\begin{thebibliography}{999}

\bibitem[Bank for International Settlements(2022)]{bis2022}
Bank for International Settlements.
\newblock Triennial Central Bank Survey: Foreign Exchange Turnover in April 2022.
\newblock {\em Bank for International Settlements}, {\bf 2022}.
\newblock {\url{https://www.bis.org/statistics/rpfx22.htm}}.

\bibitem[Box et~al.(2015)Box, Jenkins, Reinsel, and Ljung]{box2015time}
Box, G.E.; Jenkins, G.M.; Reinsel, G.C.; Ljung, G.M.
\newblock {\em Time Series Analysis: Forecasting and Control}; John Wiley \&
  Sons:  \hl{Hoboken, NJ, USA,} %newly added information, please confirm. Response: Confirmed
  2015.
\newblock {\url{https://doi.org/10.1111/jtsa.12194}}.

\bibitem[Bollerslev(1986)]{bollerslev1986garch}
Bollerslev, T.  
\newblock Generalized autoregressive conditional heteroskedasticity.  
\newblock {\em J.\ Econometrics} {\bf 1986}, {\em 31}, 307--327.  
\newblock \url{https://doi.org/10.1016/0304-4076(86)90063-1}.

\bibitem[Hochreiter and Schmidhuber(1997)]{hochreiter1997lstm}
Hochreiter, S.; Schmidhuber, J.  
\newblock Long short-term memory.  
\newblock {\em Neural Comput.} {\bf 1997}, {\em 9}, 1735--1780.  
\newblock \url{https://doi.org/10.1162/neco.1997.9.8.1735}.

\bibitem[Fischer and Krauss(2018)]{fischer2018lstm}
Fischer, T.; Krauss, C.  
\newblock Deep learning with long short‐term memory networks for financial market predictions.  
\newblock {\em Eur.\ J.\ Oper.\ Res.} {\bf 2018}, {\em 270}, 654--669.  
\newblock \url{https://doi.org/10.1016/j.ejor.2017.11.054}.

\bibitem[Lea et~al.(2017)Lea, Flynn, Vidal, Reiter, and Hager]{lea2017temporal}
Lea, C.; Flynn, M.D.; Vidal, R.; Reiter, A.; Hager, G.D.
\newblock Temporal convolutional networks for action segmentation and
  detection.
\newblock In Proceedings of the IEEE Conference on Computer
  Vision and Pattern Recognition, \hl{Honolulu, HI, USA, 21--26 July} 2017; \linebreak pp. 156--165.

\bibitem[Bai et~al.(2018)Bai, Kolter, and Koltun]{bai2018empirical}
Bai, S.; Kolter, J.Z.; Koltun, V.
\newblock An empirical evaluation of generic convolutional and recurrent
  networks for sequence modeling.
\newblock {\em arXiv} {\bf 2018}, arXiv:1803.01271.
\newblock {\url{https://doi.org/10.48550/arXiv.1803.01271}}.

\bibitem[Wan et~al.(2019)Wan, Mei, Wang, Liu, and Yang]{electronics8080876}
Wan, R.; Mei, S.; Wang, J.; Liu, M.; Yang, F.
\newblock Multivariate Temporal Convolutional Network: A Deep Neural Networks
  Approach for Multivariate Time Series Forecasting.
\newblock {\em Electronics} {\bf 2019}, {\em 8}, \hl{876}%MDPI: Newly added information, please confirm, same below. Response: Confirmed

\bibitem[Lim et~al.(2021)Lim, Arik, and Pfister]{lim2021tft}
Lim, B.; Arik, S.O.; Pfister, T.  
\newblock Temporal Fusion Transformers for interpretable multi-horizon time-series forecasting.  
\newblock {\em Int.\ J.\ Forecast.} {\bf 2021}, {\em 37}, 1748--1764.  
\newblock \url{https://doi.org/10.1016/j.ijforecast.2021.03.012}.

\bibitem[Vaswani et~al.(2017)Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, and Polosukhin]{vaswani2017attention}
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A.N.; Kaiser, Ł.; Polosukhin, I.  
\newblock Attention is All You Need.  
\newblock In Proceedings of the {\em 31st International Conference on Neural Information Processing Systems (NeurIPS)}, Long Beach, CA, USA, 2017; pp. 6000--6010.  
\newblock \url{https://doi.org/10.48550/arXiv.1706.03762}.

\bibitem[Devlin et~al.(2019)Devlin, Chang, Lee, and Toutanova]{devlin2019bert}
Devlin, J.; Chang, M.-W.; Lee, K.; Toutanova, K.  
\newblock BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.  
\newblock In Proceedings of the {\em 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)}, Minneapolis, MN, USA, 2019; pp. 4171--4186.  
\newblock \url{https://doi.org/10.48550/arXiv.1810.04805}.

\bibitem[Radford et~al.(2018)Radford, Narasimhan, Salimans, and Sutskever]{radford2018gpt}
Radford, A.; Narasimhan, K.; Salimans, T.; Sutskever, I.  
\newblock Improving Language Understanding by Generative Pre-Training.  
\newblock {\em OpenAI}, 2018.  
\newblock \url{https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf}.

\bibitem[Araci(2019)]{araci2019finbert}
Araci, D.  
\newblock FinBERT: Financial sentiment analysis with pre-trained language models.  
\newblock arXiv:1908.10063, 2019.

\bibitem[Yang et~al.(2023)Yang, Xu, McGinnis, and Shen]{yang2023fingpt}
Yang, A.; Xu, H.; McGinnis, C.; Shen, Y.  
\newblock FinGPT: Large language models for financial text.  
\newblock arXiv:2306.06031, 2023.

