scholarly journals The Interval Slope Method for Long-Term Forecasting of Stock Price Trends

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Chun-xue Nie ◽  
Xue-bo Jin
Keyword(s):  
Time Series ◽  
Stock Price ◽  
Time Series Data ◽  
Investment Strategy ◽  
Cumulative Distribution ◽  
Series Data ◽  
Time Intervals ◽  
Stock Price Prediction ◽  
Price Trends

A stock price is a typical but complex type of time series data. We used the effective prediction of long-term time series data to schedule an investment strategy and obtain higher profit. Due to economic, environmental, and other factors, it is very difficult to obtain a precise long-term stock price prediction. The exponentially segmented pattern (ESP) is introduced here and used to predict the fluctuation of different stock data over five future prediction intervals. The new feature of stock pricing during the subinterval, named the interval slope, can characterize fluctuations in stock price over specific periods. The cumulative distribution function (CDF) of MSE was compared to those of MMSE-BC and SVR. We concluded that the interval slope developed here can capture more complex dynamics of stock price trends. The mean stock price can then be predicted over specific time intervals relatively accurately, in which multiple mean values over time intervals are used to express the time series in the long term. In this way, the prediction of long-term stock price can be more precise and prevent the development of cumulative errors.

Economic Turmoil in Islamic Banking Investment

ETIKONOMI ◽  
2020 ◽  
Vol 19 (2) ◽  
Author(s):  
Budiandru Budiandru ◽  
Sari Yuniarti
Keyword(s):  
Interest Rates ◽  
Stock Price ◽  
Time Series Data ◽  
Series Data ◽  
Islamic Banking ◽  
Vector Error Correction ◽  
Investment Financing ◽  
Vector Error ◽  
Operational Activities

Investment financing is one of the operational activities of Islamic banking to encourage the real sector. This study aims to analyze the effect of economic turmoil on investment financing, analyze the response to investment financing, and analyze each variable's contribution in explaining the diversity of investment financing. This study uses monthly time series data from 2009 to 2020 using the Vector Error Correction Model (VECM) analysis. The results show that the exchange rate, inflation, and interest rates significantly affect Islamic banking investment financing in the long term. The response to investment financing is the fastest to achieve stability when it responds to shocks to the composite stock price index. Inflation is the most significant contribution in explaining diversity in investment financing. Islamic banking should increase the proportion of funding for investment. Customers can have a larger business scale to encourage economic growth, with investment financing increasing.JEL Classification: E22, G11, G24How to Cite:Budiandru., & Yuniarti, S. (2020). Economic Turmoil in Islamic Banking Investment. Etikonomi: Jurnal Ekonomi, 19(2), xx – xx. https://doi.org/10.15408/etk.v19i2.17206.

scholarly journals Assessing Stream-Aquifer Connectivity in a Coastal California Watershed

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 416
Author(s):  
Bwalya Malama ◽  
Devin Pritchard-Peterson ◽  
John J. Jasbinsek ◽  
Christopher Surfleet
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Water Level ◽  
Time Series Data ◽  
Series Data ◽  
Low Permeability ◽  
Pumping Tests ◽  
Stream Flows ◽  
The Impact

We report the results of field and laboratory investigations of stream-aquifer interactions in a watershed along the California coast to assess the impact of groundwater pumping for irrigation on stream flows. The methods used include subsurface sediment sampling using direct-push drilling, laboratory permeability and particle size analyses of sediment, piezometer installation and instrumentation, stream discharge and stage monitoring, pumping tests for aquifer characterization, resistivity surveys, and long-term passive monitoring of stream stage and groundwater levels. Spectral analysis of long-term water level data was used to assess correlation between stream and groundwater level time series data. The investigations revealed the presence of a thin low permeability silt-clay aquitard unit between the main aquifer and the stream. This suggested a three layer conceptual model of the subsurface comprising unconfined and confined aquifers separated by an aquitard layer. This was broadly confirmed by resistivity surveys and pumping tests, the latter of which indicated the occurrence of leakage across the aquitard. The aquitard was determined to be 2–3 orders of magnitude less permeable than the aquifer, which is indicative of weak stream-aquifer connectivity and was confirmed by spectral analysis of stream-aquifer water level time series. The results illustrate the importance of site-specific investigations and suggest that even in systems where the stream is not in direct hydraulic contact with the producing aquifer, long-term stream depletion can occur due to leakage across low permeability units. This has implications for management of stream flows, groundwater abstraction, and water resources management during prolonged periods of drought.

