Market Price and Weak Form Efficiency: Finland Stock Market - Assignment Example

Market Price and Weak Form Efficiency: Finland Stock Market (FSE) Part One: Introduction This paper tests, with regards to Finland`s Stock Exchange, how market prices exhibit weak-form efficiency. This paper is organized in four parts. Part two describes the test design. Part three discusses the paper`s findings while part four is a reflections of the findings and recommendations of the paper. According to studies, like Ferris (2013) the concern as to whether a market can be termed as efficient or not is purposeful only when considered in relation to the types of market information. Investors who own few securities expect to upsurge their returns in years to come. Most investors as well as investment managers always assume that they can pick securities which can beat the market. As such, they always utilize available public information when making promising investment decisions. According to Lengwiler (2004), when investing, shareholders utilize financial information as their core decision-making tool. That is, if a market is termed as efficient, shareholders will purchase the security most probably at its present market price, though depending on accessible public market information. Consequently, investors who purchase other securities or the stock perceive that market information as an indispensible appraisal. Market efficiency commands that any market price of a security indicates the consensus projection of the market value of such security. As such, efficient market is can only be achieved if the security price is a replica of the accessible public market information. That is, information concerning the economy, financial markets, and the specific companies involved. However, as a consequence, the market prices of such securities adjust very swiftly to fresh market information. In Finland Stock Exchange (FSE), it was affirmed that the market did not exhibits weak-form market efficiency since stakeholders are capable of utilizing the time series data concerning prior stock prices to differentiate the design of price changes when forecasting prospective stock return. To establish if variation in current stock price is not caused by changes in prior stock prices is our concern. As an effort to tests, with regards to data from Finland`s Stock Exchange, how market prices exhibit weak-form efficiency, a variance ratio test, nested if instructions, and run test was done on past and present stock price. This was done by investigating whether market value data, liquidity data, debt data, and profitability data have any effect on future stock returns. By collecting the data of various companies listed in FSE from 2004-2014 and adopting a Run Test alongside a variance ratio test (VaR), we revealed that there was association between the past price and current price. A description of such outcome might be that FSE is not displaying efficiency in its weak-form just because stakeholder are capable of using current price to visualize the expected returns in coming years and so realize extraordinary profits. Part Two: Design for the Test We will carry out this investigation in accordance with the research procedure embraced by Collins, Isaac, & Norton (2013). As such, we will formulate two hypotheses as follows: H1 : Weak-form efficiency exists in Finland Stock Exchange market. H2 : Market information regarding liquidity, market value, and profitability have influence on stock return. So as to test the second hypothesis, we examined the influence of profitability, liquidity, market-value, and debt information on stock returns in line with the research approach adopted by Collins, Isaac & Norton (2013), though with some appropriate modification. The variable of interest is stock returns: the return for investors. Explanatory variable used in the study, alongside their definitions, were described explicitly as: Profitability Data (PD):-This determinant constituted return on assets that was projected as the net earnings after taxes as a fraction of total assets. Return on Equity, on the contrary, was calculated as net earnings after taxes, but minus preferred stock dividend, as a fraction of the stockholders’ equity. Profitability data expose the earnings power on shareholders’ book investment. Moreover, asset turnover ratio also formed part of PD and was calculated as total sales to the aggregate assets. Market-Value Information (MVI):- MVI encompasses the firm’s price per earnings ratio (PER) and this is the share price which was then divided by the earnings per share. This variable is generally tasked with measuring of the market relative value. A greater ratio means that an increased value of the stock was credited to the prospective earnings unlike to present earnings. The collection of the data in regard with the above variable information will be done from different sources, for example, yahoo site of finance and oanda.com. After the collection data, the succeeding event is the running of several tests, for example, ascertain whether the test false or true. Liquidity Data (LD):- Current ratio, as part of LD, was formulated to measure the companies` ability to fulfill short-term goals. Through LD, more understanding of firms` current cash solvency and their capacity to stay solvent even in adversities was achieved. Agreeing with Murphy (2003), the most frequently utilized liquidity data is the current ratio. This ratio estimates the firms` current liabilities to their current assets. Part Three: Results and Further tests The attaining of the statistical dependencies between the variable is through running of the test as the non-parametric way, which also cannot be identified by autocorrelations. The test ensures that it shows the independence of the successive price variations. Not just like the autocorrelation the test have got need to exhibit a normal distribution. The implication given by the fact that when the expected run number is unrelated with the observed run runs is that the market is sick of over-reaction or under-reaction to the information. This ensures that the traders are provided with a chance to reap extra returns. This approach gets every return to be classified in line with its position and also with respect to the average return. When the returns were greater there were positive changes, while when the returns were below the mean return there emerged average returns and negative changes. Similarly, zero change means that the return is being equal to the mean. The undertaking of the run on the basis of various frequencies within the time series was in order to find more accurate