\bibitem[Fatouros et~al.(2023)Fatouros, Soldatos, Kouroumali, Makridis, and Kyriazis]{fatouros2023chatgpt}
Fatouros, G.; Soldatos, J.; Kouroumali, K.; Makridis, G.; Kyriazis, D.  
\newblock Transforming sentiment analysis in the financial domain with ChatGPT.  
\newblock {\em Machine Learning with Applications}, 14:100508, 2023.  
\newblock doi: \url{https://doi.org/10.1016/j.mlwa.2023.100508}.

\bibitem[Sezer et~al.(2020)Sezer, Gudelek, and Ozbayoglu]{sezer2020survey}
Sezer, O.B.; Gudelek, M.U.; Ozbayoglu, A.M.  
\newblock Financial time series forecasting with deep learning: A systematic literature review 2005–2019.  
\newblock {\em Appl.\ Soft Comput.} {\bf 2020}, {\em 90}, 106181.

\bibitem[Loughran and McDonald(2011)]{loughran2011dictionary}
Loughran, T.; McDonald, B.  
\newblock When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks.  
\newblock {\em J.\ Finance} {\bf 2011}, {\em 66}, 35--65.  
\newblock \url{https://doi.org/10.1111/j.1540-6261.2010.01625.x}.

\bibitem[Nassirtoussi et~al.(2014)Nassirtoussi, Aghabozorgi, Wah, and Ngo]{nassirtoussi2014survey}
Nassirtoussi, A.K.; Aghabozorgi, S.; Wah, T.Y.; Ngo, D.C.L.  
\newblock Text mining for market prediction: A systematic review.  
\newblock {\em Expert Syst.\ Appl.} {\bf 2014}, {\em 41}, 7653--7670.  
\newblock \url{https://doi.org/10.1016/j.eswa.2014.06.009}.

\bibitem[Tetlock(2007)]{tetlock2007media}
Tetlock, P.C.  
\newblock Giving content to investor sentiment: The role of media in the stock market.  
\newblock {\em J.\ Finance} {\bf 2007}, {\em 62}, 1139--1168.  
\newblock \url{https://doi.org/10.1111/j.1540-6261.2007.01232.x}.

\bibitem[Zhang and Skiena(2010)]{zhang2010news}
Zhang, X.; Skiena, S.  
\newblock Trading strategies to exploit blog and news sentiment.  
\newblock In Proceedings of the {\em 4th International AAAI Conference on Weblogs and Social Media (ICWSM)}, 2010.

\bibitem[Ding et~al.(2015)Ding, Zhang, Liu, and Duan]{ding2015deepnews}
Ding, X.; Zhang, Y.; Liu, T.; Duan, J.  
\newblock Deep learning for event-driven stock prediction.  
\newblock In Proceedings of the {\em 24th International Joint Conference on Artificial Intelligence (IJCAI)}, 2015.

\bibitem[Gatev et~al.(2006)Gatev, Goetzmann, and Rouwenhorst]{gatev2006pairs}
Gatev, E.; Goetzmann, W.N.; Rouwenhorst, K.G.  
\newblock Pairs trading: Performance of a relative-value arbitrage rule.  
\newblock {\em Rev.\ Financ.\ Stud.} {\bf 2006}, {\em 19}, 797--827.  
\newblock \url{https://doi.org/10.1093/rfs/hhj020}.

\bibitem[Herzen et~al.(2022)Herzen, LÃ¤ssig, Piazzetta, Neuer, Tafti, Raille,
  Pottelbergh, Pasieka, Skrodzki, Huguenin, Dumonal, KoÅ›cisz, Bader,
  Gusset, Benheddi, Williamson, Kosinski, Petrik, and Grosch]{JMLR:v23:21-1177}
Herzen, J.; LÃ¤ssig, F.; Piazzetta, S.G.; Neuer, T.; Tafti, L.; Raille, G.;
  Pottelbergh, T.V.; Pasieka, M.; Skrodzki, A.; Huguenin, N.;  et~al.
\newblock Darts: User-Friendly Modern Machine Learning for Time Series.
\newblock {\em J. Mach. Learn. Res.} {\bf 2022}, {\em
  23},~1--6.

% Transformer-based architectures for time series forecasting

    % Architecture Chronos is based on
\bibitem{raffel2023exploringlimitstransferlearning}
    Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu.
    \newblock Exploring the limits of transfer learning with a unified text-to-text transformer.
    \newblock {\em arXiv preprint arXiv:1910.10683}, 2023.

    % Chronos paper
\bibitem{ansari2024chronoslearninglanguagetime}
    Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Hao Wang, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, and Yuyang Wang.
    \newblock Chronos: Learning the language of time series.
    \newblock {\em arXiv preprint arXiv:2403.07815}, 2024.

    % PatchTST, uses patches, which is what Chronos-bolt and Toto use
\bibitem{nie2023timeseriesworth64}
    Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam.
    \newblock A time series is worth 64 words: Long-term forecasting with transformers.
    \newblock {\em arXiv preprint arXiv:2211.14730}, 2023.

    %Chinese Finbert Paper
\bibitem[Xu et al.(2025)]{xu2025finbert2}
Xu, X.; Wen, F.; Chu, B.; Fu, Z.; Lin, Q.; Liu, J.; Fei, B.; Yang, Z.; Zhou, L.; Li, Y.
 \newblock FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models.
 \newblock In {\em Proc.\ 31st ACM SIGKDD Conf.\ on Knowledge Discovery and Data Mining (KDD~'25)}, {\bf 2025}.
\newblock \url{https://doi.org/10.1145/3711896.3737219}.

\end{thebibliography}
