Fuzzy feed forward neural network (FFFNN) model for the Jakarta Islamic index (JII) forecasting

Abstract

Feed Forward Neural Network (FFNN) model is the best model to forecast the time series data. In this research, The Fuzzy Feed Forward Neural Network (FFFNN) with backward propagation method is used to predict the Jakarta Islamic Index (JII) data time series in 2018. Fuzzy is used as input to the FFNN model because it is overcome the weaknesses of the inaccurate results of the FFNN when the data is unclear or incomplete. The purposes of this research are to explain the procedure to generate the FFFNN model. The steps of the FFFNN model prepare the input data to become a fuzzy number using Growth-S Curve fuzzification; the second is divide the data into two training and testing data, the third is determining the best neural network architecture with different neurons and hidden layers to get the best weights that used for the forecasting model. In this research, the best FFFNN model is built by 19 neurons and one hidden layer with 90% and 10% training and testing data, respectively. Therefore with the model obtained, forecasting produces the value of MSE 0.0018 in training and 0.0004 in testing. From the MSE values obtained, it can be concluded that the forecasting using FFFNN model is reasonable to predict.

Deep learning with small and big data of symmetric volatility information for predicting daily accuracy improvement of JKII prices

“…In addition, this study accomplishes that using small data of symmetric volatility information is better than using big data sample, this result indicates that small data of symmetric volatility information is full of high-quality information for better prediction accuracy, therefore, this result is consistent with Yudelson et al (2014) and Faraway and Augustin (2018) when they demonstrated that small data outperforms big data in prediction accuracy when small data generate superior inferences than the low-quality large sample. In conclusion, this paper supports the findings of Das et al (2017), Wibowo et al (2017), Alkhoshi and Belkasim (2018), Livieris et al (2019), Aslam et al (2020), Peng and Tang (2020), Wang et al (2020), Irsalinda et al (2020), Yu and Yan (2020), Gandhmal and Kumar (2020), and Peng and Tang (2020), in which modern techniques like machine learning, artificial intelligence and DL are effective tools in the capital markets by affording advanced knowledge to the financial investors for the well-organized managing of portfolios, to reduce trading risk and to make right financial decisions, which leads to the inevitability of using new technological techniques in Islamic capital markets due to the effectiveness of those modern techniques in the predicting process than other classical statistical tools which remained paralyzed to evaluate big data time series, this inevitability of updating the classical statistical tools obliges the financial investors and decision-makers to employ new techniques which only machine learning, artificial intelligence and DL can offer.…”

“…One of the newest studies that applied ANNs in Islamic capital markets, a study by Aslam et al (2020), predicted that daily closing prices of the Islamic securities (KMI-30) index using ANNs, their results showed that ANNs provided high accuracy in the prediction process. Correspondingly, Irsalinda et al (2020) predicted the JKII using the neural networks based on the fuzzy feed-forward method, their results showed that the predicted model produced high prediction ability in reducing the error rate in the training and testing process.…”

“…According to Aslam et al. (2020) and Jannes (2018), the use of artificial intelligence, machine learning and DL in predicting the stock index movements of Islamic capital markets became essential for making the right trading decisions by investors, particularly for the Jakarta Islamic Index (JKII) in the Indonesian Islamic stock market which witnessed deep volatilities and unstable movements as stated by Irsalinda et al. (2020).…”

“…However, Ben Rejeb and Arfaoui (2019) proved that Islamic stock indices are more volatile than their conventional equivalents in terms of volatility. Since this research is focussing on the factor of volatility in the JKII, Irsalinda et al. (2020) showed that the JKII is highly volatile and witnessed unstable movements.…”

“…According to Irsalinda et al. (2020) and Ledhem (2021), there is a lack of using modern statistical techniques in the Indonesian Islamic capital markets and particularly in predicting the JKII price movements, and since the JKII was witnessing volatilities and unbalanced movements, investors were obliged to employ the modern methods like artificial intelligence, machine learning and DL in predicting the JKII prices.…”