The operational strategies of shipping companies including fleet-level decisions are impacted by freight rates (Charemza and Gronicki 1981). Since the lead-time to construct a cargo ship can exceed one or more years, forecasting freight rates accurately can better predict the level of needed ship construction. Predicting the operational decision whether to add to the fleet is generally in the hands of company financial decision-makers. These financial decision-makers have the talent to forecast. They generally rely on typical fundamental econometric analysis to predict trends. Many have applied these techniques to freight rates (Cullinane et al. 1999; Chen et al. 2012; Chung and Weon 2013; Zeng et al. 2016).
There is generally a reliance on typical econometric methods including econometric modeling, simultaneous equations models, and time-series analysis methods to predict freight rate trends (Tinbergen 1959; Shimojo 1979; Charemza and Gronicki 1981; Hampton 1991; Hale and Vanags 1992; Cullinane 1992; Beenstock and Vergottis 1993; Glen 1997; Kavussanos 1996, 1997). Other econometric methods include moving averages, autoregression, and smoothing methods (Holt 1957; Winters 1960; Box and Jenkins 1976; Bowerman and O’Connell 1979; Harvey 1990), judgmental forecasting (Sanders 1992; Goodwin and Wright 1993), and Delphi-based and expert opinion-based methods (Ariel 1989; Duru and Yoshida 2008a, 2008b, 2009). Many of these methods and models rely on various assumptions of the user.
Alternative approaches who have considered judgmental forecasting in addition to statistics models or used time-varying coefficient models have used these methods in comparing the Baltic Dry Bulk Index (BDI) (Duru and Yoshida 2009, 2011; Ko 2013). These studies have concluded that expectation formation in the shipping market is not well incorporated into present BDI.
Recent studies have tried to incorporate novel approaches to forecasting bulk freight rates. Angelopoulos (2017) has used the dynamic spectral content of the BDI to discuss its cyclical behavior through time-frequency analysis; and Tsioumas et al. (2017) have integrated the Dry Bulk Economic Climate Index (DBECI) into a VARX model to improve the accuracy of BDI forecasts. Although these methods provide a good basis for freight rate prediction, they often miss the psychology of the market impact on the rates.
Fundamental analysis relies on variables on the overall economy to predict market trends. Certain variables are leading indicators of what the economy will do and other variables are lagging indicators describing what has already occurred. Business analytics or analysis of these variables has helped businesses make better decisions and improve performance (Trkman et al. 2010). How and where to best use analytics is evolving. There is evidence that business analytics may be useful in the maritime industry through supply chain decisions (Trkman et al. 2010). The concern with this type of analysis is whether the forecasting models and trends have sufficient parameters in the design to be accurate. Therefore, much of the process of prediction involves trial and error (Kaastra and Boyd 1996).
In analyzing the psychology of a market, a different approach is taken. We assume that certain information that affects the pricing of commodities is not immediate and is not yet incorporated into the current market price (Blume et al. 1994). Psychological trends often take the shape of cycle or “waves.” These cycles are perceived to have a rhythm. These cycles are similar to physical processes, such as tides (Roberts 1959). Sometimes the trends have momentum that eventually regresses to a mean in the market. Similarly, the momentum indicator is developed through the moving average of a variable (Daniel et al. 1998).
Further, understanding the history of a market is important to the predictability of trends (Roberts 1959). Therefore, there is an element of subjective judgment in the application of psychological analysis (not unlike judgmental forecasting) but it must be put in the context of history and current circumstances. Some believe that chance behavior in markets itself produces “patterns” that invite spurious interpretations (Roberts 1959). However, there is good empirical evidence that technical analysis improves performance significantly (Neely et al. 1997) although technical analysis has been long regarded by academics with a mixture of suspicion and contempt (Neely et al. 1997). Malkiel (1981) describes Technical analysis as the anathema to the academic world.
The efficient market hypothesis is also under siege, as it cannot explain all variations in price (Brock et al. 1992). Again, studies do show that technical analysis helps to predict price changes in markets. The patterns uncovered by technical rules cannot be explained by traditional statistical analysis such as autocorrelation, random walk, etc. (Brock et al. 1992).
