Machine Learning and Expert Judgement: Analyzing Emerging Topics in Accounting and Finance Research in the Asia–Pacific

In this paper, we focus on the question to what extent machine learning (ML) tools can be used to support systematic literature reviews. We apply a ML approach for topic detection to analyze emerging topics in the literature—our context is accounting and finance research in the Asia–Pacific region. To evaluate the robustness of the approach, we compare findings from the automated ML approach with the results from a manual analysis of the literature. The automated approach uses a keyword algorithm detection mechanism whereby the manual analysis uses common techniques for qualitative data analysis, that is, triangulation between researchers (expert judgement). From our paper, we conclude that both methods have strengths and weaknesses. The automated analysis works well for large corpora of text and provides a very standardized and non-biased way of analyzing the literature. However, the human researcher is potentially better equipped to evaluate current issues and future trends in the literature. Overall, the best results might be achieved when a variety of tools are used together.     

How government-led land consolidation efforts achieve grain production stability? An empirical analysis in Hubei Province,China

China has been conducting massive land consolidation (LC) projects since the late 1990s to ensure national food security which has been one of the common issues in the world. How the implementation of LC projects affects grain production stability is a question should be answered. Basing on an empirical analysis at the county level in Hubei Province, China, this paper separates the trend component and the fluctuation component from a time series of grain production over 1991-2016 using the H-P filtering method. Then we estimate the impacts of LC projects and climate change on the trend and fluctuation rate of grain production for the entire Hubei Province. Moreover, the effects on the production of different major crops for the central, eastern, southwestern, northwestern and northern areas are also examined, respectively. The results show that: (1) the production trends for five subareas and the whole area all have followed a trajectory of “increase-decrease-increase” from 1991 to 2016. The magnitude of fluctuation rate was large but became smaller in recent years. (2) The contribution of LC to the long-term trends of grain production for the entire samples in Hubei Province mainly benefits from the growth in the cultivated land area through LC; more LC investment and newly-added arable land area promote the sudden increase in grain production above the long-term trends, while the impacts of LC area are negative. (3) The impacts of LC on the major crop production vary across areas. LC exerts significant impacts on the production of major crops in the central and eastern Hubei, and this effect persists to the following year. Similarly, this influence also exists in the southwestern and northern area, while becomes insignificant in the following year. No significant effect of LC variables is found in the northwestern area. These findings may provide reference for the government to address LC inefficiency and food security problems, and an effective and innovative mechanism combining the agricultural production, land consolidation and meteorological factors needs to be established to ensure food security and long-term and stable agricultural production. In addition, the regional difference in natural conditions and LC orientations should also be considered.     

中国旅行社业发展的回顾与前瞻

本文首先对中国旅行社业的发展历程进行了梳理和回顾，认为二十余年来的发展进程基本上可以划分为四个阶段，即初步形成、快速发展、结构调整和全面开放，并在此基础上对中国旅行社业的现状进行了简要剖析。对未来的发展趋势则分别从市场基础、竞争主体、企业制度、品牌和分工体系等角度进行了分析，并据此提出了市场、产业、企业和产品等旅行社不同运作层面上的一系列观点。     

Steady state adjusting trends using a data-driven local polynomial regression

Economic variables usually follow a dynamic trend pattern. However, it is difficult to estimate this trend precisely as numerous economically- and statistically-based estimation methods exist. This contribution proposes a data-driven nonparametric trend that is local polynomial, to improve arbitrary trend estimations of commonly used methods concerning the selection of the smoothing parameter and the dependence structure. An iterative plug-in (IPI) algorithm determines the bandwidth endogenously and allows a theory-based interpretation of the length of growth processes. This length of the bandwidth reflects the lengths of the steady state periods. Consequently, the bandwidth identifies the time period of stable economic conditions and can detect economic changes. To demonstrate the power of this estimation approach, an extensive simulation study is performed. Furthermore, examples using US and UK GDP data along with a guide for the optimal choice of algorithms for empirical applications are provided. This proposed method yields new insights for growth dynamics, cyclical movements and their dependence.     

