Stock market prediction of S&amp;#x00026;P 500 and DJIA using Bacterial Foraging Optimization Technique

Majhi, Ritanjali; Panda, Ganapati; Sahoo, G.; Dash, Puja; Das, Debashish

doi:10.1109/cec.2007.4424794

Cited by 39 publications

(19 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many institution and academic researchers are trying to propose a method for predicting next day behaviors of stock indexes in order to be better than the other methods, like a research that Majhi and other friends [8] did via applying bacterial foraging optimization technique for predicting stock market and S&P500 indexes in short and long terms, and they made a linear combiner model which its weights updated by BFO and comparing it with MultiLayer Perceptron (MLP) based method showed that Majhi and other friend's method has less calculative complexity and more precision to MLP method.…”

Section: Previous Workmentioning

confidence: 99%

A Linear Regression Approach to Prediction of Stock Market Trading Volume: A Case Study

Gharehchopogh¹,

Bonab²,

Khaze³

2013

IJMVSC

View full text Add to dashboard Cite

Predicting daily behavior of stock market is a serious challenge for investors and corporate stockholders and it can help them to invest with more confident by taking risks and fluctuations into consideration. In this paper, by applying linear regression for predicting behavior of S&P 500 index, we prove that our proposed method has a similar and good performance in comparison to real volumes and the stockholders can invest confidentially based on that.

show abstract

Section: Previous Workmentioning

confidence: 99%

A Linear Regression Approach to Prediction of Stock Market Trading Volume: A Case Study

Gharehchopogh¹,

Bonab²,

Khaze³

2013

IJMVSC

View full text Add to dashboard Cite

show abstract

“…Three behaviors were modeled by Passino in his original proposal [26]: 1) Chemotaxis, 2) reproduction and 3) elimination-dispersal. BFOA has been successfully applied to solve different type of problems like forecasting [27], transmission loss reduction [28] and identification of nonlinear dynamic systems [29].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of Fused Deposition Modelling (FDM) Process Parameters Using Bacterial Foraging Technique

Panda¹,

Padhee²,

Sood³

et al. 2009

IIM

108

View full text Add to dashboard Cite

Fused deposition modelling (FDM) is a fast growing rapid prototyping (RP) technology due to its ability to build functional parts having complex geometrical shapes in reasonable build time. The dimensional accuracy, surface roughness, mechanical strength and above all functionality of built parts are dependent on many process variables and their settings. In this study, five important process parameters such as layer thickness, orientation, raster angle, raster width and air gap have been considered to study their effects on three responses viz., tensile, flexural and impact strength of test specimen. Experiments have been conducted using central composite design (CCD) and empirical models relating each response and process parameters have been developed. The models are validated using analysis of variance (ANOVA). Finally, bacterial foraging technique is used to suggest theoretical combination of parameter settings to achieve good strength simultaneously for all responses.

show abstract

“…In 2002, inspired by the researches on the foraging behavior of E. coli bacteria, Prof. K. M. Passino proposed Bacterial Foraging Optimization Algorithm (BFOA) [4], which also has been applied to many engineering problems [5][6][7][8][9]. In the foraging process, if bacteria find no better food in the original direction, it will turn to a new direction.…”

Section: B Social Foragingmentioning

confidence: 99%

Adaptive Bacterial Foraging Optimization Algorithm Based on Social Foraging Strategy

Shen¹,

Zhu²

2014

JNW

View full text Add to dashboard Cite

Stock market prediction of S&P 500 and DJIA using Bacterial Foraging Optimization Technique

Cited by 39 publications

References 8 publications

A Linear Regression Approach to Prediction of Stock Market Trading Volume: A Case Study

A Linear Regression Approach to Prediction of Stock Market Trading Volume: A Case Study

Optimization of Fused Deposition Modelling (FDM) Process Parameters Using Bacterial Foraging Technique

Adaptive Bacterial Foraging Optimization Algorithm Based on Social Foraging Strategy

Contact Info

Product

Resources

About