scholarly journals Predicting Stock Price Trend Using MACD Optimized by Historical Volatility

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jian Wang ◽  
Junseok Kim
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Moving Average ◽  
Rapid Development ◽  
Price Trend ◽  
Technical Indicators ◽  
Volatility Index ◽  
Fixed Weight ◽  
The Stability

With the rapid development of the financial market, many professional traders use technical indicators to analyze the stock market. As one of these technical indicators, moving average convergence divergence (MACD) is widely applied by many investors. MACD is a momentum indicator derived from the exponential moving average (EMA) or exponentially weighted moving average (EWMA), which reacts more significantly to recent price changes than the simple moving average (SMA). Traders find the analysis of 12- and 26-day EMA very useful and insightful for determining buy-and-sell points. The purpose of this study is to develop an effective method for predicting the stock price trend. Typically, the traditional EMA is calculated using a fixed weight; however, in this study, we use a changing weight based on the historical volatility. We denote the historical volatility index as HVIX and the new MACD as MACD-HVIX. We test the stability of MACD-HVIX and compare it with that of MACD. Furthermore, the validity of the MACD-HVIX index is tested by using the trend recognition accuracy. We compare the accuracy between a MACD histogram and a MACD-HVIX histogram and find that the accuracy of using MACD-HVIX histogram is 55.55% higher than that of the MACD histogram when we use the buy-and-sell strategy. When we use the buy-and-hold strategy for 5 and 10 days, the prediction accuracy of MACD-HVIX is 33.33% and 12% higher than that of the traditional MACD strategy, respectively. We found that the new indicator is more stable. Therefore, the improved stock price forecasting model can predict the trend of stock prices and help investors augment their return in the stock market.

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.

Macroeconomic Factors and Pakistani Equity Market”

2004 ◽  
Vol 43 (4II) ◽  
pp. 619-637 ◽  
Author(s):  
Muhammad Nishat ◽  
Rozina Shaheen
Keyword(s):  
Stock Market ◽  
Industrial Production ◽  
Stock Prices ◽  
Stock Price ◽  
Consumer Price Index ◽  
Reverse Causality ◽  
Macroeconomic Factors ◽  
Macroeconomic Variables ◽  
Equilibrium Relationships

This paper analyzes long-term equilibrium relationships between a group of macroeconomic variables and the Karachi Stock Exchange Index. The macroeconomic variables are represented by the industrial production index, the consumer price index, M1, and the value of an investment earning the money market rate. We employ a vector error correction model to explore such relationships during 1973:1 to 2004:4. We found that these five variables are cointegrated and two long-term equilibrium relationships exist among these variables. Our results indicated a "causal" relationship between the stock market and the economy. Analysis of our results indicates that industrial production is the largest positive determinant of Pakistani stock prices, while inflation is the largest negative determinant of stock prices in Pakistan. We found that while macroeconomic variables Granger-caused stock price movements, the reverse causality was observed in case of industrial production and stock prices. Furthermore, we found that statistically significant lag lengths between fluctuations in the stock market and changes in the real economy are relatively short.

Maximal Overlap Discrete Wavelet Transform, Graph Theory And Backpropagation Neural Network In Stock Market Forecasting

2018 ◽  
Vol 5 (1) ◽  
pp. 41-46
Author(s):  
Rosalina Rosalina ◽  
Hendra Jayanto
Keyword(s):  
Neural Network ◽  
Graph Theory ◽  
Wavelet Transform ◽  
Stock Market ◽  
Discrete Wavelet Transform ◽  
Stock Price ◽  
Moving Average ◽  
Discrete Wavelet ◽  
Stock Market Forecasting ◽  
Artificial Neural

The aim of this paper is to get high accuracy of stock market forecasting in order to produce signals that will affect the decision making in the trading itself. Several experiments by using different methodologies have been performed to answer the stock market forecasting issues. A traditional linear model, like autoregressive integrated moving average (ARIMA) has been used, but the result is not satisfactory because it is not suitable for model financial series. Yet experts are likely observed another approach by using artificial neural networks. Artificial neural network (ANN) are found to be more effective in realizing the input-output mapping and could estimate any continuous function which given an arbitrarily desired accuracy. In details, in this paper will use maximal overlap discrete wavelet transform (MODWT) and graph theory to distinguish and determine between low and high frequencies, which in this case acted as fundamental and technical prediction of stock market trading. After processed dataset is formed, then we will advance to the next level of the training process to generate the final result that is the buy or sell signals given from information whether the stock price will go up or down.

