Predicting Movement of Stock of Apple Inc. using Sutte Indicator

One of the most challenging financial decisions is when to buy and sell stocks. Frontier markets offer high profit opportunities but also have high risk. Consequently, technical analysis is used to assist in properly timing entry and exit points from stock trades. Previous research presented applications of technical analysis in developed and emerging markets since they, unlike frontier markets, exist in an environment of political stability, regulations, and liquidity. This paper shows how trade signals generated from Simple Moving Average (SMA) confirmed by Moving Average Convergence Divergence (MACD) can be used to minimise trading risk in frontier markets such as Dubai Financial Market (DFM). The results show that the standard time-periods for SMA and MACD do not apply well to frontier markets and that trade signals generated from SMA and confirmed by signals generated from medium to long-term MACD or vice versa result in excellent hit ratios.

Classification and Prediction of Stock Market Index Based on Fuzzy Metagraph

Nowadays , nonlinear time series and artificial neural networks (ANN) models are used for forecasting in the field of business, agriculture and soon. Recent studies have shown, ANN have been successfully used for forecasting of financial and agriculture data series The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. ANN have more advantages that can approximate to model both linear and nonlinear structures in time series, they are not able to handling both structures equally well. The autoregressive integrated moving average (ARIMA) model and two ANN models namely, Radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) methods were applied to Hyderabad airport traffic data. The data obtained for 15 years from 2002–2003 to 2016–2017 about domestic and international passenger of International Airport of Hyderabad, India. In this research paper, we compared the performances of ARIMA, RBFNN and ERNN were based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results showed that RBFNN obtained the smallest MAE, MAPE and RMSE in both the modeling and forecasting processes. The performances of the three models ranked in ascending order were: ARIMA, ERNN and the RBFNN model. Keywords: T ime series, forecasting, artificial neural networks, ARIMA models, radial basis function neural networks, and Elman recurrent neural networks Cite this Article R. Ramakrishna, Berhe Aregay, Tewodros Gebregergs. The Comparison in Time Series Forecasting of Air Traffic Data by Autoregressive Integrated Moving Average Model, Radial Basis Function and Elman Recurrent Neural Networks. Research & Reviews: Journal of Statistics . 2018; 7(3): 75–90p.

Electricity demand forecasting is important for planning and facility expansion in the electricity sector. Accurate forecasts can save operating and maintenance costs, increased the reliability of power supply and delivery system, and correct decisions for future development.  Universiti Tun Hussein Onn Malaysia (UTHM) which is a developing university in Malaysia has been growing since its formation in 1993.  Thus, it is important for UTHM to forecast the electricity consumption in future so that the future development can be determined. Hence, UTHM electricity consumption was forecasted by using the simple moving average (SMA), weighted moving average (WMA), simple exponential smoothing (SES), Holt linear trend (HL), Holt-Winters (HW) and centered moving average (CMA).  The monthly electricity consumption from January 2011 to December 2017 was used to forecast January to December 2018 monthly electricity consumption.  HW gives the smallest mean absolute error (MAE) and mean absolute percentage error (MAPE), while CMA produces the lowest mean square error (MSE) and root mean square error (RMSE).  As there is a decreasing population of UTHM after the moving of four faculties to Pagoh and HW forecasted trend is decreasing whereas CMA is increasing, hence HW might forecast better in this problem.

Investing in the Malaysian stock market can be overwhelming due to the abundance of options, which necessitates informed decision-making to navigate the volatile market. This study addresses a common problem faced by investors venturing into the stock market, where instability and fluctuations pose significant risks, leading to financial losses stemming from inadequate knowledge about suitable stocks for investment. Unlike many studies that focus on long-term forecasting methods, this research adopts the Geometric Brownian Motion (GBM) model for short-term investment analysis. The study aims to identify the most effective volatility measurement model, develop a forecasting model using GBM based on the chosen volatility model, and evaluate the accuracy of the GBM model using Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Deviation (MAD). Four volatility models, which include simple, log, high-low, and high-low-closed volatility are analysed to determine the most effective volatility measurement model. Four months of daily stock data were collected to ensure accuracy excluding factors such as seasonality, politics, natural disasters, and wars. Findings indicate that the simple volatility model is the most suitable for forecasting stock market trends using the GBM model, demonstrating high accuracy based on MSE, MAPE and MAD. These results suggest that employing the simple volatility model within GBM model can offer a practical and accurate approach for short-term market analysis in Malaysia, potentially aiding investors in mitigating risks and optimizing their trading strategies.

