Simple Trading Rules and the Market for Internet Stocks

Simple Trading Rules and theMarket for Internet Stocks*WAI MUN FONG AND YAT WAI HODepartment of Finance and Accounting, National University of SingaporeABSTRACTWe investigate the profitability of moving average trading rules forInternet stocks based on the Dow Jones Internet Composite Index.Consistent with previous studies e.g. Brock et al. (1992), returns afterbuy signals exceed returns after sell signals. The average buysell spread islarge and significant even after accounting for transaction costs. Bootstrapsimulations based on a version of the dynamic CAPM show that the modelis able to replicate the pattern of buy and sell returns. Simulated buysellspreads amount on average to more than 39% of the actual spread.However, actual profits are still too large to be explained in terms of riskcompensation.I. INTRODUCTIONWhether technical trading rules can indeed generate abnormal profits has longbeen a key issue in empirical finance. Research on this issue has produced mixedresults. Brock et al. (1992) apply simple technical trading rules to a century ofdaily data on the Dow Jones Index and find strong evidence that support theprofitability of technical analysis. They find that buy signals consistentlygenerate positive profits, and sell signals negative profits. Standard t-testsindicate that the mean buysell spread is significant for the entire sample period.Brock et al. noted that since negative returns after sell signals cannot be readilyexplained by standard equilibrium models, the predictive power of technicalrules indicates that the market may be inefficient.Brock et al.s (1992) study did not take into account transactions cost. This is aserious limitation since technical trading rules may generate very frequenttransactions. Moreover, they did not assess the significance of trading rule profitsin terms of asset pricing models. Unless risk premia implicit in trading profits areproperly assessed, we cannot be sure whether the detected profits are unusualcompared to risk assumed.The profitability of technical trading rules has also been examined in othermarkets, such as the currency market. Recent examples include Taylor and AllenInternational Review of Finance Ltd. 2001. Published by Blackwell Publishers, 108 Cowley Road,Oxford OX4 1JF, UK and 350 Main Street, Malden, MA 02148, USA.International Review of Finance, 2:4, 2001: pp. 247268* We wish to thank K. C. Chan, Lilian Ng and Sheridan Titman, the Managing Editor, forproviding helpful comments, and Weijin Lin for research assistance. As usual, all errors andomissions are ours.(1992) and Kho (1996). Both of these studies employ moving average rules thatare widely used by practitioners in the foreign currency market. Kho (1996)applies these rules to test for efficiency in currency futures where transactioncosts are low. His study is one of the few papers to evaluate trading rule profitsusing an equilibrium asset pricing model. Specifically, he simulates the empiricaldistribution of trading rule profits based on a general version of the conditionalCAPM with time-varying covariance risk premia. His results show that simplemoving average rules generate profits that are too large to be explained bytransaction costs. However, the profits are statistically insignificant when time-varying risk premia are taken into account. Kho (1996) concludes that tradingrule profits in the currency futures market are not due to market inefficiency butcompensation for risk.In this paper, we adopt a methodology similar to Kho (1996) to study theprofitability of using simple moving average rules to trade in Internet stocks. Weuse daily data on the Dow Jones Internet Composite (DJIC) Index, which tracksthe price movements of 40 major Internet companies listed on NASDAQ and theMain Board. We assume that there is a hypothetical index fund that mimics theDJIC. Our study takes into account reasonable transaction costs for trading anindex fund. Like Kho (1996), we use a bootstrap procedure to simulate theempirical distribution of trading rule profits based on a general version of theconditional CAPM.Internet stocks have attracted much publicity in recent years, partly due to thespectacular price appreciation of such stocks as Yahoo, A and E-bay,among others.1Even before steep correction of technology stocks in March 2000,there was growing consensus among financial observers and academics that theInternet sector has been over-hyped and that the seemingly unstoppable rise ofInternet stock prices resembled a speculative bubble (e.g. Nee 1999; Perkins andPerkins 1999). The recent slump in the prices of technology stocks appears toconfirm the speculative bubble hypothesis.Whether there was indeed an Internet speculative bubble is the subject ofseveral recent studies using different methodologies. Schultz and Zaman (2001)noted that the number of Internet initial public offerings (IPOs) acceleratedbetween January 1999 and March 2000. In this relatively short space of 15months, 321 Internet companies went public, compared to a total of 420 firmsover their four-year sample from January 1996 period to March 2000. Most of theInternet companies that went public had little revenue. The vast majority were1 In 1994, there were fewer than ten pure play Internet stocks. Investors enthusiasm forInternet stocks probably started in late 1995 after Netscapes spectacular IPO debut, which sawthe stock price doubling from the offer price of $28 to close at $58.25 after an intra-day high of$74.75. Public euphoria over Internet stocks gained momentum in 1996 with the listing ofother prominent firms, such as Excite, Lycos, Yahoo and Infoseek. Before the year ended, morethan 25 stocks could be called Internet. Schultz and Zaman (2001) record that, of 420 Internetfirms which went public between January 1996 and March 2000, 260 firms saw their stockprice rise by at least 50% on the first day of IPO. The comparable percentage rise in