scholarly journals Building stock market resilience through digital transformation: using Google trends to analyze the impact of COVID-19 pandemic

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Ding Ding ◽  
Chong Guan ◽  
Calvin M. L. Chan ◽  
Wenting Liu
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Digital Transformation ◽  
Google Trends ◽  
Building Stock ◽  
Market Sentiment ◽  
Price Performance ◽  
Novel Coronavirus ◽  
The Impact

Abstract As the 2019 novel coronavirus disease (COVID-19) pandemic rages globally, its impact has been felt in the stock markets around the world. Amidst the gloomy economic outlook, certain sectors seem to have survived better than others. This paper aims to investigate the sectors that have performed better even as market sentiment is affected by the pandemic. The daily closing stock prices of a total usable sample of 1,567 firms from 37 sectors are first analyzed using a combination of hierarchical clustering and shape-based distance (SBD) measures. Market sentiment is modeled from Google Trends on the COVID-19 pandemic. This is then analyzed against the time series of daily closing stock prices using augmented vector autoregression (VAR). The empirical results indicate that market sentiment towards the pandemic has significant effects on the stock prices of the sectors. Particularly, the stock price performance across sectors is differentiated by the level of the digital transformation of sectors, with those that are most digitally transformed, showing resilience towards negative market sentiment on the pandemic. This study contributes to the existing literature by incorporating search trends to analyze market sentiment, and by showing that digital transformation moderated the stock market resilience of firms against concern over the COVID-19 outbreak.

scholarly journals The Impact of COVID-19 on the Stock Price of Socially Responsible Enterprises: An Empirical Study in Taiwan Stock Market

2021 ◽  
Vol 18 (4) ◽  
pp. 1398
Author(s):  
Kuo-Jung Lee ◽  
Su-Lien Lu
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Abnormal Returns ◽  
Recovery Phase ◽  
Socially Responsible ◽  
Cumulative Impact ◽  
Study Approach ◽  
Price Performance ◽  
The Impact

This study examines the impact of the COVID-19 outbreak on the Taiwan stock market and investigates whether companies with a commitment to corporate social responsibility (CSR) were less affected. This study uses a selection of companies provided by CommonWealth magazine to classify the listed companies in Taiwan as CSR and non-CSR companies. The event study approach is applied to examine the change in the stock prices of CSR companies after the first COVID-19 outbreak in Taiwan. The empirical results indicate that the stock prices of all companies generated significantly negative abnormal returns and negative cumulative abnormal returns after the outbreak. Compared with all companies and with non-CSR companies, CSR companies were less affected by the outbreak; their stock prices were relatively resistant to the fall and they recovered faster. In addition, the cumulative impact of the COVID-19 on the stock prices of CSR companies is smaller than that of non-CSR companies on both short- and long-term bases. However, the stock price performance of non-CSR companies was not weaker than that of CSR companies during times when the impact of the pandemic was lower or during the price recovery phase.

EVOLUTION OF SHANGHAI STOCK MARKET BASED ON MAXIMAL SPANNING TREES

2012 ◽  
Vol 27 (03) ◽  
pp. 1350022 ◽  
Author(s):  
CHUNXIA YANG ◽  
YING SHEN ◽  
BINGYING XIA
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Spanning Tree ◽  
Stock Price ◽  
Spanning Trees ◽  
Turning Points ◽  
Single Step ◽  
P Value ◽  
Power Law Distribution ◽  
Structure Variation

In this paper, using a moving window to scan through every stock price time series over a period from 2 January 2001 to 11 March 2011 and mutual information to measure the statistical interdependence between stock prices, we construct a corresponding weighted network for 501 Shanghai stocks in every given window. Next, we extract its maximal spanning tree and understand the structure variation of Shanghai stock market by analyzing the average path length, the influence of the center node and the p-value for every maximal spanning tree. A further analysis of the structure properties of maximal spanning trees over different periods of Shanghai stock market is carried out. All the obtained results indicate that the periods around 8 August 2005, 17 October 2007 and 25 December 2008 are turning points of Shanghai stock market, at turning points, the topology structure of the maximal spanning tree changes obviously: the degree of separation between nodes increases; the structure becomes looser; the influence of the center node gets smaller, and the degree distribution of the maximal spanning tree is no longer a power-law distribution. Lastly, we give an analysis of the variations of the single-step and multi-step survival ratios for all maximal spanning trees and find that two stocks are closely bonded and hard to be broken in a short term, on the contrary, no pair of stocks remains closely bonded for a long time.

scholarly journals Disposition Effect and Return Predictability in the Tunisian Stock Market

