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The purpose of this paper is to resolve three problems in ship routing and scheduling systems. Problem 1 is the anticipation of the future cargo transport demand when the shipping models are stochastic based on this demand. Problem 2 is the capacity of these models in processing large number of ships and cargoes within a reasonable time. Problem 3 is the viability of tramp shipping when it comes to real problems.

A commodity-trade forecasting system is developed, an information technology platform is designed and new shipping elements are added to the models to resolve tramp problems of en-route ship bunkering, low-tide port calls and hold-cleaning cost caused by carrying incompatible cargoes.

More realistic stochastic cargo quantity and freight can now be anticipated, larger number of ships and cargoes are now processed in time and shipping systems are becoming more viable.

More support goes to ship owners to make better shipping decisions.

New norms are established in forecasting, upscaling and viability in ship routing and scheduling systems.

Antitrust exemptions to shipping alliances in the liner shipping sector have prevailed for many years. This study aims to examine anti-competition of ocean shipping alliances from a legal perspective of the USA, the European Union (EU) and People’s Republic of China (PRC).

Adopting the standard “doctrinal approach to legal research and analysis” in legal literatures, this paper reviews landmark court cases and legislations in the USA relating to shipping conference system from its beginning to its erosion, followed by its latest transition to non-ratemaking agreements, with discussions on the EU and some PRC treatments on shipping conferences.

Although antitrust exemptions to shipping conferences in the liner shipping sector were eliminated in the trades to/from the USA and the EU, there is a lack of evidence of the deterioration found in the viability of liner shipping carriers in both parts of the world trades. For the USA, shipping alliances will shift the focus to sharing resources for improvement of collective operational efficiencies, whereas the shipper groups in the EU have worried that a protected system of sharing information may lead to price fixing conducts among the carriers.

Through the discussions on the legal treatments of shipping conferences from the USA, the EU and PRC perspectives, this paper provides legal researchers with not only a new research direction on raising collective operational efficiencies through resource sharing but also an insight into shifting their research focus from purely price determination to the area of merger.

This paper reviews landmark court cases and related legislations about the treatments of different regulatory regimes, including the USA, the EU and PRC, to explore the illegitimacy of anti-competition conducts in ocean shipping alliances.

Before describing the methods used by the National Ports Council to forecast UK international trade it will be useful to explain the background to the forecasts, in particular the role of the National Ports Council in the British ports industry, and hence the requirement for the work.

The forecasting power of commodity futures is a matter of intensive research as evidenced by a number of related publications. The purpose of this paper is to illustrate how advanced forecasting techniques improve the predictability of sugar futures in the Indian commodity market.

The forward premium is estimated using ordinary least square regression technique. Different linear and nonlinear models are used to forecast the sugar future spot prices from the futures prices. The forecasting accuracy of each pair of models is then compared by estimating the corresponding Diebold-Mariano test statistics.

From the estimated forward premiums, it is found that there is more volatility toward the date of maturity for a three-month horizon compared to six-month, and 12-month horizons. It is established that the futures prices of sugar, when used in a model, are able to generate better forecasts for the future spot prices. Moreover, the forecasting accuracy is found to be better for a shorter futures horizon.

The present study is restricted only to sugar. If sufficient data are available, the same study could be extended to other commodities as well. The findings imply that technical traders would benefit by using advanced forecasting techniques along with futures prices of sugar to determine the expected future spot prices.

The findings in this paper suggest that though simple statistical models may be adopted to relate future spot prices to futures prices, more accurate prediction of the price behavior is possible with advanced forecasting methods like the artificial neural network.

The findings will help market participants such as traders to be better informed about the future spot prices and hence get a better deal.

This is one of the first investigations to assess the predictability of commodity futures by employing advanced forecasting techniques.

In this paper the authors examine evidence on exchange rate predictability through commodity prices for a set of countries categorized as commodity import- and export-dependent developed and emerging countries.

