Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange

Rezaee, Mustafa Jahangoshai; Jozmaleki, Mehrdad; Valipour, Mahsa

doi:10.1016/j.physa.2017.07.017

Cited by 83 publications

(35 citation statements)

References 31 publications

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“…Typically, trading systems are classification algorithms, which deal with labeling the predictor variables into classes [8]. There are many different trading systems: daily trading system [21], fuzzy logic rules trading system [10], and others [14,22,30,51].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Stock Market Trading: A Superior Trading Strategy?

Fister¹,

Mun²,

Jagrić³

et al. 2019

NNW

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Deep-learning initiatives have vastly changed the analysis of data. Complex networks became accessible to anyone in any research area. In this paper we are proposing a deep-learning long short-term memory network (LSTM) for automated stock trading. A mechanical trading system is used to evaluate its performance. The proposed solution is compared to traditional trading strategies, i.e., passive and rule-based trading strategies, as well as machine learning classifiers. We have discovered that the deep-learning long short-term memory network has outperformed other trading strategies for the German blue-chip stock, BMW, during the 2010-2018 period. system (MTS) to sequentially (day-by-day) execute trading signals (decisions), and thus trade with real stocks [66] in virtual time. Consequently, the quality and effectiveness of a trading system over a period of a few years can be evaluated in a matter of seconds.The MTS works by giving three common trading signals, i.e., buy, hold, and sell, for each of the stocks. It is given an initial amount of cash to buy stocks that can be held in the portfolio or be sold at a later date. Buying and selling stocks can generate profits if trading signals are given rationally or generate loss if they are not. Profits and losses can be monitored and reviewed at the end of the trading period to realize strengths and weaknesses of the trading decisions [47]. Trading decisions are typically given by trading strategies, i.e., automated algorithms that constantly monitor market behaviour and react accordingly. Multiple trading strategies are usually incorporated within the trading system, by each giving unique trading decisions. The latter can be evaluated quickly and inexpensively either in realworld time or using the MTS.In this paper we are proposing a concept of an automated, single stock, trading system using the MTS, examining its potential by five different trading strategies, realizing their strengths and weaknesses, and proving the correctness of an optimal strategy. Based on this, we encourage further analysis and the design of an automated portfolio trading system for many similarly treated stocks in parallel. Here we employ three different trading strategies: (1) passive, (2) rule-based -relative strength index (RSI) and moving average convergence/divergence (MACD) technical indicators, and (3) surrogate model trading strategies using machine learning classifiers (MLC) and long short-term memory network (LSTM). Each of them can provide three trading signals: buy, hold, or sell.Since deep learning (DL) [37] is primarily intended for engineering applications, such as image and sound processing, we have not found many examples of DL in the fields of finance, banking, or insurance. In line with this, we would like to apply the DL to the area of finance, particularly mechanical trading systems as an alternative, and thus test whether the DL can be successfully deployed into this area.The structure of the paper is as follows: Section 2 presents the literature review, MLC application...

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Section: Introductionmentioning

confidence: 99%

Deep Learning for Stock Market Trading: A Superior Trading Strategy?

Fister¹,

Mun²,

Jagrić³

et al. 2019

NNW

View full text Add to dashboard Cite

show abstract

“…The outputs of each model have been integrated using Elastic net regression. Rezaee, Jozmaleki & Valipour (2018) provided an approach to predict the stock market using dynamic fuzzy C-means, Data Envelopment Analysis, and multilayer perceptron (MLP). In another study, a novel ensemble method has been introduced for time series forecasting integrating various machine learning models (Adhikari, 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

An ensemble approach based on transformation functions for natural gas price forecasting considering optimal time delays

Saghi

Rezaee

2021

PeerJ Computer Science

Self Cite

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Natural gas, known as the cleanest fossil fuel, plays a vital role in the economies of producing and consuming countries. Understanding and tracking the drivers of natural gas prices are of significant interest to the many economic sectors. Hence, accurately forecasting the price is very important not only for providing an effective factor for implementing energy policy but also for playing an extremely significant role in government strategic planning. The purpose of this study is to provide an approach to forecast the natural gas price. First, optimal time delays are identified by a new approach based on the Euclidean Distance between input and target vectors. Then, wavelet decomposition has been implemented to reduce noise. Moreover, fuzzy transform with different membership functions has been used for modeling uncertainty in time series. The wavelet decomposition and fuzzy transform have been integrated into the preprocessing stage. An ensemble method is used for integrating the outputs of various neural networks. The results depict that the proposed preprocessing methods used in this paper cause to improve the accuracy of natural gas price forecasting and consider uncertainty in time series.

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“…Tao et al, (2013) presented a hybrid model to conducting performance measurements using DEA and axiomatic fuzzy set clustering. Jahangoshai et al, (2018) integrated dynamic fuzzy C-means and Arti cial Neural Network with a DEA model to solve a multiple criteria optimization problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Cluster-Adjusted DEA Efficiency in the presence of Heterogeneity: An Application to Banking Sector

2020

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This paper improves on the issues of extreme data points and heterogeneity found in the linear programming data envelopment analysis (DEA) by presenting a cluster-adjusted DEA model (DEA with cluster approach). This analysis, based on efficiency, determines the number of clusters via Gap statistic and Elbow methods. We use the December quarterly panel data consisting of 122 U.S agricultural banks across 37 states from 2000 to 2017 to estimate the cluster-adjusted DEA model. Empirical results show differences in the estimated DEA efficiency measures with and without a clustering approach. Furthermore, using non-parametric tests, the results of Ansari-Bradley, Kruskal Wallis, and Wilcoxon Rank Sum tests suggest that the cluster-adjusted DEA model provides statistically better efficiency measures in comparison to the DEA model without a clustering approach.

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Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange

Cited by 83 publications

References 31 publications

Deep Learning for Stock Market Trading: A Superior Trading Strategy?

Deep Learning for Stock Market Trading: A Superior Trading Strategy?

An ensemble approach based on transformation functions for natural gas price forecasting considering optimal time delays

Cluster-Adjusted DEA Efficiency in the presence of Heterogeneity: An Application to Banking Sector

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