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Item Anomalous behavior of the volatility of DJIA over the last century(Southern New Hampshire University, 2006) Hamid, Shaikh A.; Dhakar, Tej S.Show more This study explores month effects in terms of standard deviations of monthly and daily percentage changes of the Dow Jones Industrial Average. During the last century, the standard deviation of the monthly percentage changes of April (6.63%) is significantly higher than the standard deviations for the other months. The monthly standard deviations of daily percentage changes as a measure of volatility exhibit a slightly rising trend, peaking in October and are all significantly different from zero. The mean monthly standard deviation of daily percentage changes for October (1.08%) was the maximum and also significantly higher than the means of the other months. The DJIA became less volatile in terms of monthly as well as daily percentage changes during the second half of the last century compared to the first half. If we divide the data for the last century into decades, the thirties stand out as the most volatile period in terms of monthly as well as daily percentage changes. Based on both dimensions, the decades prior to 1940 experienced higher standard deviations compared to the subsequent decades. So it appeared that the stock market became more volatile in recent times – but that was in points, not in percentage terms.Show more Item Behavior of monthly total returns of U.S. government bonds : 1926-2007(American Society of. Business and Behavioral Sciences (ASBBS), 2010-03) Hamid, Shaikh A.; Habib, AbrahamShow more We explore for presence of monthly seasonality in monthly total returns of U.S. long term government bonds from January 1926 to December 2007. We test three types of effects with respect to monthly seasonality. We further partition the data into three sub-periods and explore monthly seasonality. In addition, we explore monthly seasonality based on Republican and Democratic presidencies. We look at the nature of monthly returns during contraction and expansion periods, as well as periods of crisis. The mean of monthly total returns of long term government bonds for the entire data set (0.47%) was significantly greater than zero. The mean of monthly returns of none of the months was significantly greater than the mean of the other eleven months stacked together. We find evidence of month effect with respect to variances of monthly returns. When we partition the data into three sub-periods, we do not find any discernible monthly seasonality. We also find the mean of monthly returns during the Republican presidencies to be significantly higher than during the Democratic presidencies. Government bond returns were on average significantly higher during contraction periods than during expansion periods. The Great Depression was good for the bond market; war periods were comparatively not as attractive for bond investing because governments tend to peg interest rates during such periods. Though not fully efficient, the U.S. long term government bond market exhibits a high degree of efficiency.Show more Item The behavior of the Consumer Price Index : 1913 to 2003(Southern New Hampshire University, 2005) Hamid, Shaikh A.; Dhakar, Tej S.Show more This paper analyzes the seasonality in the monthly consumer price index (CPI) over the period January 1913 to December 2003. We examine three types of month effects: if the mean of monthly CPI changes of the entire data set, and of a given month were significantly different from zero; if the mean of monthly CPI changes of a given month was different from the mean of the other months; and if the variance of the monthly CPI changes for a given month was different from the variance of the other months. The mean of monthly CPI changes for the entire data set (0.27%) was found to be significantly greater than zero. The means of monthly changes show a downward trend from September to December. When the data are sliced into three sub-periods, we find an increasing trend in the means and medians of monthly changes but a decreasing trend in the standard deviations of the monthly changes. The mean of monthly CPI changes during the Republican presidencies (0.15%) was significantly lower than during the Democratic presidencies (0.38%). A revised version of this paper has since been published in the journal Applied Economics. Please use this version in your citations.Show more Item The behavior of U.S. Producer Price Index : 1913 to 2004(Southern New Hampshire University, 2006) Hamid, Shaikh A.; Dhakar, Tej S.; Thirunavukkarasu, ArulShow more This paper analyzes the behavior of U.S. PPI over the period January 1913 to March 2004 using monthly “all commodities index” values. The mean of monthly percentage index changes for the entire data set (0.23%) was significantly greater than zero. January, July and November had mean monthly percentage changes which were significantly greater than the mean changes of the other months over the entire period. March, May and September had mean percentage changes significantly lower than the other months. We find that there is some periodicity to all commodities index. The mean of monthly commodities index changes during the Republican presidencies (0.08%) was significantly lower than the mean changes during the Democratic presidencies (0.38%) and so were the medians. We slice the entire data into three sub-periods. We find that though the means and medians have significantly increased over the three sub-periods, the standard deviations of the means have decreased. Granger causality tests reveal that while oil prices affected the all commodities index and the finished goods index, the causal relationship is not true the other way at the 99% significance level. The findings have implications for policy makers, analysts, investors, and manufacturers.Show more Item The behavior of US corporate bonds : 1926-2008(Southern New Hampshire University, 2010-03-09) Hamid, Shaikh A.Show more Item Can neural networks learn the Black-Scholes model? A simplified approach(Southern New Hampshire University, 2005) Hamid, Shaikh A.; Habib, AbrahamShow more Neural networks have been shown to learn complex relationships. It would be interesting to see if the networks can be trained to learn the nonlinear relationship underlying Black-Scholes type models. Interesting hypothetical questions that can be raised