CoChat
Sign Up
Cited by 15Stock Market Forecasting MethodsTime Series Analysis and ForecastingForecasting Techniques and Applications

Prediction of Amazon’s Stock Price Based on ARIMA, XGBoost, and LSTM Models

Open access
Zhu, Zhe, He, Kexin·2022·Proceedings of Business and Economic Studies
DOI 10.26689/pbes.v5i5.4432
Open this paper in CoChat — free accountOpen PDF

Abstract

Finding the best model to predict the trend of stock prices is an issue that has always garnered attention, and it is also closely related to investors’ investment dynamics. Even the commonly used autoregressive integrated moving average (ARIMA), extreme gradient boosting (XGBoost), and long short-term memory (LSTM) have their own advantages and disadvantages. We use mean squared error (MSE) to judge the most suitable model for predicting Amazon’s stock price from many aspects and find that LSTM is the model with the best fitting effect and the closest to the real curve. However, the LSTM model still needs to improve in terms of performance so as to reduce the bias. We anticipate the discovery of more models that are apt for predicting stocks in the future.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.26689/pbes.v5i5.4432

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Based on the evaluation parameters, XG-Boost outperformed ARIMA and LSTM.

    “Based on the evaluation parameters, XG-Boost outperformed ARIMA and LSTM (Zhu and He 2022).”

    From Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications (2023) · cites this paper as Zhu and He 2022

    In the MACD section, the paper says LSTM had a much smaller MSE than both ARIMA and XGBoost for predicting Amazon’s stock price. The Abstract likewise identifies LSTM as the best-fitting model. The paper’s own evaluation therefore contradicts the claim that XGBoost outperformed both models. (LLM, conf=0.94)

    Checked by a reasoning model — may be wrong.

    Experimental — accuracy not yet measured

  • XG-Boost is an ensembled learning technique that uses decision trees sequentially and gradient boosting iteratively to obtain better results; it is sensitive to hyperparameters and will not work as well on large datasets as random forest.

    “XG-Boost is an ensembled learning technique that uses decision trees but in a sequential manner and uses gradient boosting in an iterative manner to obtain better results.XG-Boost is sensitive to hyperparameters and will not work as well on large datasets as random forest (Zhu and He 2022).”

    From Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications (2023) · cites this paper as Zhu and He 2022

    Section 3.3 describes XGBoost as based on gradient-boosted decision trees and says each additive training step depends on the previous one, supporting the sequential, iterative part of the claim. It discusses customization parameters but does not say XGBoost is sensitive to hyperparameters; nor does it say XGBoost performs worse than random forest on large datasets. The retrieved copy provides no page numbers for these passages. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • XG-Boost is a good prediction model for stock forecasting because it works on a sequential model that considers the gradient for each iteration so that the weights are updated for each iteration of the decision tree.

    “It is a good prediction model for stock forecasting as it works on a sequential model that considers the gradient for each iteration so that the weights are updated for each iteration of the decision tree (Zhu and He 2022).”

    From Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications (2023) · cites this paper as Zhu and He 2022

  • XG-Boost achieved MSE 360.0.

    “XG-Boost has the following evaluation parameter: MSE = 360.0(Zhu and He 2022).”

    From Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications (2023) · cites this paper as Zhu and He 2022

    The experiment discussion reports XGBoost validation RMSE approaching 600, but does not give an XGBoost MSE of 360.0. Table 2, labeled “MSE of three models,” is cut off after its header in our copy, so the claimed value cannot be checked. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 9

  1. [1]1. Asness CS, Frazzini A, Pedersen LH, 2012, Leverage Aversion and Risk Parity. Financial Analysts Journal, 68(1): 47-59.
  2. [3]3. Ho SL, Xie M, 1998, The Use of ARIMA Models for Reliability Forecasting and Analysis. Computers & Industrial Engineering, 35(1-2): 213-216.
  3. [7]7. Ariyo AA, Adewumi AO, Ayo CK, 2014, Stock Price Prediction Using the ARIMA Model. 2014 UKSim- AMSS 16th International Conference on Computer Modelling and Simulation, IEEE, 106-112.
  4. [9]9. Gers FA, Schraudolph NN, Schmidhuber J, 2002, Learning Precise Timing with LSTM Recurrent Networks. Journal of Machine Learning Research, 3: 115-143.
  5. [10]10. Graves A, Fernandez S, Schmidhuber J, 2005, Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition. International Conference on Artificial Neural Networks, Springer, 799- 804.
  6. [11]11. Chen K, Zhou Y, Dai F, 2015, A LSTM-Based Method for Stock Returns Prediction: A Case Study of China Stock Market. 2015 IEEE International Conference on Big Data (Big Data), IEEE, 2823-2824.
  7. [12]12. Fu R, Zhang Z, Li L, 2016, Using LSTM and GRU Neural Network Methods for Traffic Flow Prediction. 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), IEEE, 324- 328.
  8. [13]13. Liao J, Zhang R, 2018, Dynamic Weighting Multi Factor Stock Selection Strategy Based on XGBoost Machine Learning Algorithm. 2018 IEEE International Conference of Safety Produce Informatization (IICSPI), IEEE, 868-872.
  9. [16]16. Gumelar AB, Setyorini H, Adi DP, et al., 2020, Boosting the Accuracy of Stock Market Prediction using XGBoost and Long Short-Tergumem Memory. 2020 International Seminar on Application for Technology of Information and Communication (iSemantic), IEEE, 609-613.

Cited by

Sorted by citations

Related

Authors
Zhu, Z., & He, K.
Affiliations
Wenzhou-Kean University (CN)
ORCID
Zhu, Zhe
Venue
Proceedings of Business and Economic Studies
Publisher
Bio-Byword Scientific Publishing, Pty. Ltd.
Published
2022-10-21
Volume
5
Issue
5
Pages
127-136
ISSN
  • 2209-2641 (ISSN-L)
  • 2209-265X
DOI
10.26689/pbes.v5i5.4432
OpenAlex
W4306970998
Cited by
15
References
8
Citations by year
2026: 5 · 2025: 4 · 2024: 4 · 2023: 2
FWCI
1.53
Type
journal article
Language
en
Open access
Locations
Proceedings of Business and Economic Studies (publishedVersion)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
  • Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
Keywords
stock price prediction, Amazon stock, ARIMA, XGBoost, LSTM, time series forecasting, model comparison, mean squared error

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review