The research focuses on finding a superior forecasting technique to predict stock movement and behavior in the Shanghai Stock Exchange. The author’s interest is in stock market activities during high volatility, specifically 13 years from 2002 to 2015. This volatile period, fueled by events such as the dot-com bubble, SARS outbreak, political leadership transitions, and the global financial crisis, is of interest. The study aims to analyze changes in stock prices during an unstable period. The author used advanced computer sciences, Machine Learning through information processing and training, and the traditional statistical approach, the Multiple Linear Regression Model, with the least square method. Both techniques are accurate predictors measured by Absolute Percent Error with a range of 1.50% to 1.65%, using a data file containing 3,283 observations generated to record the daily close prices of individual Chinese companies. The t-test paired difference experiment shows the superiority of Neural Network in the finance sector and potentially not in other sectors. The Multiple Linear Regression Model performs equivalent to the Neural Network in other sectors.
This paper considers the production planning problem of a firm that produces a single product using a process that has multiple production lines (or machines) in parallel, each with a finite production capacity. Specifically, the firm has m parallel production lines, each with capacity of P units per period. If needed, the firm can adjust the production rate in a period by adjusting the number of lines it operates in the period. The firm faces time‐varying demands. The objective is to find a production plan that meets the demands over the problem horizon and minimizes the sum of setup, holding and variable production costs. The paper develops an efficient forward dynamic programming algorithm and uses it to develop managerial insights on the effect of process design on “volume flexibility,” which is defined as “the ability to be operated profitably at different output levels.” Some forecast horizon results are also developed. The firm can use the results in the paper to optimize the process design for the demand it faces.
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