Applying Clustering Algorithms to Construct a Stock Trend Decision Model

2013 ◽  
Vol 311 ◽  
pp. 81-86
Author(s):  
Chung Min Wu ◽  
Sheng Chun Chou ◽  
Horng Twu Liaw
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Clustering Algorithms ◽  
Investment Strategy ◽  
Investment Strategies ◽  
Volume Data ◽  
Accurate Identification ◽  
Research Results ◽  
Clustering Model

The stock market is a well-developed and mature market. Nevertheless, it is not immune to international financial market changes, where volatility has reigned in recent years. Investors who misgauge stock trends can suffer dramatic losses. Accurate identification of market trends can still achieve outstanding performance and has become a major investor concern. This paper proposes a new stock price trend clustering model using a genetic algorithm to search for optimal investment strategies. Daily stock prices and trading volume data from the Taiwan stock exchange weighted index (TAIEX) was used to examine the proposed trend clustering model’s performance. The model was also compared to other popular stock market investment strategies to verify its validity. Research results confirmed that the trend clustering model correctly identified three different trends in the stock market. Furthermore, the trend investment strategy model using genetic algorithms performed better than other investment strategies, i.e. Granville’s rules for buy and hold strategies, in both bull and bear markets. Research results confirmed trend investing outperformed the other two investment strategies in return and capital distribution, both during the training period and the testing period.

scholarly journals Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock Market

2021 ◽  
Vol 13 (3) ◽  
pp. 1011
Author(s):  
Seung Hwan Jeong ◽  
Hee Soo Lee ◽  
Hyun Nam ◽  
Kyong Joo Oh
Keyword(s):  
Genetic Algorithm ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Trading Volume ◽  
Weighted Average ◽  
Market Liquidity ◽  
Average Price ◽  
Volume Data ◽  
The Impact

Research on stock market prediction has been actively conducted over time. Pertaining to investment, stock prices and trading volume are important indicators. While extensive research on stocks has focused on predicting stock prices, not much focus has been applied to predicting trading volume. The extensive trading volume by large institutions, such as pension funds, has a great impact on the market liquidity. To reduce the impact on the stock market, it is essential for large institutions to correctly predict the intraday trading volume using the volume weighted average price (VWAP) method. In this study, we predict the intraday trading volume using various methods to properly conduct VWAP trading. With the trading volume data of the Korean stock price index 200 (KOSPI 200) futures index from December 2006 to September 2020, we predicted the trading volume using dynamic time warping (DTW) and a genetic algorithm (GA). The empirical results show that the model using the simple average of the trading volume during the optimal period constructed by GA achieved the best performance. As a result of this study, we expect that large institutions will perform more appropriate VWAP trading in a sustainable manner, leading the stock market to be revitalized by enhanced liquidity. In this sense, the model proposed in this paper would contribute to creating efficient stock markets and help to achieve sustainable economic growth.

scholarly journals The Impact of Economic Agents Perceptions on Stock Price Volatility

2015 ◽  
Vol 63 (4) ◽  
pp. 1229-1234
Author(s):  
Jaroslav Bukovina
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Negative Impact ◽  
Positive Impact ◽  
Price Volatility ◽  
Investment Strategy ◽  
Economic Agents ◽  
Market Indexes ◽  
The Impact

This paper studies perceptions of economic subjects and its impact on stock prices. Perceptions are represented by stock market indexes and Facebook activity. The contribution of this paper is twofold. In the first place, this paper analyzes the unique data of Facebook activity and proposes the methodology for employment of social networks as a proxy variable which represents the perceptions of information in society related to the specific company. The second contribution is the proposal of potential link between social network principles and theories of behavioral economics. Overall, the author finds the negative impact of Facebook activity on stock prices and the positive impact of stock market indices. The author points the implications of findings to protection of company reputation and to investment strategy based on the existence of undervalued stocks.

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.

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 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

scholarly journals The effects of dividend policy to stock prices of listed companies in Vietnam stock market

2020 ◽  
Author(s):  
Thị Lam Hồ ◽  
Thùy Phương Trâm Hồ
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Dividend Policy ◽  
Corporate Value ◽  
Business Managers ◽  
Dividend Payment ◽  
Share Prices ◽  
Finance Management ◽  
The Impact

Dividend policy is one of the most important policies in corporate finance management. Understanding the impact of dividend policy on the distribution of profits, corporate value and thus on the stock price is important for business managers to make policies and for investors to make investment decisions. This study is conducted to evaluate the impact of dividend policy on share prices for companies listed on Vietnam’s stock market in the period from 2010 to 2018, based on the availability of continuous dividend payment data. Using the FGLS method with panel data of 100 companies listed on the HoSE and HNX, we find evidence of the impact of dividend policy on stock prices, supporting supports the bird in the hand and the signal detection theories. The findings of this study help to suggest a few recommendations for business managers and investors.

Sign in / Sign up

Export Citation Format

Share Document