilistic method | Sign prediction | Signed social networks | Trust