Stochastic dominance in stock market 4

Chapter The “New Economy” versus the “Old Economy”, which is preferred? 4.1 Introduction From 1998 to the first quarter of 2000, investors were extremely optimistic about Internet companies. They were mesmerized by the “dot-com” fad. Any company with a .com in its name was rewarded with a high valuation. The quantum of investment money and the number of company formations have “skyrocketed”, observed Bill Gates (Perkins and Perkins 1999). Internet stocks became the new favorite on Wall Street and hailed as the “new economy”. However, prosperity sustainable did not last for long. Many investors as well as investment analysts, who had happily enjoyed the great initial gains from Internet stocks for more than two years, were shocked by the dramatic downturn in the spring of 2000. Since then, they have been jolted from the dot-com sweet dream and started experiencing the painful truth of this new economy. The prices of Internet stocks went up and down at more extreme volatile rates than other conventional stocks between 1998 and 2000. Hence, investments in Internet stocks are likely to either generate big wins or enormous losses. Looking at the statistics, the NASDAQ 100 Index increased 140% compared to 33% in the S&P 500 Index from June 1998 to March 2000. After the peak on March 2000, NASDAQ 100 decreased 70% by the end of 2000 while S&P 500 only declined by 14%. In 1999, the Dow Jones Industrial Average (DJIA) increased 20%, the NASDAQ was up 86%, and Peter S. Cohan & Associates’ Internet Stock Index rose 339%. In 2000, however, 91 the Internet Stock Index had lost 67% of its value, while the DJIA depreciated 6% and the NASDAQ fell 39%. Over the two years under analysis, Internet stocks rose by 6%, the DJIA appreciated 18%, and the NASDAQ had gained 13% of its value (Cohan 2001). Demers and Lewellen (2003) reported that 294 Internet firms went public in 1999 and raised more than $20 billion in capital. By March 1, 2000, Internet firms had a combined market value of $1.7 trillion. Between January 1999 and February 2000, the Internet Stock Index more than tripled in value. However, by 2001, the industry suffered a total decline of 90% (Lewellen 2003). Academics and analysts started to question this dramatic rise and fall of Internet stocks prices. What are the causes of this Internet bubble burst which occurred within a two-year time period? Are new economy stocks overpriced? Is the dramatic rise and fall of Internet stocks due to irrational investors’ behavior or insider action? Will the risk-averse and risk-seeking investors have different preferences on Internet stocks to maximize their utility? Many studies have attempted to explain the downfall of Internet stocks especially since the bubble burst in the spring of 2000. Although many studies have examined misperceptions on Internet stocks, there is still a persistent lack of literature on the comparisons between the old and new economy stocks. All the literature to date uses individual companies as sample in their studies and none looked at the market from a broader perspective to study the market indices. The downfall may be attributed to investors’ behavioral or fundamental account variables, lack of broad picture focus on the risk-based preference of investors. Hence, this study tries to fill the gap mentioned here. The issue of market efficiency and rationality is not the key 92 point here as it has been widely discussed in the literature and the imperfect knowledge of the current asset pricing benchmarks. The main objective of this study is to investigate whether investors’ enthusiasm for Internet stocks is consistent with utility maximization. A more general framework for analyzing utility choices, stochastic dominance (SD), is used in this study. The findings of this study will hold an important lesson for investors on how to deal with similar bubbles if they arise in the future. In addition, the stocks preferences of different types of investors will also be examined. The empirical results show that neither S&P 500 nor NASDAQ 100 stochastically dominates each other at first order for the whole sample period and the two sub-periods. Surprisingly, there is no evidence of the new economy dominance even during the Internet boom. S&P 500 dominates at the left-hand (negative returns) side while NASDAQ 100 dominates at the right-hand (positive returns) side. However, S&P 500 stochastically dominates NASDAQ 100 at second order implies that riskaverse investors prefer old economy stocks to new economy stocks. Furthermore, evidence of old economy stocks dominance is generally stronger at third-order SD than the second-order SD. This implies that investors who prefer more positive skewness would also have chosen to buy old economy stocks only. These results suggest that the Internet stocks would never be better than the old economy stocks for the entire sample period. The evidence of old economy stocks dominating is even stronger after the bubble burst. It is also found that risk lovers prefer new economy stocks to old economy stocks while investors with S-shaped or reverse S-shaped utility function have no preference between old and new economy stocks. Invertors 93 may use these findings as a reference for their investment decisions on old and new economy stocks. This chapter is organized as follows. Section comprises of a literature review; section describes the SD methodology. Section discusses the sample and data; section constructs the SD tests, section reports the SD results, section discusses some special cases and section concludes this chapter. 