Performance analysis of the parallel code execution for an algorithmic trading system, generated from UML models by end users

2015 ◽  
Author(s):  
Gaetan Hains ◽  
Chong Li ◽  
Nicholas Wilkinson ◽  
Jarrod Redly ◽  
Youry Khmelevsky
Keyword(s):  
Performance Analysis ◽  
End Users ◽  
Algorithmic Trading ◽  
Trading System ◽  
Parallel Code ◽  
Uml Models

A Multistage Framework to Defend Against Phishing Attacks

Cyber Crime ◽  
2013 ◽  
pp. 245-262
Author(s):  
Madhusudhanan Chandrasekaran ◽  
Shambhu Upadhyaya
Keyword(s):  
Performance Analysis ◽  
Structural Properties ◽  
Web Site ◽  
End Users ◽  
Analysis Study ◽  
Second Stage ◽  
Phishing Attacks ◽  
Two Stages ◽  
E Mail

Phishing scams pose a serious threat to end-users and commercial institutions alike. E-mail continues to be the favorite vehicle to perpetrate such scams, mainly due to its widespread use combined with the ability to easily spoof them. Several approaches, both generic and specialized, have been proposed to address this growing problem. However, phishing techniques, growing in ingenuity as well as sophistication, render these solutions weak. To overcome these limitations, we propose a multistage framework – the first stage aims at detecting phishing based on their semantic and structural properties, whereas in the second stage we propose a proactive technique based on a challenge-response technique to establish the authenticity of a Web site. Using live e-mail data, we demonstrate that our approach with these two stages is able to detect a wider range of phishing attacks than existing schemes. Also, our performance analysis study shows that the implementation overhead introduced by our tool is negligibly small.

Visualised Algorithmic Trading System for Strategy Prototyping

2013 ◽  
Vol 5 (2) ◽  
pp. 809-818
Author(s):  
Allen Y. Chang ◽  
ChiaHan Chou ◽  
ChangSung Yu
Keyword(s):  
Algorithmic Trading ◽  
Trading System

scholarly journals Using UML models for the performance analysis of network systems

2005 ◽  
Vol 49 (5) ◽  
pp. 627-642 ◽  
Author(s):  
Nico de Wet ◽  
Pieter Kritzinger
Keyword(s):  
Performance Analysis ◽  
Network Systems ◽  
Uml Models

scholarly journals Algorithmic trading based on the fear of Covid-19 in Europe

2021 ◽  
Vol 10 (2) ◽  
pp. 295-304
Author(s):  
Raúl Gómez Martínez ◽  
Camilo Prado Román ◽  
Gabriel Cachón Rodríguez
Keyword(s):  
Financial Markets ◽  
Behavioral Finance ◽  
Algorithmic Trading ◽  
Trading System ◽  
Technical Indicators ◽  
The Future

he spread of Covid-19 in Europe has affected our way of living, thinking, and even investing. The fear of the epidemic caused a context of maximum uncertainty and volatility in financial markets, which were driven by fear of the spread of the epidemic. In this article we propose an algorithmic trading system on the future of the Eurostoxx 50 that, instead of following technical indicators, follows the number of cases confirmed by Covid-19 in Europe. The back test of this system carried out throughout the weeks of confinement shows that the system is profitable. In this context, confirmed cases data is useful to assess investors’ mood and anticipate the evolution of the market. Therefore, an alternative way of investing arises for maximum uncertainty contexts, based exclusively on behavioral finance.

Performance analysis of aspect-oriented UML models

2007 ◽  
Vol 6 (4) ◽  
pp. 453-471 ◽  
Author(s):  
Dorina C. Petriu ◽  
Hui Shen ◽  
Antonino Sabetta
Keyword(s):  
Performance Analysis ◽  
Uml Models

scholarly journals Effects of COVID-Induced Public Anxiety on European Stock Markets: Evidence From a Fear-Based Algorithmic Trading System

2022 ◽  
Vol 12 ◽  
Author(s):  
Yunpeng Sun ◽  
Haoning Li ◽  
Yuning Cao
Keyword(s):  
Panel Data ◽  
Stock Markets ◽  
Algorithmic Trading ◽  
Government Officials ◽  
Market Returns ◽  
Trading System ◽  
Stock Market Returns ◽  
Back Testing ◽  
The Media ◽  
European Stock Markets

The effect of COVID-induced public anxiety on stock markets, particularly in European stock market returns, is examined in this research. The search volumes for the notion of COVID-19 gathered by Google Trends and Wikipedia were used as proxies for COVID-induced public anxiety. COVID-induced public anxiety was shown to be linked with negative returns in European stock markets when a panel data method was used to a sample of data from 14 European stock markets from January 2, 2020 to September 17, 2020. Using an automated trading system, we used this finding to suggest investment methods based on COVID-induced anxiety. The findings of back-testing indicate that these techniques have the potential to generate exceptional profits. These results have significant consequences for government officials, the media, and investors.

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