Forecasting Analytics

Stock market trading has evolved over years from human intervention to automated bots. This gives a huge wave of opportunities for data scientists to analyze and predict the stock price using various machine learning and deep learning techniques. There is a trade off between the traditional techniques and deep learning techniques for accuracy and performance in time series related problems. The techniques used as part of this thesis include Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) for forecasting analytics as an attempt to validate the combination of deep learning and machine learning techniques. The biggest advantage LSTM provides over traditional machine learning technique is the ability to remember the longer sequences of input data with the memory. The major question is whether LSTM alone is sufficient enough to achieve better accuracy or with additional training would be necessary to improve the prediction. However, the thesis question is whether an ensemble of LSTM and ARIMA can achieve better accuracy. The objective also includes other external techniques such as Natural Language Processing (NLP) to perform sentiment analysis and validate the final results. The end results show that with additional training and considering the ensemble of networks can provide better results. It is also observed that including NLP can help in understanding the domain, public response and improve the predictio

Human capital in economic development: from labour productivity to macroeconomic impact

Micro-econometric evidence reveals high private returns to education, most prominently in low-income countries. However, it is disputed to what extent this translates into a macro-economic impact. This paper projects the increase in human capital from higher education in Malawi and uses a dynamic applied general equilibrium model to estimate the resulting macroeconomics impact. This is contingent upon endogenous adjustments, in particular how labour productivity affects competitiveness and if this in turn stimulates exports. Choice among labour market assumptions and trade elasticities results in widely different outcomes. Appraisal of such policies should consider not only the impact on human capital stocks, but also adjustments outside the labour market

An econometric analysis of the link between biodiesel demand and Malaysian palm oil market

The objective of this study is to describe the important factors affecting Malaysian palm oil industry especially biodiesel demand. To that end a market model representing palm oil production, import, world excess demand, domestic consumption, export demand, rest of the world excess supply and palm oil prices is formulated.A system of equations of eight structural equations and four identities is estimated by two stage least squares method using annual data for the period 1976-2008.The domestic price equation is formed to investigate the link between biodiesel demand and the Malaysian palm oil market. The domestic price is significantly affected by Malaysian ending stock, world palm oil price, biodiesel demand and lagged domestic price. The elasticity of Malaysian palm oil domestic price with respect to biodiesel demand is then obtained. Results suggest that biodiesel demand has a positive impact on the Malaysian palm oil domestic price. Thus, significant growth in biodiesel demand is important in explaining Malaysian palm oil price determination

Responses of the EU feed and livestock system to shocks in trade and production

Dit rapport gaat in op de mogelijke effecten van meervoudige en/of langdurige calamiteiten die de beschikbaarheid van landbouwproducten verminderen op de Europese voedsel- en voersector in 2020