The Test of Market Price for Weak Form Efficiency - Essay Example

Financial Economics The Test of Market Price for Weak Form Efficiency Registration: Module Leader: Workshop Due Introduction Weak form efficiency tests are concerned with whether an investor might consistently earn higher than normal returns based on knowledge of historical price sequences. Proving the weak form of efficiently can be difficult as there are infinite numbers of approaches to forecasting of future returns when considered against past returns. It should be noted that there are other tests which show efficiency or otherwise with regard to a given sequence or pattern of prices. An investor who is equipped with the knowledge of a test showing market inefficiency might expect to earn higher than normal return or face a market impediment preventing the investor from achieving such efficiency. Conversely, any test of market inefficiency is actually a combined test for the specific inefficiency and one of model showing normal returns in an efficient market. Hence, market efficiency cannot be completely discarded except if a person is sure that the right model for normal returns has been selected for the particular market. According to Teall (2012: 286) ‘the presence of costly information retrieval and processing and must lead to sequences of returns that are not normal’. As a result the market can never be efficient and certainly, the suitable standard for market efficiency tests should probably not be hypothetical perfectly efficient market. Therefore, if weak form efficiency exists then, the present prices reflect all historical information, and that includes all the information that might be given through the examination of past price movements and trading volume data (Besley & Brigham, 2012). There are a number of tests that can be used to test a market price for weak form efficiency, and these include the auto correlation test, unit root test, Dublin-Watson test, variance ratio test and runs test amongst others. Test Design Unit root test Through the use of regression analysis, given time series can be seen contain unit roots and these show that such data follow a random walk process and can e seen to be non stationary. Normally one can argue that series that are not stationary can be assumed to follow the random walk, and in such a situation that is observable, there is the likelihood for the weak form of market efficiency hypothesis. A series is stationary give that it has a constant mean, variance and covariance across time periods. In such situations, both the augmented Dickey-Fuller (ADF) and Philips-Perron (PP) tests for unit root are used in such analysis, and the two tests are used together considering that the coefficients gotten from the tests are roughly similar, and there is no autocorrelation among the variables provided that ADF test is a parametric tests for stationarity, whereas the PP are non parametric stationarity test. Mathematical connotation for the unit root test can be provided in the following format: k β0 + β1 Y t-1 + σt-1 + Ʋt t=1 The null and alternative hypothesis is normally in the following format: H0: non-stationarity, and hence there is the presence of unit root H1: Stationarity and this implies the absence of the unit root. Autocorrelation The test for autocorrelation in the study of weak form of market efficiency has been pursued by different researchers, and it is normally presented with the Breusch-Godfrey Lagrange multiplier test for sequential correlation in conjunction with the autocorrelation function gotten from the correlogram of the sample. Those tests probably try to estimate the link that exists between stock returns currently under review compared to periods in the past so as to determine whether the correlation are different from