scholarly journals BULL VS BEAR MARKET- AN INVESTMENT GAME ANALYSIS USING MOVING AVERAGE METHOD

2017 ◽  
Vol 5 (11) ◽  
pp. 425-443
Author(s):  
Biswajit Rout ◽  
Ayesha Mohanty ◽  
Akash Kumar Kacharia
Keyword(s):  
Stock Prices ◽  
Banking Sector ◽  
Moving Average ◽  
Technical Analysis ◽  
Two Phase ◽  
Game Analysis ◽  
Financial Decisions ◽  
Bear Market ◽  
Oil And Natural Gas ◽  
Official Site

This paper aims to analyse and interpret the investor perception about investing in stock market. The market is often referred as to bull or bear market. This is key importance for financial decisions and economic analysis. The market behaves differently in these two phase. The bull market is identified when there is constant rise of stock prices whereas bear market is referred when there is fall in stock prices. These phases occur due to different trends of market or economy. Investor sentiments get affected by this. The paper tries to identify and provides understanding about the factors that causes and how it affects the psychology of investors. There are different analysis techniques used by analysts. The popular and common analysis theories are Fundamental Analysis and Technical Analysis. This paper is based on technical analysis of different category of stock with respect to wide spread industry like FMCG sector, Banking sector, Oil and Natural Gas sector, Automobiles sector and Pharmaceutical Sector etc. The paper also tries to establish whether the market is having a Bull Run or bear. The movement of stock prices is analysed in technical analysis. The data of stock prices are collected from NSE official site. The analysis in done for 5 years span starting from April 2012 to Mar 2017. Even to understand better, analysis of the stock is done on 100days moving average. Prevailing news during those times are also considered to interpret the behaviour of the investors.

scholarly journals Technical Analysis and Malaysian Banking Sector during COVID-19 Pandemic

2020 ◽  
Vol 4 (1) ◽  
pp. 41-46
Author(s):  
Kelvin Yong Ming Lee
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Banking Sector ◽  
Moving Average ◽  
Investment Decision ◽  
Movement Control ◽  
Technical Analysis ◽  
Price Movement ◽  
Technical Indicator ◽  
Control Order

The announcements of Movement Control Order and Loan Moratorium caused a significant impact on the stock prices of Malaysian banks during the COVID-19 pandemic. This study aims to investigate the effectiveness of technical analysis in predicting the stock price movement and the ability of the technical analysis in generating returns. In doing so, six moving average rules used as the proxy of technical analysis and tested in this study. Majority of the MA rules shown positive returns before the various announcements dates. Specifically, this study revealed that MA rules of (2,5) and (2,10) were among the best performing MA rules during the COVID-19 pandemic. This study also recommends the investors to use the signals emitted by the technical indicator as the reference for their investment decision in the banks’ stock.

scholarly journals FORECASTING HARGA SAHAM MENGGUNAKAN METODE SIMPLE MOVING AVERAGE DAN WEB SCRAPPING

2019 ◽  
Vol 21 (3) ◽  
pp. 234-241
Author(s):  
Dessy Tri Anggraeni
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Moving Average ◽  
Technical Analysis ◽  
Secondary Market ◽  
A Value ◽  
Price History ◽  
The Difference ◽  
Absolute Percent Error ◽  
Error Method

Abstract:  The fluctuative of stock prices in a secondary market provide the possibility for investors/traders to gain profits through the difference in stock prices (capital gain). In order to obtain these benefits, it is necessary to analyze before buying shares, through fundamental and technical analysis. One of several methods in Technical Analysis is Simple Moving Average Method. This method can be used to predict (forecast) stock prices by calculating moving average of the stock price history. Historical stock prices can be obtained in real time using the Web Scrapper technique, so the results is more quickly and accurately. Using the MAPE (Mean Absolute Percent Error) method, the level of accuracy of forecasting can be calculated. As a result, the program was able to run successfully and was able to display the value of forecasting and the level of accuracy for the entire data tested in LQ45. Besides forecasting with a value of N = 5 has the highest level of accuracy that reaches 97,6 % while the lowest one is using the value of N = 30 which is 95,0 %.

scholarly journals Prediction of Banking Sectors Financial Data of Dhaka Stock Exchange Using Autoregressive Integrated Moving Average (ARIMA) Approach

2020 ◽  
pp. 64-70 ◽  
Keyword(s):  
Stock Prices ◽  
Banking Sector ◽  
Stock Exchange ◽  
Moving Average ◽  
Absolute Error ◽  
Percentage Error ◽  
Data Series ◽  
Data Set ◽  
Autoregressive Integrated Moving Average ◽  
National Bank

