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Studies of the relationship between stock price and company's performance have already started since 1970s in western academies. The generalized measures of company's performance consist of financial indicators, non-financial indicators and subjective indicators (Chee & Wim, 2006). The purpose of these studies is going to test the effectiveness of historical financial information which may provide the suggestion for the investors. This chapter will discuss the previous studies which tested the relationships between stock price and various financial indicates including accounting earnings, dividends, also the non-financial indicators such as company's announcements and news volume. On the basis of this process, different kinds of indicators can be compared. Moreover, the major indicators which have been proved that they have strong correlation with stock price will be paid more attention to. These literatures are going to help building the concepts of this type of research.
The Relation between Stock Price And Company's Earnings
According to Market Efficiency Hypothesis, the change of stock price follows the Random Walk Model which means that the stock price changes randomly. The reason is because that the information of the companies are totally contained in the stock price, therefore the future stock price are unpredictable. However, Market Efficiency is just an ideal hypothesis (Gordon, William & Jeffery, 2001). The announcements and other information from companies can not be absorbed into stock price immediately and fully in realistic stock market. This characteristic of stock price and company's information provide the opportunities for the analysts to utilize the historical information for future stock price forecasting. To the ordinary investors, they are hardly able to analyze the whole financial information of companies in their portfolios. They prefer to pay more attention on specific indicators which are strongly associated with stock price changes. Therefore, the scholars have done plenty of researches to seek out those indicators which may be valuable for investing actions.
Most of the early researches regarded the companies' accounting income information as an important indicator which can present the variation of stock prices. The related concepts with this type of indicators include realistic earning numbers, future or unpredictable earnings, and earning announcements and reports.
Ray and Philip (1968) reviewed the theory development of three major issues including: expected an unexpected income changes, reaction of stock market and econometric factors. By clarifying the definitions, the authors developed the model for empirical study with two variables: EPS and net income. There are three types of data sources: income statements, report announcements, and the change of stock prices during the period of announcements issuing. The 21 years income data collected from 1946 to 1966. The reports announcement come form Wall street Journal during the same period. The data of stock prices are collected form every month's data of stock prices on New York Stock Exchange. To investigate the change of stock performance by influence of income reports, API (abnormal performance index) is used in the test equation. The experiment result denoted that the income information has the relation with the movement of market reaction. In the regression model consisted with income number and EPS, the calculations are processed in other definitions of income: cash flow and net income before nonrecurring items but the result showed there is no sign of the stock return react to EPS or net income. The study above gave the result that there is a relation between stock price and income numbers. However, there are many sources of information in the stock market, the further test show that net income information is just one part of information and it has limitations. The investors can use net income number to analyze the future pattern of stock prices but they can not just rely on it. The study imply further question of net income reports. This paper is one of the earliest investigations of the relation between some determinants with stock prices. It provided the method of regression model and naïve model to test the relation between two variables.
Henny and Charhes (1977) determine the definition of "standardized unexpected earning" (SUE) which can be the indicator of content of quarterly earnings. The measure of stock price is "month holding period return" (HPR). The purpose of this paper is to study the cross-section association between these two variables above. The sample price data collected form 975 stocks from 1971 to 1974. The data are process in the "Matrix of Spreads" and provide the evidence of the correlation coefficients of the variables for the hypothesis that SUE has the highly positive relation with spreads in HPR. After that, the data are classified into different portfolios with high SUE and low SUE and calculated the Betas and correlation coefficients of these portfolios. The result showed that the portfolio with high SUE recorded high performance in HPR spreads. The next test of portfolios determined that the same result also exist in every quarter data. This paper told a simple conclusion that stock prices are highly associated with unexpected earning. There is also a correlation between every quarter's unexpected earning. The conclusion of this paper improves the study of the correlation between Stock Price and income information compared with the former research. The later researcher can study the same research objects in different data samples. Also, the researches after this can study the variables which have the related concepts with the "standardized unexpected earning" on the basis of this paper.
