The use of open source internet to analysis and predict stock market trading volume

The objective of this paper is to evaluate the impact of information demand and supply on stock market trading volume. Few studies have demonstrated the role of Google search data in analyzing trading volume activity. In this study, we employ a proxy for information demand which is derived from weekly internet search volume. The latest is from Google Trends database, for 25 of the largest stocks traded on CAC40 index, between April 2007 and March 2014. We use news headlines as a proxy for information supply. We use Garch model to analyze and predict trading volume.The empirical results present new evidences. First, information supply has an impact on trading volume but information demand's impact is much more important. Secondly, by applying MCA to results found, it could be concluded that the impact of public information on transaction volume is conditioned by two elements: the firm and market news disclosure and the second element relates to the characteristics of the market participants, more precisely their news interpretations and their risk aversion. Thirdly, we used Chow structural break test to verify the stability of our model. We found that for securities with structural changes, information demand is the responsible variable of the change in our model. Finally, we found that information variables have a predictive power on transaction volume.This paper contributes to existing literature by incorporating open source internet-based data into the analysis and prediction of transaction volume. Using internet information about the stock market, which has appeared recently as an interesting research for financial empiricists, computer scientists and practitioners, will have a very important utility because quantifying demand and supply of information becomes possible.     

Impact of the Sloping Land Conversion Program on rural household income: An integrated estimation

The objective of this study is to identify the pathways of the potential impacts of the Sloping Land Conversion Program (SLCP) on household income and quantify these impacts along the identified pathways. We fulfill this objective by developing an integrated analytic framework and using data collected from Wuqi county of Shaanxi for the period 2004–2010. This integrated analytic framework enables us to formulate a structural equation model for testing hypotheses regarding the direct and indirect impacts of the SLCP. It is found that the direct effect of the SLCP on household income is positive but very small and insignificant, suggesting that retiring cropland has, at least, not reduced income from farming. Moreover, the SLCP has had a much higher and even increasing indirect impact on household income through promoting labor transfer and relaxing liquidity constraints. Overall, the SLCP's total impact on household income ranged from 3% in 2004 to 9% in 2010 excluding the insignificant direct impact. The research and policy implications of our work are discussed.     

物联网商业模式的问题及对策研究

现今物联网应用领域十分广泛,对于推动经济发展、加快中国经济发展体制的转型、提高中国自主创新能力发挥着重大作用,它已成为当前最具发展潜力的产业之一。但是产业发展中仍然存在障碍。中国的物联网产业仍然处于初级阶段,成熟、稳定、有利可图的商业模式还没有形成。物联网作为一个全新的研究领域和研究对象,它的商业模式的创新才是决定物联网产业大范围、长期应用的关键所在。介绍从现存的物联网模式展开分析,并提出改进物联网商业模式发展的建议。     

The impact of FIFA’s official announcements on the stock market of Qatar: The case of the 2022 World Cup

This study examines the impact of the FIFA’s official announcements on Doha Stock Exchange (DSE) of Qatar with respect to the 2022 World Cup. Using the abnormal unsystematic volatility method of Hilliard and Savickas (2002), our empirical findings reveal that the DSE market is sensitive to FIFA’s announcements about the 2022 World Cup. We find that four out of six FIFA announcements act as primary drivers to the DSE market volatility. The significant reactions of the DSE market to these announcements unveil the investors’ sentiments about the fate of the governmental and private expenditures on medium- and long-term projects undertaken in anticipation of hosting the 2022 World Cup. The results have some implications to investors in this newly emerging market related to this global sporting event. Any future announcements, good or bad, are likely to impact share prices in DSE market and trigger portfolio reallocation by local and international investors, leading to increased volatility.     

Maximizing benefits in project selection: a hybrid approach

Charitable foundations and government programmes should endeavour to allocate their limited resources to best serve their constituents. Yet, mathematical programming techniques are rarely used despite overwhelming evidence of their superiority in selecting projects that yield higher levels of total benefits. We present a novel ‘hybrid selection model’ that combines binary linear programming and heuristic rank-based models applied to two case studies. The first case focuses on providing services to women and shows a hybrid model would have selected the top three ‘signature’ projects and maintained an above-average overall project benefit while securing a 180% improvement in the number of projects funded, a 66% improvement in the number of women served and a 132% improvement in the total benefit achieved. In the second case, we apply the hybrid approach to data from the US government’s largest forest preservation programme and demonstrate that the hybrid approach could allow the programme to select up to 11 top-scoring projects while still achieving a 97% gain in the total overall benefit compared to their traditional method. These case studies show that the hybrid approach has the potential to be applied in a variety of settings and improve how foundations and programmes achieve their goals.     

Modelling the implied volatility surface based on Shanghai 50ETF options

We develop a dynamic factor model to forecast the implied volatility surface (IVS) of Shanghai Stock Exchange 50ETF options. Based on the assumption that dynamic change in IVS is mean-reverting and Markovian, we use a state space model to capture the dynamics of IVS, and set the latent factors to be the Ornstein–Uhlenbeck processes. We obtain the optimal estimations of parameters using the Kalman filter algorithm. Empirical results show that our model performs better than the traditional IVS model in terms of fitting ability and prediction performance.     

Wheat variety yield data: Do commercial and public performance tests provide the same information?

Wheat yields from reported performance test results are of economic importance to wheat producers, since their profits depend on selecting the optimal variety for their location. However, our data shows differences in absolute and relative wheat yields between commercial and public wheat breeding program's performance test data in Kansas. Newly available data are used to test if the difference in yields arose from potential selectivity bias, and to determine the contribution of private and public wheat breeding programs to varietal yield improvement during 2007–2012. Both Heckman selection models and multiple regression showed no statistical evidence of the potential presence of selectivity bias rather, managerial practices, agronomic conditions, field location, and inherent genetic traits of the seed variety were identify as the source of yield differences.     

Short-time at-the-money skew and rough fractional volatility

The Black–Scholes implied volatility skew at the money of SPX options is known to obey a power law with respect to the time to maturity. We construct a model of the underlying asset price process which is dynamically consistent to the power law. The volatility process of the model is driven by a fractional Brownian motion with Hurst parameter less than half. The fractional Brownian motion is correlated with a Brownian motion which drives the asset price process. We derive an asymptotic expansion of the implied volatility as the time to maturity tends to zero. For this purpose, we introduce a new approach to validate such an expansion, which enables us to treat more general models than in the literature. The local-stochastic volatility model is treated as well under an essentially minimal regularity condition in order to show such a standard model cannot be dynamically consistent to the power law.     

Addressing climate change cause and effect on land cover and land use in South Asia

This paper evaluates the role of trade liberalization and agricultural intensification in mitigating climate change cause and effects on land use and emissions using a computable general equilibrium model. Our results indicate that cropland expansion triggered by climate-induced crop productivity changes results in deforestation and increases emissions in South Asia and globally. Global full trade liberalization on all goods is the optimum policy for South Asia despite significant global deforestation, but for the world, unilateral partial trade liberalization on all goods is a more appropriate policy while ensuring a considerable emissions reduction for South Asia. These results indicate that mitigation responses to climate change are location specific and no one trade policy is suitable at the regional and global levels. Lastly, agricultural intensification by improving productivity growth is the best strategy in land-based emissions mitigation, thereby avoiding the transformation of forest and pasture lands for agricultural cultivation both at regional and global levels.