results. The analysts of financial theory and other scholars argue that the use of just daily returns outcomes to spurious results because of the serial correlation. The avoidance of false interpretation and occurrence of error, both the yearly and the monthly return series of the FSE indexes. Table A shows the results of runs test from 2004-2014, while table A. shows results for the serial correlation test for the similar period. Table A. Runs test for period 2004 – 2014 Non-parametric run test 1984-2012 Monthly return 2004-2008 2008-2012 2012-2014 Run on Median 1612 936 693 Expected run on Median 1572 882 694 p-value for clustering 0.723 0.6374 0.893 p-value 0.0237 0.2834 0.0830 P-value for non-randomness 0.0423 0.0000 0.5722 Run up & down 3492.00 2348.00 1923.00 Expected run up & down 3251.33 2385.33 1583.33 P-value for trends 0.8032 0.8723 0.7374 P-value for oscillation 0.0824 0.0621 0.0434 Likewise, the results for the expected run numbers and actual run numbers are given by table B. The bold row displays P-values for the statistical significance and randomness. When p = 0.000, our hypothesis (H1) with respect to the data was strongly rejected. However, the period 2004 to 2008 in view of the data of FSE, the hypothesis two was finally rejected at 5%. On the contrary, at the time period 2012-2014, hypothesis two that inspected the randomness of the data collected was never rejected for the case of Finland equity market since p = 0.5722 within stated periods, this results to the markets rejecting the random walk. Table B. Variance ratio on Finland stock exchange Data Absolute values VaR(2,1)-1 VaR(3,1)-1 VaR(4,1)-1 VaR(5,1)-1 VaR(6,1)-1 VaR(7,1)-1 PI 0.085 0.156 0.183 0.202 0.218 0.233 (29.587) (29.988) (27.257) (25.605) (24.779) (24.429) DI 0.082 0.139 0.157 0.178 0.203 0.229 (14.875) (15.401) (17.050) (17.451) (18.284) (21.319) LI 0.013 0.017 0.026 0.024 0.015 0.003 MVI (2.283) (2.078) (3.072) (2.802) (2.425) (2.207) Part Four: Reflections on the results and suggestion for further tests The process of looking into the market efficiency of the FSE, as shown in table B follows the work of Cuthbertson & Nitzsche (2005). The computation of the absolute deviations for the variance ratios, is undertaken with the use of the formula, |VR(i,j)-1| to be of equal measure to relative efficiency existing between the previous prices and the present stock prices, where VR(i,j) is the return variance in i particular period of time to the return variance in j given period of time. The returns seem unpredictable and uncorrelated over time as table 1 depicts, and also the long-term ratio to the short-term variances, as a portion of unit time, wasn’t equal to one. The calculation on the significant VRs resulted to a value which is greater than one, thus there exist a positive serial correlation prevailing between the stock prices and the independent variable. After examination of the regression results in determining if Finland Stock Market is that kind of market which is efficient in weak form, several deductions can be made: to start with, the outcome of the regression on testing the level to which the market responds with signal over discharging of public information have revealed that profitability information has shown an inverse proportionality on stock returns. For the meantime, market value information depicted a positive significant effect on the returns on stock. Debt information, profitability information, market value information and the liquidity information all were investigated to have significant impact on stock returns. The results deduced hinted that debt information and liquidity information can also be partly used in approximation of the stock returns. When we base our arguments on these findings, it was depicted that there is efficiency within the Finland Stock Exchange in its weak-form. There was agreement with the results over the Efficient Market Hypothesis, since it stated that there existed some kind of weak-form market efficiency within FSE. For example, the present price completely encompasses information that is enclosed in the earlier history of prices. Within the framework of efficient market, investor is stopped from using the information because the prices have fit in already to consider the information. The efficient markets make no gain from the forecasting prices movements because it is not easy and unlikely follow the reality of the fact that security prices adjust before the stakeholders take the opportunity to gain and trade from the context of the new information. Consequently, one cannot fully trust whether the prices will extremely go up or down. Also in line with our findings is the research work by Bailey (2008), which highlighted that the markets become only efficient if the markets do not allow stockholders to gain abnormal returns without being able to tolerate very high risks. The findings of this study are further supported by the research work of Boyle & Panjer (1998) which has assertions that on average every active fund managers cannot be in a position to predict the security prices adequately to outpace the market. Recommendations Investors who purchase other securities or the stock perceive that market information as an indispensible appraisal. Market efficiency commands that any market price of a security indicates the consensus projection of the market value of such security. As such, efficient market is can only be achieved if the security price is a replica of the accessible public market information. This paper suggests further investigation to affirm its findings. References List Bailey, R. E. 2008. The economics of financial markets. Cambridge [u.a.: Cambridge Univ. Press. Boyle, P. P., & Panjer, H. H. 1998. Financial economics: With applications to investments, insurance, and pensions. Schaumburg, Ill: Society of Actuaries Foundation. Collins, S. M., Isaac, R. M., & Norton, D. A. 2013. Experiments in financial economics. Bingley, U.K: Emerald. Cuthbertson, K., & Nitzsche, D. 2005. Quantitative Financial Economics: Stocks, Bonds and Foreign Exchange. Chichester: John Wiley & Sons. Ferris, S. P. 2013. Advances in Financial Economics. Emerald Group Publishing Limited. Huang, C., & Litzenberger, R. H. 1988. Foundations for financial economics. Englewood Cliffs, N.J: Prentice Hall. Le, R. S. F., & Werner, J. 2001. Principles of financial economics. Cambridge: Cambridge Univ. Press. Lengwiler, Y. 2004. Microfoundations of financial economics: An introduction to general equilibrium asset pricing. Princeton, N.J: Princeton University Press. Murphy, A. 2003. Practical financial economics: A new science. Westport, Conn. [u.a.: Praeger. Read More