Jeffery and Brian (1997) investigating the correlation between changes in fundamental signals and security prices. While this is done an assessment is made of how efficiently analyses use the signals and to see if the signals the analysis's used really have an impact on the earning predictions. Its been observed that analysts predicted revision does not include the whole information about the future earnings in the fundamental signals and also the revision forecasts do not include all the signals the reasons for this is because the signals contain information unrelated to earning but related to value. Another probable caused is that the investors do not believe that the revision predictions contain the information in the signals. The sample data comes from "Compustat PST Active File" in 1992 analyzed by regression method. On further investigation of analysts forecast errors it became clear that the generalised under-reaction to accounting information if eliminated will also eliminate the under-reaction to annual earnings. A number of fundamental relations in broad industry sectors were used to draw general conclusions, sectors included were manufacture of primary, service, wholesale and retail products, if the Inventory INV, Gross margin GM, and labour force LF signals were looked at for these sectors it is clear that the INV signal is informative only if there is a substantial investment but what's a mystery is that how is there a lack of INV signal in the analysts one year ahead forecasts, the GM signal should be informative for all sectors like the firms operating at thing margins but proof of the GM signal being a mixed one is surfacing these results cant be explained with any other explanations other than randomness. LF is the most informative the relation between LF and forecast errors shows the analysts under-reaction to the information of future earnings. Overall it can be said that even though analysts use the information in the fundamental signals they do not use it efficiently, macroeconomic trends like GDP and inflation have every little effect on how informative the fundamental signals are for future earnings this excludes variables like the GM and the AR.
The researches above study the correlation between companies' earnings and the stock price using the direct quantitative data of these indicators such as earning numbers and unexpected earning. However, the other researchers focus on the qualitative information of companies' earnings which expressed in the form of reports and announcements of the companies. Carl (1997) compared the influence of quality and quantity information of earning announcement on the change in stock price. Also, the study samples of 3 American stock exchanges are divided into different groups according to the size of companies, trading volume, and the volume of analysis' predictions. By using regression model, the authors concluded that although these quality factors have effects on the change in stock prices, the effects of them are very weak.
Robert (1990) studies the influence of current market earning announcement on the relation between stock price and earning. This study borrow the sample of 145 companies to test four hypotheses which show the relations between "earning predictability" and " earning persistence" and the changes of stock prices variance and response coefficient. Then, the formulas which present the four variables above are specified from three basic equations. The result determined that the alternative information has significant association with the relation between stock return and accounting earning numbers. After developing the formulas, they are applied in presetting the hypotheses. The further calculation in new equations forecast that response coefficient has positive relation with persistence and the predictability of earning. The changes of stock price variance have positive relation with persistence of earning but negative relation with predictability. In the empirical stage, the formulas are used to analyze the data from the sample. The realistic data analysis gives the evidence to support the theory prediction of the relations between these 4 variables. In order to give more accurate result, the authors use sensitivity test to define three possible factors which lead to fake relations in the result. The three factors are the assumption of "cross-sectional constant interest rate", the differential risk and the firm size. Additionally, the paper focuses on the measurement errors and investigates the way to control it. The distinction between economic earning and accounting earning is clarified by the test of relation between them. The article provides the implication of study of the relation between stock price and earning of firms. The predictive information of earning has effect on the relation between stock price and earning.
Charles and Robert (1970) try to explore the relationship between earrings reports and the trends of "intermediate" security price. First, the report introduces the different theory systems of the explanation for the relationship between stock price and the historical information of companies. The theory of Market Efficiency Hypothesis is not support the viewpoint that the historical can used to analysis and forecast the trends of stock price. On the other hand, the other researchers such as fundamentalists believe that the data of the financial statement is valuable in some particular circumstances. The authors of this article find that there is a possibility than the high positive earning reports will cause the change of stock price. The study collected two study samples of 510 and 618 firms respectively from 1962 to 1965 and from 1964 to 1967. Six variables emerge from the data sample. The correlation coefficients are calculated to refine the data sample. The refining study sample is summarized by compared with the Standard and Poor's Index. According to the comparison, the results show that the positive earning announcement inspire the increase of the stock price. However, the influence of the negative earning announcement is not significant as the positive one. This finding reject the Market Efficiency Hypothesis, therefore the information of the companies will be contained in the stock price immediately. The historical data of the financial statement or report can be used to predict the future stock price trends. Furthermore, the systematic risk test produces the result that the investors should be careful of the stocks which they put in their portfolio. It is important for the investors and analysts to notice that the moment when the information of earning announcement is launched.
