By Sanjana Medipally

Introduction
The stock market is both efficient and competitive and in a competitive environment, there is no room for one single organization to be able to change the price of any individual stock. In regards to market efficiency in the stock market, the prices of individual stocks are reflected by the information available publicly. Hence the information available in the public domain should effectively ‘price-in’ any stock, this also implies that a single entity/individual cannot have a competitive advantage in the stock market unless they have access to undisclosed information – also known as insider trading (which is an illegal practice). From these two features of a stock market functioning, it can be concluded that the stock market provides an efficient allocation of potential capital gains which is derived from equity investment strategies. So with the absence of a competitive edge, there should be no potential investment strategies that make a person better off at the expense of another. But with the evolution of the internet and media – information has become increasingly transparent and dispersed and with this emerged the information cascades which lead to irregular market valuations.

Due to the herd mentality of the market, stock prices are bid up beyond their intrinsic value. When there is an abrupt rise in a particular stock price it indicates that the demand for these shares far exceeds the sellers. Thus, clouding the judgments of individual investors’ by making them believe that the stock is worth more than what it actually is. This rise in stock price is seen as a ‘positive signal’ in the information cascade, emphasizing that this stock is seen as a good value by other investors leading many other individual investors to follow the crowd and purchase the stock. This results in a further rise in prices of the stock. On the other hand, a ‘negative signal’ is sent to the investors when the particular stock price declines abruptly and is an inadequate investment which is to be sold (Prieto and Perote, 2017). Such beliefs have given rise to ‘momentum trading’ where some investors exploit sudden swings in the market to their advantage. Though a single individual cannot alter stock prices a ‘herd’ of irrational investors can.

The dot-com bubble- amongst many other bubbles- depicts the impact of information cascades and herding in the stock market. In the 90s and early 2000s, the many saw the impact the internet would have in generating a companies’ revenue which led to a wild speculation where public internet companies were sold on the stock exchange and companies which were not generating any revenues were abruptly valued high. Therefore sending ‘positive signals’ to the investors leading to further bloating for the bubble as stock market indices like NASDAQ reaching an all-time high (Valliere and Peterson, 2004). The dot-com bubble eventually burst like the others when the investors have understood that they were clouded by irrational herd mentality. This led to a decline in market prices of these companies causing huge losses to investors due to a speculation. By understanding the impacts of information cascades in a herd mentality, investors can clearly understand the causes of price changes in the stock market and also avoid making inadequate investment decisions.

Inside the Dotcom Bubble

With the creation of the World Wide Web the 90s mark the first steps of the internet network. But it was a tool which was reserved for connoisseurs because there were only 9% of the internet users among the American population at the begging of this period (Colombo, 2012). It is only from 1994 that the internet has democratized and arrived in the homes of the developed countries. The ‘dot-com’ companies were growing exponentially, especially in regions of the Silicon Valley. At the same time there was abundant capital available for the markets and indeed there were very accommodative monetary policies in the United States and Japan while the baby boomers were preparing for their retirement.

The change in the Internet market has occurred when Netscape – the first public browser was introduced in 1995 by Jim Clark. With this introduction, the company’s stock rose from $28 to $75 in one day and reached $2 billion in market capitalization. The start-ups in the IT and telecommunications sectors have realised that the Initial Public Offering (IPO) was a tremendous vector of growth. This was the beginning of the euphoria around the companies of the new information and communication technologies which was quickly referred to as the ‘new economy’. This has inspired some economists to speculate that we were in a ‘new economy’ in which inflation was virtually non-existent and where recessions were a relic of the past. According to this argument, the ‘Old Economy’ represented traditional brick-and motor businesses. The growth forecasts of the ‘new economy’ were around 10% per year in the telecommunications market which has been just opened up for competition (Goodnight and Green, 2010). The gains promised the investors while attracting the available capital. The increase in start-ups was facilitated by low interest rates and the development of venture capitalists, business angels, and incubators. In 1999 almost 500 IPOs took place and most of the internet start-ups were financed by bank loans and credits (Colombo, 2012).