4.2 Literature Review Several researchers have examined investors’ behavior when valuing the Internet stocks. Cohan (2001) observes the manifestations of investor’s fear from the stock indices. First, investors sell everything and put the earnings into the most rapidly growing sectors because they fear of losing out on rapid growth. Panic selling and drops in these rapidly growing sectors then lead the investors to put their money into old economy stocks which they believe of preserving the gains they have made in the fastest-growing sectors. The relationship between the performance of Internet stocks and their peers is very complex. When investors are afraid of falling behind, Internet stocks tend to go up the most because investors feel that the Internet stocks are in the fastest-growing sector of the economy. When investors are afraid of losing their gains, they tend to sell the Internet stocks and invest their earnings in the old economy stocks that are likely to hold their value better in a slowing economy. During these periods, the Internet stocks tend to perform less well than their peers. (Cohan 2001) 94 Wheale and Amin (2003) explain the burst from the change of investors’ behavior on valuing the Internet stocks before and after the burst. Six measures, namely, return on assets, return on equity, price-sales ratio, price-earnings ratio, book value and free cash flow are selected as indicators of corporate performance. They examine the relationship between stock returns and these six indicators before and after the collapse. The evidence from their study suggests that only price-sales ratio, price-earnings ratio, book value and free cash flow are value-relevant before the burst. However, after the burst, all six indicators are value-relevant. Hence, they claim that investors’ valuation on Internet stocks have changed from emphasizing on revenue to profits. Therefore, they stress the importance of behavioral finance in classical financial theory. Ofek and Richardson (2003), however, hypothesize on heterogeneous investors with short sales restrictions (via IPO) to explain the Internet bubble burst in Spring 2000. They study various characteristics like volume, share turnover, short interest, rebate rate etc. of Internet companies. From the beginning of 1998 to 2000, there were many optimistic investors willing to pay high prices for the Internet stocks. On the other hand, there were some pessimistic investors willing to short these stocks at high prices. However, the short sales restrictions lead to the rises of Internet stocks prices. During the spring and latter half of 2000, many lockups expired. Thus, pessimistic investors and insider sales cause the Internet stocks prices to drop. In addition, the holding of retail trader for Internet stocks more than institutional traders shows heterogeneity among investors. Hence, the market is more prone to behavioral biases. Moreover, retail day traders have driven momentum investing in recent years (Perkins and Perkins 1999). 95 On the other hand, traditional accounting model does not carry sufficient information about the growth opportunities and intellectual assets that may make up major components of Internet firms’ values. Several researchers have studied systematic relationships between stock prices, accounting variables and non-financial measures of resources and performance (see Trueman, Wong and Zhang 2000, Rajgopal, Venkatachalam, Kotha and Erickson 2002). In addition, there are widespread claims that stocks in this sector were overpriced in 1999, and other researchers (for example, Demers and Lev 2001) have investigated the factors associated with the Internet “pricing shakeout” in early 2000, with a focus on nonfinancial value drivers. Existing Internet valuation studies find mixed results when examining the relation between traditional financial measures and market values of Internet firms during 1999 and early 2000. Their results provide some support for the importance of cash availability for Internet firms, particularly after the downturn in Spring 2000 (Keating, Lys and Magee 2003). Jahnke (2000) discusses new approaches like top-line revenue growth, customer growth, website visits, peer group comparison and momentum to value Internet stocks. He points out that the new economy stocks are overpriced. The price being set by investors to play the Internet revolution is too high relative to the profits the industry is likely to produce in the future. Investors have