Eugene, Stanley and Wayne (1984) also research the correlation between stock price and the expectations of earrings. The variable of the report is EPS expectation and collected the data from 3,000 firms from 1975 to 1980. The data is divided into portfolio with 20 stocks each. The group of data called "Screen #1"containing the stocks that be predicted to increased by the analysts. The data of these portfolios is compared with the data of I/B/E/S Universe and Standard & Poor's 500 for different length of periods including 3 months, 6 months, and 1 year. The regression method is adopted to analyze whether these portfolios is supported by the CAPM model. The result after risk adjustment is not support the CAPM model. After that, the sample data is compared with the portfolios which selected randomly. The outcomes present that the "Screen #1" reach the average level of the total random data. In the analysis of the returns with same risks, the results also indicate that "Screen #1" is the best choice for the investors. From the analysis above, the Market Efficiency does not exist in this stock market and the expectations have effect on the change of stock price.
This paper of Bradford and Wayne (1989) discuss the discounted cash flow model of that predicts that changes in stock price are in accordance with future earnings and the current earnings forecast error, it also predicts the impact of earning announcements on the stock prices , early work done pointed out that the response of stock prices to earning innovation depended on the extent to which it is considered to be permanent, evidence in this regard concluded that the information in the current annual earnings innovation is present in the revision of the next years earning forecasts but not the other way around, all these results are based on quarterly data and further reinforce the predications of the DFC model, however the results were found to be different across the various quarters, for temporary announcements the error coefficient was found to be insignificant, for the year ahead it got more significant and in the fourth quarter the coefficient of error is insignificant but the revision coefficient is important, also there is a increase in the "explanatory power" of regression, these observations can be explained as: the analysts focus on the earnings ahead of the quarter, they find that the year ahead revision has more explanatory power but this is not the case for the audited annual announcement, here the forecast error and not the year ahead regression provides the explanatory power, these findings suggest that the forecast revisions explain most of the variation in the stock prices associated with earning announcements.
The previous reports presents above are all focus on the correlation between stock price and earning information of the companies. The results determined that stock price have positive correlation with these financial or non-financial indicators. In the later literatures, the researchers move on to the other indicators including dividend and ROE ratio.
The Relation between Stock Price And Company's Dividend
The later research explored that there is also a relationship between stock price and dividend of companies. Similar with the study objects as earrings of firms, the dividend of company are represented as dividend numbers and dividend announcements.
Marshall (1980) provides the evidence for the correlation between stock price and dividend yield. The survey of investors emerged a favour for dividend payout even while the retained earnings were decreased. There was a grater preference for dividends and thus a greater preference for the income of an investor however this pattern is inconsistent and suggests a more complicated relation between the dividend yield and common stocks. People prefer dividends even with reductions on retained earnings, cross sectional regressions that were estimated with quarterly returns and those that were estimated through a time line of over 41 years showed that there was a increase in risk adjusted returns on dividend paying stock with a dividend yield. During the same time line for 30 years, the average return on all dividend and non dividend paying issues were the same, various explanations were thought of to interpret this monotonic relation and the most common one was that the market did not anticipate the growth of dividends for high yielding stocks compared to lower yielding stocks.
Robert and Krishna (1982) find out that there is a positive correlation between security return and dividend yield. The influence of dividend yields has always been a controversial subject of the investigation of the relation between common stock returns and dividend yields. The prediction rule is uniquely relied on information that is available to the investor before it happened. From the empirical evidence consistent with the TAX-Clientele CAPM, the first procedure adopted to exam the expected dividend yield. It is similar to yield variables used in earlier studies, here because the dividends yield variable incorporates the knowledge of the ex-dividend months the results suffer a bias. The coefficients on predicted dividend yield (conditional on it being an ex month) for stocks that announced their dividends prior to the ex-month stocks versus the stocks that announced their dividend during the ex-month. It is still an open question if the effects of the dividend yield on common stock returns can be attributed to taxes or due to some omitted variable.
Robert (1983) investigates the correlation between change in dividends and the stock price. First, the correlation between these two variables is expressed by the equation of Pt = Dt/(r-g). From the analysis, if the growth rate of dividends is high than the discount rates, the stock price will change. However, the author speculates that the change of the stock price is not related to the change in dividend. He provides the examples to illustrate that the change of stock price does not affect the dividend paid by the companies. The usual methods used for test the correlation between stock price and dividend is not suitable. With the data sample of Standard and Poor's from 1872 to 1979, the standard deviation of dividend is lower than the one of stock price. Although the author believer that dividend will not affect the change of stock price, it does not mean than dividend is stable. At the end, he also introduces other factors may influence on the change in stock price such as nationalization with instance.