Additionally, the turnover of the IT sector was stimulated by the adaptation of the computer systems in the year 2000 and the introduction of the euro as a single currency in Europe. The NASDAQ stock index was multiplied by 5 between July 1995 and September 2000 from 1006 to 5046 points. The ‘Dot-com’ companies were raising high amounts of capital, but these start-ups lacked a business model nor generated any profits but were celebrating their IPOs by spending millions of dollars. The valuation of companies was exaggerated in relation to their real turnovers along with disproportionate profitability forecasts in the medium term. It is in the early 2000s when investors realized that the dot-com fantasy has transformed into a classic speculative bubble. The US Federal Reserve has just increased its long-term interest rates six times in a row since 1999, raising them from 5% to 6.50% (Joosten, 2012). This rise in interest rates accompanies by the awareness of investors about the non-profitable ‘dot-com’ companies – the bubble burst on March 13, 2000. Within a few weeks, the NASDAQ index lost 40% of its value (M.Patterson and Sharma, 2007).

Herding, Cascades, and Momentums.

A human being is a social animal who is certainly clever but his cognitive powers are limited. We do not know how the world develops around us and especially in making market decisions like – to buy or sell, lend or borrow at what interest rates – human beings operate in the conditions of uncertainty. At times it may seem that a very important change is occurring and this change will rationalise paying higher prices for a particular set of assets. Hence in the 1990s, during the information technology revolution where the use of the internet was doubling every quarter, people assumed (rationally and reasonably) that the dot-com companies were going to make a great deal of money. The question here was how much money? This depended on how long and how fast the use of the internet would continue to increase. People involved in the markets have estimated the likely earnings of the dot-com companies and took a well-informed guess about how much the use of internet would increase (Miller, 2010). However, they avoided the fact that there was nothing like the internet before and no one can be sure of the increase in its usage. But with their estimates and guesses, individuals have bid up the stock market prices of dot-com companies. Though the use of internet has increased significantly, it did not increase as much as the people expected it would. It was then realised that the dot-com stock prices were too high as the growth rate of internet usage was slowing and people started selling them leading to a dot-com price crash. The bubble was burst.

As seen in the case of the dot-com bubble, speculative bubbles occur when asset prices steadily and unusually exceed their intrinsic values. Bubbles tend to burst and they happen over an extended period of time and cause great losses while generating financial crashes and risk contagion. Bubbles are therefore described as a special case of ‘contagion’ that change ordinary rules for evaluating risk and information. The causes of bubbles have been argued to lie in the psychological aspects of human behaviour such as overconfidence, cognitive biases, emotions, irrational beliefs or herding behaviour. In assessing risk, investors chose to avoid loss before making a gain but a bubble overturns this decision. New assumptions evolve in the investing community and information cascades occur amongst peers. These investors ‘ignore their private information and follow the crowd by imitating recent actions’ (Polock, Rindova, and Maggitti, 2008) of those who have achieved successes. To an extent, behavioural theorists argue that under certain conditions it is reasonable to follow the crowd who seem better informed, but bubbles are induced when there is an overflow of investors who support a popular enterprise despite having a lot of difference in their private information. This contagious movement is referred to as ‘herd behaviour’ which explains the abandonment of constraints due to the irrationality of crowds (Lux, 1995).

The theoretical models of herd behaviour either produces rational prices in efficient markets or leads to price bubbles and crashes. According to Hirshliefer et al herd behaviour sometimes produces rational prices because investors follow the same stock, the same sources of information and trade rationally in response to the acquired information. Given that prices do not respond immediately to training, many investors can trade profitably using the same piece of information leading to informed investors trading in the market though they are not imitating each other. This argument of information acquisition presents that herd behaviour can lead to prices moving towards their fundamental values. However, the herding tends to cease when the prices reveal the acquired information and hence there is no room for predicting the price change once the herding stops.

The informational cascade is where the investors have useful information by observing the actions of other investors. As the investors ignore their personal information and follow the ‘herd’ the investors are in an informational cascade leading to irrational price distortions away from their fundamental values. Coupled with information cascade is the role of momentum which leads to a development of the high-tech bubble (Redhead, 2008). Here the investors are divided into fundamentalists and followers (also known as herd traders). The former believed in reversing of stock prices towards their fundamental values while the latter believed that a direction of price movement would continue. In the late 1990s, most of the investors were herd traders who were trend following and this group of investors was dominant for several years. This was consistent with a strong momentum in the formation of the bubble as high technology stocks were highly vulnerable to momentum trading (or trend following).

It is also hypothesized that particular kind of stocks become popular for economic reason and investors still strive to purchase large holdings of these stocks. It is important to note the role of media hype in regards to the internet stocks during the bubble and the crash.

Herding to the Dotcom Bubble.

