Purpose of this study
The index of NASDAQ-100 comprises of hundred largest international and domestic non-financial enterprises listed on the Stock Market of NASDAQ on the basis of market capitalization. This Index reflects companies from major industrial groups which include software, hardware, retail/wholesale, biotechnology, and telecommunications. However, it contains no financial security of the companies. NASDAQ 100 Index incepted in January 1985 and is a current representation of the most colossal non-financial international and domestic securities featured on The NASDAQ Stock Market determined by market capitalization (Neifar, Halioui, & Ben Abdelaziz, 2016).
The latest trend of this stock market is unconventional in various manners. Stock prices of technology companies have dwindled enormously for some time when conventional firms have been experiencing certain stability (Biktimirov & Xu, 2019). The post-market performance of several initial public offerings (IPOs) has been astonishing with substantial positive returns followed by similarly humongous declines. This paper’s objective is to examine the facts and traits of the present market in order to comprehend the nature and reasons of what has taken place.
NASDAQ is the major provider of clearing, reading, technical information, listing, public companies and information services with significant presence in over six continents. Owing to its multifaceted solution portfolio, NASDAQ facilitates the customers to optimize, plan and carry out their commercial vision with intense determination. It utilizes technologies that uphold transparency and knowledge of navigation in international capital markets. Being the creator of the very first electronic-based stock market, NASDAQ’s technology supports almost seventy marketplaces across fifty countries and one out of ten transactions of world securities. Numbers of companies accommodated by NASDAQ go beyond 3,500 amounting to $9.5 trillion market value. It further provides services to ten thousand corporate clients.
NASDAQ-100 is the refined market capitalization base index. The index value is tantamount to index shared weight’s aggregate value, called as Index shares, of per Index securities that are multiplied by per security’s Last sale cost and dividing the same by index’s divisor. The divisor is imperative for measuring aggregate value in ascending order magnitude that is better suitable for reporting of Index purposes. Similarly, the latest Last Sale Price is utilized if security trading is ceased in the primarily listed market prior to the opening of the market. On 31st January 1985, the index started off with an adjusted base value at 125.00.
The present research is based on NASDAQ-100 closing values and the number of shares sold (volume) during a period of 01-03-2017 to 11-02-2019. From the Yahoo Finance website, the data was retrieved. Table 2 (Appendix) presents the collected data along with weighted Fisher’s price index for the time period of 01-03-2017 to 11-02-2019.
The histogram with frequency distribution provides an image of the closing prices ($) of NASDAQ-100 in from 01-03-2017 to 11-02-2019. This sample of NASDAQ-100 closing prices was taken from Yahoo-Finance website. It can be noted that the closing prices ($) of NASDAQ-100 were clustered around the $7000 mark from 01-03-2017 to 11-02-2019. However, closing prices varied from $ 5436.23 to $ 7654.55 in the time frame of the research. However, a significant number of observations were discovered where the closing prices of NASDAQ-100 was higher than the average closing price of $ 6551.97.
Figure 1: Histogram of Closing prices ($) of NASDAQ-100 from 01-03-2017 to 11-02-2019
Figure 2: Number of Shares sold & Closing prices ($) of NASDAQ-100 from 01-03-2017 to 11-02-2019
For more information on the closing market prices of NASDAQ-100, the scholar noted the statistics of the sample in the above table. The lowest closing price is noted to be $ 5436.23 and the top closing price was up to $ 7654.55 on 01-08-2018. Median, the average, gives the closing price of NASDAQ-100 was less than $6605.57 in 50% of the sample observations from 01-03-2017 to 11-02-2019. The arithmetical average, another measure of the central tendency, is more than the median at $ 6551.97. Because the average is affected by the extreme closing prices, where it is more than $ 6949.99 and the median is not. The median is the best measure of the average closing price for NASDAQ-100. The average closing price is calculated as $ 6551.97, but, the standard deviation of $615.52 suggests the average closing of NASDAQ-100 was not always close to the average closing price.
Table 1: Descriptive statistics of NASDAQ-100 from 01-03-2017 to 11-02-2019
The summary of “Five Number ” is also included in table 1. Analysis of the sample in quarters yielded that 25% of the observations of the sample with closing prices less than $ 5988.60 is in the first quartile. Top 25% of the closing prices of were more than $ 6949.99 is in the third quartile. As a result, NASDAQ-100 closing prices in the middle 50 percent fluctuated by $ 961.39. It can be deduced that individuals who bought stocks at the average price have a reasonable agreement of $ 6551.97 in the time period from 01-03-2017 to 11-02-2019. However, individuals who spent more than $ 6949.99 bought shares suffered a loss.
