Narrow Results

The main purposes of this introduction chapter are (i) to give an overview of the following 109 papers, which discuss investment analysis, portfolio management, and financial derivatives; (ii) to classify these 109 chapters into nine topics; and (iii) to classify the keywords in terms of chapter numbers.

This chapter first presents a review of various theoretical models and six estimation methods to the optimal futures hedge ratios. Then we use data to show how some of the hedge ratios can be applied to estimate hedge ratio in terms of S&P 500 future. We also show the estimation procedure on how to apply OLS, GARCH, and CECM models to estimate optimal hedge ratios through R language. These approaches are theoretically derived in terms of minimum variance, mean-variance, expected utility, and Value-at-Risk. Various ways of estimating these hedge ratios are also discussed, ranging from simple ordinary least squares to complicated heteroskedastic cointegration methods. Under martingale, joint-normality conditions, and some other conditions, different hedge ratios can be shown that this different ratio can be converted to the minimum variance hedge ratio. Otherwise, the optimal hedge ratios based on the different approaches are in general different. Finally, our empirical findings suggest the importance of capturing the heteroskedastic error structures including the long-run equilibrium error term in conventional regression model.

In this paper, by using the R language, the published Google stock data obtained from the New York Stock Exchange are used to test the performance of the ARIMA model, KNN model and artificial neural network model for stock price prediction. Experimental results show that the prediction accuracy of the neural network model is higher than that of the other two models. This finding will give us some guidance when we choose the stock price and forecast model.

This paper comes up with Rdp, a novel implementation of programming abstraction for R language under the circumstance of distributed parallel system. It keeps the traditional programming habits of R users and use MPI underneath to accelerate the execution performance. Furthermore, experiments are taken to prove the efficiency and usability of the new method.