scholarly journals Moving Average Convergence-Divergence (MACD) Trading Rule: An Application in Nepalese Stock Market "NEPSE"

2020 ◽  
Vol 1 (6) ◽  
Author(s):  
Rashesh Vaidya
Keyword(s):  
Stock Market ◽  
Intrinsic Value ◽  
Moving Average ◽  
Technical Analysis ◽  
Future Trend ◽  
Trading Rule ◽  
Minimal Period ◽  
Current Pattern ◽  
Signal Line ◽  
Market Convergence

There are two types of analysis done for a stock market. One is fundamental analysis, where an investor looks at an intrinsic value of the stock, and another is technical analysis, where investors determine the future trend of the market looking at the current pattern or trend of the market. This paper is focused on one of the technical analysis tools, i.e., Moving Average Convergence-Divergence. It is a tool based on the three exponential moving average (9-12-26 EMA Rule). The MACD analysis, with the help of a single line, was helpful to find out the exact bullish and the bearish trend of the Nepse. A signal line is a benchmark to determine the stock market moving either to a bullish or bearish trend. It can help an investor, where the market is going in a direction. A market convergence, divergence, and crossover were better identified with the help of the MACD histogram. The paper found that the Nepse return was stable for a very minimal period from 1998-99 to 2019-20. The shift from the bullish to bearish or vice-verse were seen easily identified with the help of a MACD histogram. Finally, a better-combined knowledge of moving average and candlestick chart analysis will help an investor, to put a clear picture of a market trend with the help of MACD analysis.

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 & 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&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&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.

Data Snooping on Technical Analysis: Evidence from the Taiwan Stock Market

2011 ◽  
Vol 14 (02) ◽  
pp. 195-212 ◽  
Author(s):  
Cheng-Wei Chen ◽  
Chin-Sheng Huang ◽  
Hung-Wei Lai
Keyword(s):  
Stock Market ◽  
Transaction Costs ◽  
Market Efficiency ◽  
Technical Analysis ◽  
The Other ◽  
Trading Rule ◽  
Data Snooping ◽  
Empirical Results

The main purpose of this paper is to investigate the validity and predictability of technical analysis in the Taiwan stock market. Bootstrapped tests of White (2000) and of Hansen (2005) are employed to ascertain whether there exists a superior trading rule among two broadly used sets of technical analysis. One coming from Brock et al. (1992) and the other from Sullivan et al. (1999). Moreover, this study brings together powerful bootstrapped tests along with two institutional adjustments to ascertain the efficacy of technical analysis: (1) non-synchronous trading and (2) transaction costs. The empirical results indicate that this triad-data snooping, non-synchronous trading and transaction costs, has a great impact on the performance of technical analysis. In fact, the Taiwan stock market stands for market efficiency, and economical profits cannot be rendered from technical analysis in this market.

scholarly journals Optimization of investment management in Warsaw stock market

2014 ◽  
Vol 15 (2) ◽  
pp. 143-156 ◽  
Author(s):  
Maciej Janowicz ◽  
Arkadiusz Orłowski ◽  
Franciszek Michał Warzyński
Keyword(s):  
Stock Market ◽  
Time Perspective ◽  
Moving Average ◽  
Technical Analysis ◽  
Relative Strength ◽  
Optimal Management ◽  
Brute Force ◽  
Exchange Market ◽  
Strength Index ◽  
Optimal Values

Abstract Application of simple prescriptions of technical analysis on the Warsaw Exchange Market (GPW) has been analyzed using several stocks belonging to WIG20 group as examples. Only long positions have been considered. Three well-known technical-analysis indicators of the market have been investigated: the Donchian channels, the Relative Strength Index, and Moving Average Convergence-Divergence indicator. Optimal values of parameters of those indicators have been found by „brute force“ evaluation of (linear) returns. It has been found that trading based on both Donchian channels and Relative Strength Index easily outperform the „buy and hold“ strategy if supplied with optimal values of parameters. However, those optimal values are by now means universal in the sense that they depend on particular stocks, and are functions of time. The optimal management of capital in the stock market strongly depends on the time perspective of trading. Finally, it has been argued that the criticism of technical analysis which is often delivered by academic quantitative financial science is unjustified as based of false premises.

scholarly journals Effectiveness of Technical Analysis Signals Around the Earning Announcements in Malaysian Stock Market

2018 ◽  
Vol 7 (3.21) ◽  
pp. 109
Author(s):  
Kelvin Lee Yong Ming ◽  
Mohamad Jais
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Moving Average ◽  
Technical Analysis ◽  
Relative Strength ◽  
Kuala Lumpur ◽  
Earning Announcement ◽  
Strength Index ◽  
Corporate Announcements ◽  
Technical Indicator

