scholarly journals NEPSE in Bollinger Bands

2021 ◽  
Vol 3 (4) ◽  
pp. 439-451
Author(s):  
Rashesh Vaidya ◽  
Keyword(s):  
Line Graph ◽  
Moving Average ◽  
Investment Decision ◽  
Technical Analysis ◽  
Fiscal Year ◽  
Daily Return ◽  
Market Return ◽  
Clear Signal ◽  
Analysis Tools ◽  
Graphical Presentation

<abstract> <p>An investor uses the graphical presentation of Bollinger Bands to get signals of the ups and downs, as well the volatility of the market from the expansion and tightening of the UBB and LBB, reflecting higher and lower volatility. The percent (%) b helps determine the opportunities during extreme periods from the market, looking at the concentration of line graph at the value "0" or "1" reflecting the bearish and bullish trend, respectively. The Bandwidth Index was able to picture out the bullish trend with a squeeze at the upper band. The positive unimodality of Q for NEPSE daily return for the period of the fiscal year 1998–1999 to the fiscal year 2019–2020 indicated normality for the market return. Nevertheless, the results for the trading signals based on the Bollinger bands are seen as useful for an investor by giving a clear signal to "buy" or "sell". At the same time, relying only on Bollinger Bands with a specific period MA, i.e. the Bollinger Bands with a shorter moving average (MA) shows higher fluctuations and vice-versa, hence, could show false signals while choosing inappropriate MA, therefore, help of other technical analysis tools should be taken while going for an investment decision.</p> </abstract>

scholarly journals Predicting Movement of Stock of Apple Inc. using Sutte Indicator

2017 ◽  
Author(s):  
Ansari Saleh Ahmar
Keyword(s):  
Decision Making ◽  
Moving Average ◽  
Investment Decision ◽  
Technical Analysis ◽  
Percentage Error ◽  
Decision Making Process ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Investment Decision Making ◽  
The Mean

The purpose of this study is to apply technical analysis e.g. Sutte Indicator in Stock Market that will assist in the investment decision-making process to buy or sell of stocks. This study took data from Apple Inc. which listed in the NasdaqGS in the period of 1 January 2008 to 26 September 2016. Performance of the Sutte Indicator can be seen with comparison with other technical analysis e.g. Simple Moving Average (SMA) and Moving Average Convergence/Divergence (MACD). Comparison of the reliability of prediction from Sutte Indicator, SMA, and MACD using the mean of square error (MSE), mean absolute deviation (MAD) and mean absolute percentage error (MAPE).

scholarly journals Optimizing the Performance of the Fractal Adaptive Moving Average Strategy: The Case of EUR/USD

2016 ◽  
Vol 8 (2) ◽  
pp. 171 ◽  
Author(s):  
Ikhlaas Gurrib ◽  
Elgilani Elshareif
Keyword(s):  
Moving Average ◽  
Market Timing ◽  
Technical Analysis ◽  
Performance Measure ◽  
Trading Strategies ◽  
Average Model ◽  
Average Technique ◽  
Analysis Tools ◽  
Moving Average Model ◽  
Us Dollar

Most technical analysis tools focus traditionally on the simple and exponential moving average technique. This study looks at the performance of an optimized fractal adaptive moving average strategy over different frequency intervals, where the Euro/US Dollar currency pair is analyzed due to the increased correlation between the Euro Index and EUR/USD, and the Dollar Index and EUR/USD over the last year compared to the last 15 years. The optimized strategy is evaluated against a buy-and-hold strategy over the 2000- 2015 period, using annualized returns, annualized risk and Sharpe performance measure. Due to the existence of different number of long and short trades in every trading scenario, this paper proposes the use of a new measure called the Sharpe/Total trades ratio which takes into account the number of trades when evaluating the different trading strategies. Findings strongly support the use of the adaptive fractal moving average model over the naïve buy-and-hold strategy where the former yielded higher annualized returns, lower annualized risk, a higher Sharpe value, although it was subject to more trades than the buy-and-hold strategy. The best market timing strategy occurred when using 131 daily fractal data with a Sharpe/Total trades ratio of 0.31%.

scholarly journals Technical Financial Analysis Tools for Stata

2017 ◽  
Vol 17 (3) ◽  
pp. 736-747 ◽  
Author(s):  
Mehmet F. Dicle ◽  
John D. Levendis
Keyword(s):  
Financial Analysis ◽  
Moving Average ◽  
Technical Analysis ◽  
Relative Strength ◽  
Analysis Tool ◽  
Investment Strategies ◽  
Moving Averages ◽  
Strength Index ◽  
Analysis Tools

In this article, we provide four financial technical analysis tools: moving averages, Bollinger bands, moving-average convergence divergence, and the relative strength index. The tftools command is used with four subcommands, each referring to a technical analysis tool: bollingerbands, macd, movingaverage, and rsi. We provide examples for each tool. tftools allows researchers to backtest their own investment strategies and will be of interest to investors, researchers, and students of finance.

