Last Update:

Monday, April 21, 2025

Opening Value:

505.4

Highest Value:

509.27

Lowest Value:

494.68

Adj. Closing Value:

501.36

  • The forecast plot shows the forecasted The Goldman Sachs Group, Inc. stock prices for the next 12 months.

  • The plot also shows the 80% prediction interval in dark purple and the 95% prediction interval in light purple.

  • The numeric values for these forecasted values and prediction intervals are shown in the next tab.

The table below shows the forecast values and 80% and 95% prediction intervals for the 12 requested forecasts for the The Goldman Sachs Group, Inc. stock.

Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
May 2025 550.7221 526.6929 574.7514 513.9726 587.4717
Jun 2025 552.9943 519.0118 586.9768 501.0225 604.9660
Jul 2025 555.2664 513.6465 596.8863 491.6143 618.9185
Aug 2025 557.5385 509.4800 605.5971 484.0394 631.0377
Sep 2025 559.8107 506.0796 613.5417 477.6361 641.9852
Oct 2025 562.0828 503.2234 620.9422 472.0651 652.1005
Nov 2025 564.3549 500.7795 627.9304 467.1247 661.5852
Dec 2025 566.6271 498.6621 634.5921 462.6836 670.5706
Jan 2026 568.8992 496.8114 640.9870 458.6505 679.1479
Feb 2026 571.1713 495.1841 647.1585 454.9590 687.3837
Mar 2026 573.4435 493.7474 653.1395 451.5589 695.3280
Apr 2026 575.7156 492.4758 658.9554 448.4113 703.0199
  • In March of 2026, the The Goldman Sachs Group, Inc. stock price is forecasted to be 573 dollars.

  • The width of the 80% prediction interval for this forecast in March of 2026 is 159 dollars.

  • These three residual plots allow the analyst to examine the distribution of the residuals of the modeled time series.

  • Despite increasing volatility, our stock price model is estimated to be 93.5% accurate.

  • This doesn’t guarantee that forecasts will be 93.5% accurate but it does improve our chances of accurate forecasting.


This dashboard was created using Quarto in RStudio, and the R Language and Environment.

The dataset used to create this dashboard was downloaded from Yahoo Finance.

Software Citations

Allaire, J., Teague, C., Scheidegger, C., Xie, Y., & Dervieux, C. (2024). Quarto (Version 1.5.57). doi.org/10.5281/zenodo.5960048 https://doi.org/10.5281/zenodo.5960048

Arnold J (2024). ggthemes: Extra Themes, Scales and Geoms for ‘ggplot2’. R package version 5.1.0, https://CRAN.R-project.org/package=ggthemes.

Bache S, Wickham H (2022). magrittr: A Forward-Pipe Operator for R. R package version 2.0.3, https://CRAN.R-project.org/package=magrittr.

Hyndman R, Athanasopoulos G, Bergmeir C, Caceres G, Chhay L, O’Hara-Wild M, Petropoulos F, Razbash S, Wang E, Yasmeen F (2025). forecast: Forecasting functions for time series and linear models. R package version 8.24.0, https://pkg.robjhyndman.com/forecast/.

Hyndman RJ, Khandakar Y (2008). “Automatic time series forecasting: the forecast package for R.” Journal of Statistical Software, 27(3), 1-22. doi:10.18637/jss.v027.i03 https://doi.org/10.18637/jss.v027.i03.

Neuwirth E (2022). RColorBrewer: ColorBrewer Palettes. R package version 1.1-3, https://CRAN.R-project.org/package=RColorBrewer.

Posit team (2024). RStudio: Integrated Development Environment for R. Posit Software, PBC, Boston, MA. URL http://www.posit.co/.

R Core Team (2025). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

Rinker, T. W. & Kurkiewicz, D. (2017). pacman: Package Management for R. version 0.5.0. Buffalo, New York. http://github.com/trinker/pacman

Vanderkam D, Allaire J, Owen J, Gromer D, Thieurmel B (2018). dygraphs: Interface to ‘Dygraphs’ Interactive Time Series Charting Library. R package version 1.1.1.6, https://CRAN.R-project.org/package=dygraphs.

Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” Journal of Open Source Software, 4(43), 1686. doi:10.21105/joss.01686 https://doi.org/10.21105/joss.01686.

Xie Y (2025). knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.50, https://yihui.org/knitr/.

Yihui Xie (2015) Dynamic Documents with R and knitr. 2nd edition. Chapman and Hall/CRC. ISBN 978-1498716963

Yihui Xie (2014) knitr: A Comprehensive Tool for Reproducible Research in R. In Victoria Stodden, Friedrich Leisch and Roger D. Peng, editors, Implementing Reproducible Computational Research. Chapman and Hall/CRC. ISBN 978-1466561595

Zhu H (2024). kableExtra: Construct Complex Table with ‘kable’ and Pipe Syntax. R package version 1.4.0, https://github.com/haozhu233/kableExtra, http://haozhu233.github.io/kableExtra/.