Impact of Value Creation on Stock Prices: A Study of Amazon.Com, Inc






Esha Jain (1)
Manish Madan (2)
Sonia Singh (3)


(1) Dr. Esha Jain, School of Management,
G. D. Goenka University, Gurgaon
(2) Prof. (Dr.) Manish Madan,
Rukmini Devi Institute of Advanced Studies, Rohini, Delhi
(3) Dr. Sonia Singh, School of Business (Adjunct Faculty)
Al Falah University, Dubai

Correspondence:
Dr. Sonia Singh, School of Business (Adjunct Faculty)
Al Falah University, Dubai
Email: sonia23singh@gmail.com

Abstract

The handwriting has been on the wall for a long time, but a lacklustre economy and ongoing financial turmoil have underscored the broad trend that has fundamentally altered how private equity investors make money. Private equity firms can no longer rely solely on the power of leverage and ever-expanding price-earnings multiples to generate superior returns. More private equity firms wielding more capital have bid up acquisition prices, putting pressure on potential investment gains. In this study, one of the most common indicators, Relative Strength Index (RSI) was used to analyse the market movements of Amazon.com, Inc. over a period of the last 290 days. As the study describes the existing price movements of the selected company, the research design followed was descriptive and analytical research design. The company was chosen completely on the basis of convenient sampling technique which is non-probability in nature. This study is significant for investors and traders as it leads to identify the level of price movement that further helps in understanding buying and selling situations in the market by identifying support and resistance levels.

Key words: Market movements, relative strength index, stock exchange, value creation.

1. Introduction
Value creation is performance of actions that increase the worth of goods, services or even a business. Many business operators now focus on value creation both in the context of creating better value for customers purchasing its products and services, as well as for shareholders in the business who want to see their stake appreciate in value. Value creation is a corporation's raison d'être, the ultimate measure by which it is judged. In the 1990s, the main emphasis of executives was on creating value for shareholders-a value that was reflected in movements of the company's stock price. But measures based on stock market values are subject to the same wild fluctuations as the market itself. In a rising tide, all boats get raised. But when macroeconomic changes force up markets generally, it does not mean that the value of each individual company in that market has changed similarly. Markets are moved by sentiment that has little to do with the underlying value of individual corporations.(1) Amazon strives to be Earth's most customer-centric company where people can find and discover virtually anything they want to buy online. By giving customers more of what they want - low prices, vast selection, and convenience - Amazon continues to grow and evolve as a world-class e-commerce platform. It is the largest Internet-based retailer in the United States.(2) Founded by Jeff Bezos, the Amazon.com website started in 1995 as a place to buy books because of the unique customer experience the Web could offer book lovers. Bezos believed that only the Internet could offer customers the convenience of browsing a selection of millions of book titles in a single sitting. During the first 30 days of business, Amazon fulfilled orders for customers in 50 states and 45 countries - all shipped from his Seattle-area garage.(3) Amazon's evolution from Web site to e-commerce partner to development platform is driven by the spirit of innovation that is part of the company's DNA. The world's brightest technology minds come to Amazon.com to research and develop technology that improves the lives of shoppers and sellers around the world. In 2015, Amazon surpassed Walmart as the most valuable retailer in the United States by market capitalization. (4)

Amazon's stock is listed on NasdaqGS. The NASDAQ Stock Market, commonly known as the NASDAQ, is an American stock exchange. It is the second-largest exchange in the world by market capitalization, behind only the New York Stock Exchange. On July 1, 2006, the NASDAQ National Market was renamed the NASDAQ Global Market. In conjunction with this, NASDAQ created the new NASDAQ Global Select Market, a segment of the NASDAQ Global Market with the highest initial listing standards of any exchange in the world. (5)

Footnotes
1. http://www.economist.com/node/14301714
2. Jopson, Barney (July 12, 2011). "Amazon urges California referendum on online tax". Financial Times.
3. https://www.linkedin.com/company/amazon
4. http://www.nytimes.com/2015/08/16/technology/inside-amazon-wrestling-big-ideas-in-a-bruising-workplace.html?_r=0
5. http://www.nasdaq.com/about/Top_Tier_Splash.stm

2. Review of Literature
Errunza, V. R., & Losq, E. (1985) investigated the behaviour of stock prices for a group of well-established and newly emerging LDC securities markets and the derived results suggested that the probability distributions to be consistent with a lognormal distribution with some securities exhibiting non-stationary variance. LDC markets, even though not as efficient as major DC markets, are quite comparable to the smaller European markets and the behaviour of security prices as reported in their study appears to be generalizable for the heavily traded segments of LDC markets.

