Testing Market Price for Weak Form Efficiency - London Stock Exchange - Report Example

Testing Market Price for Weak Form Efficiency: A case of London Stock Exchange SECTION ONE: INTRODUCTION Market efficiency dictates that a market price of any security signifies the consensus guesstimate of the security`s value in the market. Thus, efficient financial market is achieved when security prices replicate all accessible public information concerning the economy, concerning financial markets, and concerning the specific firm involved. The repercussion is that the securities` market prices adjust very quickly to new information (Bierman, 2010, pg.39). Every investor who own security anticipate increasing the return in times to come. Many investors, counting investment managers, consider that they may select securities which will outdo the market. Hence, they need to utilize accessible public information to take them to favorable investment decisions. Hadi (2006) claims that while investing, the stockholders could utilize financial reports as the main decision-making vessel. If that market is efficient, stockholder will buy the security probable at its existing market price depending on all accessible public information. Thus, investors who buy the stock or other securities perceive that information as superior appraisal. According to literatures, such as Paola (2005) the issue of whether the market is efficient or not is meaningful simply relative to certain type of information. As such, three general kinds of information are significant when defining three systems of market efficiency: weak-form, semi-strong-form, and strong-form efficient market. In London Stock Exchange (LSE), we undertake that the market is efficient though in weak-form since investors are unable to utilize the time series of previous stock price to distinguish the pattern of price variations in forecasting future stock return. Thus, we are fascinated to inspect whether stock price variations is independent of previous variations or not. We also examined the level to which market responded with signal on discharging public information in London Stock Exchange. So as to answer the questions, the serial correlation between past and present stock price was tested by examining whether liquidity, debt, profitability, and market value data have any influence on stock returns in order to that all these data to be used to approximate the stock returns in future. By using Run Test and variance ratio test and gathering the data of various firms in LSE for a period of 1984-2012, the test results has revealed that a noteworthy lag amount is 16, hence there exist autocorrelation between present price and past price. The explanation of the finding would be that the stock market is never efficient in weak-form, implying that stockholder can use price from single period to envisage returns in future periods and realize higher profits. Variance result of regarding testing the level to which stock market responded with signal on discharging public information revealed that ROE, ROA, and even dividend might predict the upcoming shifts in stock returns whereas current ratio and PER could partly be used to approximate the stock returns. This paper is separated into 4 parts. Part 2 explains the test design: How the tests will be carried out. Part 3 discusses the results and further tests and part 4 presents the reflections on findings and recommendations for further tests. SECTION TWO: TEST DESIGN We will inspect the serial connection between the present stock price and previous stock price over past periods. This will be done in line with the research methodology adopted by Hadi (2006). The two hypotheses for is as follows: H1 : There is some kind of weak-form market efficiency in LSE: the current price completely incorporates information enclosed in previous history of prices. H2 : Debt information, liquidity information, profitability information, and market value data have effect on stock return. For testing hypothesis two, we scrutinized the effect of liquidity, debt, profitability, and market-value data on stock returns through the application of research methodology used by Paola (2005), but with suitable modification. The independent variable selection and definitions used in the study were as follow, with dependent variable being stock returns. Stock Return was the return for investors. PI. Profitability Information Part of this variable was ROA (return on assets), which was estimated as net returns after the taxes to over-all assets. ROE (return on Equity) was also measured as net returns after taxes less the preferred stock dividend and as a percentage of the shareholders’ equity. The ratio revealed to us the profit power on investors’ book investment. In addition, asset turnover ratio was measured as aggregate sales to aggregate assets. The ratio revealed to us the comparative efficiency with which companies utilizes their resources so as to generate output. LI. Liquidity Information In this variable it was the current ratio in play. This ratio was used to gauge the firms` capability as far as meeting short-term goals is concerned. From them, more insight was obtained in the current cash solvency of the firms and their ability to stay solvent in the face of adversities. According to Bierman (2010), the best and frequently utilized liquidity ratios will always be the current ratio since it measures the current liabilities to current assets. DI. Debt Information This variable included debt-to-equity ratio and was computed by just dividing the aggregate debt of the companies (plus current liabilities) by the investors’ equity. Part of this variable was also the long-term debt-to-equity ratio, which was computed by just dividing long-term debt of companies by the investors’ equity. Final part was the aggregate debt to the assets ratio which was calculated by dividing aggregate liabilities by the total Assets. DI was used to estimate the companies` relative obligations. MVI. Market-Value Information MVI, on the contrary, entailed PER (price to earnings ratio) of the firms and was simply the share price which was divided by earnings per share. The ratio was labeled as the measure of market relative value. Higher ratio implied more that more value of stock was being credited to prospective earnings contrary to present earnings. Data regarding the above variables will be collected from various sources such as oanda.com and yahoo site of finance. After data is gathered, we will run various test such as Variance ratio test and run test to ascertain if our hypothesis is true or false. SECTION THREE: RESULTS AND FURTHER TESTS The runs test was also another non-parametric method