wrongly assumed that glamour-investing produces superior investment returns. He reminds the investors that great technological innovation not necessarily translate into great investment opportunities for the typical investor. Producing high rates of revenue and earning 96 growth rates over many years is rare. As companies get bigger, it becomes harder to grow at above average rates. King (2000) suggests that the best way to value a single e-business company is to apply traditional valuation methodologies. Follow the usual approach of valuation, first, the present value of the future cash flows is determined. Then, the projected cash flows are discounted at an appropriate discount rate. An indication of the value for an e-business company is the sum of these discounted amounts. Keating, Lys and Magee (2003) show that traditional financial variables and new economy measures can explain much of the cross-sectional variation in prices and returns of Internet stocks in the spring of 2000. However, Lewellen (2003) argues that their results tell more on investor’s irrationality or misperception than as suggested by them on the agency cost and information asymmetries. This irrational view suggests why prices rise so dramatically in the first place. Besides investors’ behavior and stock valuation methods, there is literature focus on IPO underpricing in investigating the Internet bubble. There is widespread belief among both academics and practitioners that the prices of Internet IPO cannot be justified by economic fundamentals. Managers and bankers are taking the advantage of irrationally high prices to sell Internet stocks. Ritter (1991), Loughran and Ritter (1995) and many others show that Internet IPOs significantly underperform as compared to other stocks of similar size in the same industry for five years after going public. 97 According to Perkins and Perkins (1999), as capital begins to flood the market, companies not only start up faster but also go public sooner. A financial food chain includes the entrepreneurs, the venture capitalists, the investment bankers and certain large institutional investors and mutual funds play an important role in IPO. In the Internet boom era, venture capitalists are pushed by both their investors and entrepreneurs they invest in to shoot quickly for an IPO. On the other hand, the investment banks willing to serve their investors for Internet IPO so they can collect their 7% underwriting fees and engage new clients to manage their follow-on offerings. At the end, companies that shouldn’t be public are public. Perkins and Perkins (1999) also state that narrow float which means the limited number of company shares available to public investors drive up the Internet stocks prices. When demand is high and the supply is limited, the prices of these stocks skyrocket. Furthermore, insiders keep more of the stocks from their Internet startups for themselves. In the first half of 1998, these companies offered only 31% of their total capitalization to public investors. This allows them to sell their stocks at a greater profit following the significant share appreciation typical in bubble markets. Schultz and Zaman (2001) find that the Internet firms sell smaller proportion of their equity and insiders sell fewer of their own shares in the IPO. This implies that insiders expect prices to remain high for a long time and therefore there is no hurry to sell or they feel the IPOs are underpriced and prefer to sell later. Ljungqvist and Wilhelm (2003) suggest that the unique characteristics on ownership structure and insider selling behavior of firm during the “dot-com bubble” 98 cause the IPO underpricing. The decline of CEO, venture capitalists and investment banks stakes cause the ownership to be more fragmented. These changes of ownership then cause the secondary sales to decrease sharply. How true does the media hype sharing market gossip via Internet impact on the Internet stocks prices? Demers and Lewellen (2003) explore the potential marketing benefits of going public and IPO underpricing. Internet companies experience high publicity surrounding their IPO. They suggest that the marketing benefits of underpricing extend beyond the Internet sector and the “hot issues” market in the late 1990s. If underpricing attracts media attention and creates valuable publicity for issuing firms, this effect should be reflected in an increased number of website visitors following the IPO. Beneath the surface of the statistics, there are unique activities that drive the movement of money in and out of Internet stocks. Day traders, message boards, influential analysts and the pervasive influence of the cable-TV network CNBC all have a real impact on the day-to-day flow of money in the markets. In some cases, these money drivers are simply new technologies that have speeded up the traditional process of sharing market gossip. In other cases, these phenomena are new and surprisingly powerful (Cohan 2001). Grodinsky (1953) points out that when new industries are born, there often is a