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Fulltext Application of empirical mode decomposition with local linear quantile regression in financial time series forecasting

Jaber AM, Ismail MT, Altaher AM

ScientificWorldJournal, 2014;2014:708918.
PMID: 25140343 DOI: 10.1155/2014/708918

This paper mainly forecasts the daily closing price of stock markets. We propose a two-stage technique that combines the empirical mode decomposition (EMD) with nonparametric methods of local linear quantile (LLQ). We use the proposed technique, EMD-LLQ, to forecast two stock index time series. Detailed experiments are implemented for the proposed method, in which EMD-LPQ, EMD, and Holt-Winter methods are compared. The proposed EMD-LPQ model is determined to be superior to the EMD and Holt-Winter methods in predicting the stock closing prices.

Matched MeSH terms: Commerce/trends*
Fulltext The ability of analysts' recommendations to predict optimistic and pessimistic forecasts

Biglari V, Alfan EB, Ahmad RB, Hajian N

PLoS One, 2013;8(10):e73853.
PMID: 24146741 DOI: 10.1371/journal.pone.0073853

Previous researches show that buy (growth) companies conduct income increasing earnings management in order to meet forecasts and generate positive forecast Errors (FEs). This behavior however, is not inherent in sell (non-growth) companies. Using the aforementioned background, this research hypothesizes that since sell companies are pressured to avoid income increasing earnings management, they are capable, and in fact more inclined, to pursue income decreasing Forecast Management (FM) with the purpose of generating positive FEs. Using a sample of 6553 firm-years of companies that are listed in the NYSE between the years 2005-2010, the study determines that sell companies conduct income decreasing FM to generate positive FEs. However, the frequency of positive FEs of sell companies does not exceed that of buy companies. Using the efficiency perspective, the study suggests that even though buy and sell companies have immense motivation in avoiding negative FEs, they exploit different but efficient strategies, respectively, in order to meet forecasts. Furthermore, the findings illuminated the complexities behind informative and opportunistic forecasts that falls under the efficiency versus opportunistic theories in literature.

Matched MeSH terms: Commerce/trends*
Fulltext Integrating contextual sentiment analysis in collaborative recommender systems

Osman NA, Mohd Noah SA, Darwich M, Mohd M

PLoS One, 2021;16(3):e0248695.
PMID: 33750957 DOI: 10.1371/journal.pone.0248695

Recently. recommender systems have become a very crucial application in the online market and e-commerce as users are often astounded by choices and preferences and they need help finding what the best they are looking for. Recommender systems have proven to overcome information overload issues in the retrieval of information, but still suffer from persistent problems related to cold-start and data sparsity. On the flip side, sentiment analysis technique has been known in translating text and expressing user preferences. It is often used to help online businesses to observe customers' feedbacks on their products as well as try to understand customer needs and preferences. However, the current solution for embedding traditional sentiment analysis in recommender solutions seems to have limitations when involving multiple domains. Therefore, an issue called domain sensitivity should be addressed. In this paper, a sentiment-based model with contextual information for recommender system was proposed. A novel solution for domain sensitivity was proposed by applying a contextual information sentiment-based model for recommender systems. In evaluating the contributions of contextual information in sentiment-based recommendations, experiments were divided into standard rating model, standard sentiment model and contextual information model. Results showed that the proposed contextual information sentiment-based model illustrates better performance as compared to the traditional collaborative filtering approach.

Matched MeSH terms: Commerce/trends*
Toxic trade: the impact of preferential trade agreements on alcohol imports from Australia in partner countries

Schram A, Aisbett E, Townsend B, Labonté R, Baum F, Friel S

Addiction, 2020 07;115(7):1277-1284.
PMID: 31808205 DOI: 10.1111/add.14925

BACKGROUND AND AIMS: Trade liberalization is hypothesized to increase the availability of imported alcoholic beverages in importing countries. This study provides the first longitudinal analysis of the impact of preferential trade agreements (PTAs) on alcohol imports.
DESIGN: Panel data comprising alcohol-product (n = 15) by importing country (n = 16) observations from 1988 to 2016 constructed from global databases. The relationship between PTA status, tariff level and alcohol imports were assessed using a log-linear model. Unobserved heterogeneity was addressed through a combination of differencing and product-year fixed-effects.
SETTING: Australia and its 16 free trade partners (PTA year in parentheses), classified by low [ 50%: Chile (2009), China (2015), Japan (2015), Korea (2014), Laos (2010), New Zealand (1983, 2010), Philippines (2010), Singapore (2003, 2010) and United States (2005)] percentage of alcohol consumers in the population.
MEASUREMENTS: Independent variables were the existence of a PTA with Australia and tariff (border tax) rate on Australian products. Outcomes were (log) Australian imports; and a binary indicator of any imports from Australia.
FINDINGS: Introducing a PTA has been associated with a statistically significant increase in the share of Australian alcoholic beverage imports in its partner country's total alcoholic beverage import supply, mainly from trade in new alcoholic beverage categories (0.067, P

Matched MeSH terms: Commerce/trends*