Adaptive Trading System of Assets for International Cooperation in Agricultural Finance Based on Neural Network

Tong, Guangji; Yin, Zhiwei

doi:10.1007/s10614-021-10136-3

Cited by 4 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the proposed approach was validated and tested by using 16 practical time-series and showed its capability for the selected datasets of the stock exchange. Tong and Yin ( 2021 ) investigated the prediction of financial time series and adaptive trading based on LSTM model to increase the international cooperation in agricultural finance. Ünvan and Ergenç ( 2021 ) used the fuzzy COPRAS (Complex Proportional Assessment) as a fuzzy multi-criteria decision-making technique to investigate the financial performance analysis for the banks.…”

Section: Fewer Research Questions Diverse Fieldsmentioning

confidence: 99%

Deep Learning for Financial Engineering

et al. 2022

View full text Add to dashboard Cite

Section: Fewer Research Questions Diverse Fieldsmentioning

confidence: 99%

Deep Learning for Financial Engineering

et al. 2022

View full text Add to dashboard Cite

“…It is possible to find in the literature several works in different areas that use this type of recurrent network to build machine learning models. Due to the ability to learn data sequences, numerous papers use this architecture for language processing and text classification [36][37][38][39][40][41], financial predictions [23,[42][43][44][45], and other problems involving time series [19,[46][47][48][49][50].…”

Section: Lstm Networkmentioning

confidence: 99%

Forecasting Brazilian Ethanol Spot Prices Using LSTM

et al. 2021

View full text Add to dashboard Cite

Ethanol is one of the most used fuels in Brazil, which is the second-largest producer of this biofuel in the world. The uncertainty of price direction in the future increases the risk for agents operating in this market and can affect a dependent price chain, such as food and gasoline. This paper uses the architecture of recurrent neural networks—Long short-term memory (LSTM)—to predict Brazilian ethanol spot prices for three horizon-times (12, 6 and 3 months ahead). The proposed model is compared to three benchmark algorithms: Random Forest, SVM Linear and RBF. We evaluate statistical measures such as MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and accuracy to assess the algorithm robustness. Our findings suggest LSTM outperforms the other techniques in regression, considering both MSE and MAPE but SVM Linear is better to identify price trends. Concerning predictions per se, all errors increase during the pandemic period, reinforcing the challenge to identify patterns in crisis scenarios.

show abstract

Profit Suggestion Technique in Agronomics Using a New Heuristic-Based Barnacle Mating Honey Badger Algorithm I-BMHBA

Dharsini

Babu

2022

Cybernetics and Systems

View full text Add to dashboard Cite

Adaptive Trading System of Assets for International Cooperation in Agricultural Finance Based on Neural Network

Cited by 4 publications

References 24 publications

Deep Learning for Financial Engineering

Deep Learning for Financial Engineering

Forecasting Brazilian Ethanol Spot Prices Using LSTM

Profit Suggestion Technique in Agronomics Using a New Heuristic-Based Barnacle Mating Honey Badger Algorithm I-BMHBA

Contact Info

Product

Resources

About