Stock prediction, trading simulation and options volatility prediction using FASCOM++ (fuzzy associative cortical maps architecture) by Malik, Arpit.

“…The author aims to validate the modified architecture of FASCOM by conducting benchmarking experiments and observing the improvement in the performance of the system over other systems. …”
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Parasite trading model using neuro-fuzzy system by Ng, Wei Jie

“…Directional change of Event and Exit Day for trade was predicted from our parasite trading model, the results were used against Price Time-Series approach (SeroTSK and GSETSK) in a benchmarking, to analyze the practical result on applying our proposed parasite model in financial domain.…”
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DNN-FES & Fole : objective loss estimator for integrating deep neural-network into fuzzy systems by Dandapath, Soham

“…FOLE is an objective loss function in the sense that it not only tries to minimize the error incurred from the target and predicted value, but also the loss incurred in making inadmissible or unjustifiable rule. In most of the benchmarking tests, DNN-FES has proved to produce state-of-the-art results. …”
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Intelligent option trading by Manan Maheshwari.

“…SaFIN++ is implemented and run over several benchmarking simulations to demonstrate its efficiency and accuracy as a self-organizing neural fuzzy system. …”
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Fuzzy-embedded long short-term memory (FE-LSTM) with application in stock trading by Lim, Tammy Lee Xin

“…The implemented FE-LSTM is benchmarked against common neural networks and fuzzy inference systems through several time-series benchmark experiments and the prediction of stock prices. …”
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Increasing interpretability using a fuzzy-embedded recurrent neural network (FE-RNN) with its application in stock ETF trading by Tan, James Chee Min

“…They produce good results in benchmark experiments but suffer in the Nakanishi dataset, where the training data is sparse. …”
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Evolving deep fuzzy ensemble network for portfolio management by Yu, Xinhui

“…The system is applied to a carefully constructed portfolio for both allocation and dynamic rebalancing, and its performance is benchmarked against other popular trading strategies. …”
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Deep neural network with fuzzy inputs for portfolio management by Lim, Alston Khian Heng

“…The performance of the model will be evaluated through back testing and comparing against the performance of a benchmark.…”
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Structural evolving appetitive reward-based pseudo-outer-product fuzzy neural network SE-ARPOP-CRI(S) by Do, The Anh

“…Experimental results from benchmark applications support the model with promising results. …”
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A novel incremental rough set-based pseudo outer-product with ensemble learning (ieRSPOP) neuro-fuzzy system for forecasting volatility by Tor Das, Ronald.

“…The performance of ieRSPOP is evaluated through several time series benchmark experiments and stock data and analyzed against existing incremental and non-incremental architectures, and results are promising. …”
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Reinforcement trading for multi-market portfolio with crisis avoidance by Cai, Lingzhi

“…The experiment result shows that the models trained by the framework performed as well as buy-and-hold strategy benchmark in the bullish period of 2015-2019. Furthermore, very much accredited to the crisis avoidance algorithm, the models acted 17% better than buy-and-hold during all testing windows no less than 5 years in 2000-2019.…”
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Generic self-evolving TSK fuzzy neural network with rough set (GSETSK+RS) by Qiu, Huiqian

“…The performance is benchmarked against GSETSK and other fuzzy neural networks. …”
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Reinforcement learning (RL) based stock trading system via support vector machine by Ong, Zhi Yuan.

“…It is found to generate significant profits as compared to other benchmark systems despite the recent economic crisis the world face.…”
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An evolving type-2 neural fuzzy inference system with fuzzy rule interpolation (eT2FIS++) with its application in straddle option trading by Zeng, Ye

“…The proposed eT2FIS++ model is benchmarked against several models by using datasets with different properties. …”
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CERS-DR : cycle-based ETF rotation strategy with dynamic rebalancing with the application of reinforcement learning and neural network by Miharja, Toni

“…To conduct dynamic rebalancing, CERS-DR utilises RL and Convolutional Neural Network (CNN) to summarise the market environment, assess the potential of each portfolio constituent and assign optimal portfolio weights subsequently. CERS-DR is benchmarked against other rebalancing schemes and S&P 500 index. …”
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Recurrent neural network embedded fuzzy (RNNEFS) system with its applications in stock market forecasting & MACD trading strategies by Chen, Yinya

“…The performance of the proposed trading system is benchmarked against existing strategies and the results are highly encouraging.…”
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Support vector fuzzy parallel embedded system by Book, Jeremy Kay Yip

“…The experimental results showed that the portfolio management incorporated with the proposed SVFPS has outperformed benchmarks of commonly used investing strategies.…”
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Self-evolving neural fuzzy system with application in portfolio management by Yap, Jia Le

“…The proposed trading strategy is then benchmarked against the “Buy and Hold” strategy and vanilla MACD strategy and the performance of the portfolio is evaluated under different market conditions. …”
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A pseudo-incremental rough set-based pseudo outer-product with ensemble learning (ieRSPOP++) fuzzy neural network by Iyer, Aparna.

“…The performance of ieRSPOP++ is evaluated and compared against existing incremental and non-incremental architectures, through several time series benchmark experiments and prediction of future stock prices. ieRSPOP++ is also used as a regressor for artificial ventilation modeling and volatility prediction for option trading. …”
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Dynamic modeling of type 1 diabetic metabolism by Jivesh Ramduny

“…Further experimental benchmarks were conducted which capitalized on the fitting of the predicted blood glucose levels given an amount of insulin bolus after a meal declaration. …”
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