rush by many companies to enter the field in this period of initial and rapid growth. This is followed by a shakeout period with only a relatively few survivors and by a continuing period of strong growth, although the rate of growth is slower than the 99 initial period. Finally, industries are expected to stop growing, either living a relatively stable existence for an extended period of time or dying. Grodinsky points out the great risk of selecting stocks in the pioneering stage, where little information about participants may be available. There is little or no past record to guide investors or aid them in preparing future projections. Any new industry will follow the four stages of industry life cycle. Internet companies are in the start-up stage. In the long run, this new industry will develop and mature to become old economy and everything will return to normal. Although many have suffered from the Internet bubble burst, Koller (2001) advises that investors shouldn’t abandon the Internet stocks. Nevertheless, they should understand the basic principles of value creation and generate new insights into the potential value of Internet opportunities. Investors should look forward to a better tomorrow. All the literature above try to explain the downfall of Internet stocks attributed to investor’s behavior, accounting methods, IPO underpricing etc. while this study contributes to the literature by looking at different direction that is investor’s utility maximization. 4.3 Stochastic Dominance The SD approach is used to examine whether the new economy stocks dominates the old economy stocks or vice versa in this study. The SD approach provides a general framework for studying economic behavior under uncertainty. Hadar and Russell (1969), Hanoch and Levy (1969), Rothschild and Stiglitz (1970), Whitmore (1970) lay the foundation of SD analysis. Levy (1992, 1998) provides an up-to-date summary 100 Kahneman (1992) suggest that this type of investors’ behavior can be characterized by S-shaped utility function which the utility (value) function is concave for gains and convex for losses. On the other hand, Markowitz (1952) suggest that individuals are risk averse for losses and risk seeking for gains, as long as the possible outcomes are not very extreme. The utility function for this type of individual is called reverse S-shaped utility function. It is convex for the positive returns and concave for the negative returns. Let X and Y be two distinct prospects with CDFs F and G respectively. Levy (1998) and Levy and Levy (2002) develop Prospect SD (PSD) and Markowitz SD (MSD) to determine the dominance of one investment alternative over another for all S-shaped and reverse S-shaped utility functions respectively. Theorem 1: PSD. X dominates Y for all S-shaped utility functions denoted X f PSD Y , if and only if ∫ [G(t ) − F (t )]dt ≥ for all y ≤ and ∫ [G(t ) − F (t )]dt ≥ for all x ≥ . x y Theorem 2: MSD. X dominates Y for all reverse S-shaped utility functions denoted X f MSD Y , if and only if ∫ [G(t ) − F (t )]dt ≥ for all y ≤ and ∫ [G(t ) − F (t )]dt ≥ for all x ≥ . y −∞ ∞ x It is noted that MSD is generally not “the opposite” of PSD. In other words, if X dominates Y by PSD, this does not necessarily mean that Y dominates X by MSD. PSD and MSD theorems are employed into the DD test in this study. The unreported 119 results show that investors with S-shaped and reverse S-shaped utility functions not show any preference on neither old nor new economy stocks in the whole sample period, the bull sub-period as well as the bear sub-period. 4.7.3 Portfolio Diversification Since it may be optimal for investors to hold diversified portfolios comprising both old and new economy stocks, it is clearly interesting to examine portfolio choice from the viewpoint of diversified investors. To study the effects of diversification, portfolios with different combinations of NASDAQ 100 stocks and S&P 500 stocks are formed. Results of this exercise are reported in Table 4.7. The results show that none of the DD statistics are significantly negative while around 36% of the DD statistics for second-order SD are significantly positive. Moreover, the proportion of positive DD statistics is relatively constant across all the portfolios. This suggests that diversification does not affect the previous result, namely that S&P 500 stocks are the optimal choice for risk-averse investors as compared to portfolios containing Internet stocks. The results for third-order SD is also unaffected by diversification since majority of the DD statistics are significantly positive and none are negative. 120 Table 4.7: DD Test Results for Diversified Portfolios of New and Old Stocks vs. the S&P 500 Portfolio S&P 500 % in New Stocks % in Old Stocks SSD TSD 100 36 81 95 37 80 90 10 36 81 85 15 36 81 80 20 35 80 75 25 36 79 70 30 36 78 65 35 36 77 60 40 35 76 55 45 35 75 50 50 35 74 45 55 35 73 40 60 36 72 35 65 36 71 30 70 36 69 25 75 36 68 20 80 37 67 15 85 37 65 10 90 37 63 95 38 62 Note: New stocks are represented by the NASDAQ 100 index. Old stocks are represented by the S&P 500 index. The sample period is January 1998 through December 2003. Numbers reported under SSD and TSD refer to the percentage of DD statistics which are significantly positive at the 5% significance level for second- and third-order SD respectively. 