Enhancing Profitability through Interpretability in Algorithmic Trading with a Multiobjective Evolutionary Fuzzy System

2012 ◽  
pp. 42-51 ◽  
Author(s):  
Adam Ghandar ◽  
Zbigniew Michalewicz ◽  
Ralf Zurbruegg
Keyword(s):  
Fuzzy System ◽  
Algorithmic Trading

Modeling of a Variable Speed Drive Using Adaptive Fuzzy System

2016 ◽  
Vol 10 (1) ◽  
pp. 19
Author(s):  
Sekhar J.N. Chandra ◽  
Marutheswar G.V. ◽  
◽  
Keyword(s):  
Fuzzy System ◽  
Variable Speed ◽  
Variable Speed Drive ◽  
Adaptive Fuzzy ◽  
Adaptive Fuzzy System

The profitability of algorithmic trading systems based on football sentiment

2020 ◽  
Vol 42 (1) ◽  
pp. 33-46
Author(s):  
Raúl Gómez-Martínez ◽  
Camila Marqués-Bogliani ◽  
Jessica Paule-Vianez
Keyword(s):  
Financial Markets ◽  
Investment Decisions ◽  
Futures Market ◽  
Algorithmic Trading ◽  
Investment Strategies ◽  
Measurable Effect ◽  
Behavioural Finance ◽  
Trading Systems ◽  
European Football

Behavioural finance has shown that investment decisions are the result of not just rational but also emotional brain processes. On the assumption that emotions affect financial markets, it would seem likely that football results might have a measurable effect on financial markets. To test this, this study describes three algorithmic trading systems based exclusively on the results of three top European football teams (Juventus, Bayern München and Paris St Germain) opening long or short positions in the next market season of the futures market of the index of each country (MIB (Milano Italia Borsa), DAX (Deutscher Aktien Index) and CAC (Cotation Assistée en Continu). Depending on the outcome of the last game played a long position was taken after a victory and a short position after a draw or defeat. The results showed that the algorithmic systems were profitable in the case of Juventus and Bayern whereas in the case of PSG, the system was profitable, but in an inverse way. This study shows that investment strategies that take account of sports sentiment could have a profitable outcome.

Penerapan Logika Samar dalam Peramalan Data Runtun Waktu

2011 ◽  
Vol 3 (2) ◽  
pp. 11-15
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Time Series Analysis ◽  
Fuzzy Sets ◽  
Fuzzy System ◽  
Time Series Data ◽  
Fuzzy Logic System ◽  
Series Data ◽  
Series Analysis ◽  
Logic System

Recently, there are so many soft computing methods been used in time series analysis. One of these methods is fuzzy logic system. In this paper, we will try to implement fuzzy logic system to predict a non-stationary time series data. The data we use here is Mackey-Glass chaotic time series. We also use MATLAB software to predict the time series data, which have been divided into four groups of input-output pairs. These groups then will be used as the input variables of the fuzzy logic system. There are two scenarios been used in this paper, first is by using seven fuzzy sets, and second is by using fifteen fuzzy sets. The result shows that the fuzzy system with fifteen fuzzy sets give a better forecasting result than the fuzzy system with seven fuzzy sets. Index Terms—forecasting, fuzzy logic, Mackey-Glass chaotic, MATLAB, time series analysis

Impact of Algorithmic Trading on Liquidity and Its Persistency

2014 ◽  
Author(s):  
Siyi Shen ◽  
Liu Jing
Keyword(s):  
Algorithmic Trading

Does Trade Clustering Reduce Trading Costs? Evidence from Periodicity in Algorithmic Trading

2014 ◽  
Author(s):  
Dmitriy Muravyev ◽  
Joerg Picard
Keyword(s):  
Algorithmic Trading ◽  
Trading Costs

Does Algorithmic Trading Affect Analyst Research Production?

2020 ◽  
Author(s):  
Pawel Bilinski ◽  
Irene Karamanou ◽  
Anastasia Kopita ◽  
Marios A. Panayides
Keyword(s):  
Algorithmic Trading ◽  
Research Production

Future of Commodity Markets and Investments

2018 ◽  
Author(s):  
Hunter M. Holzhauer
Keyword(s):  
Supply And Demand ◽  
Commodity Markets ◽  
Investment Banks ◽  
Algorithmic Trading ◽  
Excess Supply ◽  
The Future ◽  
Change Concerns ◽  
Global Population ◽  
Regulatory Trends

This chapter begins with a breakdown of recent growth trends for the overall commodities market. However, the long-term future of the market will heavily depend on three pressing issues: excess supply, increased regulations, and algorithmic trading. The section on excess supply explores how traders are changing strategies to adjust to the current imbalance between supply and demand, especially in the steel industry, and how that imbalance might change in the future based on global population trends and climate change concerns. The next section examines several regulatory trends, including the dramatic exodus of some investment banks from certain segments of the commodities market followed by a section focusing on how algorithmic trading is influencing how commodities are traded. A discussion of potential scenarios for the commodities market follows. The chapter concludes by examining a few ways in which the market and commodity traders may both survive and even thrive in the future.

A new classifier combination method based on TSK-type fuzzy system

2006 ◽  
Author(s):  
Liu Ming ◽  
Yuan Bao-zong ◽  
Feng Ze-shu ◽  
Chen Jiang-feng
Keyword(s):  
Fuzzy System ◽  
Combination Method ◽  
Classifier Combination

Reset Method Based Unknown Input Observer Design for Continuous-Time T-S Fuzzy System

2020 ◽  
Author(s):  
Z. Echreshavi ◽  
M. Farbood ◽  
M. Shasadeghi ◽  
M.H. Asemani
Keyword(s):  
Continuous Time ◽  
Fuzzy System ◽  
Observer Design ◽  
Unknown Input ◽  
Unknown Input Observer

scholarly journals An Evolving Neuro-Fuzzy System based on Uni-Nullneurons with Advanced Interpretability Capabilities

2021 ◽  
Author(s):  
Paulo Vitor de Campos Souza ◽  
Edwin Lughofer
Keyword(s):  
Fuzzy System ◽  
Neuro Fuzzy
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