scholarly journals Testing Weak Form of Stock Market Efficiency at The Indonesia Sharia Stock Index

2019 ◽  
Vol 10 (1) ◽  
pp. 17
Author(s):  
Isnaini Nuzula Agustin
Keyword(s):  
Time Series ◽  
Stock Price ◽  
Moving Average ◽  
Arima Model ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Stock Index ◽  
Time Series Modelling ◽  
Current Price ◽  
Run Test

AbstractEfficient Market is the market where all traded securities prices reflects all available information. Market Efficient Hypotesis in the Weak Form stated that past stock price movement incorporated with current securities’s prices, thus it can be used to predicting the current price or return. The objective of this research is to examine the weak form of Efficient Market Hypothesis (EMH) in Indonesia Sharia Stock Index (ISSI) over the period of January 3rd2017 -February 8th 2019. To Examine the EMH, some appropriate tests are developed, these are: Run Test, Autocorrelation Test, Autoregressive Integrated Moving Average (ARIMA), and Paired Sample t-test. The result findings showing that ISSI is not efficient in the weak form during the period of the study. Moreover, in accordance with time series modelling result, the fitted model is ARIMA (1,1,1) with accuracy level of 78%. This result proved that ARIMA model successfully and accurately in forecasting ISSI indices. It can be implied that the historical stock index data in the past still described the stock index information in the future. Thus, technical analysis is still feasible to do as the guide for investors in conducting transactions in the capital market.AbstrakPasar yang efisien adalah pasar dimana semua harga sekuritas yang diperdagangkan telah mencerminkan semua informasi yang tersedia. Teori pasar efisien bentuk lemah menyatakan bahwa perubahan harga masa lalu tidak berhubungan dengan harga sekuritas sekarang, sehingga tidak dapat digunakan untuk memprediksi harga atau return dari sekuritas. Penelitian ini bertujuan untuk melakukan pengujian hipotesis pasar efisien bentuk lemah pada Indeks Saham Syariah Indonesia (ISSI). Data diambil pada periode 3 Januari 2017 – 8 Februari 2019. Pada tahap awal penelitian, Run test dan Autocorrelation test dilakukan untuk melihat apakah pasar efisien bentuk lemah berlaku pada ISSI. Selanjutnya dilakukan pembentukan pemodelan time series ARIMA untuk melihat teknik prediksi yang sesuai untuk memprediksi Indeks Saham ISSI. Hasil Run test dan Autocorrelation test menunjukkan bahwa hipotesis pasar efisien bentuk lemah tidak terbukti. Pada pembentukan model ARIMA, terlihat bahwa model yang sesuai adalah ARIMA (1,1,1) menghasilkan tingkat akurasi sebesar 78%. Hal ini membuktikan bahwa model ARIMA berhasil dan akurat digunakan untuk memprediksi Indeks Harga Saham ISSI. Oleh karena itu, analisis teknikal masih dapat digunakan oleh investor untuk menjadi pedoman dalam melakukan transaksi perdagangan di pasar modal.

An Empirical Study on Efficient Market Hypothesis with reference to FMCG Sector

GIS Business ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. 109-126
Author(s):  
Nitin Tanted ◽  
Prashant Mistry
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Abnormal Returns ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Controversial Issues ◽  
Efficient Market ◽  
Price Movements ◽  
Run Test ◽  
Stock Price Movements

One of the highly controversial issues in the area of finance is “Efficient Market Hypothesis”. Efficient Market Hypothesis states that, “In an efficient market, all available price information is reflected in the stock prices and it is not possible to generate abnormal returns compared to other investors.” A lot of studies conducted previouslyto test the Efficient Market Hypothesis, confirmed the theory until recent years, when some academicians found it to be non-applicable in financial markets. According to them, it is possible to forecast the stock price movements using Technical Analysis. The results of various studies have been inconclusive and indefinite about the issue. This study attempted to test the efficiency of FMCG Sector stocks in India in its weak form. For the study, closing prices of top 10 stocks from Nifty FMCG index has been taken for the 5-year period ranging from 1st October 2014 to 30th September 2019. Wald-Wolfowitz Run test has been used to test the haphazard movements in the stock price movements. The results indicated that FMCG sector stocks does support the Efficient Market Hypothesis and exhibit efficiency in its weak form. Hence, it is not possible to accurately predict the price movements of these stocks.

scholarly journals A New Strategy of Hybrid Models using ARIMA, ANN, and DWT in Time Series Modelling

