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The paper seeks to answer the question of how price forecasting can contribute to which techniques gives the most accurate results in the futures commodity market. A total of two families of models (decision trees, artificial intelligence) were used to produce estimates for 2018 and 2022 for 21- and 125-day periods. The main findings of the study a...
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... the second estimation (2022), the learning database covers the period between 1 January 2014 and 28 February 2022 (98 months), while the validation interval between 1 March 2022 and 31 August 2022 (6 months). The descriptive statistics of the commodity market for the full dataset are presented in Table 6. The two periods (2018 and 2022) reflect a significantly different general economic situation, which is not reflected in the descriptive statistics for the whole period. ...Similar publications
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... These findings underscore CCI's adaptability in refining financial forecasting models, though its dependence on high-quality input data necessitates robust preprocessing mechanisms. Vancsura, Tatay, and Bareith (2023) reinforced the value of CCI by showcasing its effectiveness in speculative transactions, particularly when combined with relative strength index strategies, further demonstrating its versatility in diverse trading scenarios. ...
The growing intricacy of international financial markets requires sophisticated approaches to managing investments and minimizing losses. This paper evaluates the use of Structural Equation Modeling (SEM) to improve forecast accuracy by integrating multiple technical indicators within the Bank Nifty Index. The study employs SEM to estimate the effect of key technical indicators such as the Simple Moving Average (SMA), Relative Strength Index (RSI), Volume Weighted Average Price (VWAP), and Moving Average Convergence Divergence (MACD) on trading volumes and closing values. The model considers both direct and indirect relationships among these indicators to determine their overall impact. The study highlights the significance of certain technical indicators in predicting market trends. It demonstrates SEM’s effectiveness in estimating interrelationships among these indicators and formulating predictive models. This study underscores SEM’s effectiveness in financial forecasting by showing that incorporating multiple technical indicators enhances prediction accuracy and improves decision-making in financial markets. Investors and traders can use these findings to develop better trading strategies, improve market stability, and maximize returns. This analysis supports the case for a multi-indicator approach in forecasting models.
... Traditional forecasting models such as Linear Regression, ARIMA, and Simple Exponential Smoothing (SES) have been widely used; however, their effectiveness is often limited by their inability to fully capture the complex dynamics of commodity markets. The primary limitation of these models lies in their assumption of linearity and stationarity, which often does not hold true in real-world financial data characterized by sudden market shifts and non-linear interactions (Ma, 2020;Vancsura et al., 2023). ...
... First, it contributes to the academic discourse on economic forecasting by offering a detailed comparative analysis of different models, thereby providing insights into their relative strengths and weaknesses. Previous studies have indicated that while models like ARIMA are effective in specific contexts, they often fail to account for the non-linear and chaotic nature of financial markets, necessitating the exploration of more advanced hybrid approaches (Vancsura et al., 2023). Second, this research serves a practical purpose by guiding policymakers and economic stakeholders in Indonesia in making informed decisions to mitigate the adverse effects of price volatility. ...
In this study, we compare the performance of both hybrid and non-hybrid forecasting models, explicitly focusing on Linear Regression, ARIMA, Simple Exponential Smoothing, Hybrid ARIMA-LSTM, and EWMA in predicting commodity prices within the volatile market of Central Java, Indonesia. The primary objective is to evaluate which hybrid and non-hybrid models provide the most accurate and reliable forecasts under various conditions. Analyzing daily price data from the SiHaTi platform, an official service provided by Bank Indonesia, the Hybrid ARIMA-LSTM model emerges as the most accurate, achieving a forecast accuracy of 92.5%, compared to the 78.3% and 84.7% accuracies of Linear Regression and ARIMA, respectively. These findings underline the potential advantages of combining machine learning with statistical methods to improve predictions in dynamic market conditions, providing invaluable insights for policymakers and market analysts. However, it should be noted that only one hybrid model was compared, and future research should explore multiple hybrid models to ensure a comprehensive evaluation of their effectiveness.
Predictions of prices for a wide variety of commodities have been relied upon by governments and investors over the course of history. The purpose of this study is to investigate the difficult challenge of predicting daily palladium prices for the United States by utilizing time series data ranging from January 5, 1977, to March 26, 2024. When it comes to this crucial evaluation of commodity prices, estimates have not been given sufficient consideration in earlier research. In this context, price predictions are generated by the utilization of Gaussian process regression algorithms, which are estimated through the utilization of cross-validation processes and Bayesian optimization approaches. With a relative root mean square error of 0.4598%, our empirical prediction approach produces price estimates that are generally accurate for the out-of-sample phase that spans from March 24, 2017, to March 26, 2024. In order to make educated choices about the palladium industry, governments and investors can utilize price prediction models to get the information they need.
After 2010, the consumer price index fell to a low level in the EU. In the euro area, it remained low between 2010 and 2020. The European Central Bank has even had to take action against the emergence of deflation. The situation changed significantly in 2021. Inflation jumped to levels not seen for 40 years in the EU. Our study aims to use artificial intelligence to forecast inflation. We also use artificial intelligence to forecast stock index changes. Based on the forecasts, we propose portfolio reallocation decisions to protect against inflation. The forecasting literature does not address the importance of structural breaks in the time series, which, among other things, can affect both the pattern recognition and prediction capabilities of various machine learning models. The novelty of our study is that we used the Zivot–Andrews unit root test to determine the breakpoints and partitioned the time series into training and testing datasets along these points. We then examined which database partition gives the most accurate prediction. This information can be used to re-balance the portfolio. Two different AI-based prediction algorithms were used (GRU and LSTM), and a hybrid model (LSTM–GRU) was also included to investigate the predictability of inflation. Our results suggest that the average error of the inflation forecast is a quarter of that of the stock market index forecast. Inflation developments have a fundamental impact on equity and government bond returns. If we obtain a reliable estimate of the inflation forecast, we have time to rebalance the portfolio until the inflation shock is incorporated into government bond returns. Our results not only support investment decisions at the national economy level but are also useful in the process of rebalancing international portfolios.
Agricultural commodity prices have significant impacts on economies by leading to changes and regulations in both fiscal and monetary policies. These also have effects on household economies and consumer purchasing power particularly in developing countries. Thereby, instability and variability in these prices constitute adverse effects on these economies. On the other hand, assets of the commodity markets become popular just as bonds and stocks. Because of this growing interest, needs for managing risks, stable prices and lowering transaction costs has led to establishment of the commodity exchanges. In this context, Turkey put the licensed warehousing system into operation by founding the Turkish Mercantile Exchange (TMEX) to operate trades of Electronic Warehouse Receipts (EWRs). In this study, a model including US Dollar-Turkish Lira exchange rate (USD/TRY), Brent crude-oil prices, overnight interest rate and a daily dataset for the 01/04/2021-20/02/2023 period were used to assess several machine learning regression methods in predicting the TMEX Wheat Index (TMXWHT). As verified by comparisons with actual values and considering performance evaluation criteria, all methods yielded successful outcomes, furthermore, tree-based methods revealed better overall performance.
