scholarly journals Fuzzy Time Series and Geometric Brownian Motion in Forecasting Stock Prices in Bursa Malaysia

2019 ◽  
Vol 14 (2) ◽  
pp. 240-250
Author(s):  
Nor Hayati Shafii ◽  
Nur Ezzati Dayana Mohd Ramli ◽  
Rohana Alias ◽  
Nur Fatihah Fauzi
Keyword(s):  
Time Series ◽  
Brownian Motion ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Exchange ◽  
Investment Decisions ◽  
Geometric Brownian Motion ◽  
Fuzzy Time Series ◽  
Good Information ◽  
The Mean

Every country has its own stock market exchange, which is a platform to raise capital and is a place where shares of listed company are traded. Bursa Malaysia is a stock exchange of Malaysia and it is previously known as Kuala Lumpur Stock Exchange. All over the world, including Malaysia, it is common for investors or traders to face some loss due to wrong investment decisions. According to the conventional financial theory, there are so many reasons that can lead to bad investment decisions. One of them is confirmation bias where an investor has a preconceived notion about an investment without a good information and knowledge. In this paper, we study the best way to provide good information for investors in helping them make the right decisions and not to fall prey to this behavioral miscue. Two models for forecasting stock prices data are employed, namely, Fuzzy Time Series (FTS) and Geometric Brownian Motion (GBM). This study used a secondary data consisting of AirAsia Berhad daily stock prices for a duration of 20 weeks from January 2015 to May 2015. The 16-weeks data from January to April 2015 was used to forecast the stock prices for the 4-weeks of May 2015. The results showed that FTS has the lowest values of the Mean Absolute Percentage Error (MAPE) and the Mean Square Error (MSE), which are 1.11% and MYR20.0011, respectively. For comparison, for GBM, the MAPE is 1.53% and the MSE is MYR2 0.0017. The findings imply that the FTS model provides a more accurate forecast of stock prices. Keywords: Forecasted values, stock market, Fuzzy Time Series, Geometric Brownian Motion

scholarly journals A Multifactor Fuzzy Time-Series Fitting Model for Forecasting the Stock Index

Symmetry ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1474 ◽  
Author(s):  
Ming-Chi Tsai ◽  
Ching-Hsue Cheng ◽  
Meei-Ing Tsai
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Trading Volume ◽  
Stock Exchange ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
Sequential Data ◽  
Proposed Model ◽  
Market Indexes ◽  
Fitting Model

Fuzzy time series (FTS) models have gotten much scholarly attention for handling sequential data with incomplete and ambiguous patterns. Many conventional time series methods employ a single variable in forecasting without considering other variables that can impact stock volatility. Hence, this paper modified the multi-period adaptive expectation model to propose a novel multifactor FTS fitting model for forecasting the stock index. Furthermore, after a literature review, we selected three important factors (stock index, trading volume, and the daily difference of two stock market indexes) to build a multifactor FTS fitting model. To evaluate the performance of the proposed model, the three datasets were collected from the Nasdaq Stock Market (NASDAQ), Taiwan Stock Exchange Index (TAIEX), and Hang Seng Index (HSI), and the RMSE (root mean square error) was employed to evaluate the performance of the proposed model. The results show that the proposed model is better than the listing models, and these research findings could provide suggestions to the investors as references.

scholarly journals DAILY VARIATION AND PREDICTING STOCK MARKET RETURNS FOR THE FRANKFURTER BÖRSE (STOCK MARKET)

2008 ◽  
Vol 9 (3) ◽  
pp. 189-198 ◽  
Author(s):  
Jeffrey E. Jarrett ◽  
Janne Schilling
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Returns ◽  
Stock Prices ◽  
Stock Exchange ◽  
Arima Model ◽  
Unit Root Test ◽  
Unit Roots ◽  
Stock Market Returns ◽  
Daily Stock Returns

In this article we test the random walk hypothesis in the German daily stock prices by means of a unit root test and the development of an ARIMA model for prediction. The results show that the time series of daily stock returns for a stratified random sample of German firms listed on the stock exchange of Frankfurt exhibit unit roots. Also, we find that one may predict changes in the returns to these listed stocks. These time series exhibit properties which are forecast able and provide the intelligent data analysts’ methods to better predict the directive of individual stock returns for listed German firms. The results of this study, though different from most other studies of other stock markets, indicate the Frankfurt stock market behaves in similar ways to North American, other European and Asian markets previously studied in the same manner.

scholarly journals Forecasting Cyclical and Non-cyclical Stock Prices on the Stock Exchange of Thailand

2021 ◽  
Vol 17 (5) ◽  
pp. 550-565
Author(s):  
Rapin Sunthornwat ◽  
Yupaporn Areepong
Keyword(s):  
Decision Making ◽  
Brownian Motion ◽  
Stock Prices ◽  
Stock Exchange ◽  
Geometric Brownian Motion ◽  
Percentage Error ◽  
Initial Value ◽  
Minimum Error ◽  
Stock Exchange Of Thailand ◽  
And Performance