2021 ◽  
Vol 25 (1) ◽  
pp. 27-50
Author(s):  
Tsung-Lin Li ◽  
◽  
Chen-An Tsai ◽  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Real Data ◽  
Hybrid Models ◽  
Discrete Wavelet ◽  
Data Sets ◽  
Model Combining ◽  
Time Series Modelling ◽  
Significant Difference

Time series forecasting is a challenging task of interest in many disciplines. A variety of techniques have been developed to deal with the problem through a combination of different disciplines. Although various researches have proved successful for hybrid models, none of them carried out the comparisons with solid statistical test. This paper proposes a new stepwise model determination method for artificial neural network (ANN) and a novel hybrid model combining autoregressive integrated moving average (ARIMA) model, ANN and discrete wavelet transformation (DWT). Simulation studies are conducted to compare the performance of different models, including ARIMA, ANN, ARIMA-ANN, DWT-ARIMA-ANN and the proposed method, ARIMA-DWT-ANN. Also, two real data sets, Lynx data and cabbage data, are used to demonstrate the applications. Our proposed method, ARIMA-DWT-ANN, outperforms other methods in both simulated datasets and Lynx data, while ANN shows a better performance in the cabbage data. We conducted a two-way ANOVA test to compare the performances of methods. The results showed a significant difference between methods. As a brief conclusion, it is suggested to try on ANN and ARIMA-DWT-ANN due to their robustness and high accuracy. Since the performance of hybrid models may vary across data sets based on their ARIMA alike or ANN alike natures, they should all be considered when encountering a new data to reach an optimal performance.

scholarly journals Time Series Modelling of The Volume of Overland Timber Export in The Bolgatanga Municipality

2020 ◽  
pp. 124-138
Author(s):  
Henry M. Kpamma ◽  
Silverius K. Bruku ◽  
John A. Awaab
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Preliminary Investigation ◽  
Arima Model ◽  
Estimation Method ◽  
Long Run ◽  
Time Series Modelling ◽  
The Future ◽  
Equilibrium Relationships ◽  
The Right

Aims/ Objectives: This research was carried out with the intention of using time series to model the volume of overland timber exported within Bolgatanga municipalityPlace and Duration of Study: Study of the time series was based on a historical data of the volume of timber exported for twenty consecutive years, from 1999 to 2019 within Bolgatanga municipality.Methodology: The three-stage iterative modeling approach for Box Jenkins was used to match an ARIMA model and to forecast both the amount of timber export and the confiscated lumber. ARIMA method incorporates a cycle of autoregressive and a moving average. The three-stage iterative modeling technique of Box Jenkins which were used are model recognition, parameter estimation and/or diagnostic checks were also made. Results: From the preliminary investigation, the study showed that the amount of timber exported in municipality is skewed to the right, suggesting that much of the amount of timber exported is below the average. This, together with the high volatility in the volume of timber exported, indicates that the amount of timber exported within the municipalities during the twenty-year period was low. The plots from the trends also showed robust variations in the volume of timber exported indicating that timber exporters do not have better grips with the concepts and applications of export technology, hence the erratic nature of the volume of timber exported over the period. The quadratic pattern and the ARIMA (1,1,1) model best represented the amount of timber exported.The analysis further indicated that there will be a further decrease in the amount of timber export from the five years projection into the future. Over the last two decades the Bayesian approach to VAR has gained ground. For a future report, this estimation method will be followed to examine the ”long-run equilibrium relationships” between timber export volumes and climate change.Conclusion: The quadratic pattern and the ARIMA (1,1,1) model best represented the amount of timber exported. There will be a further decrease in the amount of timber export from the five years projection into the future.

scholarly journals Forecasting Stock Price PT. Telkom Using Hybrid Time Series Regression Linear– Autoregressive Integrated Moving Average Model

2022 ◽  
Vol 18 (2) ◽  
pp. 293-307
Author(s):  
Kartika Ramadani ◽  
Sri Wahyuningsih ◽  
Memi Nor Hayati
Keyword(s):  
Time Series ◽  
Linear Model ◽  
Stock Price ◽  
Moving Average ◽  
Arima Model ◽  
Residual Value ◽  
Time Series Regression ◽  
Autoregressive Integrated Moving Average ◽  
Trend Pattern ◽  
Closing Price