Bong-soo (1995) tried to explore the relation between stock price and dividends stocks in both short term and long term. Since the former studies of stock return and dividends showed that these two variables series have a unstable process, the short dividends were assumed to just reflect the intermittent kind of dividends. In the first place, the author showed the difference between "bivariate moving-average representation model (BMAR) and bivariate value- representation model" (BVAR). Then, on the basis of BMAR, the formulas which present the relation between stock return and dividends are developed. Further discussion of the association between BVAR and BMAR can clarify one component in the equation of BMAR. The data used in calculation comes from CRSP during 1926 and 1991. The data are refined into quarterly data. The primary process of data of stock price series and dividend series used Dickey and Fuller regression test. After that, the co integrating test of stock price series and dividend series support the hypothesis of spreads of the variables are stable. The line chart of historical stock price response to both long-term and short tem dividends stocks noted that the relation of stock price and the two types of shocks are similar. The other outcome of this study is that the spreads of stock price and dividend are highly responding to short-term shocks. The methodology applied in this study is bivariate model, therefore the variation of stock prices are related to shocks to dividend. The positive relation in the finding provide an implication of the idea that dividend of one firm will have some influence on the performance of the stock prices in different length of duration.
There are a few researches base on Singapore Exchange to investigate the relation between stock price and expected dividends. Gab and Soon (1996) applied the "dividend-ratio" model to determine the level of fluctuation of stock prices compared with their initial values in SGX. The sample data used in this study are borrowed from OCBC series between 1975 and 1991. After the data analysis by this existing model, the results explain the usefulness of this model and "present value model". It also mentions that the dividend-price ratio can be predicted in the present value model. This conclusion provides a speculation that the correlation between stock price and dividend can be discussion in theoretical methods.
H. Thomas et al (2000) adopt DPS ratio, cash flow per share and EPS ratio in this investigation to seek out whether these indicators can give more significant prediction of stock price than Standard & Poor's 500 Index. They selected 1300 firms' data of these three variables from 1980 to 1997. According to certain standard, the sample data are spited into 22 portfolios. Pearson correlation coefficient method is applied to examine the relation between changes in stock price with these three indicators. With the comparison of results of these portfolios, EPS ratio denoted the strongest positive correlation with security prices. Cash flow per share is anther indicator which presents a positive correlation with stock price second to EPS. Nonetheless, it seems that there is no correlation between stock prices with DPS ratios though DPS has relation between companies' cash flow. The authors speculated that there is anther way for the firms to pay the shareholders instead of paying out dividends. Although this study does not provide a viewpoint that DPS is related to stock price, the Pearson Correlation Coefficient applied is a good way to explore the correlation between two variables.
James (2001) did not study the relationship between stock price and dividend but the usefulness of dividend in assessment in companies' equity. However, this paper did provide a discussion about the role of different accounting indicators. The paper bases on the pure inference of several models which had been introduced in the former studies, including EBD (earnings, book values, dividends) model, RIV (residual in come valuation) model and PVED (present value of expected dividends) model. The purpose of further explaining these models is to give some assumptions for these accounting indicators when they are used to evaluate the value of companies. In the first place, EBD model and RIV model are compared with each other. Earnings, book values and dividends are present as variables in these model formulas. Through the inference and test of coefficients of the variables in the equations, the consequence of the experimental study is that the models should be integrated for the study of financial indicators because each of them has limitation. For example, the EBD model does not involve "the other information" therefore the principle of this model is too simple. As a result, the implication of this EBD model shows that the three indicators can be used to express the value of companies but this conclusion is not exist in the real world. From this study, the finding is not directly associated with dividend and stock price. However it really indicated that the value of companies or other performance factors of companies can not be totally measured by some particular indicators. The more factors involved in the experiment, the more accurate result will be given.