According to the study conducted by (Singh, 2012) the results show that herding for all stocks was higher during the time period of dot com bubble than in the previous periods and herding was higher for internet stocks compared to the other stocks during this period – this suggests that the mutual identity of the internet stocks could have been partially responsible for the high levels of herding. This can be concluded based on the study by (M.Patterson and Sharma, 2007) here herding was generally higher for internet stocks than that of non-internet stocks during this period. Also, these internet companies have experienced more buy herding in the bubble period and more sell herding during the crash period as compared to the non-internet firms.

But if we consider the overall herding intensity, this would disguise the direction of the herding. For instance the in the total period of the dot com bubble a herding intensity of 6.76% in the internet stocks does not inform us if the institutional investors were buying or selling in herds. The mean herding intensity during the bubble period was 7.26% with buy herding aggregated to 8.47% whereas sell herding amounted to only 3.62% (Singh, 2012). Hence it can be concluded that buy herding was more evident than sell herding in the bubble-period suggesting that institutional investors were herding larger set of stocks on the buy side instead of on the sell side during the bubble period. Conversely, in the post-bubble period the herding intensity has dropped to 6.48% with buy and sell herding at 3.99% and 8.56%, this shows that buy herding has reduced in the post-bubble period (Singh, 2012).

Also as mentioned above that herding is also driven by an informational cascade, it is safe to assume that herding will be more evident in younger stocks where the information is very scarce. But in the overall bubble period, the buy herding intensity was higher for new internet stocks than the older stocks. However, it is also likely that institutional investors were buying less of old stocks or selling these old stocks in order to buy more new stocks to exploit the returns from the internet companies. But in the after the crash period, the buy and sell herding was mostly occurring in the old stocks and this could be due to the realisation of the investors on the dotcoms which resulted in fewer new entrants of internet companies in the market.

Though the institutions were mostly chasing trends in prices in the daily horizon, the buy-side institutions bought stocks which were of average and below average performance in the overall period from 1998-2001 and proved that the ‘new economy’ sector was attractive despite its previous performances (Prieto and Perote, 2017). Conversely, the selling side of the market dumped stocks in the lowest return areas. On the other hand, it is important to pose some questions on if institutional herding during the dot com bubble had any impact on prices and if these price changes which are a result of herding were supported based on the information available. It has been deduced that the institutional herding had a large impact on the prices from the buy side and this was not completely based on the information. Alternatively, institutional sell herding is based on information (Singh, 2012).

It can be understood that herds develop and become dominant when there are substantial price movements in the markets and these herds are easily formed in the stocks which are ‘hot’. Throughout the bubble from January 1998 to March 2000, investors were the net buyers of these ‘new economy’ stocks. These investors range from banks, mutual funds, university endowments, foundations etc who crowded into the new economy stocks and bought in herds all stocks of the internet companies despite their size and past performances. Either these institutions were ignorant of the bubble or were greedy to generate profits.

Conclusion

Innovation can be the driving force of economic growth and sometimes they are also the main cause of cyclical fluctuations in an economy (Schumpeter, 1966). Most importantly, the internet enabled technologies to seem to have the characteristics of a fundamental technological innovation with a power to transform the global and economic development which can justify the ‘investors’ enthusiasm. In the early phases of a diffusion of a new innovation new firms emerge to exploit this new technology and investment, employment tends to expand in the respective industries. The demand for this internet technology which was derived the potential of the commercial internet implementation by public and private organization which reinforced the favourable internet climate in the 1990s from the newly created internet firms. These firms funded by venture capitalists claimed that internet-enabling technologies would change the structure of the stock market and the corporate landscape.

It can be seen that during the surge in prices from 1997 to 2000, institutional investors have herded into the internet stocks with comparatively high and increasing intensity. Institutions remained the net buyers of the internet stocks at the peak of the bubble. It also suggests that institutions as a group did not demonstrate the ability to time their investments in internet firms relative to the price peaking. Herding during the bubble, particularly buy herding has surpassed what can be explained by momentum trading because ‘it was present in prior return quintiles’ (Kallinterakis, Munir, and Radovic-Markovic, 2010). According to (Singh, 2012) the institutional herding of internet stocks was more than what can be explained by momentum trading strategies and during the surge in internet stock prices, institutional buying has led to temporary price pressures contributing to the bubble.