The hybrid method is a method of combining two forecasting models. Hybrid method is used to improve forecasting accuracy. In this study, the Time Series Regression (TSR) linear model will be combined with the Autoregressive Integrated Moving Average (ARIMA) model. The TSR linear model is used to obtain the model and residual value, then the residual value of the TSR linear model will be modeled by the ARIMA model. This combination method will produce a hybrid TSR linear-ARIMA model. The case study in this research is stock closing price (daily) of PT. Telkom Indonesia Tbk. The stock closing price (daily) of PT. Telkom Indonesia Tbk in 2020 showed an decreasing and increasing trend pattern. The results of this study obtained the best model of hybrid TSR linear-ARIMA (2,1,1) with the proportion of data training and testing is 70:30. In the best model, the MAD value is 56.595, the MAPE value is 1.880%, and the RMSE value is 78.663. It is also found that the hybrid TSR linear-ARIMA model has a smaller error value than the TSR linear model. The results of forecasting the stock price of PT. Telkom Indonesia Tbk for the period 02 January 2021 to 29 January 2021 formed a decreasing trend pattern.

Forecasting of Stock Price Using Autoregressive Integrated Moving Average Model

2019 ◽  
Vol 16 (8) ◽  
pp. 3519-3524
Author(s):  
Loh Chi Jiang ◽  
Preethi Subramanian
Keyword(s):  
New York ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Stock Exchange ◽  
Moving Average ◽  
Arima Model ◽  
Stock Index ◽  
Series Data ◽  
Autoregressive Integrated Moving Average

Finance sector is highly volatile where the stock prices fluctuate rapidly and it is usually challenging to forecast. The unstable conditions and rapid changes can drastically modify the monetary value of an organization or an individual. Hence, the prediction of stock prices continues to remain as one of the sizzling and vital topics in the applications of data mining in the finance sector. This forecasting is significant as it has the potential to reduce the losses that happen mainly due to erroneous intuitions and blind investment. Moreover, the prediction of stock prices endure to increase in complexity with accumulation of more and more historical data. This paper focuses on American Stock Market (New York Stock Exchange and NASDAQ Stock Exchange). Taking into account the complexity of the prediction, this research proposes Autoregressive Integrated Moving Average (ARIMA) model for estimating the value of future stock prices. ARIMA demonstrated better results for prediction as it can handle the time series data very well which is suitable for forecasting the future stock index.

scholarly journals Testing of weak form of efficient market hypothesis: evidence from the Bahrain Bourse

2017 ◽  
Vol 14 (2) ◽  
pp. 376-385 ◽  
Author(s):  
Iqbal Thonse Hawaldar ◽  
Babitha Rohit ◽  
Prakash Pinto
Keyword(s):  
Random Walk ◽  
Stock Price ◽  
Goodness Of Fit ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Goodness Of Fit Test ◽  
Efficient Market ◽  
Share Prices ◽  
The Individual ◽  
Run Test

Efficient market hypothesis (EMH) states that financial markets are “informationally efficient”, implying that current prices fully reflect all available information. The present study aims at testing the weak form of market efficiency of the individual stocks listed on the Bahrain Bourse for the period 2011 to 2015. Weak form of EMH is tested using the Kolmogorov-Smirnov goodness of fit test, run test and autocorrelation test. The K-S test result concludes that in general the stock price movement does not follow random walk. The results of the runs test reveals that share prices of seven companies do not follow random walk. Autocorrelation tests reveal that share prices exhibit low to moderate correlation varying from negative to positive values. As the study shows mixed results, it is difficult to conclude the weak form of efficiency of Bahrain Bourse.

scholarly journals SEMI-STRONG FORM EFFICIENCY OF NIFTY 50 INDEX: AN EMPIRICAL TESTING IN RELATION TO RIGHTS ISSUE ANNOUNCEMENTS

2019 ◽  
pp. 192-196
Author(s):  
Mr.Ch Naveen ◽  
Prof. G. Satyanarayana
Keyword(s):  
Event Study ◽  
Stock Price ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Strong Form ◽  
Stock Index ◽  
Efficient Market ◽  
Price Series ◽  
Study Methodology ◽  
Rights Issue