Technical analysis is an analysis that widely applied by the investor in the stock market. However, various corporate announcements could cause the market to react, and the most significant corporate announcement is the earnings announcement (1). Thus, this study examines the effectiveness of technical analysis signals around the earning announcements dates in Malaysian stock market. In doing so, this study applied and tested four technical indicators, namely Simple Moving Average (SMA), Relative Strength Index (RSI), Stochastic (K line), and Moving Average Convergence/Divergence (MACD) in Malaysian stock market. The sample of this study consisted of 30 largest capitalization companies from the main market of Kuala Lumpur Stock Exchange (KLSE). Meanwhile, the sample period covered from 2nd January 2014 to 31st March 2016. This study found that Moving Average Convergence/Divergence (MACD) significantly produced higher returns as compared to the other technical indicator before the earning announcement dates in financial year 2014 and 2015. The combined indicator of MA-MACD also found to have higher return in financial year 2015. The findings conclude that the technical analysis signals can be used to generate returns before earning announcement dates.  

Development of Stock Trading Optimization Analysis System Using Moving Average Method

2011 ◽  
Vol 66-68 ◽  
pp. 1703-1707
Author(s):  
Pang Wen Ling
Keyword(s):  
Stock Market ◽  
Average Method ◽  
Moving Average ◽  
Technical Analysis ◽  
Market Potential ◽  
Optimization Analysis ◽  
Stock Trading ◽  
Analysis System

The Taiwan stock market has millions of investors. If each investor spends $5,000 NTD each month on a professional technical analysis system (the current use price is $5,000~10,000 NTD) and it is conservatively estimated 100,000 investors would use the system, the market potential will be $500 million NTD. Developing an effective stock trading analysis system can help investors obtain profits. At the same time, the system buyers can also make considerable profits. The study is intended to develop a stock trading technical analysis system with moving average method.

scholarly journals Stock market prediction using technical analysis

2006 ◽  
Vol 51 (170) ◽  
pp. 125-146 ◽  
Author(s):  
Aleksandra Bradic-Martinovic
Keyword(s):  
Stock Market ◽  
Trading Volume ◽  
Moving Average ◽  
Supply And Demand ◽  
Wave Theory ◽  
Technical Analysis ◽  
Ease Of Use ◽  
Future Price ◽  
Mathematical Methods ◽  
Price Movements

Technical analysis (TA) is a form of analyzing market encompassing supply and demand of securities according to the study of their prices and trading volume. Using the appropriate methods, TA aims to identify price movements in the stock market, futures or currencies. In short, TA analysis is the process by which "future price movements are formulated according to the price history". TA originates from the work of Charles Dow and his conclusions about the global behavior of the market, as well as from Elliot Wave Theory. Dow did not regard its theory as a tool for stock market movement prediction, nor as a guide for investors, but as a kind of barometer of general market movements. The term TA methods encompasses all the methods used in tracking prices aiming to clearly predict future events. Many different methods, mainly statistical, are used in technical analysis, the most popular ones being: establishing and following trends using moving average, recognizing price momentum, calculating indicators and oscillators, as well as cycle analysis (structure indicators). It is also necessary to point out that TA is not a science in the true meaning of the term, and that methods it uses frequently deviate from the conventional manner of their use. The main advantage of these methods is their relative ease of use, aiming to give as clear picture as possible of price movements, while at the same time avoiding the use of complicated and complex mathematical methods. The reason for this is simple and is reflected in the dynamics of financial markets, where changes occur during short periods of time and where prompt decision-making is of vital importance.

scholarly journals A Study on Stock Price Prediction using LSTM Model

2021 ◽  
Vol 9 (VI) ◽  
pp. 2618-2622
Author(s):  
Koushal Saini
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Time Series Data ◽  
Moving Average ◽  
Technical Analysis ◽  
Relative Strength ◽  
Trend Detection ◽  
Series Data ◽  
Future Price ◽  
Stock Price Prediction

