A self-adaptive fuzzy-based optimised functional link artificial neural network model for financial time series prediction

Das, Soumya Ranjan; Patra, A.; Mishra, Sarojananda; Senapati, Manas Ranjan

doi:10.1504/ijbfmi.2015.075358

Cited by 10 publications

(6 citation statements)

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“…Table 4 provides a descriptive summary of the data scaling, feature selection, and hybrid model categories identified in the sample of articles each year, with responses indicated as either “yes” or “no.” The use of preprocessing techniques is quite common, particularly for neural networks (Qi, 2002; Tseng et al, 2012), and there are numerous ways to do so. The most widely used techniques include normalization using min‐max (Bhowmick et al, 2019; Das & Padhy, 2018; Das et al, 2015; Hajibabaei et al, 2014; Hegde et al, 2018; Shrivastava & Panigrahi, 2011) or normal distribution approximation (Van Gestel et al, 2006; Yazdani‐Chamzini et al, 2012), also referred to as standardization (McNally et al, 2018), as well as softmax function (Jiang et al, 2018), log‐transform (Gradojevic & Yang, 2006; Kumar, 2010; Kumar & Thenmozhi, 2012; Matyjaszek et al, 2019), differences (Cao & Tay, 2001; Gradojevic & Yang, 2006), and those articles that do not specify any data transformation. Among these, min‐max normalization is the most prevalent in the articles.…”

Section: Resultsmentioning

confidence: 99%

A review of artificial intelligence quality in forecasting asset prices

Barboza

Silva

Fiorucci

2023

Journal of Forecasting

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Researchers and practitioners globally, from a range of perspectives, acknowledge the difficulty in determining the value of a financial asset. This subject is of utmost importance due to the numerous participants involved and its impact on enhancing market structure, function, and efficiency. This paper conducts a comprehensive review of the academic literature to provide insights into the reasoning behind certain conventions adopted in financial value estimation, including the implementation of preprocessing techniques, the selection of relevant inputs, and the assessment of the performance of computational models in predicting asset prices over time. Our analysis, based on 109 studies sourced from 10 databases, reveals that daily forecasts have achieved average error rates of less than 1.5%, while monthly data only attain this level in optimal circumstances. We also discuss the utilization of tools and the integration of hybrid models. Finally, we highlight compelling gaps in the literature that provide avenues for further research.

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Section: Resultsmentioning

confidence: 99%

A review of artificial intelligence quality in forecasting asset prices

Barboza

Silva

Fiorucci

2023

Journal of Forecasting

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“…Each of the above models can be used for time series data prediction. Das et al gave forward a FLANN model trained by fuzzy for prediction of the closing price of Yahoo Inc, Nokia and Bank of America [31]. The result is then put in comparison to the constraints of the FLANN model trained by GA. A trigonometric FLANN model projected by Majhi et al for short term and long term prediction [32] of prices of S&P 500 and DJIA stock price.…”

Section: Flannmentioning

confidence: 99%

Applications of neural network based methods on stock market prediction: survey

Mohapatra¹,

Das²,

Das³

et al. 2018

IJET

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Financial forecasting is one of the domineering fields of research, where investor's money is at stake due to the rise or fall of the stock prices which unpredictable and fluctuating. Basically as the demand for stock markets has been rising at an unprecedented rate so its prediction becomes all the more exciting and challenging. Prediction of the forthcoming stock prices mostly Artificial Neural Network (ANN) based models are taken into account. The other models such as Bio-inspired Computing, Fuzzy network model etc., considering statistical measures, technical indicators and fundamental indicators are also explored by the researchers in the field of financial application. Ann's development has led the investors for hoping the best prediction because networks included great capability of machine learning such as classification and prediction. Most optimization techniques are being used for training the weights of prediction models. Currently, various models of ANN-based stock price prediction have been presented and successfully being carried to many fields of Financial Engineering. This survey aims to study the mostly used ANN and related representations on Stock Market Prediction and make a proportional analysis between them.

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“…To get a good return, the individual should know about the operations of the stock market, movements of the stock price and other factors. Generally, the stock market is noisy in nature which shatters the investors’ confident (Das et al , 2015). Due to numerous underlying factors (Weckman et al , 2008), stock market data are considered to be the complex time series data, and the prediction is viewed as the most difficult task due to its inconsistent nature.…”

Section: Introductionmentioning

confidence: 99%

An investigational analysis on forecasting intraday values

Manickavasagam

Visalakshmi

2019

BIJ

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Purpose The algorithmic trading has advanced exponentially and necessitates the evaluation of intraday stock market forecasting on the grounds that any stock market series are foreseen to follow the random walk hypothesis. The purpose of this paper is to forecast the intraday values of stock indices using data mining techniques and compare the techniques’ performance in different markets to accomplish the best results. Design/methodology/approach This study investigates the intraday values (every 60th-minute closing value) of four different markets (namely, UK, Australia, India and China) spanning from April 1, 2017 to March 31, 2018. The forecasting performance of multivariate adaptive regression spline (MARSplines), support vector regression (SVR), backpropagation neural network (BPNN) and autoregression (1) are compared using statistical measures. Robustness evaluation is done to check the performance of the models on the relative ratios of the data. Findings MARSplines produces better results than the compared models in forecasting every 60th minute of selected stocks and stock indices. Next to MARSplines, SVR outperforms neural network and autoregression (1) models. The MARSplines proved to be more robust than the other models. Practical implications Forecasting provides a substantial benchmark for companies, which entails long-run operations. Significant profit can be earned by successfully predicting the stock’s future price. The traders have to outperform the market using techniques. Policy makers need to estimate the future prices/trends in the stock market to identify the link between the financial instruments and monetary policy which gives higher insights about the mechanism of existing policy and to know the role of financial assets in many channels. Thus, this study expects that the proposed model can create significant profits for traders by more precisely forecasting the stock market. Originality/value This study contributes to the high-frequency forecasting literature using MARSplines, SVR and BPNN. Finding the most effective way of forecasting the stock market is imperative for traders and portfolio managers for investment decisions. This study reveals the changing levels of trends in investing and expectation of significant gains in a short time through intraday trading.

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A self-adaptive fuzzy-based optimised functional link artificial neural network model for financial time series prediction

Cited by 10 publications

References 0 publications

A review of artificial intelligence quality in forecasting asset prices

A review of artificial intelligence quality in forecasting asset prices

Applications of neural network based methods on stock market prediction: survey

An investigational analysis on forecasting intraday values

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