Gongmeng et al (2002) choose the emerging stock marketing PRC rather than the mature stock market in the former studies as the data base to test whether the stock return will be affected by the earning reports and dividend announcements. The history of China stock market development showed that it is necessary to provide the suggestion for the investors of whether the information of firms earning and dividend are helpful for analysis of stock prices. Additionally, the cash dividend and earning are given at the same time in China stock market, thereby these tow items are highly related. Also, the accounting number of cash dividend and stock dividend are the same. The sample data come form A-share companies' form 1994 to 1997. The data are classified and selected through summary statistics and present a positive relation between earnings and stock dividend. Then, the authors use regression test to examine the relations between earning, cash dividend and stock dividends changes. The results showed that earning and stock dividend has the highest relations with each other. Additional sensitivity test proved the strength of the results of regression test. Even the change of sample into B-share listed firms got the same result as the one use A-share listed firms. To summarize, there is a positive association between earning announcement and stock prices. Since the stock dividends are highly related to earning announcement, it will influence the stock prices indirectly through earning announcement. On the other hand, cash dividend has the lowest relation with stock return among the tree indication. Compared with China, the stock market in Singapore is more international and mature but it also different form the western stock market due to regional characteristics. There is still a necessity to investigate the effect of profitability performance of firm on the stock prices.
In the form of numbers, most of the previous study gave the evidence to demonstrate that the influence of dividend on stock returns or stock price exist both directly and indirectly. Similar to the earnings of company, weather dividend and stock price still have correlation when the dividend present as the increase announcement. Some investigators study from this point of view to explore the relationship between stock price and dividends.
Robert et al (2000)' paper adopt a measure of compensation called pay-performance sensitivity (PPS) to test the relation between this measure and the reaction of stock price to dividends. The result showed whether the relation between these two variables is positive or not. The data of study sample of PPS collected form 1,000 firms in Securities and Exchange Commission in United States in 1993. The dividends increase firms' data comes from1992 Wall Street Journal Index of 345 announcements. Market-to- book ratios and the mean and standard deviations of PPS are calculated by using the summary statistics. According to these numbers, the sample is further classified into high and low MTB. Z-statistic is applied to examine the mean abnormal return in announcement-period. The results illustrated in table explained that there are no linear relation between PPS and stock prices reaction. Moreover, the negative relation between these two variables just exists in low market-to-book ratio firms based on the outcomes. The second stage of this study is using regression test to clarify the relation between PPS and respond of stock prices to dividend with more detail conditions changes including market-to book influence, volume of dividend increase and rules. The consequence of the test displayed that the third factor has more effect on respond of stock pieces that the other two factors. The third stage is and additional test which examine the influence of agency cost and over optimism prediction. The sample data is put into categories according to company size and CEO ages. The conclusion produced from this test is that if the agency cost decline, the reaction of stock prices will negatively relate to PPS.
The Relation between Stock Price And Company's ROE ratio
Colin and Mounir (2007) tested the effectiveness of forecasting book-to-market and ROE expectation for evaluating the stock pricing. This research based on the former concepts of "fundamental valuation" and "risky proxy". The authors apply the variables in "fundamental valuation" and "risky proxy" into their research model. The data used in the research collected from financial listed companies in UK from 1980 to 2000. The variables from those two concepts above are tested in the "cross sectional-model" and both annually and monthly stock return regression are calculated. According to the statistic results of the regressions, both book-to-market and ROE expectation are associated with the stock return. Furthermore, due to the inefficient market in the real stock market, the predicting ROE also has the relationship with future stock returns. This current study exams the situation of UK stock market by exploring the correlation between combination of "fundamental valuation" and "risk proxy" variables.
Ray et al (1993) study the economic factors which can determine the association between change in earnings of firms and stock returns. Actually, the "earnings" in this study is specified into return on equity. The authors argue that the risk of investment can be an indicator for change in earnings therefore it is included in the relationship between earnings and stock returns. In their study, the data are collected from 1950 to 1988 and the observed firm is 764 every year. On the basis of the sample data, these variables are defined in equations to calculate the "time-series regression" and then the results are used to rank the portfolios of this study sample. Depend on the result, the authors summarized that in terms of return on equity, change in earnings has more significant relation with stock return. At the same time, there is also a positive correlation between investment risk and earnings. The implication contained in this study given evidence that ROE ratio may has relationship with stock price in some extent.
The Relation between Stock Price and Other Factors
Apart from the common accounting ratio indicators for prediction of stock price, the other researchers discovered different indicators which may be related to stock prices. They can be economic indicators or other non-financial indicators.