Image processing is a technique to create an image that appears and can be converted into light that describes 2 dimensions. The K-Nearest Neighbor (K-NN) algorithm is known for its efficiency and effectiveness in classification. Although K-NN is often used, a deep understanding of Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Deviation (MAD) especially in the context of image classification, still requires further exploration. This study aims to emphasize and measure the behavior of MSE, MAPE, and MAD when applied to K-NN classification results. In this study, calculating statistical features such as mean, standard deviation, skewness, and kurtosis are extracted from the histogram of chicken egg images. These features are then normalized and used as input for the K-NN algorithm. Classification performance is evaluated using MSE, MAPE, and MAD. The results show that K-NN is able to classify chicken egg images with performance measured by MSE of 0.35114462, MAPE of 0.14237803%, and MAD of 0.52930142. The differences in these values, especially the much smaller MAPE value and in percentage units, underscore the importance of selecting the right metrics according to the needs of interpretation and practical applications. This study provides a clearer understanding of how different error metrics characterize the performance of K-NN in image classification, while also highlighting the need for comparative metric considerations in future research.

Stock market volatility is important for investment, option pricing and financial market regulation. In recent years, stock market analysis and prediction have the greatest significance for many professionals in the fields of finance and stock exchange. There are many methods available in the literature to solve the problem of future prediction. The present study provides a detailed comparison of Single Exponential Smoothing (SES) model and Autoregressive Integrated Moving Average (ARIMA) model. Future values are forecasting using SES and ARIMA model. Forecasted values for different iś values are calculated from SES method and ARIMA (0, 2, 3). Also, Mean Square Error (MSE), Mean Absolute Deviation (MAD), Root Mean Square (RMSE) and Mean Absolute Percentage Error (MAPE) are calculated individually for both methods.

The implementation of tools such as Genetic Algorithms has not been exploited for asset price prediction despite their power, robustness, and potential application in the stock market. This paper aims to fill the gap existing in the literature on the use of Genetic Algorithms for predicting asset pricing of investment strategies into stock markets and investigate its advantages over its peers Buy & Hold and traditional technical analysis. The Genetic Algorithms strategy applied to the MACD was carried out in two different validation periods and sought to optimize the parameters that generate the buy-sell signals. The performance between the machine learning-based approach, technical analysis with the MACD and B&H was compared. The results suggest that it is possible to find optimal values of the technical indicator parameters that result in a higher return on investment through Genetic Algorithms, beating the traditional technical analysis and B&H by around 4%. This study offers a new insight for practitioners, traders, and finance researchers to take advantage of Genetic Algorithms for trading rules application in forecasting financial asset returns under a more efficient and robust methodology based on historical data analysis.

Stock is one of the investment instruments that many investors choose, both short and long term. Meanwhile, the stock price index is an essential indicator for investors deciding whether to buy, sell, or hold the stock. This study aims to determine what methods are suitable for predicting the Stock Price Index of Construction Companies Listed on the Indonesia Stock Exchange in 2015-2019. By selecting a model that matches the existing time series data, to evaluate the results of the forecasting, the researcher uses a measure of accuracy with Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD), and Mean Squared Deviation (MSD). This type of research is a quantitative study with a research population of 16 companies listed on the Indonesia Stock Exchange. Only four samples were used that fit the specified criteria, and only five years of research were conducted, namely in 2015 to 2019. data can be seen from historical data or actual data and tested using Minitab software version 19. The results showed that Double Exponential Smoothing (Holt's) and Double Moving Average Method could be used to forecast the Construction Company Stock Price Index. Obtaining the smallest error value of the four construction companies, namely WSKT company with MAPE = 7.3, MAD = 148.8, and MSD = 40506.0 for the Holt'sand MAPE method = 5.3, MAD = 110.1, and MSD = 22006.9 for the Double Moving Average method.

The aim of this study is to apply technical analysis Sutte Indicator at stock market that will assist in the decision-making process in investment to buy or sell stocks. This study took data from Stock of “Y” which listed in the NasdaqGS from the period 18 May 2012 to 30 August 2016. The performance of the Sutte Indicator can be checked with comparison with Moving Average Convergence/Divergence and Simple Moving Average. For comparison of the reliability of prediction, we can use the mean absolute percentage error, mean absolute deviation, and mean of square error.

