A Technical Analysis Indicator Based On Fuzzy Logic

AbstractIn this paper an indicator for technical analysis based on fuzzy logic is proposed, which unlike traditional technical indicators, is not a totally objective mathematical model, but incorporates subjective investor features such as the risk tendency. The fuzzy logic approach allows representing in a more “human” way the decision making reasoning that a non-expert investor would have in a real market. Such an indicator takes as input, general market information like profitability and volatility of the stock prices, while the outputs are the buy and sell signals. In addition to present the detailed formulation of the indicator, in this paper a validation for the same is presented, which makes use of a multi-agent based simulation platform within which the behavior and profits obtained by agents that used traditional technical indicators such as MA, RSI and MACD, are compared against those obtained by agents that use the fuzzy indicator for the decision making process

Modelos de predicción de índices de mercados de valores mediante el uso de la lógica difusa

Treballs Finals del Màster de Ciències Actuarials i Financeres, Facultat d'Economia i Empresa, Universitat de Barcelona, Curs: 2017-2018, Tutor: Ana María Gil LafuenteMediante el siguiente estudio, se va a ahondar en la evaluación de la predicción de una serie histórica del NASDAQ-100, aplicando la metodología ANFIS, concretamente mediante el uso de la neuro-fuzzy. Por lo tanto, se puede hablar de la aplicación de modelos de lógica difusa a una serie de datos históricos, a fin de establecer una teoría predictiva del comportamiento de los valores y su proyección, para la toma de decisiones por parte de los traders. Con todo, compararemos la predicción con dicha metodología con la encontrada a través de la metodología Box-Jenkins sobre los mismos datos históricos

Applying GMDH-Type Neural Network and Genetic Algorithm for Stock Price Prediction of Iranian Cement Sector

The cement industry is one of the most important and profitable industries in Iran and great content of financial resources are investing in this sector yearly. In this paper a GMDH-type neural network and genetic algorithm is developed for stock price prediction of cement sector. For stocks price prediction by GMDH type-neural network, we are using earnings per share (EPS), Prediction Earnings Per Share (PEPS), Dividend per share (DPS), Price-earnings ratio (P/E), Earnings-price ratio (E/P) as input data and stock price as output data. For this work, data of ten cement companies is gathering from Tehran stock exchange (TSE) in decennial range (1999-2008). GMDH type neural network is designed by 80% of the experimental data. For testing the appropriateness of the modeling, reminder of primary data were entered into the GMDH network. The results are very encouraging and congruent with the experimental result

Application of neuro-fuzzy methods for stock market forecasting: a systematic review

Predicting stock prices is a challenging task owing to the market's chaos and uncertainty. Methods based on traditional approaches are unable to provide a solution to the market predictability issue. Thus, contemporary models using accurate neuro-fuzzy systems are found to be the most effective approach to tackling the problem. However, the existing literature lacks a detailed survey of the application of neuro-fuzzy techniques for stock market prediction. This paper presents a systematic literature review of the use of neuro-fuzzy systems for predicting stock market prices and trends.  On this basis, articles issued in various reputed international journals from 2000 to July 2022 were examined, 11 duplicates and 4 non-exclusive articles were removed and, as consequent, 24 eligible studies were retrieved for inclusion. Thus, analysis and discussions were based on two major viewpoints: predictor techniques and accuracy metrics. The review reveals that the researchers, based on their knowledge and research interests, applied a diverse neuro-fuzzy technique and shown stronger preference for certain neuro-fuzzy methods, such as ANFIS. To draw conclusions about the model performance, researchers chose different statistical and non-statistical metrics according to the technique used. It was finally observed that neuro-fuzzy approaches outperform, within its limits, conventional methods. However, each has its own set of constraints regarding the challenges involved in putting it into practice. The complexity of the presented approaches is the most significant potential obstacle that they face. Therefore, stock market prediction is a difficult undertaking, and multiple elements should be considered for accurate prediction. Yet, despite the subject's prominence, there are still promising new frontiers to explore and develop. Keywords: Fuzzy logic, Artificial neural network, Neuro-fuzzy, stock market forecasting JEL Classification: F37 Paper type: Theoretical Research  Predicting stock prices is a challenging task owing to the market's chaos and uncertainty. Methods based on traditional approaches are unable to provide a solution to the market predictability issue. Thus, contemporary models using accurate neuro-fuzzy systems are found to be the most effective approach to tackling the problem. However, the existing literature lacks a detailed survey of the application of neuro-fuzzy techniques for stock market prediction. This paper presents a systematic literature review of the use of neuro-fuzzy systems for predicting stock market prices and trends.  On this basis, articles issued in various reputed international journals from 2000 to July 2022 were examined, 11 duplicates and 4 non-exclusive articles were removed and, as consequent, 24 eligible studies were retrieved for inclusion. Thus, analysis and discussions were based on two major viewpoints: predictor techniques and accuracy metrics. The review reveals that the researchers, based on their knowledge and research interests, applied a diverse neuro-fuzzy technique and shown stronger preference for certain neuro-fuzzy methods, such as ANFIS. To draw conclusions about the model performance, researchers chose different statistical and non-statistical metrics according to the technique used. It was finally observed that neuro-fuzzy approaches outperform, within its limits, conventional methods. However, each has its own set of constraints regarding the challenges involved in putting it into practice. The complexity of the presented approaches is the most significant potential obstacle that they face. Therefore, stock market prediction is a difficult undertaking, and multiple elements should be considered for accurate prediction. Yet, despite the subject's prominence, there are still promising new frontiers to explore and develop. Keywords: Fuzzy logic, Artificial neural network, Neuro-fuzzy, stock market forecasting JEL Classification: F37 Paper type: Theoretical Research &nbsp