This paper investigates the predictability with the banking sector data of the Dhaka Stock Exchange (DSE) by using the Autoregressive Integrated Moving Average (ARIMA) process. Through different formal tests on the data set, the best-fitted model selected was ARIMA (0,2,1) for the data series. This study was select five banks from DSE such as Al-Arafah bank limited, EXIM bank limited, Islami bank limited, National bank limited, and one bank limited and use these data to train the model and checks the predictive power of the model. Only analyzed results of Al-Arafah bank limited are presented in this paper because the same results have been produced for other remaining companies. The obtained results show that all the companies closing stock prices are non-stationary. It is also found that the original value curve and the predicted value curve are very much identical. So, the fitted model is performed better. For the validity of the model, the root means squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were checked.

scholarly journals Technical analysis on select stocks of banking sector

2016 ◽  
Vol 6 (3) ◽  
pp. 231-242
Author(s):  
Sharmila R ◽  
Kavitha R ◽  
Ananthi S
Keyword(s):  
Banking Sector ◽  
Weather Forecasting ◽  
Stock Exchange ◽  
Moving Average ◽  
Technical Analysis ◽  
Relative Strength ◽  
Rate Of Change ◽  
Strength Index ◽  
Price Movements ◽  
Over Time

Technical Analysis is the forecasting of future financial price movements based on an examination of past price movements. Like weather forecasting, technical analysis does not result in absolute predictions about the future. Instead, technical analysis can help investors anticipate what is “likely” to happen to prices over time. Technical analysis uses a wide variety of charts that show price over time. This study is based on the analysis of four Nifty Bank Index stocks namely Axis Bank, Bank of Baroda, State Bank of India and ICICI bank listed in National Stock Exchange. Technical indicators such as Relative strength index (RSI), Rate of change (ROC) and Moving Average (MA) are used in the study. This paper aims at carrying out Technical Analysis of the securities of the selectedbanking stocks and to assist investment decisions in this Indian Market.

scholarly journals ANALISIS TEKNIKAL PERGERAKAN HARGA SAHAM DENGAN MENGGUNAKAN INDIKATOR STOCHASTIC OSCILLATOR DAN WEIGHTED MOVING AVERAGE

KEUNIS ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 36
Author(s):  
Nur Alviyanil 'Izzah ◽  
Dina Yeni Martia ◽  
Maria Imaculata ◽  
Moh Iqbal Hidayatullah ◽  
Andhika Bagus Pradana ◽  
...  
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Moving Average ◽  
Technical Analysis ◽  
First Semester ◽  
Share Prices ◽  
Stochastic Oscillator ◽  
Industrial Sectors ◽  
The Right ◽  
Weighted Moving Average

<p><em>Investments in the stock market are closely related to the price movement risk. Investors used technical analysis to minimize the risk caused by changes in stock prices. Hence, investors get the right decision to buy or sell the stocks. Weighted Moving Average and Stochastic Oscillator are technical analysis indicators that investors often use due to their ease and accuracy predictions. This study combines the Stochastic Oscillator and Weight Moving Average (WMA) indicators to predict stock price movements in various industrial sectors during the 2015 to 2019 period and the first semester of the 2020 covid-19 pandemic outbreak in Indonesia. This combination aims to provide better predictive results by completing the weaknesses of each indicator. To provide recommendations for the right investment decisions for investors interested in investing their funds, especially in various industrial sectors. Using the combination of WMA and SI indicator charts from the investing.com website resulting in a better prediction of the right time to buy or sell stocks in various industry sectors. While the shares of SCCO, INDR, INDS appear stable during 2020, the movement of KLBM and KLBI's share prices seems to be affected by the Covid-19 pandemic in Indonesia.</em></p>

scholarly journals Accuracy Combination Test of Classical and Modern Technical Analysis: A Case Study in Stock of PT Wijaya Karya Tbk

2016 ◽  
Vol 7 (1) ◽  
pp. 71
Author(s):  
Agustini Hamid
Keyword(s):  
Stock Prices ◽  
Average Rate ◽  
Moving Average ◽  
Computer Software ◽  
Investment Decisions ◽  
Technical Analysis ◽  
Share Price ◽  
Average Return ◽  
Support Resistance

The research aimed to measure the accuracy and combination of Classic and Modern Technical Analysis. PT Wijaya Karya Tbk (WIKA)’s stock in two periods is the sample of research. Technical analysis was used to predict stock prices by observing changes in historical share price. Practically, technical analysis is divided into Classic Technical and Modern. Research was conducted by library study and using a computer software. Microsft Excel was used for the simulation and Chart Nexus for analyzing Modern Technical Analysis. The research period started in January 1, 2013 until December 31, 2013 and January 1, 2014 until December 31, 2014. The Classic Technical Analysis used Support, Resistance, Trendline, and Flag Patern. Meanwhile for Modern Technical Analysis used Moving Average, Stochastic, Moving Average Convergence Divergence (MACD) indicator. The Classical Technical Analysis gave less result than Modern Technical Analysis. The classical give 14 investment decisions in two periods. The average return of Classical Technical is 15,50%. Meanwhile the Modern Technical Analysis gave 18 investment decisions in two periods. The average return of Modern Technical is 18,14%. Combining Classic Technical Analysis and Modern Technical Analysis gave 20 investment decisions with the average rate of return 20,41%.