Forecasting is an important role in organizations for decision making and planning. This research is to forecast the cyclical and non-cyclical weekly stock prices on the Stock Exchange of Thailand by using the models of Geometric Brownian motion, Fourier’s series, and Cauchy initial value problem. The accuracy and performance of the models are based on the minimum root mean squared percentage error which is the error between actual and forecasted stock prices. The results showed that Geometric Brownian motion is suitable for forecasting both cyclical and non-cyclical stock prices because of minimum error. Moreover, the confidence intervals of forecasted stock prices are demonstrated. Therefore, Geometric Brownian motion should be selected to describe the movement of stock prices in Thailand.

scholarly journals Analysing the Nepali Stock Market with Stochastic Models

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Karan Singh Thagunna ◽  
Radal M Lochowski
Keyword(s):  
Brownian Motion ◽  
Stock Market ◽  
Diffusion Model ◽  
Stock Prices ◽  
Jump Diffusion ◽  
Efficient Market Hypothesis ◽  
Geometric Brownian Motion ◽  
Brownian Motion Model ◽  
Jump Diffusion Model ◽  
Daily Returns

In this article we analyse the behaviour of the Nepali stock market and movements of stock prices of selected companies using (i) Efficient Market Hypothesis (EMH) (ii) geometric Brownian motion model (gBm) and (iii) Merton’s jump-diffusion model. Using the daily returns of the NEPSE index and the daily returns of stock prices of selected companies we estimate the geometric Brownian motion model and Merton’s jump-diffusion model. Further, we compare both models to identify the best fit for the Nepali stock market data. Keywords: Black-Scholes model, Efficient Market Hypothesis, geometric Brownian motion, Merton’s jump-diffusion Model, Variance Ratio Test

Stochastic Forecasting of Stock Prices in Nigeria: Application of Geometric Brownian Motion Model

2021 ◽  
Vol 6 (2) ◽  
pp. 1-35
Author(s):  
Adolphus Joseph Toby ◽  
Samuel Azubuike Agbam
Keyword(s):  
Brownian Motion ◽  
Stock Prices ◽  
Stock Exchange ◽  
Investment Strategy ◽  
Time Frame ◽  
Geometric Brownian Motion ◽  
Percentage Error ◽  
Unique Contribution ◽  
Holding Period ◽  
Stochastic Forecasting

Purpose:  The purpose of the study is to model and simulate the trends and behavioral patterns in The Nigerian Stock Market and hence predict the future stock prices within the Geometric Brownian Motion (GBM) framework. Methodology: The methodology involves a comparison of forecasted daily closing prices to actual prices in order to evaluate the accuracy of the prediction model. Based on the model assumptions of the GBM with drift: continuity, normality and Markov tendency, the study investigated four years (2015 - 2018) of historical closing prices of ten stocks listed on The Nigerian Stock Exchange. The sample for this study is based on the most continuously traded stocks. Findings: The results show that in the simulation there are some actual stock prices located outside trajectory realization that may be from GBM model. Thus, the model did not predict accurately the price behavior of some of the listed stocks.  The predictive power of the model is declining towards the longer the evaluated time frame proven by the higher value of the mean absolute percentage error. The value of the MAPE is 50% and below for the one- to two-year holding periods, and above 50% for the three-year holding period. Unique Contribution to theory, Practice and Policy:  The MAPE and directional prediction accuracy method provide support that over short periods the GBM model is accurate. Meaning that the GBM is a reasonable predictive model for one or two years, but for three years, therefore, it is an inaccurate predictor. It is recommended that the technical analyst whose primary motive is to make gain at the expense of other participants should identify high volatile portfolio in any holding period for effective prediction Investors with long-range holding position as investment strategy should concentrate more on low capitalized stocks rather than stocks with large market capitalization. This is a unique contribution to theory, practice and policy. 

A Novel Intuitionistic Fuzzy Time Series Method Based on Bootstrapped Combined Pi-Sigma Artificial Neural Network

2021 ◽  
Vol 01 ◽  
Author(s):  
Eren Bas ◽  
Erol Egrioglu ◽  
Emine Kölemen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Stock Exchange ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Intuitionistic Fuzzy ◽  
Forecasting Methods ◽  
Time Series Method ◽  
Artificial Neural