The authors perform in-sample and out-of-sample forecasting analysis. The commodity prices are modeled to predict the exchange rate and to analyze whether this commodity price model can perform better than the random walk model (RWM) or not. These two models are compared and evaluated in terms of exchange rate forecasting abilities based on mean squared forecast error and Theil inequality coefficient.

The authors find that primary commodity prices better predict exchange rates in almost two-thirds of export-dependent developed countries. In contrast, the RWM shows superior performance in the majority of export-dependent emerging, import-dependent emerging and developed countries.

Previous studies examined the exchange rate of commodity export-dependent developed countries mainly. This study examines both developed and emerging countries and finds for which one the changes in prices of export commodities (in case of commodity export-dependent country) or prices of major importing commodities (in case of import-dependent countries) can significantly predict the exchange rate.

This work aims at designing an indicator for detecting and forecasting price volatility and speculative bubbles in three markets dealing with agricultural and soft commodities, i.e. Intercontinental Exchange Futures market Europe, (IFEU), Intercontinental Exchange Futures market United States (IFUS) and Chicago Board of Trade (CBOT). This indicator, designed as a demand/supply odds ratio, intends to overcome the subjectivity limits embedded in sentiment indexes as the Bull and Bears ratio by the Bank of America Merrill Lynch.

Data evidence allows for the parameter estimation of a Jacobi diffusion process that models the demand share and leads the forecast of speculative bubbles and realised volatility. Validation of outcomes is obtained through the dynamic regression with autoregressive integrated moving average (ARIMA) error. Results are discussed in comparison with those from the traditional generalized autoregressive conditional heteroskedasticity (GARCH) models. The database is retrieved from Thomson Reuters DataStream (nearby futures daily frequency).

The empirical analysis shows that the indicator succeeds in capturing the trend of the observed volatility in the future at medium and long-time horizons. A comparison of simulations results with those obtained with the traditional GARCH models, usually adopted in forecasting the volatility trend, confirms that the indicator is able to replicate the trend also providing turning points, i.e. additional information completely neglected by the GARCH analysis.

The authors' commodity demand as discrete-time process is capable of replicating the observed trend in a continuous-time framework, as well as turning points. This process is suited for estimating behavioural parameters of the agents, i.e. long-term mean, speed of mean reversion and herding behaviour. These parameters are used in the forecast of speculative bubbles and realised volatility.

This study aims to validate the “expectancy theory” of asset pricing and explores the price discovery process in metals futures markets.

This paper adopts the Johansen cointegration and vector error correction model approach to investigate the potentials of Pairs trading in the metals market during the period 2008–2019.

The results find the price movements in metal markets are not random walk and the current “futures” prices are the reasonable estimate of the “spot” metal prices in future. This study does not notice any significant differences in the price efficiency across metals markets, which signal the effects of limited idiosyncratic forces in price transmission.

The research suggests the covert use of metal futures to make gains from arbitrage trading.

The study emphasizes the potential of “pair trading” in commodity market context that is seldom discussed in academic papers.

We propose a Machine Learning (ML) approach that will be trained from the available financial data and is able to gain the trends over the data and then uses the acquired knowledge for a more accurate forecasting of financial series. This work will provide a more precise results when weighed up to aged financial series forecasting algorithms. The LSTM Classic will be used to forecast the momentum of the Financial Series Index and also applied to its commodities. The network will be trained and evaluated for accuracy with various sizes of data sets, i.e. weekly historical data of MCX, GOLD, COPPER and the results will be calculated.

Desirable LSTM model for script price forecasting from the perspective of minimizing MSE. The approach which we have followed is shown below. (1) Acquire the Dataset. (2) Define your training and testing columns in the dataset. (3) Transform the input value using scalar. (4) Define the custom loss function. (5) Build and Compile the model. (6) Visualise the improvements in results.