2017 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Cheïma Hmida ◽  
Ramzi Boussaidi
Keyword(s):  
Stock Market ◽  
Behavioral Finance ◽  
Stock Prices ◽  
Stock Price ◽  
Return Predictability ◽  
Capital Gains ◽  
Disposition Effect ◽  
Individual Investors ◽  
Long Run ◽  
Return Reversal

The behavioral finance literature has documented that individual investors tend to sell winning stocks more quickly than losing stocks, a phenomenon known as the disposition effect, and that such a behavior has an impact on stock prices. We examined this effect in the Tunisian stock market using the unrealized capital gains/losses of Grinblatt & Han (2005) to measure the disposition effect. We find that the Tunisian investors exhibit a disposition effect in the long-run horizon but not in the short and the intermediate horizons. Moreover, the disposition effect predicts a stock price continuation (momentum) for the whole sample. However this impact varies from an industry to another. It predicts a momentum for “manufacturing” but a return reversal for “financial” and “services”.

scholarly journals Is Abnormally Large Volume a Clue?

2016 ◽  
Vol 8 (9) ◽  
pp. 226
Author(s):  
Tsung-Hsun Lu ◽  
Jun-De Lee
Keyword(s):  
Stock Market ◽  
Large Volume ◽  
Stock Prices ◽  
Stock Price ◽  
Trading Volume ◽  
Sensitive Analysis ◽  
Support Level ◽  
Out Of Sample ◽  
Sample Test ◽  
Abnormal Trading Volume

This paper investigates whether abnormal trading volume provides information about future movements in stock prices. Utilizing data from the Taiwan 50 Index from October 29, 2002 to December 31, 2013, the researchers employ trading volume rather than stock price to test the principles of resistance and support level employed by technical analysis. The empirical results suggest that abnormal trading volume provides profitable information for investors in the Taiwan stock market. An out-of-sample test and a sensitive analysis are conducted for the robustness of the results.

scholarly journals PENGARUH VOLUME PERDAGANGAN DAN SIMPLE MOVING AVERAGE TERHADAP HARGA SAHAM

2020 ◽  
Vol 1 (1) ◽  
pp. 1-16
Author(s):  
Gama Paksi Baskara ◽  
Suyanto Suyanto ◽  
Sri Retnaning Rahayu
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Trading Volume ◽  
Qualitative Data ◽  
Moving Average ◽  
Analytical Tool ◽  
T Test ◽  
Quantitative Approach ◽  
F Test ◽  
Regression Tests

Trading volume is a sheet of company shares traded on a particular transaction and has beenagreed between the seller and the buyer, Simple Moving Average is a method that studies themovement of the previous stock price based on the number of certain days in order to predict thestock price that will occur to the next.The objective of the study is to find out how much influenceTrade Volume and Simple Moving Average on Stock Prices is and what are the most dominantaspects in influencing Stock Prices. The type of the research uses a quantitative approach, namely anapproach in which the data are in the form of numbers or qualitative data that have been used asnumbers. The technique of collecting data uses documentation. The analytical tool used is multiplelinear regression tests including T Test, F Test and Coefisein R² Determination processed usingEviews. The results of the study show that partially the trading volume variable does not have asignificant effect on Stock Prices and the Simple Moving Average variable shows a positive andsignificant effect on stock prices while the results of the research simultaneously show that theTrading Volume and Simple Moving Average variables simultaneously affect the Stock Price .

scholarly journals The Effect of The Match Results on Bali United’s Stock Price Movements

2020 ◽  
Vol 3 (1) ◽  
pp. 26
Author(s):  
Agung Novianto Margarena ◽  
Arian Agung Prasetiyawan
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
European Countries ◽  
Football Club ◽  
Study Results ◽  
Price Movements ◽  
Positive Effect ◽  
Stock Price Movements

This study was conducted due to differences in the study results inseveral countries related to the effect of the match results on stockmovements. Dimic et. al (2019) stated the match results effect themovement of stock prices, while Mishra & Smyth (2010) stated thevice versa. Then, Floros (2014) put forward different results throughthe study of four clubs in four European countries. Thus, this studyreexamines the effect of the match results on the stock pricemovement of Bali United. Moreover, Bali United is the first SoutheastAsian football club to be listed on the stock market. This study uses aquantitative method with a sample of 31 Bali United’s matches afterlisted on the stock market. The data were analyzed using simple linearregression with SPSS 21 with either won, drawn or lost match resultsrepresented by goal margins. The stock price movements arerepresented by stock prices after the results of the match. It was foundthat the results of the match had a positive effect on the stockmovement of Bali United

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