Stock price series is a wandering one. Investors put their money after analysing the behavior of the price using technical or fundamental analysis. The assumption behind these models is that the stock price behaviour is quite orderly and not random. Many researchers questioned this assumption and argued that the stock price behaviour is random. Efficient market hypothesis is explained in three levels. Weak form, semi-strong forma and strong form. With this background an attempt was made to anlayse the efficiency of the leading stock index in India i.e. Nifty 50 Index in weak-form in relation to rights issue. In this study rights issue of Nifty 50 companies announced during 2009-2018 were considered and event study methodology was applied to examine the randomness. The results of the study revealed that the Nifty 50 Index is not efficient in semi-strong form. KEY WORDS: Efficient Market hypothesis, Rights issue, event study, Nifty 50 index.

scholarly journals Assessment of Capital Market Efficiency in COVID-19

2021 ◽  
Vol 6 (3) ◽  
pp. 42-46
Author(s):  
Fakrul Ahmed
Keyword(s):  
Stock Returns ◽  
Capital Market ◽  
Stock Price ◽  
Stock Exchange ◽  
Arima Model ◽  
Weak Form ◽  
Weekend Effect ◽  
Special Focus ◽  
Momentum Strategy ◽  
Run Test

The study tries to focus on the efficiency of the capital market through investigating the randomness of return series of Dhaka Stock Exchange of Bangladesh. Due to COVID-19 pandemic the worldwide capital market faces higher volatility than before. The study finds the week form of efficiency level of Bangladesh capital market. Special focus on Run test, Auto correlation test, predictability of tock return using ARIMA model the weekend effect anomaly and momentum strategy investing. The study found that the hypothesis of randomness of the stock returns are rejected for stock price index changes by using random walk tests, normality of return distributions, runs test and at different lags using ARIMA and the momentum tests which assert Dhaka Stock Exchange is not efficient even in the weak form.

scholarly journals Time-series modelling and forecasting of hand, foot and mouth disease cases in China from 2008 to 2018

2019 ◽  
Vol 147 ◽  
Author(s):  
C. W. Tian ◽  
H. Wang ◽  
X. M. Luo
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
Moving Average ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Annual Peak ◽  
Time Series Modelling ◽  
Foot And Mouth

AbstractSeasonal autoregressive-integrated moving average (SARIMA) has been widely used to model and forecast incidence of infectious diseases in time-series analysis. This study aimed to model and forecast monthly cases of hand, foot and mouth disease (HFMD) in China. Monthly incidence HFMD cases in China from May 2008 to August 2018 were analysed with the SARIMA model. A seasonal variation of HFMD incidence was found from May 2008 to August 2018 in China, with a predominant peak from April to July and a trough from January to March. In addition, the annual peak occurred periodically with a large annual peak followed by a relatively small annual peak. A SARIMA model of SARIMA (1, 1, 2) (0, 1, 1)12 was identified, and the mean error rate and determination coefficient were 16.86% and 94.27%, respectively. There was an annual periodicity and seasonal variation of HFMD incidence in China, which could be predicted well by a SARIMA (1, 1, 2) (0, 1, 1)12 model.

scholarly journals Identification of monthly municipal water demand system based on autoregressive integrated moving average model tuned by particle swarm optimization

2017 ◽  
Vol 19 (2) ◽  
pp. 261-281 ◽  
Author(s):  
Sahbi Boubaker
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Water Demand ◽  
Moving Average ◽  
Arima Model ◽  
Particle Swarm ◽  
Demand System ◽  
Swarm Optimization ◽  
Municipal Water ◽  
Average Absolute Relative Error

In this paper, a modeling-identification approach for the monthly municipal water demand system in Hail region, Saudi Arabia, is developed. This approach is based on an auto-regressive integrated moving average (ARIMA) model tuned by the particle swarm optimization (PSO). The ARIMA (p, d, q) modeling requires estimation of the integer orders p and q of the AR and MA parts; and the real coefficients of the model. More than being simple, easy to implement and effective, the PSO-ARIMA model does not require data pre-processing (original time-series normalization for artificial neural network (ANN) or data stationarization for traditional stochastic time-series (STS)). Moreover, its performance indicators such as the mean absolute percentage error (MAPE), coefficient of determination (R2), root mean squared error (RMSE) and average absolute relative error (AARE) are compared with those of ANN and STS. The obtained results show that the PSO-ARIMA outperforms the ANN and STS approaches since it can optimize simultaneously integer and real parameters and provides better accuracy in terms of MAPE (5.2832%), R2 (0.9375), RMSE (2.2111 × 105m3) and AARE (5.2911%). The PSO-ARIMA model has been implemented using 69 records (for both training and testing). The results can help local water decision makers to better manage the current water resources and to plan extensions in response to the increasing need.

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