Predicting stock price of any stock is a challenging task because the Volatility of stock market the nature of stock price is dynamic, chaotic, noisy and sometimes totally unexpected. The other most difficult task is to analyze and decide financial time series data that improves investment returns and help in minimizing losses. Technical analysis is a method that help in analyzing a stock and predict its future price via evaluating securities. There are already many Indicators and other tools for technical analysis in stock market. Some famous indicators such as SMA (Simple Moving Average), EMA (Exponential Moving Average), WMA (Weight Moving Average), VWMA (Volume Weight Moving Average), DEMA (moving averages), MACD (Moving Average Convergence/Divergence), ADX (Average Di- reactional Movement Index), TDI (Trend Detection Index), Arun, VHF (trend indicators), stochastic, RSI (Relative Strength Index), SMI(Stochastic Momentum Index, volume indicators are also available for technical analysis. Here, we have used the LSTM Model to predict future price of some big companies of stock market in NSE.

scholarly journals Analisis Komparatif Penggunaan Metode Stochastic, Moving Average Dan MACD Dalam Mendapatkan Keuntungan Optimal Dan Syar’i (Study Pada Jakarta Islamic Index 2016 – 2018)

2019 ◽  
Vol 2 (1) ◽  
pp. 52
Author(s):  
Priyo Prasetyo ◽  
Nur Laely ◽  
Heri Subagyo
Keyword(s):  
Qualitative Research ◽  
Moving Average ◽  
Sampling Technique ◽  
Technical Analysis ◽  
Purposive Sampling ◽  
Stochastic Oscillator ◽  
Signal Line ◽  
The One ◽  
The Right ◽  
Stochastic Oscillators

The purpose of this study is to make a decision when it is appropriate to buy and sell shares in the Jakarta Islamic Index by using technical analysis or other stochastic oscillators, moving averages and MACD. This research is descriptive qualitative research. The population of this study is the Jakarta Islamic Index. The period of January 2016 - December 2018, by taking samples from shares listed on the Jakarta Islamic Index (JII). Sampling is done by purposive sampling technique. The research location is the shares listed on the Jakarta Islamic Index using Chart Nexsus software. Data analysis using technical analysis using three indicators, namely stochastic oscillator, moving average and MACD. Based on the results of the research of the three stochastic oscillator indicators, the moving average and MACD that the one that generates greater profit is using MACD. The right moment in using MACD is if the MACD line cuts the signal line from the bottom up or Golden Cross. And give a sell signal if the MACD line cuts the signal line from top to bottom or Death Cross. Tujuan penelitian ini yaitu untuk mengambil keputusan saat yang tepat untuk jual dan beli saham pada Jakarta Islamic Index dengan menggunakan analisis teknikal atara lain stochastic oscillator, moving average dan MACD. Penelitian ini adalah penelitian kualitatif deskriptif. Populasi penelitian ini yaitu pada Jakarta Islamic Index. Periode Januari 2016 – Desember 2018, dengan mengambil sampel dari saham yang terdaftar pada Jakarta Islamic Index (JII). Penarikan sampel dilakukan dengan teknik purposive sampling. Lokasi penelitian pada saham yang terdaftar di Jakarta Islamic Index dengan menggunakan software Chart Nexsus. Analisis data dengan menggunakan analisis teknikal dengan menggunakan tiga indikator yaitu stochastic oscillator, moving average dan MACD. Berdasarkan hasil penelitian dari ketiga indikator stochastic oscillator, moving average dan MACD bahwasannya yang menghasilkan profit lebih besar adalah dengan menggunakan MACD. Momen yang tepat dalam menggunakan MACD adalah bila garis MACD memotong garis sinyal dari bawah ke atas atau Golden Cross. Dan memberi sinyal jual bila garis MACD memotong garis sinyal dari atas ke bawah atau Death Cross.

scholarly journals Trading Performance Analysis: A Comparisons Between the Original MA Crossover and Modified MA Crossover Strategy

2018 ◽  
pp. 933-941
Author(s):  
Afiruddin Tapa ◽  
Mohd Hasimi Yaacob ◽  
Ahmad Husni Hamzah ◽  
Yean Soh Chuen
Keyword(s):  
Performance Analysis ◽  
Moving Average ◽  
Technical Analysis ◽  
Lower Number ◽  
Analysis Approach ◽  
Trading Rule ◽  
Trading Rules ◽  
Stop Loss

This paper empirically analyses the Trading Performance by using technical analysis approach. The original moving-average (MA) crossover strategy as compare with the modified moving-average crossover strategy. The modified trading rules are the rules that been established to trading rules such as entry rule, exit rule, holding rule, and stop-loss rule. The results show The MAshort of 10-period for modified strategy underperform the original strategy, except for MA (10,100). The modified MA (20,200), (50,100), (50,200), and (100,200) underperform the original strategy. Only modified MA (20,50) and (20,100) outperform the original strategy. The outperformance and underperformance due to the stricter additional trading rule that reduces trading signals, and thus lower number of trades.

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