Jegadeesh, N., & Titman, S. (1993) documented that strategies which buy stocks that have performed well in the past and sell stocks that have performed poorly in the past generate significant positive returns over a 3 to 12 months holding period. They found that the profitability of those strategies (Returns of Relative Strength Portfolios) were not due to their systematic risk or to delayed stock price reactions to common factors. The returns of the zero-cost winners minus losers' portfolio were examined in each of the 36 months following the portfolio formation date. With the exception of the first month, the portfolio realized positive returns in each of the 12 months after the formation date. However, the longer term performances of these past winners and losers revealed that half of their excess returns in the year following the portfolio formation date dispatch within the following two years.

Pruitt, S. W., & White, R. E. (1988) attempted to directly determine the profitability performance of a multi-component technical trading system incorporating price, volume, and relative strength indicators on individual security issues. The system they tested, to which they had given the acronym CRISMA to represent its component parts (Cumulative Volume, Relative Strength, Moving Average), is completely ex-ante in nature and outperformed the market over a significant interval of time, even after adjusting for problems of trade timing and risk, and after allowing for round-trip transaction costs up to 2% per security trade.

Tsaih, R., Hsu, Y., & Lai, C. C. (1998) presented a hybrid AI (artificial intelligence) approach to the implementation of trading strategies in the S&P 500 stock index futures market. The hybrid AI approach integrates the rule-based systems technique and the neural networks technique to accurately predict the direction of daily price changes in S&P 500 stock index futures. Based upon this hybrid AI approach, the integrated futures trading system (IFTS) was established and employed to trade the S&P 500 stock index futures contracts. Empirical results also confirmed that IFTS outperformed the passive buy-and-hold investment strategy during the 6-year testing period from 1988 to 1993.

Amit, R., & Zott, C. (2000) explored the theoretical foundations of value creation in e-business by examining how 59 American and European e-businesses became publicly traded corporations create value and observed that in e-business, new value can be created by the ways in which transactions are enabled. Developed a model based on study, they suggested that the value creation potential of e-businesses hinges on four interdependent dimensions, namely: efficiency, complementarities, lock-in, and novelty. They also found that no single entrepreneurship or strategic management theory can fully explain the value creation potential of e-business.

Gunasekarage, A., & Power, D. M. (2001) analysed the performance of one group of these trading rules using index data for four emerging South Asian capital markets (the Bombay Stock Exchange, the Colombo Stock Exchange, the Dhaka Stock Exchange and the Karachi Stock Exchange) and examined the implications of the results for the weak form of the efficient market hypothesis. The findings indicated that technical trading rules have predictive ability in these markets and rejected the null hypothesis that the returns to be earned from studying moving average values are equal to those achieved from a naive buy and hold strategy.

Hameed, A., & Kusnadi, Y. (2002) analysed that the momentum returns of more than 1 percent per month is observed when applied to less diversified portfolios consisting of firms with small market capitalization or high volume of trade, suggesting that price momentum is related to firm specific factors.

Wong, W. K., Manzur, M., & Chew, B. K. (2003) focused on the role of technical analysis in signalling the timing of stock market entry and exit. Test statistics were introduced to test the performance of the most established of the trend followers, the Moving Average, and the most frequently used counter-trend indicator, the Relative Strength Index. Using Singapore data, the results indicated that the indicators can be used to generate significantly positive return. It was also found that member firms of Singapore Stock Exchange (SES) tend to enjoy substantial profits by applying technical indicators and concluded it as the main reason why most member firms do have their own trading teams that rely heavily on technical analysis.

Wang, C. (2004) examined among national stock prices of four Asian Newly Industrializing Countries stock markets - Taiwan, South Korea, Singapore and Hong Kong - in models incorporating the established markets of Japan, USA, UK and Germany. The results consistently appear to suggest the relatively leading role of all established markets in driving fluctuations in the NIC stock markets. In other words, all established markets and Hong Kong, consistently were the initial receptors of exogenous shocks to the (long-term) equilibrium relationships and the other NIC markets, particularly the Singaporean and Taiwanese markets had to bear most of the burden of short-run adjustment to re-establish the long-term equilibrium relationship. In comparison to all other NIC markets, Taiwan and Singapore appear as the most endogenous, with Taiwan providing evidence of its short-term vulnerability to shocks from the established markets.