Financial series is one of the very aged techniques where a commerce person would commerce financial scripts, make business and earn some wealth from these companies that vend a part of their business on trading manifesto. Forecasting financial script prices is complex tasks that consider extensive human–computer interaction. Due to the correlated nature of financial series prices, conventional batch processing methods like an artificial neural network, convolutional neural network, cannot be utilised efficiently for financial market analysis. We propose an online learning algorithm that utilises an upgraded of recurrent neural networks called long short-term memory Classic (LSTM). The LSTM Classic is quite different from normal LSTM as it has customised loss function in it. This LSTM Classic avoids long-term dependence on its metrics issues because of its unique internal storage unit structure, and it helps forecast financial time series. Financial Series Index is the combination of various commodities (time series). This makes Financial Index more reliable than the financial time series as it does not show a drastic change in its value even some of its commodities are affected. This work will provide a more precise results when weighed up to aged financial series forecasting algorithms.

We had built the customised loss function model by using LSTM scheme and have experimented on MCX index and as well as on its commodities and improvements in results are calculated for every epoch that we run for the whole rows present in the dataset. For every epoch we can visualise the improvements in loss. One more improvement that can be done to our model that the relationship between price difference and directional loss is specific to other financial scripts. Deep evaluations can be done to identify the best combination of these for a particular stock to obtain better results.

In this paper, the authors aim to investigate the short‐run as well as long‐run market efficiency of Indian commodity futures markets using different asset pricing models. Four agricultural (soybean, corn, castor seed and guar seed) and seven non‐agricultural (gold, silver, aluminium, copper, zinc, crude oil and natural gas) commodities have been tested for market efficiency and unbiasedness.

The long‐run market efficiency and unbiasedness is tested using Johansen cointegration procedure while allowing for constant risk premium. Short‐run price dynamics is investigated with constant and time varying risk premium. Short‐run price dynamics with constant risk premium is modeled with ECM model and short‐run price dynamics with time varying risk premium is modeled using ECM‐GARCH in‐Mean framework.

As far as long‐run efficiency is concerned, the authors find that near month futures prices of most of the commodities are cointegrated with the spot prices. The cointegration relationship is not found for the next to near months futures contracts, where futures trading volume is low. The authors find support for the hypothesis that thinly traded contracts fail to forecast future spot prices and are inefficient. The unbiasedness hypothesis is rejected for most of the commodities. It is also found that for all commodities, some inefficiency exists in the short run. The authors do not find support of time varying risk premium in Indian commodity market context.

In context of Indian commodity futures markets, probably this is the first study which explores the short‐run market efficiency of futures markets in time varying risk premium framework. This paper also links trading activity of Indian commodity futures markets with market efficiency.

This paper aims to examine from commodity portfolio managers’ perspective the performance of liquidity adjusted risk modeling in assessing the market risk parameters of a large commodity portfolio and in obtaining efficient and coherent portfolios under different market circumstances.

The implemented market risk modeling algorithm and investment portfolio analytics using reinforcement machine learning techniques can simultaneously handle risk-return characteristics of commodity investments under regular and crisis market settings besides considering the particular effects of the time-varying liquidity constraints of the multiple-asset commodity portfolios.

In particular, the paper implements a robust machine learning method to commodity optimal portfolio selection and within a liquidity-adjusted value-at-risk (LVaR) framework. In addition, the paper explains how the adapted LVaR modeling algorithms can be used by a commodity trading unit in a dynamic asset allocation framework for estimating risk exposure, assessing risk reduction alternates and creating efficient and coherent market portfolios.

The optimization parameters subject to meaningful operational and financial constraints, investment portfolio analytics and empirical results can have important practical uses and applications for commodity portfolio managers particularly in the wake of the 2007–2009 global financial crisis. In addition, the recommended reinforcement machine learning optimization algorithms can aid in solving some real-world dilemmas under stressed and adverse market conditions (e.g. illiquidity, switching in correlations factors signs, nonlinear and non-normal distribution of assets’ returns) and can have key applications in machine learning, expert systems, smart financial functions, internet of things (IoT) and financial technology (FinTech) in big data ecosystems.