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The trading determinant model showed that large hedgers exhibited significant positive feedback trading behaviour in 15 markets, and significant contrarian behaviour in five markets. This was consistent with Grinblatt and Keloharju (2000) who found sophisticated investors pursued positive feedback strategies. Speculators, on the other hand, exhibited significant positive feedback trading behaviour only in seven markets, and contrarian behaviour in Eurodollars only. With 23 markets having speculators exhibiting positive feedback trading, and only significant in seven of them, the monthly data interval was found to be not synchronous enough to determine speculators’ trading decisions. Hedgers (speculators) were also found to respond negatively (positively) to market sentiment after controlling for market risk, which was consistent with De Bondt (1993) and Wang (2003). Information variables were insignificant in most markets, suggesting that the large players did not use these monthly yields significantly in their trading decisions.

Hedgers had significant market timing ability in getting a positive return in one- month’s time for silver, corn, cocoa and coffee, while exhibiting similar poor abilities in Treasury bonds, Japanese yen, soybean oil, crude oil and heating oil. This can be contrasted with speculators having significant market timing ability in wheat (Minnesota) and cocoa only. This was consistent with Khoury and Perrakis (1998) that hedgers in silver, corn and coffee properly change their net positions to increase their futures return in one month’s time, and hence are better informed than speculators in these markets, but inconsistent with Chatrath et al. (1997) that speculators were the most profitable in the 1990s. The poor or negative market timing ability for hedgers was supported by Working (1953) that short hedgers tend to lose money to speculators on their hedge

197 transactions. However, due to the poor market timing ability of speculators, a higher frequency data interval was recommended to fully test their market timing ability. Using the monthly data, the poor market timing ability of speculators do support Keynes (1930) assumption that speculators do not have any forecasting ability, thereby, giving further support to the existence of risk premium. To test the existence of risk premium, own- and cross-hedging pressure effect tests were used and revealed significant risk premium only in few markets, which was consistent with Besseminder (1993) that hedging pressures do not affect the futures returns. Further, price pressure tests were performed to test the robustness of hedging pressure tests. After controlling for price pressures, silver, crude oil and live cattle continued to exhibit significant risk premium, suggesting the transfer of risk from hedgers to speculators in those markets. Significant positive feedback trading behaviour and negative market timing ability in Japanese yen, crude oil and heating oil, suggested hedgers tend to destabilise the futures markets by pushing away prices from their fundamental values. In particular, hedgers in Japanese yen and heating oil suggested a tendency to be destabilisers, since there was no significant risk premium after controlling for price pressures. A review of the regulation regarding stringent position limits imposed upon speculators in these markets was suggested. This was further supported with the decline in speculation in these markets, and also where net positions of speculators were less than net positions of hedgers, both for the mean and standard deviation figures.

5.3.2 Performance Section

The mean equation showed that hedgers’ net positions were negatively related to returns in 18 markets, which was consistent with the negative correlation between the two variables. Speculators’ returns were significantly related with their net positions only in four markets, which was consistent with the low correlation between the two variables. Sentiment index was highly associated with returns for both players, which can be explained by the bullish trend in the US. The lagged hedging pressure variables were mostly significant for agricultural markets, which was consistent with Keynes (1930). As

198 expected from earlier findings, information variables tend to be insignificant in determining returns. An ARMA decomposition of net positions showed that expected net positions of hedgers are negatively related to returns in 17 markets, where 15 were from the agricultural group. The fewer positive expected net positions of speculators and relatively more unexpected net positions of speculators suggested that these players were less informed than hedgers in setting their net positions at the start of the month, but rather speculators changed their net positions more often than hedgers during the rest of the month with the expectation of higher returns. The expected net position coefficients for both hedgers and speculators in 15 and six markets were consistent with Canoles et al. (1998) that, in these markets, they were both financially sophisticated, well educated, and hedgers were better informed in setting a better expected net position at the start of the trading month to determine actual returns. The low significance of expected net positions for speculators also suggested other non-return motivational factors like recreation, which were further supported by the poor correlation between returns and net positions. Decomposed sentiment variables and lagged hedging pressure variables were still significantly positive and negative as found in the non-decomposed mean equation. As for decomposed information variables, unexpected T-bill yield appeared to be more negatively significant to return for speculators, and unexpected corporate spread and dividend yield to be more positively significant to returns of speculators, particularly for financials, minerals and currencies.

The decomposition of variables against idiosyncratic volatility helped in confirming that hedgers were better informed in setting a current net position level at the start of the month that would have a smaller impact of their risk levels and that speculators would rather set net positions that change more frequently to satisfy their risk appetites. Net positions of hedgers (expected and unexpected) tend to have less effect on volatility compared to speculators’ net positions (expected and unexpected) that tended to add to volatility. This was consistent with the Shalen (1993) and Chen et al. (1995) models, where speculators’ volatility was positively related with trading demand. More significant expected and particularly unexpected variables affecting volatility were found within the currency group for both players, supported by the fact that the foreign

199 exchange markets were among the most actively traded contracts. Information variables appear not to have significant effect upon volatility of large players. A decomposition of idiosyncratic volatility showed that speculators had 22 markets with significant expected volatility, with 17 being positive. Expected volatility of hedgers was significant and positive in 14 markets, and negative in seven markets. While both speculators and hedgers had significant positive expected volatility in heating oil and crude oil, the magnitude of the coefficients was larger for hedgers, suggesting more active trading in these markets by hedgers at the start of the month rather than for the rest of the month.

Using a GARCH (1, 1) model, news about volatility from the previous month was positive and significant in 10 (15) markets for hedgers (speculators), suggesting that the ARCH term was important in determining current volatility levels for hedgers (speculators), especially in agricultural futures markets. The GARCH term (lagged volatility) was significant in 24 (19) markets, which was consistent with Yang and Brorsen (1993). The greater significance of the news about volatility from the previous month for speculators suggested their greater reliance on noise trading and herding behaviour, where news from previous periods affected current volatility. Further, hedgers’ volatility in Treasury bonds and coffee, and speculators’ volatility in gold and S&P500 futures, had experienced increasing volatility persistence to shocks over the 1990s. In all remaining markets, hedgers’ and speculators’ volatility had shown a tendency to decay over time in response to shocks, supporting that both players were informed and reacted well to news volatility. The PARCH model, in contrast, exhibited more significant negative variables for both lagged volatility and news about volatility from previous month for speculators. By capturing more significant negative impact of lagged volatility and news of volatility from previous month, the PARCH was suggested to be more informative than the GARCH model for speculators’ current volatility. The PARCH model, by capturing both more negative and positive impacts of lagged volatility and news of volatility from previous month for hedgers’ current volatility, was also preferred over the GARCH model. As a robust check, model performance evaluation was carried out and the GARCH model, under normal distribution, gave the lowest RMSE for hedgers’ returns in 13 markets, which was consistent with Bracker and Smith