A.H.Alizadeh.N.K.Nomikos Transportation Research Part B 41 (2007)126-143 137 Table 4 Result of empirical simulation of trading strategies Handysize Panamax Capesize MAl2/MAI on PlE ratio Mean return 0.08611 0.14814 0.16127 St.Dev 0.12907 0.15083 0.20209 Sharpe ratio 0.66713 0.98216 0.79800 MA6/MAl on PlE ratio Mean return 0.07332 0.14017 0.15751 St.Dev. 0.13346 0.15353 0.20197 Sharpe ratio 0.54936 0.91296 0.77983 Buy and hold Mean return 0.08487 0.11459 0.08097 St.Dev. 0.18406 0.20967 0.25288 Sharpe Ratio 0.46109 0.54651 0.32019 Mean return and St.Dev.are the annualized mean returns (monthly mean return x 12)and standard deviation of returns (monthly standard deviation x v12),respectively,for the different trading strategies.Sharpe ratio is the ratio of mean returns over the standard deviation of returns. .Sample period is January 1976 to September 2004 for handysize and panamax series and April 1979 to September 2004 for the capesize series. The cumulative returns on the MA(12,1)trading rule and "buy and hold"investment strategy in the handysize,panamax and capesize markets are shown in Figs.3-5,respectively.The significant increase in cumulative returns when the active MA(12,1)trading rule is employed,compared to "buy and hold"strategy, is evident for the larger vessels(panamax and capesizes).In fact,it is also interesting to note that the proposed trading model correctly identifies the buy signal during the lucrative shipping markets of 2003-2004 when earnings increased sharply compared to ship prices. 4.2.Data snooping and the stationary bootstrap The results in the previous section are encouraging regarding the performance of our proposed trading strategies.However,an important issue which arises when evaluating technical trading rules,is that of data snooping.According to Sullivan et al.(1999)and White(2000)data snooping occurs when a dataset is used more than once for data selection and inference purposes.In other words,using the same dataset frequently for testing trading strategies,may increase the probability of having satisfactory results purely due to chance or due to the use of posterior information rather than the superior ability of the trading strategies. The method most commonly used in the literature to assess the performance of trading strategies and test for data snooping is bootstrap.The bootstrap,introduced by Efron (1979),is a resampling method that uses the empirical distribution of the statistic of interest,rather than the theoretical distribution implied by 1200 1000 800 600 400 200 0 Jan-77 9 9 1 93 95 7 99 -03 Jan Jar Jan Jan Ja MACum Ret-BHCum Ret Fig.3.Cumulative return on MA trading strategy for handysize bulk carrier.The cumulative returns on the MA(12, 1) trading rule and ‘‘buy and hold’’ investment strategy in the handysize, panamax and capesize markets are shown in Figs. 3–5, respectively. The significant increase in cumulative returns when the active MA(12, 1) trading rule is employed, compared to ‘‘buy and hold’’ strategy, is evident for the larger vessels (panamax and capesizes). In fact, it is also interesting to note that the proposed trading model correctly identifies the buy signal during the lucrative shipping markets of 2003–2004 when earnings increased sharply compared to ship prices. 4.2. Data snooping and the stationary bootstrap The results in the previous section are encouraging regarding the performance of our proposed trading strategies. However, an important issue which arises when evaluating technical trading rules, is that of data snooping. According to Sullivan et al. (1999) and White (2000) data snooping occurs when a dataset is used more than once for data selection and inference purposes. In other words, using the same dataset frequently for testing trading strategies, may increase the probability of having satisfactory results purely due to chance or due to the use of posterior information rather than the superior ability of the trading strategies. The method most commonly used in the literature to assess the performance of trading strategies and test for data snooping is bootstrap. The bootstrap, introduced by Efron (1979), is a resampling method that uses the empirical distribution of the statistic of interest, rather than the theoretical distribution implied by Table 4 Result of empirical simulation of trading strategies Handysize Panamax Capesize MA12/MA1 on P/E ratio Mean return 0.08611 0.14814 0.16127 St. Dev. 0.12907 0.15083 0.20209 Sharpe ratio 0.66713 0.98216 0.79800 MA6/MA1 on P/E ratio Mean return 0.07332 0.14017 0.15751 St. Dev. 0.13346 0.15353 0.20197 Sharpe ratio 0.54936 0.91296 0.77983 Buy and hold Mean return 0.08487 0.11459 0.08097 St. Dev. 0.18406 0.20967 0.25288 Sharpe Ratio 0.46109 0.54651 0.32019 • Mean return and St. Dev. are the annualized mean returns (monthly mean return · 12) and standard deviation of returns (monthly standard deviation  ffiffiffiffiffi 12 p ), respectively, for the different trading strategies. Sharpe ratio is the ratio of mean returns over the standard deviation of returns. • Sample period is January 1976 to September 2004 for handysize and panamax series and April 1979 to September 2004 for the capesize series. 0 200 400 600 800 1000 1200 Jan-77 Jan-79 Jan-81 Jan-83 Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 MA Cum Ret BH Cum Ret Fig. 3. Cumulative return on MA trading strategy for handysize bulk carrier. A.H. Alizadeh, N.K. Nomikos / Transportation Research Part B 41 (2007) 126–143 137