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Purpose After the fall of fix exchange rate regime in early 1970s, the nexus between the exchange rate volatility and trade flows has been of a great interest to the policy makers and researchers. Resultantly an extensive literature is available on the topic. However, the research findings are inconclusiveness so far. The purpose of this paper is to examine the exchange-rate volatility and bilateral industry trade link between the two important countries of Southeast Asia, i.e. Malaysia and Thailand. Design/methodology/approach This study employs Generalized Autoregressive Conditional Heteroskedasticity (GARCH) (1, 1) to measure exchange rate volatility and autoregressive distributed lag (ARDL) model to study the relationship between exchange rate volatility and trade flows. ARDL approach is suitable to accommodate the mix cases (i.e. stationary and first difference stationary). The paper considers 62 Malaysian exporting and 60 Malaysian importing industries with Thailand over the monthly period 2000-2013. Findings Findings suggest the influence of exchange-rate volatility on the trade flows in a limited number of industries. Large industries like instruments and apparatus experience negative influence from exchange-rate volatility. Originality/value Past literature continued to be inconclusiveness on the nexus between exchange-rate volatility and trade flows due to its over-reliance on the aggregated data. Besides, the past studies are more based on quarterly or yearly frequency data. These issues contribute to the aggregation bias. This research focusses on a country bilateral trade pair, using industry level disaggregated monthly data. Such research is rare in Malaysian-Thai bilateral trade context. This study uses a suitable estimation approach and also draws valuable implications.

The purpose of this paper is to assess the importance of geographical location in the banking sector efficiency of the Sino-ASEAN (Association of Southeast Asian Nations) region, and how the location was affected before, during and after the financial crisis. Using a panel of data from 407 banks from China, Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam from 2000–2013, this study applies data envelopment analysis, Tobit regression, bootstrapping, and Simar and Wilson double bootstrapping regression. The empirical evidence suggests that the banking market has an important and significant role in the efficiency of the banking sector in the Sino-ASEAN region. The significant country’s coefficients suggest that during the pre-crisis period, banks belonging to China and Indonesia were more likely to be efficient due to the geographical location effect. The study finds the same tendency among Chinese banks in the crisis period as in the period before the crisis. Overall, the results suggest that Chinese banks outperform banks from the ASEAN countries in terms of efficiency. This study raises some significant policy implications for improving bank efficiency.

Appropriate identification and classification of online reviews to satisfy the needs of current and potential users pose a critical challenge for the business environment. This paper focuses on a specific kind of reviews: the suggestive type. Suggestions have a significant influence on both consumers' choices and designers' understanding and, hence, they are key for tasks such as brand positioning and social media marketing. The proposed approach consists of three main steps: (1) classify comparative and suggestive sentences; (2) categorize suggestive sentences into different types, either explicit or implicit locutions; (3) perform sentiment analysis on the classified reviews. A range of supervised machine learning approaches and feature sets are evaluated to tackle the problem of suggestive opinion mining. Experimental results for all three tasks are obtained on a dataset of mobile phone reviews and demonstrate that extending a bag-of-words representation with suggestive and comparative patterns is ideal for distinguishing suggestive sentences. In particular, it is observed that classifying suggestive sentences into implicit and explicit locutions works best when using a mixed sequential rule feature representation. Sentiment analysis achieves maximum performance when employing additional preprocessing in the form of negation handling and target masking, combined with sentiment lexicons.

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