Empirical results
2. Investor sentiment and the price discovery process
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the two- to five-lag returns and the high sentiment dummy show similar patterns in signs and magnitudes.17
I show that investor sentiment has a significant impact on the lead–lag relation between the spot and futures markets. The leading role of the futures is significantly weakened when investor sentiment is high. These results imply that informed traders are less willing to leverage their information advantage on the futures market during high sentiment periods, when the noise trader risk and trading costs are high.
2. Investor sentiment and the price discovery process
In the previous section, the VECM estimation reveals that the temporary lead–lag relation between the spot and futures markets is affected by investor sentiment. Next I present the impact of investor sentiment on the information shares and GG factor weights to show whether investor sentiment affects the spot and futures prices in equilibrium. I use the intraday data to calculate the daily information shares for the ETFs and their corresponding futures. As pointed out in the research methodology section, the ordering of time series in the Hasbrouck (1995) model affects the calculations of information shares, so I focus on the average of the upper and lower bounds (i.e., the midpoint) of information shares.
Table 7 reports the changes in the information shares during different sentiment regimes.
The midpoints of the futures information shares are higher than those of the ETFs during both high and low sentiment periods. This pattern is consistent with my VECM results in the previous section and shows that the futures prices are, unconditionally, more informative than ETFs prices. Table 7, however, shows that as investor sentiment increases, the average information shares of the futures market decrease and those of ETFs increase. For example, the information share midpoint of the S&P 500 futures during high sentiment periods is 0.021 lower than that
17 The VECMs in my tables are estimated in an AR(6) framework. To save space, I only show the coefficients on the first three lags. Interested reader can obtain the complete results from the authors.
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during low sentiment periods. Similar results are obtained for the Nasdaq 100 and DJIA indexes.
From Table 7, I observe that the futures market becomes relatively less informative, whereas the spot market becomes relatively more informative, during high sentiment periods.18,19
I next perform multivariate regressions to investigate the relation between investor sentiment and the futures information shares with control variables, including realized volatility and liquidity measures. Table 8 shows that investor sentiment again has a significantly negative impact on the futures information shares. I regress the futures information shares of the S&P 500, Nasdaq 100, and DJIA indexes on the high sentiment dummies, set with respect to the sentiment index at greater than the 75th percentile, and on control variables in different model specifications and find that the coefficients on high sentiment dummies are mostly significantly negative. Take the information shares of the S&P 500 futures for example: From Table 8, the coefficients on high sentiment dummies in Models (1), (2), and (3) are –0.104, –0.044, and – 0.005, respectively, for three different sets of liquidity controls, and two of them are significant at the 1% level.
Table 8 provides similar results for the Nasdaq 100 and DJIA futures, implying that futures prices contribute relatively less to price discovery when investor sentiment is high. The results reported in Tables 7 and 8 are in line with Shleifer and Vishny (1997) and Barberis et al.
(1998) who argue that informed traders avoid exposing themselves to extreme risk when investor sentiment is high and thus are less willing to leverage their information advantages on the futures market, which in turn makes futures prices relatively less informative during such periods.
I next report the results with GG factor weights as an alternative information measure.
The larger the GG factor weights are, the more the prices contribute to the price discovery
18 The results are similar when the high sentiment period is defined as the sentiment index being greater than its 75th percentile.
19 In unreported univariate analysis, I also find the correlations between investor sentiment and information shares of the S&P 500, Nasdaq 100, and DJIA futures are significantly negative.
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process. Table 9 reports the regression results of the relation between investor sentiment and the futures GG factor weights. The coefficients on the 75th percentile sentiment dummies are all negative in various model specifications, and most of them are statistically significant. For example, with the GG factor weights of the DJIA futures as the dependent variable, the coefficients on the high sentiment dummies are respectively –0.229, –0.183, and –0.053, significant at the 1% level, for the liquidity measures of MS, SR, and TV; see Models (7), (8), and (9), respectively. Results for the S&P 500 and Nasdaq 100 futures are similar, which again imply that the futures prices contribute less to the price discovery process when investor sentiment is high.20
20 To address the endogeneity concerns, I replace all control variables in Tables 8 and 9 with their lagged terms and re-estimate the coefficients. The results are both qualitatively and quantitatively similar.
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Chapter VI
Investor sentiment and the futures trading activity
Finally, I provide supporting evidence on the increase of noise trading activity during high sentiment periods. I collect the weekly Commitment of Traders report for the S&P 500, Nasdaq 100, and DJIA E-mini futures from the CFTC and analyze the trader composition. Panel A of Table 10 summaries the combined open interests held by commercial, noncommercial, and nonreportable traders in different sentiment periods, and Panels B and C break the total open interests into long and short positions, respectively. The last four columns in Table 10 show the absolute and percentage changes in open interests for different types of traders between high and low sentiment periods. High (low) sentiment periods are those months in which Baker and Wurgler’s (2006) investor sentiment index is above (below) its median.