Welfare Use in Japan: Trends and Determinants

2007 ◽  
pp. 88
Author(s):  
Wataru Suzuki ◽  
Yanfei Zhou
Keyword(s):  
Time Series ◽  
Longitudinal Data ◽  
Time Series Data ◽  
Positive Impact ◽  
Series Data ◽  
Negative Effects ◽  
Long Time ◽  
Welfare Use ◽  
The Impact

This article represents the first step in filling a large gap in knowledge concerning why Public Assistance (PA) use recently rose so fast in Japan. Specifically, we try to address this problem not only by performing a Blanchard and Quah decomposition on long-term monthly time series data (1960:04-2006:10), but also by estimating prefecturelevel longitudinal data. Two interesting findings emerge from the time series analysis. The first is that permanent shock imposes a continuously positive impact on the PA rate and is the main driving factor behind the recent increase in welfare use. The second finding is that the impact of temporary shock will last for a long time. The rate of the use of welfare is quite rigid because even if the PA rate rises due to temporary shocks, it takes about 8 or 9 years for it to regain its normal level. On the other hand, estimations of prefecture-level longitudinal data indicate that the Financial Capability Index (FCI) of the local government2 and minimum wage both impose negative effects on the PA rate. We also find that the rapid aging of Japan's population presents a permanent shock in practice, which makes it the most prominent contribution to surging welfare use.

scholarly journals Uncertainty quantification of the effects of biotic interactions on community dynamics from nonlinear time-series data

2018 ◽  
Vol 15 (147) ◽  
pp. 20180695 ◽  
Author(s):  
Simone Cenci ◽  
Serguei Saavedra
Keyword(s):  
Time Series ◽  
Community Dynamics ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Model Averaging ◽  
Biotic Interactions ◽  
Series Data ◽  
Time Intervals ◽  
Novel Approach ◽  
Data Generating Process

Biotic interactions are expected to play a major role in shaping the dynamics of ecological systems. Yet, quantifying the effects of biotic interactions has been challenging due to a lack of appropriate methods to extract accurate measurements of interaction parameters from experimental data. One of the main limitations of existing methods is that the parameters inferred from noisy, sparsely sampled, nonlinear data are seldom uniquely identifiable. That is, many different parameters can be compatible with the same dataset and can generalize to independent data equally well. Hence, it is difficult to justify conclusive assertions about the effect of biotic interactions without information about their associated uncertainty. Here, we develop an ensemble method based on model averaging to quantify the uncertainty associated with the effect of biotic interactions on community dynamics from non-equilibrium ecological time-series data. Our method is able to detect the most informative time intervals for each biotic interaction within a multivariate time series and can be easily adapted to different regression schemes. Overall, this novel approach can be used to associate a time-dependent uncertainty with the effect of biotic interactions. Moreover, because we quantify uncertainty with minimal assumptions about the data-generating process, our approach can be applied to any data for which interactions among variables strongly affect the overall dynamics of the system.

scholarly journals Analisis Intervesi Fungsi Step Efek Program Tol-Laut Terhadap Pergerakan Harga Saham TMAS.JK

2019 ◽  
Vol 5 (01) ◽  
pp. 47-54
Author(s):  
Wigid Hariadi
Keyword(s):  
Time Series ◽  
Stock Price ◽  
Time Series Data ◽  
Vice President ◽  
Step Function ◽  
Series Data ◽  
Intervention Analysis ◽  
Analysis Model ◽  
External Events ◽  
Port Construction

Abstract. Intervention analysis is used to evaluate the effect of external events on a time series data. Sea-highway program is one of the leading programs Joko Widodo-Jusuf Kalla in presidential election 2014. So the author want to modeling the effect from Sea-highway programs on stock price movement in the shipping sector, TMAS.JK (Pelayaran Tempuran Emas tbk). After analyzing, proven that it has happened intervention on movement of daily stock price TMAS.JK caused by Sea-highway programs. Intervention I, on 11 August 2014, which was efect as a result of the election of the Joko Widodo-Jusuf kalla pair as President and vice President Republic of Indonesia on 22 july 2014. Intervention II, on 10 november 2014, president Joko Widodo speech in APEC about Sea-highway Program, and offering investment in port construction to foreign country. So that the model of time series analysis that right is intervention analysis model multi input step function, where the model is ARIMA (2,1,0), StepI (b=0, s=2, r=1), StepII (b=3, s=0, r=1).  Keywords: Intervention Analysis, Multi Input, Step Function, Sea-highway.    Abstrak. Analisis intervensi digunakan untuk mengevaluasi efek dari peristiwa eksternal pada suatu data time series. Program Tol-Laut merupakan salah satu program unggulan pasangan Joko Widodo-Jusuf Kalla dalam pemilu 2014. sehingga, penulis ingin memodelkan efek dari Program Tol-Laut terhadap pergerakan harga saham dibidang pelayaran, TMAS.JK (Pelayaran Tempuran Emas tbk). Setelah dilakukan analisis data, terbukti bahwa terjadi intervensi pada pergerakan harga saham harian TMAS.JK yang disebabkan oleh efek dari program Tol-Laut. Dimana intervensi I, pada tanggal 11 Agustus 2014, yang diduga sebagai dampak dari terpilihnya pasangan Joko widodo-Jusuf Kalla sebagai presiden dan wakil presiden Republik Indonesia pada tanggal 22 Juli 2014. Intervensi II, pada tanggal 10 November 2014, pidato Presiden Joko Widodo di forum APEC mengenai program  tol  laut, dan  menawarkan investasi dibidang pembangunan pelabuhan  kepada bangsa asing. Sehingga model analisis time series yang tepat adalah model analisis intervensi multi input fungsi step, dimana modelnya adalah ARIMA (2,1,0), StepI (b=0, s=2, r=1), StepII (b=3, s=0, r=1). Kata kunci: Analisis intervensi, Multi Input, fungsi step, Tol-Laut.

