Forecasting leading industry stock prices based on a hybrid time-series forecast model

Tsai, Mi Ching; Cheng, Ching‐Hsue; Tsai, Meei-Ing; Shiu, Huei-Yuan

doi:10.1371/journal.pone.0209922

Cited by 18 publications

(9 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many SPF approaches have been proposed in recent decades, such as traditional time-series analysis and forecasting [3][4][5][6], machine learning [7][8][9][10][11][12], and deep learning [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Designing an accurate SPF system requires considering fundamental issues such as feature selection, model fitting, and prediction.…”

Section: Introductionmentioning

confidence: 99%

“…Another study [8], meanwhile, proposed using a Bayesian median autoregressive model-in contrast to a mean-based method-for time-series forecasting. Tsai et al [9] used multivariate adaptive regression splines, stepwise regression, and kernel ridge regression as featureselection methods for a time-series forecasting model. Others have combined support vector regression and genetic algorithms to increase forecasting accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Stock-Price Forecasting Based on XGBoost and LSTM

Vuong¹,

Dat²,

Khoi³

et al. 2022

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Using time-series data analysis for stock-price forecasting (SPF) is complex and challenging because many factors can influence stock prices (e.g., inflation, seasonality, economic policy, societal behaviors). Such factors can be analyzed over time for SPF. Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches. This study, therefore, proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches. First, we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant features. Then, we fed selected features into a deep long short-term memory (LSTM) network to forecast stock prices. The deep LSTM network was used to reflect the temporal nature of the input time series and fully exploit future contextual information. The complex structure enables this network to capture more stochasticity within the stock price. The method does not change when applied to stock data or Forex data. Experimental results based on a Forex dataset covering 2008-2018 showed that our approach outperformed the baseline autoregressive integrated moving average approach with regard to mean absolute error, mean squared error, and root-mean-square error.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stock-Price Forecasting Based on XGBoost and LSTM

Vuong¹,

Dat²,

Khoi³

et al. 2022

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Normal trend prediction tasks mainly take direct views on the stock prices. Based on stock prices, fundamental analysis [1], technical analysis [2,3], and historical price time series analysis [4][5][6] have been used to aid in previous stock analysis. In addition to the direct quantitative information the numeric price brings on stock trends, financial news implies qualitative relations between daily events and their effect on the stock prices.…”

Section: Introductionmentioning

confidence: 99%

“…• Empirical evidence that our proposed MIL model can achieve impressive results on the S&P 500 stock index prediction, competing with other conventional neural architectures and previous MIL methods. 1 We use the VADER sentiment analysis tools in [16] to estimate sentiment values for all news items on September 20, 2011.…”

Section: Introductionmentioning

confidence: 99%

Multiple Instance Learning Networks for Stock Movements Prediction with Financial News

Deng¹,

Yiu²

2022

IJAIA

View full text Add to dashboard Cite

A major source of information can be taken from financial news articles, which have some correlations about the fluctuation of stock trends. In this paper, we investigate the influences of financial news on the stock trends, from a multi-instance view. The intuition behind this is based on the news uncertainty in random news occurrences and the lack of annotation for every single financial news. Under the scenario of Multiple Instance Learning (MIL) where training instances are arranged in bags, and a label is assigned for the entire bag instead of instances, we develop a flexible and adaptive multi-instance learning model and evaluate its ability in directional movement forecast of Standard & Poor’s 500 index on financial news dataset. Specifically, we treat each trading day as one bag, with certain amounts of news happening on each trading day as instances in each bag. Experiment results demonstrate that our proposed multiinstance-based framework gains outstanding results in terms of the accuracy of trend prediction, compared with other state-of-art approaches and baselines.

show abstract

“…An interesting approach was the analysis of twitter feeds that were found to be correlated with the Dow Jones Industrial Average (Bollen, Mao & Zeng, 2011). A hybrid time-series model was used for forecasting leading industry stock prices in (Tsai et al, 2018). Bao, Lu & Zhang (2004) employed support vector machines regression for stock price prognosis.…”

Section: Introductionmentioning

confidence: 99%

Competitive / Collaborative Statistical Learning Framework for Forecasting Intraday Stock Market Prices: A Case Study

Belciug¹,

Săndiță²,

Costin³

et al. 2021

STUD INFORM CONTROL

View full text Add to dashboard Cite

This paper presents an intelligent decision system based on statistical learning that regards the tactics of an investor in predicting the next intraday stock price. Significant percentages can be won or lost depending on the tactics applied for buying/selling shares. This paper includes a case study regarding the efficiency of a group of machine learning techniques that work together in a competitive/collaborative manner with a view to achieving an overall price forecast for the next intraday transaction. In order to illustrate the advantages of this intelligent decision system this work provides a concrete example concerning the price forecast for the next intraday transaction for Transilvania Bank (TLV), the stock market at the Bucharest Stock Exchange (BVB), Romania. An important part of the decision system lies in the competitive stage, because only the best competitors are chosen for the ultimate decision-making process. In the collaborative stage of the statistical learning framework one uses a weighted voting system that outputs the final intraday stock price. The results obtained show that this intelligent system outperforms each stand-alone method.

show abstract

Forecasting leading industry stock prices based on a hybrid time-series forecast model

Cited by 18 publications

References 46 publications

Stock-Price Forecasting Based on XGBoost and LSTM

Stock-Price Forecasting Based on XGBoost and LSTM

Multiple Instance Learning Networks for Stock Movements Prediction with Financial News

Competitive / Collaborative Statistical Learning Framework for Forecasting Intraday Stock Market Prices: A Case Study

Contact Info

Product

Resources

About