I find that nonreportable traders, who are usually less informed and are most likely to be affected by investor sentiment (Röthig and Chiarella, 2011), tend to hold significantly more open interests in all sample E-mini futures when investor sentiment is high. For the combined long and short positions results in Panel A of Table 10, compared to low sentiment periods, nonreportable traders hold on average 85,742, 9,566, and 9,139 more open interests during high sentiment periods in the S&P 500, Nasdaq 100, and DJIA futures, respectively. These results indicate that noise traders tend to trade more actively on the futures market when investor sentiment is high.
Panel A of Table 10 also shows the percentage changes in open interests for nonreportable traders and commercial traders. The percentage changes in open interests from low to high sentiment periods for the commercial traders, who are the major participants on the E-mini index futures market, are –2.41%, 6.58%, and 37.19 for the S&P 500, Nasdaq 100 and DJIA futures, respectively, without obvious patterns. However, the percentage changes in open interests from low to high sentiment periods for nonreportable traders are sizable and positive at 22.87%, 13.57%, and 39.06% for the S&P 500, Nasdaq 100 and DJIA futures, respectively.
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This result suggests that nonreportable trading disproportionately increases during high sentiment periods.
By breaking the combined positions into long and short positions, I further find that the increased open interests held by nonreportable traders during high sentiment periods are mostly due to increases in long positions. Panel B of Table 10 shows that during high sentiment periods nonreportable traders hold significantly more long positions, both in the absolute and in percentage terms, while no clear the patterns exist for commercial traders. For example, for nonreportable traders, the percentage increases in the long positions from low to high sentiment periods are 41.83%, 45.65%, and 26.39% for the S&P 500, Nasdaq 100 and DJIA futures, respectively. However, the absolute and percentage changes of short positions in Panel C are not consistent for both commercial and nonreportable traders.21
The results that nonreportable traders tend to hold more long positions when investor sentiment is high are in line with the argument that during high sentiment periods noise traders tend to buy more stocks and that informed traders have difficulty correcting overpricing due to short-sale constraints on the stock market. These findings are consistent with Baker and Wurgler (2006) and Stambaugh et al. (2012), who find that noise traders driven by high sentiment are usually overly optimistic and push prices up, away from their fundamental values for an appreciable length of time. Overall, Table 10 shows that nonreportable traders trade more actively on the futures market during high sentiment periods, and this excessive trading increases informed traders’ risk and cost to trade on the futures market during high sentiment periods. This phenomenon, in turn, undermines the informativeness of the futures market.
21 When high (low) sentiment periods are defined as those months in which the investor sentiment index is above (below) its 75th (25th) percentile, the results are more significant. For nonreportable traders, the percentage increases in the combined positions from low to high sentiment periods are 57.79%, 3.20%, and 79.21% for the S&P 500, Nasdaq 100 and DJIA futures, respectively. For nonreportable traders, the percentage increases in the long positions from low to high sentiment periods are 79.07%, 69.76%, and 50.40% for the S&P 500, Nasdaq 100 and DJIA futures, respectively.
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Chapter VII
Conclusion
The literature extensively shows that the futures market reflects new information more quickly than the spot market does because lower trading costs in the futures market attract more informed traders who can better utilize their information. As a consequence, an asymmetric lead–lag relation is observed between the spot and futures markets. Some studies, however, suggest that the lead–lag relation can be time-varying if trading risk and trading cost change over time. I empirically investigate the effect of time-varying investor sentiment on the lead–
lag relation and on the price discovery process between the spot and futures markets. Using the trade and quote data of the S&P 500, Nasdaq 100, DIJA ETFs, and their corresponding futures contracts, I first show that investor sentiment has a positive impact on both price volatility and the bid–ask spread, which implies that informed traders bear higher trading risk and trading costs during high sentiment periods. Based on the theory of limits to arbitrage and trading cost hypothesis, I hypothesize that informed traders become less willing to leverage their information advantages on the futures market during high sentiment periods.
My investigation on the lead–lag relation and on the price discovery process between the spot and futures markets provides several findings that are consistent with the literature and my hypotheses. First, the leading role of the futures markets becomes significantly weaker during high sentiment periods, indicating that informed traders tend to trade less on the futures market when noise trader risk and trading cost are high. Second, investor sentiment negatively impacts both the information shares and the GG factor weights of the futures market. These results suggest that the futures prices become relatively less informative during high sentiment periods. Finally, nonreportable small traders tend to hold more long positions in futures during high sentiment periods, which indicates that investor sentiment indeed has a positive impact on the noise trader risk.
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My study provides support for the theory of limits to arbitrage. That is, increased noise trader risk and trading costs during high investor sentiment periods discourage informed traders from leveraging their information advantages on the futures market. This study contributes to the literature by showing that investor sentiment not only affects asset prices and volatility but also has an important effect on the price discovery process across informationally linked markets.
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