The psychological trends are dependent on the types of information investors and traders receive on the market. For example, if the information on the aggregate supply of a commodity is poor, different investors may take different actions (Blume et al. 1994). Further, the amount of information whether on economic trends or political events influences the psychological assessment of the market trend. Therefore, asymmetry in information may play a role but there may be other causes such as market inefficiency and differing market expectations or expectation errors by investors (Neely et al. 1997). However, most importantly, it is the knowledge and experience of the analyst of the market that makes the most difference to correctly seeing psychological trends (Blume et al. 1994). Again, these types of analysts are referred to as technical analysts. Pring (1980) contends that technical analysis is built on price movements, which are a reflection of mass psychology (fear, panic, confidence, etc. all encompassed). As such, technical analysis is often viewed as an art rather than the application of social science to econometrics. When well-executed it can help to identify forces and predict the peaks and bottoms of a given market. Pring (1991) further elaborates that the way information is framed and presented it makes an impact on the rational choice. As such, technical analysts usually examine the uncertainties of markets in the psychological context.
The psychology of markets often views investors in three categories: smart money, average money, and dumb money (Frazzini and Lamont 2008; Akbas et al. 2015). Smart money enters a particular market early when the market trend is going in the right direction. Smart money also exits before the “bubble” bursts meaning the smart money leaves a particular market before the trend reverses. Average money enters a particular market later than smart money but as the market is still ascending. Average money may or may not leave before the “bubble” bursts. Dumb money enters the market much later than smart or average money. It wants to get into the market where everyone is making money. The problem with dumb money is that it stays too long and generally creates the “bubble.” Dumb money contributes to overcapacity in a market overshooting the needs of operations. Another way to put this phenomenon is that smart money gets out earlier because of the principle of diminishing sensitivity where investors value incremental gains more than riskier gains (Tversky and Kahneman 1992).
In the psychology of shipping freight rates, research has identified waves and cycles. Using these waves and cycles, practical forecasting of turning points in freight rates and the “sentiment” in the shipping market is 1–4 years ahead of time. Turning points refers to signals of major market downturns and upturns.
Another study shows that one-year time charter weekly freight rates are highly volatile and predictable only under nonlinear low dimensional chaos (Goulielmos and Psifia 2009). Zeng et al. (2016) report that the empirical mode decomposition method does provide a useful technique for dry market analysis and forecasting, thus, showing that natural signals can be used. Ndikom (2006) indicates that forecasting freight rates should be based on the principles of planning, which usually uses past and present variables to postulate future extrapolation of rates. Finally, maritime technical analysis supports long term cycles of 16 to 24 years and short-term cycles of 3 to 4 years (Goulielmos 2012).
Forecasting freight rates are not fully predictive by using traditional econometric models (Manzanero and Krupp (2009). Duru et al. (2012) use the fuzzy DELPHI adjustment process for improvement in accuracy for freight rate prediction. Part of the inaccuracy attributed to typical econometric methods is that the methods require several assumptions about the data including the normality and independence that often are not met (Goulielmos and Psifia 2009, 2011).
Therefore, it appears that technical analysis is and can be a good tool to predict freight rates. The question remains whether reliable freight rate prediction assists in better ship capacity needs. Randers and Goluke (2007) identify a 20-year wave based on capacity adjustments and a 4-year cycle based on capacity utilization. Second-hand tanker ship markets also are found to have cycles (Goulielmos and Siropoulou 2006).
In this paper, technical analysis shows that freight rate market predictions can help the capacity needs decisions and technical analysis with fundamental analysis can predict rates more fully. Further, technical analysis can help reduce overcapacity by identifying the value of freight rates where the market signals a turning point. Again, the turning points refer to signals of major market downturns and upturns according to the moving average method of technical analysis.
Therefore, we hypothesize that technical analysis can show when to reduce capacity needs (number of ships) of the operations to not overshoot the market due to the psychological reaction to freight rates. We further contend that technical analysis can achieve this by identifying the market signals that a turning point has occurred.