121 4.8 Conclusion The new dot-com companies have no track record or history, no assets and virtually no profit history (while some are even in losses) but yet they capture $1 trillion in the market capitalization. King (2000) and many others predicted that this fairy tale will be gone soon and it had been proven true in the spring of 2000. The fundamentalist camp questions the valuation of Internet stocks and urges us to go back to fundamentals when valuing an Internet stock. On the other hand, the behavioral group tries to explain the burst from investors’ heterogeneity and irrationality. There are researchers who examine the IPO underpricing and insiders’ selling behavior. This study examines whether the new economy stocks dominates the old economy stocks or vice versa using the SD approach. The main objective of this study is to investigate whether investors’ enthusiasm for Internet stocks is consistent with utility maximization. The findings of this study will hold an important lesson to the investors on how to deal with similar bubbles if they arise in the future. In addition, the stocks preference by different types of investors will also be examined. The empirical results show that neither S&P 500 nor NASDAQ 100 dominates each other at first order for the whole sample period and the two sub-periods. Surprisingly, there is no evidence of the new economy dominating even during the Internet boom. The results explore the premise that the Internet stocks could never be better than the old economy stocks for the entire sample period. The evidence shows old economy stocks dominating even stronger after the bubble burst. This implies that there are rational investors who prefer old economy stocks than the new economy stocks which are undervalued by the irrational investors. Moreover, S&P 500 122 dominates at the left-hand (negative returns) side while NASDAQ 100 dominates at the right-hand (positive returns) side. It is also found that risk-averse investors prefer old economy stocks to new economy stocks. In contrast, risk lovers prefer new economy stocks to old economy stocks. Moreover, investors with S-shaped or reverse S-shaped utility function have no preference between the old and new economy stocks. In summary, Internet stocks are extremely volatile, exposing investors to substantial downside risks. From an utility viewpoint, this downside risk has clearly been too high a price to pay for the rewards of a over hyped sector of the stock market. With the aid of technical analysis or other types of analysis, it is possible to predict if the coming period will be a bull run or a bear market. 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( lim N cov(Dˆ Then N →∞ ) N Dˆ Hs ( z ) − DHs ( z ) is asymptotically normal with mean zero and covariance s X (z ), Dˆ Ys (z ')) = ((s − 1)!) ( E (z − x )+ (z '− y )+ s −1 s −1 )− D s X (z )DYs (z '). (A.2) Proof. For each distribution, the existence of the population moment of order s-1 lets us apply the law of large numbers to (A.1), thus showing that Dˆ s (z ) is a consistent estimator of D s (z ) . Given also the existence of the population moment of order 2s – 2, the central limit theorem shows that the estimator is root-N consistent and asymptotically normal with asymptotic covariance matrix as in (A.2). If X and Y are independent populations, the sample sizes NX and NY may be different. Then (A.2) applies to each with N replaced by the appropriate sample size. The covariance across the two populations is of course zero.▪ Source: Davidson, R. and J.Y. Duclos, 2000, Statistical inference for stochastic dominance and for the measurement of poverty and inequality, Econometrica, 68(6), 1435-1464. 133 Appendix B A natural alternative to the p-value simulation method is to conduct inferences using a form of the bootstrap. A possible advantage of this is that, although existence of a limiting distribution (for the test statistic) is generally needed, one does not necessarily need to be able to characterize it as in the Monte Carlo method. Therefore, the bootstrap may be applicable in more complicated situations. In this case, the sample is defined as ℵ = {X , ., X N } and computes the distribution of the random quantity, [ ( ) ( F K s = N sup D s z; FˆN* − D s z; FˆN )] , z where FˆN* = N ( ) 1 X i* ≤ z and FˆN = ∑ N i =1 N ∑1(X N i =1 i ) ≤ z for a random sample of X i* drawn from ℵ . Define a combined sample as £ = {X , ., X N , Y1 , ., YN } . Let Gˆ N* denote the empirical CDF of an independent drawn random sample of size N from £. Then compute the distribution of the random quantity K s = [ ( ) )] ( N2 sup D s z; Gˆ N* − D s z; FˆN* . 