Pan, R. K., & Sinha, S. (2007) analysed the cross-correlation matrix C of stock price fluctuations in the National Stock Exchange (NSE) of India to investigate the universality of the structure of interactions in different markets and found that this emerging market exhibits strong correlations in the movement of stock prices compared to developed markets, such as the New York Stock Exchange (NYSE). This showed the dominant influence of a common market mode on the stock prices. By comparison, interactions between related stocks, were found to be much weaker. This lack of distinct sector identity in emerging markets was explicitly shown by reconstructing the network of mutually interacting stocks. Spectral analysis of C for NSE revealed that the few largest eigenvalues deviate from the bulk of the spectrum predicted by random matrix theory, but they were far fewer in number compared to, e.g., NYSE. They showed this due to the relative weakness of intra-sector interactions between stocks, compared to the market mode, by modelling stock price dynamics with a two-factor model. They also suggested that the emergence of an internal structure comprising multiple groups of strongly coupled components is a signature of market development.

3. Objectives of the Study
The main objective of the study is to do relative strength analysis, with the help of RSI indicator, of a particular scrip to interpret buying and selling conditions in the market. Also the objective of the study is to analyse price movements over a period of last 290 days.

4. Research Methodology
The study aims at analysing the price movements of Amazon.com, Inc. over a period of last 290 days and as the study describes the existing price movements of the selected company, the research design followed was descriptive and analytical research design. The company was chosen completely on the basis of convenient sampling technique which is non-probability in nature. This study is significant for investors and traders as it leads to identify the level of price movement that further helps in understanding buying and selling situations in the market by identifying support and resistance levels. To achieve the desired objective, the daily share price movements of the selected company was absorbed for 290 days, i.e. from 1st April 2015 to 15th January 2016. As the company is listed on NasdaqGS (NASDAQ Global Select Market) so the data was collected from the website of Nasdaq and Bloomsberg. After that, the closing prices of share prices were taken and the future price movement was analysed using Relative Strength Index Indicator of technical analysis. Data was collected as available on Nasdaq and Bloomsberg website as on 16th January 2016-evening.

5. Data Analysis and Interpretations
The data was analysed by using Relative Strength Analysis, the most common and reliable indicator of technical analysis of stock markets. Relative Strength Index (RSI) is a popular momentum oscillator developed by J. Welles Wilder Jr. It is not to be confused with relative strength, which compares a stock's price performance to that of an overall market average, such as the S&P 500. Instead, the RSI analyses the recent performance of a security in relation to its own price history. RSI is a valuable tool to determine overbought/oversold levels. The Relative Strength Index compares upward movements in closing price to downward movements over a selected period. Wilder originally used a 14 day period, but 7 and 9 days are commonly used to trade the short cycle and 21 or 25 days for the intermediate cycle. The RSI value will always move between 0 and 100; the value will be 0 if the stock falls on all 14 days, and 100, if the price moves up on all the days. This implies that the RSI can also be used to identify the overbought/oversold levels in a counter. As suggested by J Welles Wilder, the developer of this indicator, most technical analysts consider the RSI value above 70 as 'overbought zone' and below 30 as 'oversold zone'. However, investors and traders need to adjust these levels according to the inherent volatility of the scrip. It is computed on the basis of the speed and direction of a stock's price movement. This means that the RSI indicator only measures the stock's internal strength (based on its past) and should not be confused with its relative strength, that is compared with other stocks, market indices, sectoral indices, etc.

Table 1 shows the Relative Strength Index for each day on the basis of 14-day RSI technique from 1st April 2015 to 15th January 2016.

Table 1: Showing Relative Strength Analysis on the basis of 14-day RSI





In this paper, signals are only tak en in the direction of the trend with the following conditions:
• Go long, in an up-trend, when RSI falls below 35 and rises back above it.
• Go short, in a down-trend, when RSI rises above 65 and falls back below it.

In the column of 14-day RSI in Table 1, the red cells show oversold zones, green cells show overbought zones and yellow cells show hold position.

According to Wilder, divergences signal a potential reversal point because directional momentum does not confirm price. A bullish divergence occurs when the underlying security makes a lower low and RSI forms a higher low. RSI does not confirm the lower low and this shows strengthening momentum. A bearish divergence forms when the security records a higher high and RSI forms a lower high. RSI does not confirm the new high and this shows weakening momentum.