Though the dot com bubble burst in the 2000s, it seems like it has been gifted a second chance in this era of bitcoin and blockchain. Cryptocurrencies are now been subject to the same variety of rampant ‘irrational exuberance’ which has infected the dot-com era. In the recent times, cryptocurrencies have given rise to the speculative behaviour led by the ‘Bit-coin’ currency. As the crypto industry is mobilizing it has been difficult to predict its trajectory and one of the ways to project this trajectory is to consider the dot-com bubble as an example to validate the future of cryptocurrencies.

References

Colombo, J. (2012). The Dot-com Bubble. [online] Thebubblebubble. Available at: http://www.thebubblebubble.com/dot-com-bubble/ [Accessed 3 May 2018].

Goodnight, G. and Green, S. (2010). Rhetoric, Risk, and Markets: The Dot-Com Bubble. Quarterly Journal of Speech, 96(2), pp.115-140.

Hirshleifer, D. and Teoh, S. (2002). Herd Behavior and Cascading in Capital Markets: A Review and Synthesis. SSRN Electronic Journal. [online] Available at: https://onlinelibrary.wiley.com/doi/pdf/10.1111/1468-036X.00207 [Accessed 5 May 2018].

Joosten, N. (2012). The effects of the Dot Com bubble and the Credit Crisis on leverage ratios of US non-financial firms. [ebook] Netherlands: Tilburg University. Available at: http://arno.uvt.nl/show.cgi?fid=127700 [Accessed 7 May 2018].Schumpeter, Innovation and Economic Growth (Cambridge, MA, Harvard University Press, 1966).

Kallinterakis, V., Munir, N. and Radovic-Markovic, M. (2010). Herd Behaviour, Illiquidity and Extreme Market States. Journal of Emerging Market Finance, [online] 9(3), pp.305-324. Available at: https://www.researchgate.net/publication/254089796_Herd_Behaviour_Illiquidity_and_Extreme_Market_StatesEvidence_from_Banja_Luka [Accessed 7 May 2018].

Levenson, N. (2018). Popping The Bubble: Cryptocurrency vs. Dot Com – Hacker Noon. [online] Hacker Noon. Available at: https://hackernoon.com/popping-the-bubble-blockchain-and-cryptocurrency-7130156f91b2 [Accessed 12 May 2018].

Lux, T. (1995). Herd Behaviour, Bubbles and Crashes. The Economic Journal, [online] 105(431), p.881. Available at: https://www.jstor.org/stable/2235156 [Accessed 5 May 2018].

 Miller, R. (2010). Morals in a Market Bubble. University of Dayton Law Review, [online] 35. Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1626019 [Accessed 17 May 2018].

Patterson, D. and Sharma, V. (2007). Did Herding Cause the Stock Market Bubble of 1998-2001?. [ebook] Michigan: University of Michigan. Available at: https://pdfs.semanticscholar.org/f04d/2d5ae95c23b1e4aafa4089c8dac159de2b26.pdf [Accessed 4 May 2018].

Morris, C. (2018). How the Bitcoin Bubble Is Mirroring the Dot-Com One, Only Happening 15 Times Faster. [online] Fortune. Available at: http://fortune.com/2018/03/20/morgan-stanley-bitcoin-bubble-mirror-dot-com-bubble-cryptocurrency/ [Accessed 8 May 2018].

Morgenstern, O. and Schumpeter, J. (1940). Business Cycles: A Theoretical, Historical, and Statistical Analysis of the Capitalist Process. Journal of the American Statistical Association, 35(210), p.423.

 Prieto, P. and Perote, J. (2017). Agents’ Behavior in Market Bubbles: Herding and Information Effects. Economics World, [online] 5(1). Available at: https://www.davidpublisher.org/Public/uploads/Contribute/581716ad46771.pdf [Accessed 5 May 2018].

 Redhead, K. (2008). A BEHAVIOURAL MODEL OF THE DOT.COM BUBBLE AND CRASH. [ebook] Coventry University Business School. Available at: http://cw.routledge.com/textbooks/9780415428620/downloads/model.pdf [Accessed 5 May 2018].

Shiller, R. (1984). Stock Prices and Social Dynamics. Brookings Papers on Economic Activity, [online] 2. Available at: https://www.brookings.edu/wp-content/uploads/1984/06/1984b_bpea_shiller_fischer_friedman.pdf [Accessed 6 May 2018].

Singh, V. (2012). Did institutions herd during the internet bubble?. Review of Quantitative Finance and Accounting, 41(3), pp.513-534.

Sanjana Medipally is a graduate from Jindal School of International Affairs, Sonipat.

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