Figure 3: Fishers Weighted Index numbers for NASDAQ-100 from 01-03-2017 to 11-02-2019
The average volume of 43143361043.92 over a period of time from 01-03-2017 to 11-02-2019 is the total amount of traded stocks in NASDAQ-100, divided by 25 months, the length of the period. Fishers Weighted Index numbers for NASDAQ-100 from 01-03-2017 to 11-02-2019 is calculated with 01-01-2018 as the base month (Fisher, Weaver, & Webb, 2010). Figure 2 represents the trend of indexed closing values for NASDAQ-100 in the time frame of the present study. The weighted index was calculated with closing price and a number of stocks sold in the time frame of 01-03-2017 to 11-02-2019 (Kharatyan, Lopes, & Nunes, 2017).
The Fisher’s index is calculated for a single commodity as the geometrical mean of Paasche’s and Laspeyre’s price indices . The advantage of Fisher’s index is that the calculation is based on closing price and volume of the base month (01-01-2018) as well as the current months. The weighted trend of US market can be interpreted from Fisher’s price index values. From Figure 3 presenting the Fishers Weighted Index numbers for NASDAQ-100 from 01-03-2017 to 11-02-2019, it can be observed that the NASDAQ market fluctuated a lot after 01-01-2018 till 01-12-2018 (Baltussen, van Bekkum, & Da, 2018). The market volatility throughout 2018 can be identified from the trend of Fishers Weighted Index numbers (Allen, 2017).
Conclusion
At an integrated level, there are testimonies to show that stock fluctuations is the major harbinger of economic activities and this volatility is frequently related to the recession. It is intriguing to know if the current growth extent of NASDAQ volatility heralds a drastic technological slowdown in the economy. Apparently, no one has till date investigated the interrelation between the industry’s volatility in stock return and the real industrial activities (?bikowski, 2015). If this link gets established then it may be inferred that huge current volatility levels in a technological segment of the stock market foretell a gigantic recession. It is widely known that trading volatility and volume occur simultaneously, further NASDAQ technological stocks’ trading volume remains higher when NASDAQ’s volatility escalates. Considering volatility is intertwined with dissimilarities in beliefs, it is thus not astonishing to note that volatility and volume occur jointly. In the nutshell, the scholar is still oblivious of the volatility implications witnessed at NASDAQ and the significance it holds for the overall macroeconomics (Fisher, Martineau, & Sheng, 2016). Similarly, it remains elusive to me what could be necessary steps or solutions for this scenario. However, the scholar reckons that more studies can be conducted on these valuable data.
References
Allen, R. G. D. (2017). Index Numbers in Economic Theory and Practice. Routledge, New York, 1st Edition. https://doi.org/10.4324/9780203788677
Baltussen, G., van Bekkum, S., & Da, Z. (2018). Indexing and stock market serial dependence around the world. Journal of Financial Economics. https://doi.org/10.1016/j.jfineco.2018.07.016
Biktimirov, E. N., & Xu, Y. (2019). Asymmetric stock price and investor awareness reactions to changes in the Nasdaq 100 index. Journal of Asset Management, Palgrave Macmillan UK. https://doi.org/10.1057/s41260-019-00108-6
Fisher, A. J., Martineau, C., & Sheng, J. (2018). Macroeconomic Attention and the Stock Market (SSRN Scholarly Paper No. ID 2703978). Rochester, NY: Social Science Research Network. Retrieved from https://papers.ssrn.com/abstract=2703978
Fisher, L., Weaver, D. G., & Webb, G. P. (2010). Removing Biases in Computed Returns: An Analysis of Bias in Equally-Weighted Return Indexes of REITs (SSRN Scholarly Paper No. ID 1625830). Rochester, NY: Social Science Research Network. Retrieved from https://papers.ssrn.com/abstract=1625830
Kharatyan, D., Lopes, J., & Nunes, A. (2017). Determinants of return on equity: evidence from NASDAQ 100. XXVII Jornadas Hispano-Lusas Gestión Científica. Retrieved from https://bibliotecadigital.ipb.pt/handle/10198/14213
Neifar, S., Halioui, K., & Ben Abdelaziz, F. (2016). The motivations of earnings management and financial aggressiveness in American firms listed on the NASDAQ 100. Journal of Applied Accounting Research, 17(4), 397–420. https://doi.org/10.1108/JAAR-05-2014-0051
?bikowski, K. (March 2015). Using Volume Weighted Support Vector Machines with walk forward testing and feature selection for the purpose of creating stock trading strategy. Expert Systems with Applications, 42(4), 1797–1805. https://doi.org/10.1016/j.eswa.2014.10.001
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