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 Comparison of the Return of Investment from Securities Screening Using Financial Ratios and Security Trading Methods: Evidence from Thailand

2021 ◽  
Vol 56 (1) ◽  
Author(s):  
Chonnawat Chunhawiksit ◽  
Sirichai Deelers ◽  
Waraton Chitadisai ◽  
Suwatjana Thammachot ◽  
Somnuk Aujirapongpan ◽  
...  
Keyword(s):  
Stock Exchange ◽  
Moving Average ◽  
Technical Analysis ◽  
Financial Ratios ◽  
Stock Selection ◽  
Return On Equity ◽  
Analysis Tools ◽  
Stock Exchange Of Thailand ◽  
Price Earnings Ratio ◽  
Trading Model

This research aims to study investment in the Stock Exchange of Thailand based on historical financial ratios, namely, the return on equity (ROE) and price-earnings ratio (P/E), and screening securities together with buy-and-sell timing using technical analysis tools for stock selection to build a stock trading model. This research utilizes financial statement data from the companies listed in the Stock Exchange of Thailand from 2014 to 2018, selecting the stocks to invest using Greenblatt's Magic Formula and identifying buy-and-sell timing by a simple moving average (SMA). The stocks were reselected annually from 2014 to 2018. The study compared returns from 3 methods; it was found that the portfolio that identified buy-and-sell timing by technical analysis tools, the 6-month simple moving average, had the best performance, followed by the 12-month simple moving average and buy-and-hold portfolio, consecutively.

MOMENTUM OSCILLATORS - A TOOL TO PREDICT STOCK PRICE BEHAVIOUR

2016 ◽  
Vol 12 (1) ◽  
Author(s):  
Shishir Kumar Gujrati
Keyword(s):  
Stock Price ◽  
Moving Average ◽  
Technical Analysis ◽  
Relative Strength ◽  
Future Price ◽  
Range Data ◽  
Strength Index ◽  
Price Trends ◽  
Past Data ◽  
Price Movements

Stock markets are always taken as the barometer of the economy. The price movement of their indices reflects every ups and downs of the economy. Although seem to be random, these price movements do follow a certain track which can be identified using appropriate tool over long range data. One such method is of Technical Analysis wherein future price trends are forecasted using past data. Momentum Oscillators are the important tools of technical analysis. The current paper aims to identify the previous price movements of sensex by using Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) tools and also aims to check whether these tools are appropriate in forecasting the price trends or not.

scholarly journals Improving MACD Technical Analysis by Optimizing Parameters and Modifying Trading Rules: Evidence from the Japanese Nikkei 225 Futures Market

2021 ◽  
Vol 14 (1) ◽  
pp. 37
Author(s):  
Byung-Kook Kang
Keyword(s):  
Moving Average ◽  
Public Information ◽  
Technical Analysis ◽  
Weak Form ◽  
Futures Market ◽  
Traditional Model ◽  
Analysis Tool ◽  
Futures Prices ◽  
Improved Performance ◽  
Parameter Values

Much research has examined performance or market efficiency by using the moving average convergence divergence (MACD) technical analysis tool. However, most tests fail to verify efficiency with the traditional parameter settings of 12, 26, and 9 days. This study confirms that applying the traditional model to Japan’s Nikkei 225 futures prices produces negative performance over the period of 2011–2019. Yet, it also finds that the MACD tool can earn significant positive returns when it uses optimized parameter values. This suggests that the Japanese market is not weak-form efficient in the sense that futures prices do not reflect all public information. Hence, the three parameters values of the MACD tool should be optimized for each market and this should take precedence over finding other strategies to reduce false trade signals. This study also tests which models are able to improve profitability by applying additional criteria to avoid false trade signals. From simulations using 19,456 different MACD models, we find that the number of models with improved performance resulting from these strategies is far greater for models with optimized parameter values than for models with non-optimized values. This approach has not been discussed in the existing literature.

scholarly journals Machine learning applied in the stock market through the Moving Average Convergence Divergence (MACD) indicator

2020 ◽  
Vol 17 (4) ◽  
pp. 44-60
Author(s):  
Alberto Antonio Agudelo Aguirre ◽  
Ricardo Alfredo Rojas Medina ◽  
Néstor Darío Duque Méndez
Keyword(s):  
Machine Learning ◽  
Genetic Algorithms ◽  
Stock Market ◽  
Moving Average ◽  
Asset Price ◽  
Technical Analysis ◽  
Investment Strategies ◽  
Historical Data Analysis ◽  
Financial Asset Returns ◽  
Technical Indicator

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 &amp;amp; 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&amp;amp;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&amp;amp;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.

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