International Journal of Business and Economy eISSN: 2682-8359 [Vol. 1 No. 2 September 2019]
http://myjms.mohe.gov.my/index.php/ijbec
STOCK MARKET ANALYSIS DURING ELECTION PERIOD IN MALAYSIA
Norkhairunnisa Redzwan1*, Nadia Musa2, Azimatul Husna Abdul Latip3, Yasmin Abdul Latif4 and Izzah Nabihah Ab Rahman5
1 2 3 4 5 Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, MALAYSIA
*Corresponding author: [email protected]
Article Information:
Article history:
Received date : 10 July 2019 Revised date : 19 August 2019 Accepted date : 15 September 2019 Published date : 30 September 2019
To cite this document:
Redzwan, N., Musa, N., Abdul Latip, A., Abdul Latif, Y., & Ab Rahman, I. (2019).
STOCK MARKET ANALYSIS DURING ELECTION PERIOD IN MALAYSIA. International Journal Of Business And Economy, 1(2), 93-102.
Abstract: During election period, investors are interested in the movement of stock prices. This study focused on the performance of 10 sectors in Malaysian stock market and its reaction to the 11th, 12th and 13th General Election. The performance of stock market was determined using Sharpe Ratio, Treynor Ratio and Jensen Ratio; and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) was used to measure the effect of General Election on stock market performance. This paper also used Markov chain model in predicting the stock market trend during pre, post and on event of the 11th, 12th and 13th General Election. The results showed that the stock performance of 10 sectors is inconsistent for pre, during and post-election except for 12th General Election by using the Sharpe ratio. The Markov chain model is found to be suitable to predict the stock market trend in short term.
Keywords: General Election, Markov chain, GARCH, stock market, performance.
1. Introduction
Starting from September 4, 1959, 13 elections had been done in Malaysia with 5 or 4-year interval.
In past decades, it is seen that Malaysian citizens are actively participating in electing new government to rule the country as it will affect the whole aspects of country, including the stock market index in Malaysia.
In the United States, the price of stock is highly correlated to cycle of presidential election. In fact, stocks’ behavior of oscillating over the time is challenging for the investors to predict the gain and loss (Wong & McAleer, 2009). During election period, firms that meet the government policies will gain benefit and attract more investors (Shen et. al, 2017). This is due to firms’ high exposure to government expenditure that will give them more advantages especially for democratic country (Belo et. al, 2013). Value-at-risk (VAR), Sharpe ratio, Treynor ratio and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) are examples of models that are used to illustrate the performance of stock market (Siti et. al, 2017), (Hussin, et. al, 2018), (Mohd et al, 2016).
In this paper, analysis on the performance of Malaysia’s economic sector were carried out by using risk-adjusted ratio on the top 10 economic sectors in Malaysia’s stock market. Then, prediction of the stock market trend was carried out by a Markov chain model. Markov chain model is a suitable method of prediction. Besides its use in prediction of the stock market, it can also be used to predict the future health state of human beings (Samsuddin & Ismail, 2017), (Samsuddin & Ismail, 2016).
2. Literature Review
There are many previous researches showing the significance relationship between the stock market and election years. According to Wong and McAleer (2009), he claimed that United States stock market frequently fluctuates during the cycle of election especially when Republican leads the country. Nath (2016) reveals from previous studies that stock market will eventually improve if a country is ruled by Democratic. This argument also is supported by Oehler, Walker, and Wendt (2013). Presently, Malaysia is one of the countries that practices Parliamentary Democracy.
Indeed, the stock market price will have positive impact if the government imposes policies that will gives benefits to the economy (Shaikh, 2017). Jacob (2018a) points out if the current government in Malaysia maintain their position against the opposition, the stock market will remain stable.
However, the main concern of the investors is when the opposition rules the country as they will have the right to transform new laws and regulations of Malaysia that will affects them. As Wong and McAleer (2009) states, that there is a possibility of reduce in stock market price if new leader is choose. A finding by Oehler et al. (2013) discover that the stock market index during 13th Malaysia General Election slumped 2 days before election date. In view of that, investors are concern regarding the government that will lead this country.
According to Koima, Mwita, and Nassiuma (2015) Autoregressive Conditional Heteroscedasticity (ARCH) was first suggested by Engle in 1982. The method used is lagged disturbance. ARCH is used to study the conditional variance of stock market in a time series. However, the collected result showed that a better version of ARCH model is needed.
GARCH model can explain volatility and movement of stock market in the time series very well (Lin, 2018). These studies have found similar result with Kristjanpoller R and Michell V (2018).
Also, Hansen and Lunde (2001) emphasized that when predicting volatility stock market return GARCH performs better than any complicated models. The model also can be used to interpret for decision making in financial as it comprises regarding portfolio selection, risk analysis and derivative pricing (Engle, 2001)
According to Choji, Eduno, and Kassem (2013), to identify and to forecast on the three states in changes of price of stock which are prices escalate, decline or stagnant, Markov chain model was being used. A Markov Chain is a stochastic process that has the Markovian property. Markovian property is a discrete parameter process where it is independent on past state given the current state.