stock pricefor non-Internet over the same period IPOs was 9.4%.248 International Review of Finance Ltd. 2001International Review of Financemaking losses. Undeterred by this, investors snapped up virtually every InternetIPO. On average, these 420 IPOs closed at a 80.7% premium over their offerprices, far exceeding the first-day premia for other IPOs. To test whether thisclustering of hot new issues indicates that investors were irrationally overpayingfor Internet stocks, Schultz and Zaman (2001) examine the actions of insiders ofthe Internet firms in their sample. They find that insiders sell fewer of their ownshares in IPOs than do insiders in other IPOs, suggesting that insiders do notthink their stock is overpriced. However, they also find evidence that Internetfirms which went public recently were more likely to sell stock as seasoned equityofferings than other IPOs after adjusting for returns in the first four weeksfollowing IPO. While Schultz and Zaman (2001) conclude that there is no strongevidence to indicate that insiders were trying to sell overpriced stock, theyacknowledge that their research does not directly address the question of whetherInternet stocks were overpriced. Several factors prevent them from drawing clearinferences about the overpricing issue. First, most of the stocks in their sample aresubject to lock-up provisions which prevent insiders from selling their stockshortly after IPO. Second, even if insiders believe their stock is overpriced, theyneed not sell stock in order to take advantage of overpricing, but can exchangethe stock for fractional ownership in diversified exchange funds offered by someinvestment banks. Third, since Internet firms may need many rounds of equityfinancing, insiders may need to retain stock to avoid sending the wrong signal tothe market. Lastly, insiders may not sell at IPO simply because of hubris, i.e. theyare overconfident about their companys success (see Heaton 2000).A more direct test of the Internet bubble hypothesis is the interesting study byCooper et al. (2001). They report a striking abnormal return of 53% in the twodays around the announcement date of corporate name changes to dotcomnames. This dotcom effect is present even in companies that derive little or norevenue from the Internet. Moreover, the effect was quite persistent. The averageabnormal return across all 95 firms in their sample remained a significant 20% forup to 15 days from the end of the two-day event window, and was even morepersistent for firms whose businesses are unrelated to the Internet. Their researchprovides fairly conclusive evidence that the spectacular rise in Internet stockprices was driven by a speculative bubble as investors frantically bought virtuallyany stock that was remotely connected to the Internet.In this paper, we study the behaviour of Internet stocks from a differentperspective. Specifically, we examine whether the market for Internet stocks isweak-form efficient based on commonly used technical trading rules. Our studyprovides a weaker test of market efficiency than the speculative bubble test ofCooper et al. (2001). While speculative bubbles can cause stock prices to take longswings away from their fundamental values and thus violate weak-forminefficiency, a market can be weak-form inefficient even without the extremeprice momentum of a speculative bubble. However, given the findings of Cooperet al. (2001), it would interesting to examine whether the apparent excesses ofInternet stocks can be exploited by using relatively simple trading rules afteradjusting for trading cost and risk. Our study is the first to apply Internet stockInternational Review of Finance Ltd. 2001 249Simple Trading Rulesdata to test for the weak-form efficiency in the context of an equilibrium model toaccount for time-varying risk premium. Apart from the trading rule results, thequestion of whether Internet stocks can be explained in terms of equilibriumpricing models is also interesting in its own right.The main results of this study are as follows. First, consistent with previousstudies, we find that buy signals generate higher profits than sell signals. Theaverage difference between buy and sell profits is highly significant across variouspermutations of moving average rules. Second, we find clear evidence of time-varying covariance risk premia for the DJIC. Third, unlike the findings of Kho(1996), our trading rule profits are too large to be explained either in terms oftransaction costs or time-varying risk premia. Our results suggest that the marketfor Internet stocks is probably weak-form inefficient.The rest of this paper proceeds as follows. Section II reviews the recentbehavioral finance literature on momentum trading and its relation to this study.Section III describes the data and the trading rule specifications used in thispaper. Section IV reports the profitability of the moving average trading rules. InSection V, we estimate an equilibrium asset pricing model for DJIC returns: aconditional CAPM with time-varying covariance risk premium. This model isused as the null model for bootstrap simulations to test whether the actualtrading profits can be explained in terms of risk compensation. Results of thebootstrap test are reported in Section VI. Section VII concludes the paper.II. MOMENTUM IN THE MARKET FOR INTERNET STOCKSThe quantum leap in Internet stock prices between 1998 and 2000 is one of themost talked about stock market phenomena in recent times. Years before theNASDAQ correction, many market observers, notably Perkins and Perkins (1999),had predicted that the euphoria over Internet stocks represents a bubble thatwould eventually burst. Whether Internet stocks were indeed rationally pricedcan never be resolved conclusively given the lack of concensus on how Internetstocks should be rationally priced, as well as wide variations in cash flow anddiscount rate projections. However, there is an increasing amount ofcircumstantial evidence, mainly