Paper Info
Title
Fast Quantitative Analysis of Stock Trading Points in Dual Period of DMAC
Abstract
We propose a novel volatility segmentation approach to detect effective trading points from 2679 stocks of NASDAQ. The buy and sell points are derived from dual periods of DMAC based on daily and weekly periods by estimating the amplitude and interval. The proposed approach is very accurate in that only 373 stocks (out of 2679) in NASDAQ have the average rate of profit of overall buy points higher than 3% and only 193 stocks (out of 2679) in NASDAQ have the average rate of stop-loss of overall sell points higher than 3%. The volatility segmentation approach reduces the uncertainty of stock estimation in single period DMAC. This approach, however, is very computationally intensive requiring 2382.82s for evaluating buy points and 2688.53s for evaluating sell points. A parallel implementation using 240 cores reduced the time to 16.01s and 13.50s, respectively.
Year
DOI
Venue
2015
10.1109/BigDataService.2015.63
BigDataService
Keywords
Field
DocType
Stock Trading Points, Dual Period DMAC, Parallel Computing, Quantitative Analysis
Econometrics,Data mining,Polynomial,Rate of profit,Segmentation,Computer science,Stock (geology),Volatility (finance),Market research,Stock trading,Statistical analysis
Conference
Citations 
PageRank 
References 
2
0.41
5
Authors
2
Name
Order
Citations
PageRank
Yi Wei130.77
Vipin Chaudhary283883.24