used to ascertain statistical dependencies between the variable, which might not be noticed by autocorrelations test. This test ascertains whether successive price variations are independent. Unlike serial correlation, the run test did not need returns to have a normal distribution. If the expected run numbers is considerably unrelated to the observed run runs, it implies the market is sick of under-reaction or over-reaction to information, thus providing a chance to make extra returns for traders. In this approach, every return was classified in line with its position and with regard to the average return. Therefore, positive changes emerged when the returns were greater compared to the average returns and negative changes arose, when returns were below the mean return. Zero change replicated return being equivalent to the mean. So as to get more precise results, the runs test was done on various frequencies of the time series. Scholars and Financial theory analysts argue that using just daily returns can cause spurious results due to the serial correlation. In order to avoid errors as well as false interpretation, we used both monthly and yearly return series of London stock market indexes. Table 1. displayed the results of runs test from 1984-2012 and Table 2. displayed results for serial correlation test for the same period. Table 1. Results of runs test for period 2000 – 2012 Non-parametric run test 1984-2012 Monthly return 1984-2012 1984-1991 1991-1998 1998-2005 2005-2012 Run on Median 1738 835 693 583 445 Expected run on Median 1674 783 694 529 467 p-value for clustering 0.8723 0.7374 0.893 0.638 0.402 p-value 0.00152 0.2394 0.0830 0.0938 0.1734 P-value for non-randomness 0.0232 0.4382 0.000 0.000 0.0504 Run up & down 3293.00 2291.00 1923.00 2837.00 2193.00 Expected run up & down 3156.33 2256.33 1819.33 2783.33 2019.33 P-value for trends 0.9304 0.8723 0.7374 0.4382 0.0342 P-value for oscillation 0.0923 0.2104 0.0234 0.0193 0.0273 SECTION FOUR: REFLECTIONS ON FINDINGS AND RECOMMENDATIONS FOR FURTHER TESTS From table1. with regard to the VRs (variance ratios), we followed Hadi (2006) to look into LSE market efficiency. Comparable to the technique adopted in autocorrelations, we computed absolute deviations from 1 for the variance ratios, that is |VR(m,n)-1| to equate relative efficiency between present stock prices and previous prices, where VR(m,n) denoted return variance in m given period to return variance in n given period. As we can see, the returns in table 1 were uncorrelated and unpredictable over time and the long-term ratio to the short-term variances, as a fraction of unit time, was not equal to one. The significant VRs was greater than one, hence, a positive serial correlation existed between the independent variable on stock prices. Similarly, table 2 above gives the results for expected run numbers and actual run numbers. P-values for the randomness and for statistical significance were displayed in bold row. In the times of p=0.000, our hypothesis regarding the data was rejected strongly. However, for the time 2007 to 2012 regarding the data of LSE, the hypothesis two was rejected at 5%. Nonetheless, during the time of 1984-1991 the hypothesis two of randomness in our data could not be rejected for London equity market since p = 0.4382. In certain periods, the markets reject the random walk. After getting the regression results to examining whether London Stock Market is one market that is efficient in weak-form, we can make the deduction. Firstly, regression outcome of testing the level to which the market responded with signal on discharging public information have revealed that profitability information had an inverse influence on stock returns. Meanwhile, market value information had positive significant effect on stock returns. Profitability information, debt information, liquidity information, and market value information all had significant influence on stock returns. The results hinted that liquidity information and debt information can also be partly used to approximate the stock returns. Based on these findings, there was a sign that London Stock Exchange was not efficient its weak-form. The results agreed with Efficient Market Hypothesis, since it stated that, there is some kind of weak-form market efficiency in LSE: the current price completely incorporates information enclosed in previous history of prices. Efficient market stops investors from using information since prices have already fit in to take into account the information. The efficient markets cannot be said to gain from forecasting price movements since it`s very hard and unlikely following the reality that security prices adjust prior to a stockholder having a chance to trade and gain from the new information. As such, there is no cause to trust that prices can be too low or too high. Our findings are also in line with nzotta (2004) who found that markets are only efficient if the markets cannot allow stockholders to earn abnormal returns minus tolerating above-average risks. The study of Odoko (2004) also supports our findings since it claims that on average any active fund managers cannot be able to foresee the security prices good enough to outpace the market. Recommendations Even though, the results suggested that stock returns are foreseeable, in ways which are equivalent to efficient market theory, the level of predictability should be researched about. This is because, generally, previous researches are unclear concerning the level of predictability of returns. References List Bierman, H. (2010). An introduction to accounting and managerial finance a merger of equals. Singapore, World Scientific. Brigham, E. F., & EHRHARDT, M. C. (2013). Financial management: theory and practice. Mason, Ohio, South-Western. Hadi, Mahdi M. (2006). “Review of Capital Market Efficiency: Some Evidence from Jordanian Market”. International Research Journal of Finance and Economics, Issue 3, pp.13-27. Hadi, Mahdi. (2005). “Predictive Power of Earnings and Cash Flows and Dividends in Forecasting Future Cash Flows Evidences for USA and Kuwait”, European of Journal Scientific Research, Vol. 6. No. 5. (2013). Weak form efficiency tests. [S.l.], Grin Verlag. Nzotta, S. M. (2004). Money, banking and finance. Owerri; Hudson-Jude Nigeria publishers. Odoko, N. S. (2004). Financial markets in Nigeria. Abuja; CBN Publication. PALAN, S. (2007). The efficient market hypothesis and its validity in todays markets. München, GRIN Verlag. Paola Roncati. (2005). “Empirical Evidence on the Implications of the Efficient Market Hypothesis in the Swiss Equity Funds Market”. Diploma Thesis in Corporate Finance at the Swiss Banking Institute University of Zurich. Read More