scholarly journals Minimum time required to detect population trends: the need for long-term monitoring programs

2017 ◽  
Author(s):  
Easton R White
Keyword(s):  
Time Series ◽  
Statistical Power ◽  
Time Series Data ◽  
Conservation Status ◽  
Monitoring Program ◽  
Minimum Time ◽  
Series Data ◽  
Series Length ◽  
Time Required

Long-term time series are necessary to better understand population dynamics, assess species' conservation status, and make management decisions. However, population data are often expensive, requiring a lot of time and resources. When is a population time series long enough to address a question of interest? We determine the minimum time series length required to detect significant increases or decreases in population abundance. To address this question, we use simulation methods and examine 878 populations of vertebrate species. Here we show that 15-20 years of continuous monitoring are required in order to achieve a high level of statistical power. For both simulations and the time series data, the minimum time required depends on trend strength, population variability, and temporal autocorrelation. These results point to the importance of sampling populations over long periods of time. We argue that statistical power needs to be considered in monitoring program design and evaluation. Time series less than 15-20 years are likely underpowered and potentially misleading.

scholarly journals A Predictive Meta Model for Forecasting Stock Price using Time Series Data

2021 ◽  
Vol 10 (1) ◽  
pp. 345-356
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Price ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Meta Model ◽  
Price Range ◽  
Stock Traders ◽  
The Right

Stock market prediction through time series is a challenging as well as an interesting research areafor the finance domain, through which stock traders and investors can find the right time to buy/sell stocks. However, various algorithms have been developed based on the statistical approach to forecast the time series for stock data, but due to the volatile nature and different price ranges of the stock price one particular algorithm is not enough to visualize the prediction. This study aims to propose a model that will choose the preeminent algorithm for that particular company’s stock that can forecastthe time series with minimal error. This model can assist a trader/investor with or without expertise in the stock market to achieve profitable investments. We have used the Stock data from Stock Exchange Bangladesh, which covers 300+ companies to train and test our system. We have classified those companies based on the stock price range and then applied our model to identify which algorithm suites most for a particular range of stock price. Comparative forecasting results of all algorithms in diverse price ranges have been presented to show the usefulness of this Predictive Meta Model

scholarly journals PENGARUH ZIS (ZAKAT, INFAK, SEDEKAH) DAN ZAKAT FITRAH TERHADAP PENURUNAN KEMISKINAN DI INDONESIA PERIODE 1998 - 2010

Media Ekonomi ◽  
2017 ◽  
Vol 20 (1) ◽  
pp. 83
Author(s):  
Jumadin Lapopo
Keyword(s):  
Time Series ◽  
Developing Countries ◽  
Regression Analysis ◽  
Poverty Reduction ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Analysis Model ◽  
Independent Variables

<p>Poverty is being a problem in all developing countries including Indonesia. Among goverment programs, poverty has become the center offattention in policy at both of the regional and national levels. Looking at thephenomenon of poverty, Islam present with solution to reduce poverty through Zakat. This study aims to analyze the effect of ZIS and Zakat Fitrah against poverty in Indonesia in 1998 until 2010, data used in this study is secondary data and uses time series data, for the dependent variabel is poverty and for independent variables are ZIS and Zakat Fitrah. The analysis tools used in this study is to use multiple regression analysis model and the assumptions of classical test using the software Eviews-4. In this study also concluded that the ZIS variables significantly affect to the reduction of poverty in Indonesia although the effect is very small. In the variable Zakat Fitrah not significantly affect poverty reduction in Indonesia because of the nature of Zakat Fitrah is for consumption and not for long-term needs. The results of this study can be used for the management of zakat to be able to develop the management and to get a better system for distribution of zakat so that the main purpose of zakat can be achieved to reduce poverty.<br />Keywords : Poverty, Zakat Fitrah, ZIS.</p>

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