Forecasting Taiwan Stocks Weighted Index Monthly Average Based on Linear Regression – Applied to Taiwan Stock Index Futures

Ha, Duy-An; Jian-de, LU; Yuan, Shyan-Ming

doi:10.1142/9789811228001_0099

Cited by 3 publications

(7 citation statements)

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“…For predicting the monthly TAIEX average, Sun et al [32] and Ha et al [33] adopted linear regression while Tan et al [34] tried both linear regression and extreme gradient boosting. All three studies used correlation analysis and statistical hypothesis testing for feature selection and worked with monthly averages for both the independent and dependent variables.…”

Section: Literature Reviewmentioning

confidence: 99%

“…All three studies used correlation analysis and statistical hypothesis testing for feature selection and worked with monthly averages for both the independent and dependent variables. The forecast distance is one and one to five months ahead in [32] and [33,34], respectively. They also used a similar strategy to our study but without a stop-loss strategy.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this study, the main structure of the framework for building models to predict MAT includes three stages: (1) identifying potential factors that have a relationship with the development of TAIEX, (2) data preprocessing, and (3) training models. For the first and second stages, we used an approach similar to that used in our previous study [33]. The details of these stages are described later in this section.…”

Section: The Framework For Predicting Monthly Average Of Taiexmentioning

confidence: 99%

“…Readers can refer to Table A1 in Appendix A for more details about these factors. In this study, we considered much more potential factors compared to previous studies [32][33][34] (References [32][33][34] inspected 16 and 22 factors, respectively), and the closing prices of these independent variables were collected daily. It is worth noting that the target variable TAIEX was also collected daily, but the opening price was used instead of the closing price (we assume that the opening and closing prices can be used interchangeably).…”

Section: Identifying Potential Factorsmentioning

confidence: 99%

“…To evaluate the performance of the models in practice, we conducted simulations on MTX trading, where the selection of MTX contracts for investment was based on the predictions of the models. Concerning trading strategy, we adopted a similar approach as in [33], which is suitable for individual investors who do not want to spend too much time tracking the volatility of the stock market. The trading fee was ignored in the simulations for simplicity but without loss of generality.…”

Section: Simulated Tradingmentioning

confidence: 99%

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Deep Learning Models for Predicting Monthly TAIEX to Support Making Decisions in Index Futures Trading

Liao

Tan

et al. 2021

Mathematics

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Futures markets offer investors many attractive advantages, including high leverage, high liquidity, fair, and fast returns. Highly leveraged positions and big contract sizes, on the other hand, expose investors to the risk of massive losses from even minor market changes. Among the numerous stock market forecasting tools, deep learning has recently emerged as a favorite tool in the research community. This study presents an approach for applying deep learning models to predict the monthly average of the Taiwan Capitalization Weighted Stock Index (TAIEX) to support decision-making in trading Mini-TAIEX futures (MTX). We inspected many global financial and economic factors to find the most valuable predictor variables for the TAIEX, and we examined three different deep learning architectures for building prediction models. A simulation on trading MTX was then performed with a simple trading strategy and two different stop-loss strategies to show the effectiveness of the models. We found that the Temporal Convolutional Network (TCN) performed better than other models, including the two baselines, i.e., linear regression and extreme gradient boosting. Moreover, stop-loss strategies are necessary, and a simple one could be sufficient to reduce a severe loss effectively.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: The Framework For Predicting Monthly Average Of Taiexmentioning

confidence: 99%

Section: Identifying Potential Factorsmentioning

confidence: 99%

Section: Simulated Tradingmentioning

confidence: 99%

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Deep Learning Models for Predicting Monthly TAIEX to Support Making Decisions in Index Futures Trading

Liao

Tan

et al. 2021

Mathematics

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Using machine learning models to forecast future prices – an exploration of investment strategies

2023

2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA)

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Prediction of university fund revenue and expenditure based on fuzzy time series with a periodic factor

Shen

et al. 2023

PLoS ONE

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Financial management and decision-making of universities play an essential role in their development. Predicting fund revenue and expenditure of universities can provide a necessary basis for funds risk prevention. For the lack of solid data reference for financial management and funds risk prevention in colleges and universities, this paper presents a prediction model of University fund revenue and expenditure based on fuzzy time series with a periodic factor. Combined with the fuzzy time series, this prediction method introduces the periodic factor of university funds. The periodic factor is used to adjust the proportion of the predicted value of the fuzzy time series and the periodic observation value. A fund revenue prediction model and a fund expenditure prediction model are constructed, and an experiment is carried out with the actual financial data of a university in China. The experimental result shows the effectiveness of the proposed model, which can provide solid references for financial management and funds risk prevention in universities.

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Forecasting Taiwan Stocks Weighted Index Monthly Average Based on Linear Regression – Applied to Taiwan Stock Index Futures

Cited by 3 publications

References 0 publications

Deep Learning Models for Predicting Monthly TAIEX to Support Making Decisions in Index Futures Trading

Deep Learning Models for Predicting Monthly TAIEX to Support Making Decisions in Index Futures Trading

Using machine learning models to forecast future prices – an exploration of investment strategies

Prediction of university fund revenue and expenditure based on fuzzy time series with a periodic factor

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