Persistence,mean reversion and non-linearities in the US housing prices over 1830–2013

The objective of this study is to provide a direct estimate of the degree of persistence of measures of nominal and real house prices for the US economy, covering the longest possible annual sample of data, namely 1830–2013. The estimation of the degree of persistence accommodates for non-linear (deterministic) trends using Chebyshev polynomials in time. In general, the results show a high degree of persistence in the series along with a component of non-linear behaviour. In general, if we assume uncorrelated errors, non-linearities are observed in both nominal and real prices, but this hypothesis is rejected in favour of linear models for the log-transformation of the data. However, if autocorrelated errors are permitted, non-linearities are observed in all cases, and mean reversion is found in the case of logged prices, though given the wide confidence intervals, the unit root null hypothesis cannot be rejected in these cases.     

Modelling interactions among the housing market and key US sectors

This study evaluates the dynamic interactions among the housing market and ten key US sectors including: consumer discretionary, consumer staples, energy, financials, industrial, technology, health care, materials, utility and telecommunications. Long-run results indicate that the housing market is integrated with each of the ten sector and that the degree of convergence has increased over time and especially after the onset of the most recent housing crisis. Moreover, the housing market contributes most heavily to the common trends indicating that the housing market is the ‘leader’ market that drives each sector towards the long-run equilibrium relationships. Short-run analyses indicate causal linkages emanating from the housing market to each sector with reciprocal feedback. Finally, impulse response function analysis reveal that shocks from each sector affect the housing market but that shocks from the housing market have a (comparatively) more profound and persistent impact on each sector.     

Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data

We utilize the Internet search data from Google Trends to provide short-term forecasts for the inflow of Japanese tourists to South Korea. We construct the Google variable in a systematic way by combining keywords to minimize mean squared or mean absolute forecasting errors. We augment the Google variable to the standard time-series forecasting models and compare their forecasting accuracies. We find that Google-augmented models perform much better than the standard time-series models in terms of short-term forecasting accuracy. In particular, Google models show better out-of-sample forecasting performance than in-sample forecasting.     

Trends and Future Directions in Online Marketing Research

Over the last decade, the increased adoption of the Internet in public life as well as in developing businesses has led to a phenomenal rise in academic research on online marketing. This article is set to extensively review scholarly articles appearing from 2000 to 2014 on the topic from 10 top-tier academic journals to understand the research trends in the domain. A literature review has reported eight major subjective categories with an analysis of online marketing effectiveness framework. This review found three most significant subject categories: (1) online marketing issues; (2) Internet usage, perception, and attitude; and (3) online shopping and e-commerce. Additionally, some new online marketing research topics such as word-of-mouth, user-generated content, and social network are also highlighted. Finally, a selection of research topics that got the maximum attention of researchers is presented along with discussion of the future research directions in the online marketing space.     

The organic industry in Australia: Current and future trends

Accurate information is critical for investment, policy alignment and to support agricultural development. In Australia information relating to the size, growth, current and future value of the organic sector has been incoherent and, in some cases, contradictory. This study seeks to address this gap by synthesising industry and government data with the results of an online quantitative survey as well as a series of in-depth interviews to capture the value added component of the organic industry in Australia. By sub-dividing the total organic industry into thirteen categories which makes for meaningful analysis and interpretation, we aimed to estimate the compound average growth rate, the current value added and future trends in the Australian organic industry. This has resulted in some interesting findings, hence the methodology could potentially serve as a benchmark for the reporting of organic food growth globally. The findings also have important implications for various stakeholders including, growers of organic products, investors, land use policy makers and industry operators.     

Current issues and research trends on open-source software communities

Open source software (OSS) projects represent a new paradigm of software creation and development based on hundreds or even thousands of developers and users organised in the form of a virtual community. The success of an OSS project is closely linked to the successful organisation and development of the virtual community of support group. This paper reviews different fields and research topics related to the OSS communities such as collective intelligence, the structure of OSS communities, their success, communities as virtual organisations, motivation, shared knowledge, innovation and learning. The main challenges, results obtained, and the knowledge areas are detailed for each topic.