Background: Intuitionistic fuzzy time series forecasting methods have been started to solve the forecasting problems in the literature. Intuitionistic fuzzy time series methods use both membership and non-membership values as auxiliary variables in their models. Because intuitionistic fuzzy sets take into consideration the hesitation margin and so the intuitionistic fuzzy time series models use more information than fuzzy time series models. The background of this study is about intuitionistic fuzzy time series forecasting methods. Objective: The study aims to propose a novel intuitionistic fuzzy time series method. It is expected that the proposed method will produce better forecasts than some selected benchmarks. Method: The proposed method uses bootstrapped combined Pi-Sigma artificial neural network and intuitionistic fuzzy c-means. The combined Pi-Sigma artificial neural network is proposed to model the intuitionistic fuzzy relations. Results and Conclusion: The proposed method is applied to different sets of SP&500 stock exchange time series. The proposed method can provide more accurate forecasts than established benchmarks for the SP&500 stock exchange time series. The most important contribution of the proposed method is that it creates statistical inference: probabilistic forecasting, confidence intervals and the empirical distribution of the forecasts. Moreover, the proposed method is better than the selected benchmarks for the SP&500 data set.

scholarly journals ANALISIS PORTOFOLIO SEBAGAI DASAR PENGAMBILAN KEPUTUSAN INVESTASI SAHAM YANG TERDAFTAR DI BEI

IQTISHODUNA ◽  
2013 ◽  
Author(s):  
Sri Yati
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Analytical Method ◽  
Stock Exchange ◽  
Investment Decisions ◽  
Portfolio Analysis ◽  
Rate Of Return ◽  
Expected Returns ◽  
Risk Level ◽  
Expected Return

This study aims to analyze rate of return and risk as the tools to form the portfolio analysis on 15 the most actives stocks listed in Indonesian Stock Exchange. Descriptive analytical method is used to describe the correlation between three variables: stock returns, expected returns of stock market, and beta in order to measure the risk of stocks to help the investors in making the investment decisions. The research materials are 15 the most actives stocks listed in Indonesian Stock Exchange during 2008-2009. The results show that PT. Astra International Tbk. has the highest average expected return of individual stock (Ri) of 308,3355685, while PT. Perusahaan Gas Negara Tbk. has the lowest of -477,0827847. The average expected return of stock market (Rm) is 0,00247163. PT. Astra International Tbk. has the highest systematic risk level of 20229,14205, while the lowest of -147,5793279 is PT. Kalbe Farma Tbk. Furthermore, the results also indicate that there are 9 stocks can be combined to form optimal portfolio because they have positive expected returns.

scholarly journals Relationship Between Deferred Tax Category and Stock Prices in Pharmacy

2018 ◽  
Vol 66 (6) ◽  
pp. 1469-1475
Author(s):  
Petr Habanec
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Stock Exchange ◽  
Negative Relationship ◽  
Accounting Standards ◽  
Earning Management ◽  
International Accounting ◽  
International Accounting Standards ◽  
Deferred Tax ◽  
The Relationship

The paper deals with relationship between stock prices and deferred tax category. Joos, Pratt and Young provided evidence that book‑tax differences are correlated with earning management. In this paper is confirmed negative relationship between stock prices and deferred tax. The relationship is assessed on sample of companies making business in pharmacy (CZNACE‑C‑21). The relationship between deferred tax category and stock prices is assessed on a sample of companies in the time series from 2005 to 2015. Sample consists of companies listed on Frankfurt stock exchange and reporting in accordance with international accounting standards IAS/IFRS. The stock prices dataset is based on Morningstar database. The results are compared with the results of author ’s previous study concerning the deferred tax materiality.

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

A Score Function-Based Method of Forecasting Using Intuitionistic Fuzzy Time Series

2018 ◽  
Vol 14 (01) ◽  
pp. 91-111 ◽  
Author(s):  
Abhishekh ◽  
Surendra Singh Gautam ◽  
S. R. Singh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Stock Exchange ◽  
Market Price ◽  
Intuitionistic Fuzzy Set ◽  
Score Function ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy

Intuitionistic fuzzy set plays a vital role in data analysis and decision-making problems. In this paper, we propose an enhanced and versatile method of forecasting using the concept of intuitionistic fuzzy time series (FTS) based on their score function. The developed method has been presented in the form of simple computational steps of forecasting instead of complicated max–min compositions operator of intuitionistic fuzzy sets to compute the relational matrix [Formula: see text]. Also, the proposed method is based on the maximum score and minimum accuracy function of intuitionistic fuzzy numbers (IFNs) to fuzzify the historical time series data. Further intuitionistic fuzzy logical relationship groups are defined and also provide a forecasted value and lies in an interval and is more appropriate rather than a crisp value. Furthermore, the proposed method has been implemented on the historical student enrollments data of University of Alabama and obtains the forecasted values which have been compared with the existing methods to show its superiority. The suitability of the proposed model has also been examined to forecast the movement of share market price of State Bank of India (SBI) at Bombay Stock Exchange (BSE). The results of the comparison of MSE and MAPE indicate that the proposed method produces more accurate forecasting results.

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