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.

Creation of Stock Analysis System Depend on the Prediction Research of Individual Stock Price Behavior by Moving Average Method

2012 ◽  
Vol 601 ◽  
pp. 547-553
Author(s):  
Pang Wen Ling
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Average Method ◽  
Moving Average ◽  
Market Potential ◽  
General Rules ◽  
Data Source ◽  
Stochastic Data ◽  
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 and it is conservatively estimated 100,000 investors would use the system, the market potential will be $500 million NTD. If we can seek out irregular general rules of stock price data on stock market and further make predictions to some extent, the return on investment may be effectively raised, and investment loss reduced for the investors. This study will take Taiwan stock market as data source and use Moving Average Method (MA) to conduct study and analysis of stochastic data of stock price. One stocks (TSMC) typical in Taiwan’s industries and with large-cap index weights are studied to explore whether there are any specific changes and regularities in seemingly irregular stock prices.. We should depend on the front research to develop an effective stock trading analysis system, and we can help investors obtain profits. At the same time, the system users can also make more considerable profits.

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.

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.  

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 Value Premium and Technical Analysis: Evidence from the China Stock Market

Economies ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 92 ◽  
Author(s):  
Lam ◽  
Dong ◽  
Yu
Keyword(s):  
Stock Market ◽  
Transaction Costs ◽  
Moving Average ◽  
Technical Analysis ◽  
Trading Strategy ◽  
Value Premium ◽  
Chinese Stock Market ◽  
New Strategy ◽  
The One ◽  
Three Factor

We find value premium in the Chinese stock market using a conventional buy-and-hold approach which longs the portfolio with the highest BM ratio and shorts the one with the lowest BM ratio. Based on the finding, we test a new strategy by combining the value premium effect and technical analysis. During the sample period (1995 to 2015), we trade the objective portfolio or risk-free asset according to the moving average timing signals, and we find excess return from such a zero-cost trading strategy. We perform various robustness tests and find that the excess returns remain significantly positive after adjusting for risks (on three factor models) and transaction costs. In general, we find that the combined trading strategy can generate significant positive risk-adjusted returns after the transaction costs.

A Stock Trading Strategy Based on Time-Varying Quantile Theory

2015 ◽  
Vol 19 (3) ◽  
pp. 417-422 ◽  
Author(s):  
Tiantian Liu ◽  
◽  
Ning Qiu ◽  
Wentao Gu
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Financial Market ◽  
Technical Analysis ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Time Varying ◽  
People's Republic Of China ◽  
Stock Trading ◽  
Republic Of China

Many of the trading strategies viewed as highly important by to financial market investors, we developed based on fundamental and technical analysis. We propose a stock trading strategy based on time-varying quantile analysis and apply it to the stock market in the People’s Republic of China. Comparing results for both the buy-and-hold strategy and a popular NARX-based neural network trading strategy showed that our strategy performed well.

Sign in / Sign up

Export Citation Format

Share Document