Chart 1: Showing Relative Strength Analysis with Support & Resistance Levels

RSI forms patterns, such as triangles or head and shoulders tops and bottoms. Breakouts from these patterns on the daily chart often precede the price breakout by one or two days -- providing the swing trader valuable advance notice. Chart 1 shows Amazon.com, Inc. with a bullish divergence in the first month of financial year (April 2015) showing the stock in overbought zone on 24th April 2015. Then suddenly for a week from 30.04.2015 to 07.05.2015, the stock formed a bearish divergence to some extent for saving the stock from overbought zone. After two days the stock again came in normal mode. Then, the stock price moved to new high from mid-July to mid-August 2015 as well as RSI formed a bullish divergence which further leads to crossing of resistance level, showing overbought zones.

But on 20th August 2015, suddenly all the investors start selling the shares of the company and the shares came in oversold zones within two trading days and the scrip gave clear indication of buying as it is expected that prices will definitely increase in the near future. During the above mentioned two-days, the prices of the shares fall by US$69.55 from US$532.92 to US$463.37 per share. The prices fell down suddenly during this period due to the news spread all over about the 'brutal' work treatment in Amazon which became headlines of leading newspapers and news channels viz. CNN Money(6), New York Times(7), BBC News(8,9), Washington Post(10), etc.

The indication of buying of shares as shown by RSI values comes to practical ground when on 26th August 2015, the share price again rose to US$500.77 per share and US$518.37 per share on 27th August 2015. The prices of the shares moved to US$548.39 per share till 21st September 2015 which generated huge profits for the investors who invested on those two trading days near 20th August 2015. During this period, the RSI values formed various bullish and bearish divergences but all trading were in the range of 35 and 65. Then on 23rd of October 2015, the RSI values again showed overbought zones due to high demand of investors over previous days. The stock was in overbought zones till around 1st December 2015 and then the prices moved to normal position. But, now since 7th January 2016, the stock is showing in oversold zones, i.e. the investors are selling its shares in a huge amount. On the last trading day of period concerned i.e. on 15th January 2016, the prices of the shares fell by US$22.82 per share.

6. http://money.cnn.com/2015/08/17/technology/amazon-nytimes/
7. http://www.nytimes.com/2015/08/16/technology/inside-amazon-wrestling-big-ideas-in-a-bruising-workplace.html?_r=0
8. http://www.bbc.com/news/business-33957484
9. http://www.bbc.com/news/magazine-33988479
10. https://www.washingtonpost.com/news/the-switch/wp/2015/08/17/is-it-really-that-hard-to-work-at-amazon/

6. Conclusion
The study was done over a period of 290 days with the help of RSI indicator out of which the trading was opened for 194 days, rest of the days were weekends and other holidays on which stock market was closed. From the study, it was analysed that the stock prices of Amazon.com, Inc. over a said period of time was moved from a period low of US$368.34 on 1st April 2015 to a period high of 696.44 on 29th December 2015 which shows a huge positive change in market capitalisation. It was further concluded that business begins with value creation. It is the purpose of the institution: to create and deliver value in an efficient enough way that it will generate profit after cost because value creation is the starting point for all businesses, successful or not; it's a fundamental concept to understand as proved by the analysis, effect of timely news and data shown.

References
Amit, R., & Zott, C. (2000). Value creation in e-business. INSEAD.
Errunza, V. R., & Losq, E. (1985). The behavior of stock prices on LDC markets. Journal of Banking & Finance, 9(4), 561-575.
Gunasekarage, A., & Power, D. M. (2001). The profitability of moving average trading rules in South Asian stock markets. Emerging Markets Review, 2(1), 17-33.
Hameed, A., & Kusnadi, Y. (2002). Momentum strategies: Evidence from Pacific Basin stock markets. Journal of financial research, 25(3), 383-397.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of finance, 48 (1), 65-91.
Pan, R. K., & Sinha, S. (2007). Collective behavior of stock price movements in an emerging market. Physical Review E, 76(4), 046116.
Pruitt, S. W., & White, R. E. (1988). The CRISMA trading system: Who says technical analysis can't beat the market?. The journal of portfolio management, 14(3), 55-58.
Tsaih, R., Hsu, Y., & Lai, C. C. (1998). Forecasting S&P 500 stock index futures with a hybrid AI system. Decision Support Systems, 23(2), 161-174.
Wang, C. (2004). Relative strength strategies in China's stock market: 1994-2000. Pacific-Basin Finance Journal, 12(2), 159-177.
Wong, W. K., Manzur, M., & Chew, B. K. (2003). How rewarding is technical analysis? Evidence from Singapore stock market. Applied Financial Economics, 13(7), 543-551.