A Technical Trading Indicator Based on Dynamical Consistent Neural Networks

Zimmermann, Hans-Georg; Bertolini, Lorenzo; Grothmann, Ralph; Schäfer, Anton Maximilian; Tietz, Christoph

doi:10.1007/11840930_68

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…In contrast to other approaches, such as multi-agent systems, this does not necessarily simulate a respective capital market players and their trading interactions in a capital market. Starting from the intended modelling of a closed system as sum of its partial systems, the system description is the sum of its observables y τ [32]. The state variables can be understood as a vector combination.…”

Section: Methodsmentioning

confidence: 99%

The Predictive Value of Data from Virtual Investment Communities

Abdel-Karim

Benlian

Hinz

2020

MAKE

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Optimal investment decisions by institutional investors require accurate predictions with respect to the development of stock markets. Motivated by previous research that revealed the unsatisfactory performance of existing stock market prediction models, this study proposes a novel prediction approach. Our proposed system combines Artificial Intelligence (AI) with data from Virtual Investment Communities (VICs) and leverages VICs’ ability to support the process of predicting stock markets. An empirical study with two different models using real data shows the potential of the AI-based system with VICs information as an instrument for stock market predictions. VICs can be a valuable addition but our results indicate that this type of data is only helpful in certain market phases.

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Section: Methodsmentioning

confidence: 99%

The Predictive Value of Data from Virtual Investment Communities

Abdel-Karim

Benlian

Hinz

2020

MAKE

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“…This is also referred to as the "hit rate", "profitability percentage", or the "accuracy" of the forecast, amongst many others variations of these names. If the win rate is high, it is then claimed that the trading scheme is "working" and thus profitable, see [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

Baseline win rates for neural-network based trading algorithms

Krause

Fairbank

2020

2020 International Joint Conference on Neural Networks (IJCNN)

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Neural networks and other machine-learning systems are used to create automatic financial forecasting and trading systems. To aid comparison of such systems, there is a need for reliable performance metrics. One such metric that may be considered is the win rate. We show how in certain circumstances the win-rate statistic can be very misleading, and to counter this, we propose and define baseline win rates for comparison. We develop empirical and closed-form models for such baselines and validate them against financial data and a neural forecaster.

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A Technical Trading Indicator Based on Dynamical Consistent Neural Networks

Cited by 2 publications

References 3 publications

The Predictive Value of Data from Virtual Investment Communities

The Predictive Value of Data from Virtual Investment Communities

Baseline win rates for neural-network based trading algorithms

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