zero. Hence it can assist in the formation of both the null and the alternative hypothesis Ho: P1 = P2 =… = Pn= 0 H1: P1; P2; …Pn 0 The formula for getting the correlation coefficient P is as follows Pk = Cov(rt rt-k)/[√Var(rt)√Var (rt-k)] Pk = Cov(rt rt-k)/ Var (rt ). Prices Date Open High Low Close Volume Adj Close* Feb 13, 2015 6.82 7.04 6.80 6.98 51,047,000 6.98 Feb 12, 2015 6.47 6.64 6.40 6.54 48,283,900 6.54 Feb 11, 2015 6.22 6.27 6.01 6.23 46,114,000 6.23 Feb 10, 2015 6.73 6.75 6.17 6.23 51,372,700 6.23 Feb 9, 2015 6.36 6.75 6.36 6.72 41,322,800 6.72 Feb 6, 2015 6.63 6.78 6.35 6.54 75,085,000 6.54 Feb 5, 2015 7.10 7.36 6.95 7.11 39,523,300 7.11 Feb 4, 2015 7.08 7.46 6.94 7.26 62,439,100 7.26 Feb 3, 2015 6.81 7.31 6.80 7.29 68,528,900 7.29 Feb 2, 2015 5.98 6.45 5.98 6.41 43,008,700 6.41 Jan 30, 2015 6.02 6.16 5.87 6.01 91,355,600 6.01 Jan 29, 2015 6.30 6.67 6.12 6.40 53,699,100 6.40 Jan 28, 2015 6.76 6.82 6.52 6.56 62,789,800 6.56 Jan 27, 2015 7.10 7.65 7.07 7.45 30,410,600 7.45 Jan 26, 2015 7.25 7.36 7.18 7.23 23,375,300 7.23 Jan 23, 2015 7.54 7.62 7.25 7.25 25,852,700 7.25 Jan 22, 2015 7.75 7.75 7.47 7.65 35,646,800 7.65 Jan 21, 2015 6.93 7.42 6.91 7.35 35,449,500 7.35 Jan 20, 2015 7.15 7.19 6.68 6.81 43,658,900 6.81 Jan 16, 2015 7.12 7.25 7.00 7.06 41,294,200 7.06 Jan 15, 2015 6.92 7.06 6.81 6.91 51,095,400 6.91 Jan 14, 2015 6.56 6.92 6.43 6.82 42,181,900 6.82 Jan 13, 2015 6.72 6.97 6.65 6.78 40,215,700 6.78 Jan 12, 2015 6.85 6.86 6.49 6.57 37,266,500 6.57 Jan 9, 2015 6.74 7.19 6.62 7.06 44,348,900 7.06 Jan 8, 2015 6.55 6.95 6.43 6.76 52,140,000 6.76 Jan 7, 2015 6.14 6.35 6.11 6.20 46,394,900 6.20 Jan 6, 2015 6.12 6.19 5.79 6.02 38,809,700 6.02 Jan 5, 2015 6.33 6.35 6.05 6.07 55,582,000 6.07 Jan 2, 2015 6.99 7.01 6.71 6.76 40,834,100 6.76 Dec 31, 2014 7.20 7.38 7.02 7.30 22,416,700 7.30 Dec 30, 2014 7.38 7.44 7.20 7.27 22,473,100 7.27 Dec 29, 2014 7.31 7.55 7.27 7.27 24,561,200 7.27 Dec 26, 2014 7.55 7.65 7.26 7.39 21,051,800 7.39 Dec 24, 2014 7.67 7.72 7.46 7.60 14,856,300 7.60 Dec 23, 2014 7.67 7.81 7.54 7.71 29,835,000 7.71 Dec 22, 2014 7.23 7.53 7.07 7.45 40,299,000 7.45 Dec 19, 2014 7.09 7.13 6.90 7.11 46,918,100 7.11 Dec 18, 2014 7.21 7.26 6.72 6.90 63,316,300 6.90 Dec 17, 2014 6.56 6.86 6.44 6.70 78,401,100 6.70 Dec 16, 2014 6.27 6.64 6.01 6.28 63,118,800 6.28 Dec 15, 2014 6.98 7.00 6.22 6.26 68,480,600 6.26 Dec 12, 2014 7.37 7.40 7.10 7.11 38,806,500 7.11 Dec 11, 2014 7.40 7.65 7.34 7.42 51,107,400 7.42 Dec 10, 2014 7.90 7.94 7.72 7.75 44,190,800 7.75 Dec 9, 2014 7.79 8.23 7.63 8.15 36,885,100 8.15 Dec 8, 2014 8.67 8.68 8.17 8.23 36,801,900 8.23 Dec 5, 2014 8.81 8.92 8.67 8.82 24,915,100 8.82 Dec 4, 2014 8.97 9.04 8.81 8.91 29,200,500 8.91 Dec 3, 2014 9.08 9.44 9.07 9.27 24,572,200 9.27 Dec 2, 2014 9.03 9.23 8.88 9.00 38,118,500 9.00 Dec 1, 2014 9.27 9.30 8.92 9.12 44,845,300 9.12 Nov 28, 2014 9.96 9.98 9.52 9.72 33,644,700 9.72 Nov 26, 2014 10.74 10.90 10.52 10.60 30,975,000 10.60 Nov 25, 2014 11.08 11.13 10.35 10.39 58,110,000 10.39 Nov 24, 2014 11.10 11.11 10.43 10.50 60,514,700 10.50 Nov 21, 2014 10.13 11.00 10.08 10.84 76,088,100 10.84 Nov 20, 2014 9.76 9.95 9.49 9.71 41,678,200 9.71 Nov 19, 2014 9.66 9.71 9.34 9.49 41,561,900 9.49 Nov 18, 2014 9.23 9.61 8.80 9.42 67,400,200 9.42 Nov 17, 2014 9.91 9.94 9.27 9.33 49,456,200 9.33 Nov 14, 2014 9.61 10.04 9.51 9.95 63,670,600 9.95 Nov 13, 2014 10.62 10.68 10.09 10.20 40,628,900 10.20 Nov 12, 2014 10.66 11.03 10.52 10.56 31,824,800 10.56 Nov 11, 2014 10.35 10.66 10.25 10.65 36,556,300 10.65 Nov 10, 2014 10.82 10.90 10.53 10.62 34,163,900 10.62 Reference List Besley, S., & Brigham, E. F. 2012. Principles of finance. Mason, Ohio, South-Western Cengage Learning. Teall, J. L. 2012. Financial trading and investing. [S.l.], Academic Press. http://www.sciencedirect.com/science/book/9780123918802. Read More