Markov Chain model is widely used by numerous researchers in predicting stock market trend.
Vasanthi, Subha, and Nambi (2011) stated, the accuracy Markov model in forecasting, is outperformed since it calculates the changes over the day in index values and computed using transition probability matrix.
3. Problem Statement
Stock market is one of the most significant variables used to measure the economy of a country other than Gross Domestic Product (GDP). In preceding year of 2008, the price of stock in Malaysia are significantly decline and most of the firms are not able to survive due to financial crisis. During that time, the government of Malaysia play an important role by taking responsibility to lessen the burden of the crisis in the country. This research paper mainly focuses on the reaction of stock market towards general election in order to help investors from facing severe losses.
4. Method
This section explains on the collection of data and methods used in this research.
4.1 Data Collection
Top 10 sectors (Construction, Consumer, Financial, Industrial Product, Industrial, Mining, Plantation, Property, Technology and Trade and Service) in Malaysia’s stock market. The data that was used in this research study were daily prices of 11th, 12th and 13th Malaysia General Elections and missing value is replaced with the mean of the observation (Tsai et. al, 2018).
In this case study, it is relevant to use timeframe event because the effect of an event can be identify (Mackinlay, 1997), (Krivin et. al, 2013). The timeframe used is divided into 3 categories; pre window, window and post window of 11th, 12th and 13th Malaysian General Election as shown in Figure 1, Figure 2 and Figure 3. Assumptions used in this study were that there were 250 trading days in a year and the average trading days in 6 months was 120 days. The pre-window event was 6 months before the event window, while the post-event window was 6 months after the event window.
The event window was a period of 10 days, which covered 5 days before and after the General Election respectively.
Table 1. Dates of the 11th, 12th and 13th General Election
General Election 11th 12th 13th
Date 21/03/2004 08/03/2008 05/05/2013
Figure 1. Timeframe of 11th General Election
Figure 2. Timeframe of 12th General Election
Figure 3. Timeframe of 13th General Election
4.1.1 Validity and Reliability
Sharpe ratio (𝑆𝑏), Treynor ratio (𝑇𝑝), and Jensen’s Alpha (𝛼𝑗) were used to measure the performance of stock market by each sector.
𝑆𝑏 =𝑟𝑝 − 𝑟𝑓 𝛿𝑝
(1)
𝑇𝑝 =𝑟𝑝 − 𝑟𝑓 𝛽𝑝
(2)
𝛼𝑗 = 𝑟𝑝 − (𝑟𝑓 + 𝛽𝑝(𝑟𝑚 − 𝑟𝑓)) (3)
where,
𝑟𝑝 = portfolio return
𝑟𝑓 = risk-free rate of the market 𝑟𝑚 = market return
𝛿𝑝 = standard deviation of the return of portfolio 𝛽𝑝 = beta of portfolio
Generalized Autoregressive Conditional Heteroscedasticity (GARCH) is a statistical model that can be used to analyze the pattern and predict the financial markets volatility described by the variance.
The formula for GARCH:
ℎ𝑡= 𝛿 + 𝛼1𝑒𝑡−12 + 𝛽1ℎ𝑡−1 (4) where,
ℎ𝑡 = conditional variance
ℎ𝑡−1 = previous conditional variance 𝛼 = ARCH effect
𝛽 = moving average
𝑒𝑡−12 = previous squared return
The trend of stock market was forecasted by using Markov chain model with three states which were Up, Unchanged/Zero and Down (Zhang & Zhang, 2009).
𝑈𝑝 𝑍𝑒𝑟𝑜 𝐷𝑜𝑤𝑛
𝑃 = 𝑈𝑝 𝑍𝑒𝑟𝑜 𝐷𝑜𝑤𝑛[
𝑢𝑢
𝑡𝑜𝑡𝑎𝑙 𝑢𝑝 𝑢𝑧 𝑡𝑜𝑡𝑎𝑙 𝑢𝑝
𝑢𝑑 𝑡𝑜𝑡𝑎𝑙 𝑢𝑝 𝑧𝑢
𝑡𝑜𝑡𝑎𝑙 𝑧𝑒𝑟𝑜
𝑧𝑧 𝑡𝑜𝑡𝑎𝑙 𝑧𝑒𝑟𝑜
𝑧𝑑 𝑡𝑜𝑡𝑎𝑙 𝑧𝑒𝑟𝑜 𝑑𝑢
𝑡𝑜𝑡𝑎𝑙 𝑑𝑜𝑤𝑛
𝑑𝑧 𝑡𝑜𝑡𝑎𝑙 𝑑𝑜𝑤𝑛
𝑑𝑑 𝑡𝑜𝑡𝑎𝑙 𝑑𝑜𝑤𝑛 ]
(5)
The initial state vector is denoted by:
𝜂(0)= (𝑥1 𝑥2 𝑥3) = (1 0 0) (6) where,
𝑢𝑢 = up to up 𝑢𝑧 = up to zero 𝑢𝑑 = up to down 𝑧𝑢 = zero to up 𝑧𝑧 = zero to zero 𝑧𝑑 = zero to down 𝑑𝑢 = down to up 𝑑𝑧 = down to zero 𝑑𝑑 = down to down
𝑥1 = up 𝑥2 = unchanged 𝑥3 = down
Equation (6) was applied to the find the probability of the following days by using:
𝜂 (𝑖+1)= 𝜂 (𝑖)∗ 𝑃 (7)
5. Results and Discussion
Data analysis is the most crucial part of the research. Data analysis summarizes collected data. It involves the interpretation of data gathered through the use of analytical and logical reasoning to determine patterns, relationships or trends. Discussion provides the explanation and interpretation of results or findings by comparing with the findings in prior studies.
