Stochastic efficiency analysis of alternative basic grain marketing strategies

Venter, Manfred; Strydom, Dirk; Grové, Bennie

doi:10.1080/03031853.2013.770952

Cited by 11 publications

(8 citation statements)

References 15 publications

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“…Regarding forecasts related to the financial market and commodities is a process challenge that involves stochastic and non-deterministic aspects. For example, the factors that influence the agricultural commodity include several variables that affect prices [18] . In addition to weather information, the factors can be categorized: i) Historical and recent market data; ii) Domestic demand and supply; iii) International demand and supply; iv) Macroeconomics; and v) Political factors.…”

Section: Introductionmentioning

confidence: 99%

On the enrichment of time series with textual data for forecasting agricultural commodity prices

2022

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Section: Introductionmentioning

confidence: 99%

On the enrichment of time series with textual data for forecasting agricultural commodity prices

2022

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“…In recent years, research gaps have been raised to enrich time series using external information about the problem domain [19]. For example, [20] presents some factors that influence the variation of agricultural commodity price series, divided into i) recent and historical market data; ii) domestic supply and demand; iii) international supply and demand; iv) macroeconomic information; and, v) political factors. The first three factors are usually contained in time series.…”

Section: Introductionmentioning

confidence: 99%

Data fusion between time series and textual information for predictive tasks in the financial market: A systematic mapping

Filho

Trindade

Martins

et al. 2022

Preprint

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Forecasting models of the financial market generally use time series data. However, external factors can influence time series, such as political events, economic crises, government macroeconomic policy, and the foreign exchange market. This information is not explicit in the time series and can influence the prediction of the variable values. Textual data can provide knowledge about external factors for time series forecasting models. Representations models of time series that include data extracted from texts have been proposed and pointed out as a research trend. The combination of different data sources for forecasting tasks is known in the literature as information fusion. Thus, this study aims to systematically map the main information fusion techniques used to forecast the financial market. More than three hundred works were done with the search strings. However, the complete reading was carried out in only fourteen works. Analyzes were performed to identify the vector representations of texts, information fusion techniques, and predictive models used in the works. As a result, we identified that 78\% of the included works are techniques based on Early Fusion, and textual representations that consider semantic features have been more used in recent works.

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“…Jordaan [8] found that most of the producers in South Africa do not use pre-harvesting marketing strategies, which is consistent with recent research by Michels et al [9], who found a limited adoption of futures contracts by agricultural producers to manage price risk. Meanwhile, Venter et al [10] argued that complex strategies discourage producers from using pre-harvest marketing strategies, since producers do not know how to apply these complex strategies. More straightforward strategies, such as routine and portfolio hedging, have been proposed as an alternative to these complex marketing strategies.…”

Section: Introductionmentioning

confidence: 99%

Developing a Moving Average Crossover Strategy as an Alternative Hedging Strategy for the South Africa Maize Market

Monteiro¹,

Grové²,

Matthews³

2022

Agriculture

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Grain marketing is complex because important decisions are made on the timing of sales and the quantities sold at every trading activity. The literature suggest various grain-hedging strategies, however these strategies are not adaptable to changing market conditions or are difficult for a producer to implement. To address these limitations, our study developed tailor-made moving average crossover (MAC) strategies that are adaptable to changing market conditions and can be easily followed by risk neutral and risk averse grain producers. The study used daily closing prices for the white maize May futures contract for the period 2009/2010 to 2019/2020. An optimization model was solved using the evolutionary algorithm embedded in Excel® to identify the optimal MAC strategy that maximizes the margin above marketing cost for a risk aversion level. The results showed that optimal MAC strategies differ amongst producers with different levels of risk aversion. Furthermore, it was found that the risk-averse producers perform best by marketing their grain early in the marketing season. Meanwhile, the risk-neutral producers perform better by spreading their marketing activities throughout the season. The results further showed that the optimal MAC strategies performed better than the previously proposed routine strategies. The conclusion is therefore that an optimal MAC strategy outperforms routine strategies because of its ability to adapt to changing market conditions, while still being easy to implement.

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Stochastic efficiency analysis of alternative basic grain marketing strategies

Cited by 11 publications

References 15 publications

On the enrichment of time series with textual data for forecasting agricultural commodity prices

On the enrichment of time series with textual data for forecasting agricultural commodity prices

Data fusion between time series and textual information for predictive tasks in the financial market: A systematic mapping

Developing a Moving Average Crossover Strategy as an Alternative Hedging Strategy for the South Africa Maize Market

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