Time-Series Forecast with Adaptive Feedback Controlled Predictor

Abstract This paper describes a novel approach to predicting time-series which blends techniques developed in the areas of observer design and numerical solvers for ODEs. The developed predictor is based on a novel feedback control architecture which leads to computationally efficient and a fairly accurate forecast even for volatile economic series. Application to series of various kinds shows that the developed forecaster possesses some basic properties of numerical solvers for ODE. In the same time it prediction horizon is favorably compared with a time step attaining in numerical simulations for the series with precisely known models whereas no knowledge of the series' global model is assumed in our forecast. We demonstrate that for noisy series the accuracy of prediction reduces to the level of noise to signal ratio as well as that reduction of noise by smoothing the series comparably increases the accuracy of prediction. It is also shown that the developed approach provides practically valuable forecast in application to volatile economic series

Support vector regression of a high fidelity helicopter flight model

The traditional technique for the dynamic modelling of helicopters and their systems involves the collection of flight data and aircraft specifications from which physics-based theoretical equations are generated and validated. It is a time consuming process that requires the availability of a significant amount of data. The data required is often proprietary or commercial in confidence. The suggestion of a black box approach using machine learning techniques may provide the answer for a more simplistic method of simulation. This would only require data that is readily available to the owner of the helicopter platform. The application of Support Vector Machines (SVMs) as a machine learning technique for helicopter simulation is chosen for this investigation. A high fidelity model of a SH-2G(A) Super Seasprite helicopter is initially developed and validated in the FLIGHTLAB simulation environment. This includes a new servo flap component designed using Chopra and Shen’s quasisteady adaption of the Theodorsen model. This flight model provides a basis of noise free flight data from which a number of SVM models are produced to simulate the longitudinal pitch dynamics of a helicopter in hover. Subsequently, the best performing SVM configuration is trained using real flight data and compared to the simulation results. The SVM results show significant promise in the ability to represent aspects of a helicopter’s dynamics at a high fidelity, provided that the following is established. Firstly, it is important to provide the machine with knowledge of past inputs that encompass the delay characteristics of the helicopter dynamic system. Secondly, the relationship, rather than the mechanics, between the significant variables that represent the dynamic system must be well understood

Application of Machine Learning to Financial Time Series Analysis

This multidisciplinary thesis investigates the application of machine learning to financial time series analysis. The research is motivated by the following thesis question: ‘Can one improve upon the state of the art in financial time series analysis through the application of machine learning?’ The work is split according to the following time series trichotomy: 1) characterization — determine the fundamental properties of the time series; 2) modelling — find a description that accurately captures features of the long-term behaviour of the system; and 3) forecasting — accurately predict the short-term evolution of the system