from the behavioural finance literature, whichpoints to investor irrationality in the pricing of Internet stocks. This sectionprovides a summary of the recent findings.As a starting point, it is useful to note the importance of individual investors inthe US stock market since these investors probably account for the bulk ofInternet stock holdings. According to Barber and Odean (2000), in 1996, 47% ofthe value of US stocks was held directly by individuals, with pension and mutualfunds holding 23 and 14% respectively. Investor profile surveys show that, first,individual investors are generally less informed than institutional investors and,second, they tend to concentrate on high-risk stocks. For example, based ondemographic and transactions data of nearly 80,000 US households at a majordiscount brokerage house, Dhar and Kumar (2001) find that less sophisticated250 International Review of Finance Ltd. 2001International Review of Financeinvestors gravitate towards risky glamour stocks, namely stocks with high betasand high price-to-book ratios. The evidence indicates that a large proportion ofinvestors of Internet stocks are likely to be individual investors.In a variety of theoretical models, less-informed investors are trend chasers,while more-informed investors act as contrarians. For example, in the positive-feedback model of DeLong et al. (1990), trend chasing results from irrationalbeliefs on the part of investors. In the microstructure model of Brennan and Cao(1996), trend chasing arises because of information asymmetry. In their model,well informed traders utilize their superior information to buy when the price islow and sell when the price is high. Lacking such information, less-informedinvestors rely more on signals revealed by price and, therefore, buy when theprice is high and sell when the price is low (Brennan and Cao 1996, p. 174). Inshort, various theoretical models suggest that trend chasing is a generic form ofbehaviour among less-informed investors. This hypothesis is supported by Dharand Kumar (2001), who find that less-sophisticated investors follow momentumtrading strategies, while more-sophisticated investors are contrarians.The concentration of less-informed investors in the market for Internetstocks has important implications for market efficiency. Experimentalevidence indicates that when a disproportionately large number ofinexperienced investors trade in assets that have highly uncertain futurecash flows, prolonged mispricing and speculative bubbles result (see Smith etal. 1988; Caginalp et al. 2000). These conditions are clearly evident in themarket for Internet stocks. Experimental evidence by Andreassen and Kraus(1990) and DeBondt (1991) also shows that investors tend to chase trendswhen trends are already in place, thus building further momentum intostock prices. DeLong et al. (1990) and Odean (1998) argue that aggressivetrend chasing may be due to investor overconfidence in their ability topredict future price movements, and that this tendency is exacerbatedduring bullish and bearish phases of the market. Investor overconfidencecould also be reason for the very high volume of trade in Internet stocks inrecent years (Ofek and Richardson 2001; Schwert 2001). The persistent rise inInternet stock prices before the crash suggests that much of that tradingvolume reflects momentum trades. If Internet stock prices were indeeddictated by momentum trades, an interesting question is whether investorscould have exploited the predictability of Internet stock prices by usingtechnical trading rules such as moving average rules that are studied in theliterature and also popular in practice. The next section describes the tradingrule specifications used in this paper and the data.III. DATA AND MOVING AVERAGE TRADING RULESWe investigate the profitability of technical rules using the Dow Jones InternetComposite Internet Index. The DJIC Index is a value-weighted index of 40companies whose main business is related to the Internet. Prominent names inInternational Review of Finance Ltd. 2001 251Simple Trading Rulesthe index includes Lycos, Yahoo, E*Trade, E-Bay, A and CNET.2Weuse daily returns data over the period July 2, 1997 to December 29, 2000 (882observations). Daily returns are computed as the log difference in the DJIC Index.We focus on moving average trading rules. The moving average is perhaps themost widely used measure of price trends in technical analysis. Cross-over rulesare commonly used with moving averages to detect buy and sell signals. Theserules operate on the assumption that buy signals are generated when the indexcrosses its moving average from below, while sell signals are generated when theindex crosses the moving average from above. The rationale is that when theindex penetrates the moving average, a trend emerges. Specifically, an upward(bullish) trend is believed to emerge when the index rises above its movingaverage, while a downward (bearish) trend is said to emerge when the index fallsbelow its moving average. Neftci (1991) shows that moving average cross-overrules are one of the few technical trading rules that are statistically well defined.The choice of moving average window has an important effect on the tradingfrequency. A 50-day moving average, for example, is much smoother than a five-day moving average and will consequently generate more buy and sell signals.Since the choice of the moving average window is arbitrary, we examine a rangeof windows. The cross-over rule is often modified by using a filter or band aroundthe moving average. The rationale of introducing a band is to confirm that atrend is indeed in place before one initiates a trade. Following Brock et al. (1992)and Kho (1996), we use a 1% band.Cross-over rules can be implemented with a fixed holding period or variableholding period. Accordingly, these are known as fixed length moving average(FMA) rules and variable length moving average (VMA) rules. Under the FMArule, the inv