The present study aims to model systematic risk using financial and accounting variables. Accordingly, the data for 174 companies in Tehran Stock Exchange are extracted for the period of 2006 to 2016. First, the systematic risk index is estimated using the ARFIMA-FIGARCH model. Then, based on the research background, 35 affective financial and accounting variables are simultaneously used with the help of the backward elimination and forward selection method for modeling. After analyzing and evaluating the variables in Eviews software, the four variables of debt ratio (CL. E), size (SIZE), net profit to sales ratio (NETP. S), and interest rate coverage ratio (ICR) are selected in the backward elimination method. In the forward selection method, in addition to the above variables, operating profit margin (OPM) is also chosen. The estimated model of these variables in both methods shows a low ratio of R2 coefficient that is approximately 7%. In the test case, the model of forward selection method has less error in all four criteria of root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and Tile coefficient (TIC) compared to the backward elimination method.

For common people, stock investing is one popular way to manage their property. As Information Technology (IT) has risen in recent years, every security company has analyzed computer systems for their customers by developing their own investing. Taiwan is an island nation, and the economy relies on international trade deeply. The fluctuations of international stock markets will impact Taiwan stock market to a certain degree. Therefore, the use of fluctuations of other stock markets as forecasting factors for forecasting the Taiwan stock market is a practical way. In this paper, the proposed model uses the fluctuations of other national stock markets as forecasting factors, employs different discretization methods (Fuzzy C-means clustering, Subtractive Clustering and Cumulative Probability Distribution Approach) to discretize stock data, utilizes a fuzzy inference system to produce understandable rules, and applies an adaptive neural network to optimize model parameters to reach the best forecasting accuracy. To evaluate the forecasting performances, the proposed model is compared with two different models, Chen’s model and Yu’s model. The experimental results indicate that the proposed model is superior to the listing methods in terms of RMSE (root mean squared error). Key words: Data mining, Adaptive Neuro-Fuzzy Inference System (ANFIS), stock forecasting, subtractive clustering, cumulative probability distribution approach.

MSMEs (Micro, Small and Medium Enterprises) in Indonesia have a major influence in terms of economic growth. NN MSMEs are MSMEs engaged in culinary. One type of NN MSME product is ready-to-eat emping. Often during the Eid al-Fitr holiday experience overwhelmed in terms of meeting the demand for ready-to-eat emping. This study aims to assist NN MSMEs in estimating the amount of consumer demand for ready-to-eat emping, as well as to find out the best results and methods in calculating forecasting in trend projection methods. The trend projection method used in forecasting calculations in this case is linear regression and least squares. Meanwhile, to calculate the error value from the forecasting results using the Mean Absolute Deviation (MAD), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE) methods. The best method from the results of this study is to use a linear regression method with demand values for 2024, 2025 and 2026 during Eid al-Fitr are 1771.23 kg, 1914.24 kg, and 2057.25 kg respectively with error values of MAD, MSE and MAPE respectively are 612.0781, 525807.4 and 48.83%.
 
 Keywords: Forecasting, Trend Projections, MSMEs, Consumer Demand

The capital market plays a crucial role in economic growth by serving as a platform for long-term financial instruments, including stocks, bonds, and mutual funds. Stock price movements are influenced by various factors, both internal and external, making accurate forecasting essential for investors to minimize risks and optimize returns. This study aims to forecast the stock prices of state-owned banks (BUMN) in Indonesia using the Trend Analysis method, specifically employing linear, quadratic, exponential growth, and S-curve models. The research utilizes historical closing price data from September 2024 to January 2025, covering four major state-owned banks: Bank Rakyat Indonesia (BBRI), Bank Mandiri (BMRI), Bank Negara Indonesia (BBNI), and Bank Tabungan Negara (BBTN). The analysis is conducted using Minitab software, and the best forecasting model is determined based on the lowest values of Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD), and Mean Squared Deviation (MSD). The results indicate that the quadratic model provides the most accurate forecasts for all four banks, capturing non-linear trends in stock price movements. The findings reveal a consistent downward trend in stock prices across all BUMN banks, highlighting potential market pressures and economic conditions that may affect future performance. This suggests that investors should approach short- to medium-term investments with caution. Additionally, while trend analysis offers valuable insights into historical patterns, integrating fundamental and technical analysis is recommended to enhance investment decision-making.

Design and development of an electronic drive and control system for micro-granular fertilizer metering unit