5.1. Performance of 10 Sectors in Malaysian Stock Market
TABLE 2: Summary Results Stock Performance of 11th, 12th and 13th General Election Time/Ratio Sharpe Ratio Treynor Ratio Jensen’s Alpha
General Election
11th
Pre-event CONSUMER PLANTATION TRADE & SERVICES
Event MINING MINING FINANCIAL
Post-event MINING MINING TRADE & SERVICES 12th
Pre-event PLANTATION TECHNOLOGY MINING
Event PLANTATION TECHNOLOGY CONSUMER
Post-event PLANTATION FINANCIAL FINANCIAL 13th
Pre-event FINANCIAL INDUSTRIAL INDUSTRIAL PRODUCT Event FINANCIAL INDUSTRIAL PRODUCT TECHNOLOGY
5.2. Reaction of Stock Market Towards General Election
Most volatile sectors were Mining, Construction and Mining for 11th, 12th and 13th General Election respectively. Likewise, the least volatile sectors for 11th, 12th and 13th General Election were Consumer, Consumer and Industrial sectors respectively. These are shown in figures 4, 5 and 6.
Figure 4. Reaction of Mining (Left) and Consumer (Right) Sectors towards 11th General Election
Figure 5. Reaction of Construction (Left) and Consumer (Right) Sectors towards 12th General Election
Figure 6. Reaction of Mining (Left) and Industrial (Right) Sectors towards 13th General Election
The result also showed that majority of the stocks was volatile that most of the stocks tend to fluctuate dramatically on the 11th election year. Property and other sectors happened to have a high volatility on post window of 11th and 13th general election. Meanwhile, on 12th general election the majority of the stocks showed a high volatility around 125th days which on the event window of election. Hence, it can be concluded that there is a reaction between the condition of stock market and general election.
5.3. Suitability of Markov Chain Model in Predicting Stock Market
The Markov chain model is applied to 10 sectors in Malaysia’s stock market and trend prediction is carried out separately using 11th, 12th and 13th Malaysia General Election. In order to check for the efficiency of the Markov model this study with the existing predicts models like Moving Average.
TABLE 3: Comparison between Markov Model and Moving Average on Predicting the Accuracy of Stock Market Trend During 11th, 12th and 13th General Election
Time Model
No.
of trials
Accurate Predictions
Accuracy of Prediction (%)
General Election
11th
Pre-event Markov model 10 8 80
Moving Average 10 6 60
Event Markov model 10 6 60
Moving Average 10 7 70
Post-event Markov model 10 9 90
Moving Average 10 9 90
12th
Pre-event Markov model 10 4 40
Moving Average 10 3 30
Event Markov model 10 8 80
Moving Average 10 4 40
Post-event Markov model 10 8 80
Moving Average 10 7 70
13th
Pre-event Markov model 10 5 50
Moving Average 10 5 50
Event Markov model 10 5 50
Moving Average 10 1 10
Post-event Markov model 10 5 50
Moving Average 10 5 50
Result shows the majority cases Markov Chain model more efficient than Moving Average in predicting stock market trend for day by day data. In the 12th General Election, for all three timeframe, Markov chain model accuracy predictions were higher than Moving Average especially at Event window as shown in Table 2. This result agrees with past study which proved that Markov Chain model shows superior results when the trend is predicted using short-term data (Vasanthi et.
al, 2011).
6. Conclusion
Results showed that the stock performance of 10 sectors is inconsistent for pre, during and post- election. The best sector is defined as the one that has highest ratio for all three risk adjusted methods.
By examining the results on 12th General Election, only Plantation sector using Sharpe ratio obtained consistent result for all the event window.
Most of the stocks showed a significant reaction to the 11th, 12th and 13th General Election. This can be shown from the GARCH graph that had been plotted. During the 11th General Election, the stock market had a high volatility at pre, during and post-election periods. For the 12th election majority of the stocks showed high volatility during the event window of the election. Meanwhile, in 13th general election most of the stocks showed a high volatility during the post event window.
The result also showed that Markov Chain model is suitable to predict trend of the stock market because of high level of accuracy predictions for majority time frame compared with Moving Average method. This research study could be continued by study further into companies listed in each sector. This is to help investors to choose specific companies to invest in and gain maximum profit during election period.
7. Acknowledgement
The authors wish to express gratitude to thank Encik Muhammad Azri Mohamad for his insights.
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