2N z We are interested in computing the probability that the random variables exceed the value of the statistic given the respective samples. Then, the p-values can be approximated by pˆ s ≈ ( ) R ∑1 K s > Kˆ s where Kˆ s = R r =1 [ ( ) ( N2 sup D s z; Gˆ N − D s z; FˆN 2N z )] and R is denoted as the number of replications in the simulation. Source: Barrett, G. and S. Donald, 2003, Consistent tests for stochastic dominance, Econometrica, 71, 71-104. 134 [...]... determination of stochastic dominance, Journal of Finance, XL(2), 41 7 -43 1 Bekaert, G., C Erb, C Harvey and T Viskanta, 1997, What matters for emerging market investments, Emerging Markets Quarterly, Summer, 17 -46 Bernard, V.L and H.N Seyhun, 1997, Does post-earnings announcement drift in stock prices reflect market inefficiency? A stochastic dominance approach, Review of Quantitative Finance and Accounting,... 2000, Statistical inference for stochastic dominance and for the measurement of poverty and inequality, Econometrica, 68(6), 143 5- 146 4 Demers, E and B Lev., 2001, A rude awakening: internet shakeout in 2000, Review of Accounting Studies, 6(2/3), 331–359 Demers, E and K Lewellen, 2003, The marketing role of IPOs: evidence from internet stocks, Journal of Financial Economics, 68, 41 3 -43 7 Dumas, B and... of Finance, 50, 44 5 -47 9 Engle, R.F., and D Sarkar, 2002, Pricing exchange traded funds, Working Paper, NYU Stern School of Business Falk, H and H Levy, 1989, Market reaction to quarterly earnings’ announcement: a stochastic dominance based test of market efficiency, Management Science, 35, 42 544 6 125 Fama, E.F and K.R French, 1996, Multifactor explanations of asset pricing anomalies, Journal of Finance,... after the crash This clearly shows that invest in Internet stocks are anytime riskier than in old economy stocks Furthermore, the standard deviation for NASDAQ 100 increases after crash while the mean returns reduce This implies that it is riskier to invest in the new economy stocks after the bubble burst 4. 5 Stochastic Dominance Tests In order to investigate in detail why risk averters / risk lovers... Finance, 51, 55- 84 Fishburn, Peter C., 19 64, Decision and value theory, Wiley: New York Fishburn, Peter C., 19 74, Convex stochastic dominance with continuous distribution functions, Journal of Economic Theory, 7, 143 – 158 Fishburn, P.C and R G Vickson, 1978, Theoretical foundations of stochastic dominance, in Stochastic dominance An approach to decision making under risk, G.A Whitmore and M.C Findlay, eds.,... presented in the 6*6 contingency tables below Table 4. 5a: Contingency Table for Old Stocks by New Stocks for Whole Period New Stocks Rank -2 -1 1 2 3 Total -3 1 0 0 0 0 0 1 -2 Old Stocks -3 4 6 1 0 0 0 11 -1 11 116 516 151 1 0 795 1 1 2 103 620 30 1 757 2 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 Total 17 1 24 620 771 31 1 15 64 Chi-Square = 961.57 1 14 Table 4. 5b: Contingency Table for Old Stocks by New Stocks for... when valuing an Internet stock On the other hand, the behavioral group tries to explain the burst from investors’ heterogeneity and irrationality There are researchers who examine the IPO underpricing and insiders’ selling behavior This study examines whether the new economy stocks dominates the old economy stocks or vice versa using the SD approach The main objective of this study is to investigate... reported in this chapter 101 “new stocks” The NASDAQ 100 Index includes 100 of the largest domestic and international non-financial companies listed on The NASDAQ Stock Market based on market capitalization The index reflects companies across major industry groups including computer hardware and software, telecommunications, retail/wholesale trade and biotechnology It does not contain financial companies including... positive, it is inferred that old economy stocks dominate new economy stocks The evidence in Table 4. 6 shows that risk lovers prefer new stocks to old stocks at second and third orders for all periods One will not be surprised to find that this conclusion is true in the bull sub-period as the Internet stocks are in a super bull run during this period However, one may be surprised to find that this conclusion... clear evidence of old economy stocks dominance This may imply that there are some rational investors who prefer old economy stocks than the new economy stocks which are undervalued by the irrational investors Although the market has attracted many inexperience investors or speculators during the extraordinary asset pricing period, the evidence of old economy stocks dominates even stronger after the . of Internet stocks and their peers is very complex. When investors are afraid of falling behind, Internet stocks tend to go up the most because investors feel that the Internet stocks are in. investments in Internet stocks are likely to either generate big wins or enormous losses. Looking at the statistics, the NASDAQ 100 Index increased 140 % compared to 33% in the S&P 500 Index. the fastest-growing sector of the economy. When investors are afraid of losing their gains, they tend to sell the Internet stocks and invest their earnings in the old economy stocks that are