scholarly journals Technical Analysis Efficiency Enhancement in Moving Average Indicator Through Artificial Neural Network

2018 ◽  
Vol 3 (SI) ◽  
pp. 1
Author(s):  
Muhamad Sukor Jaafar ◽  
Ismail Ahmad ◽  
Zetty Zahureen Mohd Yusoff
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Prices ◽  
Moving Average ◽  
Technical Analysis ◽  
Actual Output ◽  
Timing Of Entry ◽  
Artificial Neural ◽  
Changing Attitudes ◽  
Better Than

The technical approach to investment, essentially a reflection of an idea that prices move in trends which are determined by the changing attitudes of investors towards a variety of economy, monetary, political and psychological forces). The response of stock prices towards the changes in economic variables vary from one to another, hence, it makes trading decision to be very complex. Efficiency refers to the ability to produce an acceptable level of output using cost-minimizing input ratio. Thus, in technical analysis, efficiency refers to the ability of the indicators to indicate a good timing of entry and out of the market with profit. The levels of efficiencies are shown by actual output ratios versus expected output ratios. The higher the actual output ratios against the expected output ratios, the higher the efficiency level of the indicators. This research investigates several technical indicators and found none of the indicators reached the efficiency level. To improve the level, this study applies the Artificial Neural Network model that capable to learn the price and the moving average patterns and suggests a new pattern better than the previous, in term of efficiency level. This research found that the improvements are not just to the efficiency but also increase number of trading as per selected period hence, increase the changes of investor decisions to enter and to exit from the market with possibility of a better profit as compared to traditional technical analysis.

scholarly journals Analysis of Nifty Movement on Share Prices of Selected Construction Companies

2012 ◽  
Vol 1 (3) ◽  
pp. 59-65
Author(s):  
M. Gomathi ◽  
Dr.S. Nirmala
Keyword(s):  
Stock Prices ◽  
Moving Average ◽  
Technical Analysis ◽  
Relative Strength ◽  
Rate Of Change ◽  
Construction Sector ◽  
Strength Index ◽  
Share Prices ◽  
Construction Companies ◽  
Price Movements

This study aims at analyzing and predicting the price movements of construction companies stocks contributing to the NIFTY50 Index. To analyze the volatility of telecom stock and understand the behavior of stock prices in construction sector stocks i.e. (JP ASSOCIATES LIMITED, DLF LIMITED, GAMMON INDIA LIMITED, PUNJ LLOYD LIMITED, HCC LIMITED). The data for these stocks are collected from magazines, newspaper and websites. The stocks are analyzed by monitoring their respective price movements using technical tools. The technical tools used in this study are Exponential moving average, Relative strength index, Rate of change, MACD. Using these tools the trend over the recent past was deciphered. The expected trend in the immediate future was also predicted. Technical Analysis studies the price and volume movement in the market and predicts the future. It helps in identifying that the best time to buy and sell equity. Technical Analysis is a method of evaluating equities by analyzing the statistics generated by market activity, such as past prices and volume.

scholarly journals Random Regression Forest Model using Technical Analysis Variables

2016 ◽  
Vol 5 (3) ◽  
pp. 85-102 ◽  
Author(s):  
Senol Emir ◽  
Hasan Dincer ◽  
Umit Hacioglu ◽  
Serhat Yuksel
Keyword(s):  
Random Forest ◽  
Banking Sector ◽  
Mean Absolute Error ◽  
Moving Average ◽  
Technical Analysis ◽  
Absolute Error ◽  
Important Variable ◽  
Random Regression ◽  
Random Forest Method ◽  
Technical Indicator

The purpose of this study is to explore the importance and ranking of technical analysis variables in Turkish banking sector. Random Forest method is used for determining importance scores of inputs for eight banks in Borsa Istanbul. Then two predictive models utilizing Random Forest (RF) and Artificial Neural Networks (ANN) are built for predicting BIST-100 index and bank closing prices. Results of the models are compared by three metrics namely Mean Absolute Error (MAE), Mean Square Error (MSE), Median Absolute Error (MedAE). Findings show that moving average (MAV-100) is the most important variable for both BIST -100 index and bank closing prices. Therefore, investors should follow this technical indicator with respect to Turkish banks. In addition ANN shows better performance for all metrics.

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