ABSTRACT: We use neural data collected from an experimental asset market to measure regret preferences while subjects trade stocks. When subjects observe a positive return for a stock they chose not to purchase, a regret signal is observed in an area of the brain that is commonly active during reward processing. Subjects are unwilling to repurchase stocks that have recently increased in price, ...

With the advent of Web 2.0, various types of data are being produced every day. This has led to the revolution of big data. Huge amount of structured and unstructured data are produced in financial markets. Processing these data could help an investor to make an informed investment decision. In this paper, a framework has been developed to incorporate both structured and unstructured data for p...

Stock control is the most important aspect in an inventory system. It determines the safety stock in order to ensure that products are readily available when the customers require them. The decision makers in organizations tend to rely on software solution in obtaining stock control solutions since they have to manage a massive number of Stock Keeping Units (SKUs). However, managers intervene i...

Bandwidth estimation is one of the prerequisite for efficient link dimensioning. In the past, several approaches to bandwidth estimation have been proposed, ranging from rules-of-thumb providing over-provisioning guidelines to mathematically backed-up provisioning formulas. The limitation of such approaches, in our eyes, is that they largely rely on packet-based measurements, which are almost u...

Abstract In this paper we propose a modification to the standard forecasting, periodic order-up-tolevel inventory control approach to dealing with intermittent demand items, when the lead-time length is shorter than the average inter-demand interval. In particular, we develop an approach that relies upon the employment of separate estimates of the interdemand intervals and demand sizes, when de...

Time series analysis is the process of building a model using statistical techniques to represent characteristics of time series data. Processing and forecasting huge time series data is a challenging task. This paper presents Approximation and Prediction of Stock Time-series data (AP ST), which is a two step approach to predict the direction of change of stock price indices. First, performs da...