are: If option pricing model had not been developed, could a technique like neural networks have learnt the nonlinear form of the Black-Scholes type model to yield the fair value of an option? Could the networks have learnt to produce efficient implied volatility estimates? Our results from a simplified neural networks approach are rather encouraging, but more for volatility outputs than for call prices.Show more Item Monthly seasonality in U.S. long term corporate bonds(Allied Academies, 2010-04) Hamid, Shaikh A.Show more We explore monthly seasonality in high grade long term corporate bonds from January 1926 to December 2008. We test three types of month effects. In addition, we analyze the data based on Republican and Democratic presidencies. The mean of monthly total returns for the entire data set (0.50%) is significantly greater than zero. The mean return of January is significantly higher than the mean of the other eleven months stacked together; the mean of March is significantly lower. We find significantly higher or lower volatilities for some months compared to the other months. January experienced the highest mean monthly return, followed by a dip in February and March, and then an upward trend until January. The mean of monthly returns during the Republican presidencies (0.66%) is significantly higher than during the Democratic presidencies (0.33%). Though not fully efficient the U.S. corporate bond market exhibits a high degree of efficiency.Show more Item A new perspective on the anomalies in the monthly closings of the Dow Jones Industrial Average(Southern New Hampshire University, 2003) Hamid, Shaikh A.; Dhakar, Tej S.Show more This study explores three types of month effects in the Dow Jones Industrial Average: (a) for a given period, if the mean of monthly percentage changes of each month was different from zero, (b) for a given period, if the mean of monthly percentage changes for a month was different from the means of all the other months, and (c) for a given period, if the variance of the monthly percentage changes for a month was different from the variances of all the other months. For our entire data set (May 1896 to December 2002) we find that the means of monthly percentage changes of only July, August, January and December were significantly greater than zero (months put in descending order). But the means of none of these three months were significantly higher compared to the means of all the other months. With a mean percentage change of -1.25%, only September appears with significant negative returns. And this mean is significantly lower compared to the means of all the other months. In other words, for the entire data set, we have a negative September effect. Month effect with respect to variance (variance of monthly percentage changes for a month being significantly different from all the other months) was found for January, February and December (lower variances), and April (higher variance). When we look at the first half of the twentieth century versus the second half, we see more pronounced month effects in the second half - considering all three types of effects we analyze. December exhibited all three types of effects in this period. When we sub-divide the last century into four 25-year periods, we find more pronounced month effects in the last quarter than in the previous three quarters. When we sub-divide the data into 10-year periods, we do not find any consistent and discernible pattern. The month effect varies with the time period we consider and the type of effect we analyze. Though one would expect the DJIA stocks to be free from seasonal patterns since each one of them are closely followed by a large number of analysts, the existence of any type of month effect is surprising. However, given that no discernible pattern is detectable is a reflection of efficiency of the DJIA stocks to a large degree.Show more Item Philosophy and practice of Islamic economics and finance(Southern New Hampshire University, 2006) Hamid, Shaikh A.Show more An introductory paper intended for those who are uninitiated about Islamic economics and finance. (Library-derived description)Show more Item Price transmission between DJIA, S&P 500 Index, PPI and CPI(Southern New Hampshire University, 2006) Hamid, Shaikh A.; Thirunavukkarasu, Arul; Rajamanickam, MohanaShow more Our previous work on month effect in the DJIA, CPI and PPI led us to hypothesize that significant negative September effect that we found for the DJIA might have been caused by changes in the CPI and PPI. This led us to explore the nature of price transmission between the three (we add S&P 500 Index as well). Using VAR analysis and Granger causality analysis we find that the DJIA had a 2-month lagged impact on the CPI in the first two periods (1926-1945 and 1946-1972), and on the PPI in the second period (1946-1972); but in none of the three periods was the DJIA significantly impacted by the PPI or the CPI. For the period 1972-2003, the CPI and PPI were significantly unaffected by the DJIA and the S&P500 Index and also the DJIA and the S&P500 were also not affected significantly by the CPI and PPI. These results follow from both the VAR analysis and Granger causality tests.Show more Item Primer on using neural networks for forecasting market variables(Southern New Hampshire University, 2004) Hamid, Shaikh A.Show more Ability to forecast market variables is critical to analysts, economists and investors. Among other uses, neural networks are gaining in popularity in forecasting market variables. They are used in various disciplines and issues to map complex relationships. We present a primer for using neural networks for forecasting market variables in general, and in particular, forecasting volatility of the S&P 500 Index futures prices. We compare volatility forecasts from neural networks with implied volatility from S&P 500 Index futures options using the Barone-Adesi and Whaley (BAW) model for pricing American options on futures. Forecasts from neural networks outperform implied volatility forecasts. Volatility forecasts from neural networks are not found to be significantly different from realized volatility. Implied volatility forecasts are found to be significantly different from realized volatility in two of three cases. A revised version of this paper has since been published in the Journal of Business Research. Please use this version in your citations.Show more