A Review on Optimization of Least Squares Support Vector Machine for Time Series Forecasting

Yusof, Yuhanis; Mustaffa, Zuriani

doi:10.5121/ijaia.2016.7203

Cited by 16 publications

(9 citation statements)

References 60 publications

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“…These and other ANNs models [47][48][49][50][51][52][53][54][55][56] have shown that ANNs provide an important alternative to econometrics (both linear and nonlinear) in forecasting crude oil prices. Dbouk et al [57] noted, however, that the accuracy of price predictions is not a key aspect of successful investment or hedging strategies.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk

2021

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Despite the growing share of renewable energy sources, most of the world energy supply is still based on hydrocarbons and the vast majority of world transport is fuelled by oil products. Thus, the profitability of many companies may depend on the effective management of oil price risk. In this article, we analysed the effectiveness of artificial neural networks in hedging against the risk of WTI crude oil prices increase. This was reformulated from a regressive problem to a classification problem. The effectiveness of our approach, using artificial neural networks to classify observations, was verified for over ten years of WTI futures quotes, starting from 2009. The data analysis presented in this paper confirmed that the buyer of a call option was more often likely to incur a loss as a result of its purchase than make a profit after the final payoff from the call option. The results of the conducted research confirm that neural networks can be an effective form of protection against the risk of price fluctuations. The effectiveness of a network’s operation depends on the choice of assessment indicators, but analyses show that the networks which, for the indicator that was selected, gave the best results for the training set, also resulted in positive rates of return for the test set. Significantly, we also showed interdependence between seemingly unrelated indicators: percentage of the best possible results achieved in the analysed period of time by the proposed method and percentage of all available call options that were purchased based on the results from the networks that were used.

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Section: Introductionmentioning

confidence: 99%

Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk

2021

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“…Numerous studies [38][39][40][41][42][43][44][45][46][47] show that ANNs are an effective tool for forecasting crude oil prices. However, the aforementioned studies focused mostly on oil price predictions, which do not necessarily lead to successful investment strategies as shown by the authors in previous studies [48,49] (and others [50]).…”

Section: Introductionmentioning

confidence: 99%

Using Artificial Neural Networks to Support the Decision-Making Process of Buying Call Options Considering Risk Appetite

2021

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During the COVID-19 pandemic, uncertainty has increased in many areas of both business supply and demand, notably oil demand and pricing have become even more unpredictable than before. Thus, for companies that buy large quantities of oil, effective oil price risk management is crucial for business success. Nevertheless, businesses’ risk appetite, specifically willingness to accept more risk to achieve desired business benefits, varies significantly. The aim of this paper is to deepen the analysis of the effectiveness of employing artificial neural networks (ANNs) in hedging against oil price changes by searching for buy signals for European WTI (West Texas Intermediate) crude oil call options, while taking into account the level of risk appetite. The number of generated buy signals decreases with increasing risk appetite, and thus the amount of capital necessary to buy options decreases. However, the results show that fewer buy signals do not necessarily translate into lower returns generated by networks in a given class. Thus, higher levels of return on the purchase of call options may be obtained. The conducted analyses clearly proved that ANNs can be a useful tool in the process of managing WTI crude oil price change risk. Using the analyzed network parameters, up to 29.9% of the theoretical maximum possible profit from buying options every day was obtained in the test set. Furthermore, all proposed networks generated some profit for the test set. The values of all indicators used in the analyses confirm that the ANNs can be effective regardless of the level of risk appetite, so in this respect they may be described as a universal decision support tool.

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“…LSSVR can improve the training speed of solving the problem by transforming the quadratic programming problem of SVR into solving the problem of linear equations [28]. Hence, LSSVR has demonstrated superior performances [29][30][31] and has been applied to many forecasting areas, including reliability analysis [32], pH indications prediction [33], crashworthiness optimization problems [34], financial prediction [15], foreign exchange rate forecasting [35], revenue forecasting [36], tourism demand forecasting [29], beta systematic risk forecasting [37], crude oil price forecasting [38,39], robust system identification with outliers [40], solar irradiance forecasting [41], wind speed prediction [42], and so on. Therefore, this paper tends to introduce this powerful forecasting technique of LSSVR to perform prediction for gold prices.…”

Section: Introductionmentioning

confidence: 99%

“…Yusof and Mustaffa [31] have reviewed existing works and learned that optimizing the hyper-parameters of LSSVR is best implemented using the evolutionary algorithms (EA). Categorized under EA group, GA is the most prominent and widely used technique as compared with other techniques within the same domain [43].…”

Section: Introductionmentioning

confidence: 99%

Using Market Sentiment Analysis and Genetic Algorithm-Based Least Squares Support Vector Regression to Predict Gold Prices

Yuan¹,

Lee²,

Chiu³

2020

IJCIS

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Gold price prediction has long been a crucial and challenging research topic for gold investors. In conventional models, most scholars have used the historical gold price or economic indicators to forecast gold prices. The gold prices depend mainly on confidence in the current market. To reduce the time delay of economic indicators in this study, the daily online global gold news undergoes a text mining approach. An opinion score is generated by ascertaining the opinion polarity and words in the daily gold news. The opinion score represents the current market trends and used as an input predictor in the forecasting model. Subsequently, the least square support vector regression (LSSVR) that is optimized by the genetic algorithm (GA) is employed to train and predict the future gold price. The mean absolute percentage error (MAPE) is adopted to evaluate the model performance. This study is the first to use the opinion score through text mining as an input predictor to GA-LSSVR in forecasting gold prices. The experiment results demonstrate that the input predictor, opinion score, can improve the predicting ability of GA-LSSVR model in terms of MAPE.

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A Review on Optimization of Least Squares Support Vector Machine for Time Series Forecasting

Cited by 16 publications

References 60 publications

Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk

Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk

Using Artificial Neural Networks to Support the Decision-Making Process of Buying Call Options Considering Risk Appetite

Using Market Sentiment Analysis and Genetic Algorithm-Based Least Squares Support Vector Regression to Predict Gold Prices

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