According to the discovery of the association between stock price and money supply, John and Arthur (1977) examine the causal relationship between 5 variables including: "money supply, rate of change in the money supply, corporate interest rate, measure of risk". They select Standard & Poor's 500 Index to build the sample of stock price. Moody's AAA provides the data of corporate interest rate and risk measure. In order to mine the causal relationship between these variables and stock price, this study applies regression method to produce the empirical outcomes. The results of five regressions showed that there is no causal relationship between these five variables and stock price. The authors suggested that the later research should look for different indicators from the indicators tested in this study. Different from the financial indicators studies, the variables in this study are tested the correlation with stock price separately. It seems that regression method can be applied when there is a simple linear relation between two variables.
Another popular discussion is the relation between "Economic Value Added" system created by Stern Stewart company and stock returns. Robert et al (2005) study the usefulness of this EVA system on the improvement of stock returns. The study sample includes 65 companies from 1983 to 1998. In the data analyzing, the mean of annual return of stock are calculated to test the hypotheses which assume the EVA will improve the profitable performance of the companies. The consequences of the hypotheses test point out that there is no significant positive influence of application of EVA system However, there is a hidden implication of this test is that those companies who apply EVA system in advance will perform more profitable than the other firms. Worth mentioning, this study select ROE ratio and ROA ratios to represent the profitable performance of companies when they examine whether the EVA make a improvement for the companies' profit. Therefore, the ROE and ROA are common indicators for the profitable performance of firms and it is rational to adopt ROE to test the correlation between stock price and the profitable performance of listed companies. The study of James and D. Donald (2001) has the same conclusion with Robert's study about the influence of EVA on companies' stock performance by using different study samples.
X.Liang (2006) chooses web stock news volumes (WSNV) to be an indicator for the stock price. This indicator is regarded as a non-financial determinant of stock price which is different from those study objects above. Nowadays, internet becomes an important part of people's lives. The stock investors can search detail information online to analyze their investments. On the other hand, the information on several website may also influence the performance of the stocks. The author collected data from three major finance websites including: Yahoo, Quote and Smartmoney. Web stock news volumes are present in an equation and then the mean standard deviation method is applied to explore the relationship between WSNV and stock price. Through the hypothesis, it is confirmed that there is positive correlation between stock price and WSNV. This experimental result reminds that there are a wide variety of factors may have effects on the change of stock price. Besides, mean standard deviation is anther method to investigate the relationship between two variables.
The studies which select their study samples in Singapore stock market are seldom available. Except the one of "dividend-ratio model" study mentioned before, Ying (2000) investigated the relation between stock price and the economic indicator. The author selected Singapore stock market to be the research object and discovered that the effects of exchange rate are different by using various currencies. Moreover, the timing factor is also considered in this study especially the financial crisis in 1990s. The conclusion is that the influence of exchange rate differs in different periods.
In conclusion, the previous literatures studied the relationship between stock price and variety of indicators by using different methodologies and study samples. However, all of these researches provide the same answer to the Market Efficiency Hypothesis that this hypothesis is not supported in real stock market. The authors explored many factors may be used to predict the future change of stock price. It is still valuable to seek out the effective indicators for the stock analysts and investors to forecast the future trends of stock price.
Majority of the earliest studies of the correlation between stock price and earning information of companies shows that there is positive correlation between stock price and companies' earning information. However, some authors also mentioned that the investor should not analyze the stock price just depending on the earnings.
Other investigations are focus on the relationship between stock price and dividends of firms. The results of these studies are not as consistent as the results of stock price versus companies' earnings which implicate that the influences of dividend on change in stock price are different of different study samples. Compared with the studies of dividend versus stock price, the researches of the correlation between stock price and return on equity are rarely available. Furthermore, these studies present in this chapter combine ROE with other indicators to explore the relationship between stock performance and the combinations.
Besides, these literatures created or borrowed a plenty of models to determine the variables for their studies but Pearson Correlation Coefficient and Regression are two common methods to explore the correlation between variables. Some reports also mention Mean Standard Deviation method.
On the basis of literature review, the following part of this report is going to test the correlation between stock price versus DPS and ROE by using Correlation Coefficient and Regression methods of the companies listed in Singapore Stock Exchange. The results of this study may produce different result from former researches and give the particular suggestions for the investors who focus on the stocks in Singapore Stock Exchange.