Let the Laser Beam Connect the Dots: Forecasting and Narrating Stock Market Volatility
Published Online:18 Mar 2024https://doi.org/10.1287/ijoc.2022.0055
References
- (2015) Neural machine translation by jointly learning to align and translate. Bengio Y, LeCun Y, eds. 3rd Internat. Conf. Learn. Representations (ICLR, London).Google Scholar
- (1996) Long memory processes and fractional integration in econometrics. J. Econometrics 73(1):5–59.Crossref, Google Scholar
- (2013) Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Machine Intelligence 35(8):1798–1828.Crossref, Google Scholar
- (1986) Generalized autoregressive conditional heteroskedasticity. J. Econometrics 31(3):307–327.Crossref, Google Scholar
- (1976) Time Series Analysis: Forecasting and Control (Holden-Day, San Francisco), 121–124.Google Scholar
- (2020) Language models are few-shot learners. Adv. Neural Inform. Processes Systems 33:1877–1901.Google Scholar
- (2011) News-good or bad-and its impact on volatility predictions over multiple horizons. Rev. Financial Stud. 24(1):46–81.Crossref, Google Scholar
- (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. Moschitti A, Pang B, Daelemans W, eds. Proc. 2014 Conf. Empirical Methods Natl. Language Processing (Association for Computational Linguistics, Kerrville, TX), 1724–1734.Google Scholar
- (2021) Differentiable patch selection for image recognition. 2021 IEEE/CVF Conf. Comput. Vision Pattern Recognition (IEEE, Piscataway, NJ), 2351–2360.Google Scholar
- (2007) Yahoo! For Amazon: Sentiment extraction from small talk on the web. Management Sci. 53(9):1375–1388.Link, Google Scholar
- (1985) Does the stock market overreact? J. Finance 40(3):793–805.Crossref, Google Scholar
- (2018) BERT: Pre-training of deep bidirectional transformers for language understanding. Preprint, submitted October 11, https://arxiv.org/abs/1810.04805.Google Scholar
- (2014) Using structured events to predict stock price movement: An empirical investigation. Proc. 2014 Conf. Empirical Methods Natural Language Processing, 1415–1425.Google Scholar
- (2015) Deep learning for event-driven stock prediction. Proc. 24th Internat. Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), 2327–2333.Google Scholar
- (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50(4):987–1007.Crossref, Google Scholar
- (1993) Measuring and testing the impact of news on volatility. J. Finance 48(5):1749–1778.Crossref, Google Scholar
- (2013) Stock market volatility and macroeconomic fundamentals. Rev. Econom. Statist. 95(3):776–797.Crossref, Google Scholar
- (2006) Predicting volatility: Getting the most out of return data sampled at different frequencies. J. Econometrics 131(1–2):59–95.Crossref, Google Scholar
- (2021) Recurrent independent mechanisms. Ninth Internat. Conf. Learn. Representations.Google Scholar
- (2011) When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions. J. Empirical Finance 18(2):321–340.Crossref, Google Scholar
- (2023) Forecasting under long memory. J. Financial Econometrics 21(3):742–778.Crossref, Google Scholar
- (2017) Lexically constrained decoding for sequence generation using grid beam search. Proc. 55th Annual Meeting Assoc. Comput. Linguistics Proc. (Association for Computational Linguistics, Kerrville, TX), 1535–1546.Google Scholar
- (2018) Listening to chaotic whispers: A deep learning framework for news-oriented stock trend prediction. Proc. 11th ACM Internat. Conf. Web Search Data Mining (ACM, New York), 261–269.Google Scholar
- (2019) A prediction approach for stock market volatility based on time series data. IEEE Access 7:17287–17298.Crossref, Google Scholar
- (1998) Macroeconomic news and bond market volatility. J. Financial Econom. 47(3):315–337.Crossref, Google Scholar
- (2014) Auto-encoding variational Bayes. Second Internat. Conf. Learn. Representations (ICLR, Appleton, WI).Google Scholar
- (2021) A multimodal event-driven LSTM model for stock prediction using online news. IEEE Trans. Knowledge Data Engrg. 33(10):3323–3337.Crossref, Google Scholar
- (2020) On the sentence embeddings from pre-trained language models. Webber B, Cohn T, He Y, Liu Y, eds. Proc. 2020 Conf. Empirical Methods Natural Language Processing (Association for Computational Linguistics, Kerrville, TX), 9119–9130.Google Scholar
- (2009) Long-term memory in volatility: Some evidence from international securitized real estate markets. J. Real Estate Finance Econom. 39(4):415–438.Crossref, Google Scholar
- (2020) Predicting shareholder litigation on insider trading from financial text: An interpretable deep learning approach. Inform. Management 57(8):103387.Crossref, Google Scholar
- (1991) Long-term memory in stock market prices. Econometrica (1986-1998) 59(5):1279–1313.Google Scholar
- (2017) A neural stochastic volatility model. Proc. AAAI Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), 32.Google Scholar
- (2015) Effective approaches to attention-based neural machine translation. Proc. 2015 Conf. Empirical Methods Natural Language Processing (Association for Computational Linguistics, Linguistics, Kerrville, TX), 1412–1421.Google Scholar
- (2019) Definitions, methods, and applications in interpretable machine learning. Proc. Natl. Acad. Sci. USA 116(44):22071–22080.Crossref, Google Scholar
- (1991) Conditional heteroskedasticity in asset returns: A new approach. Econometrica 59(2):347–370.Crossref, Google Scholar
- (2005) A hybrid ARIMA and support vector machines model in stock price forecasting. Omega 33(6):497–505.Crossref, Google Scholar
- (2018) Fast lexically constrained decoding with dynamic beam allocation for neural machine translation. Walker M, Ji H, Stent A, eds. Proc. 2018 Conf. North Amer. Chapter Assoc. Comput. Linguistics Human Language Tech. (Association for Computational Linguistics, Kerrville, TX), 1314–1324.Google Scholar
- (2019) Language models are unsupervised multitask learners. OpenAI Blog 1(8):9.Google Scholar
- (1986) Learning representations by back-propagating errors. Nature 323:533–536.Crossref, Google Scholar
- (2000) Long-term memory in stock market volatility. Appl. Financial Econom. 10(5):519–524.Crossref, Google Scholar
- (2006) A multivariate long memory stochastic volatility model. Physica A 362(2):450–464.Crossref, Google Scholar
- (2015) End-to-end memory networks. Adv. Neural Inform. Processes Systems 28:2440–2448.Google Scholar
- (2007) Giving content to investor sentiment: The role of media in the stock market. J. Finance 62(3):1139–1168.Crossref, Google Scholar
- (2013) Working memory capacity and retrieval from long-term memory: The role of controlled search. Memory Cognition 41(2):242–254.Crossref, Google Scholar
- (2019) AlphaStock: A buying-winners-and-selling-losers investment strategy using interpretable deep reinforcement attention networks. Proc. 25th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 1900–1908.Google Scholar
- (2016) The interplay of hippocampus and ventromedial prefrontal cortex in memory-based decision making. Brain Sci. 7(1):4.Crossref, Google Scholar
- (2015) Memory networks. 3rd Internat. Conf. Learn. Representations (ICLR, Appleton, WI).Google Scholar
- (1921) Logisch-philosophische abhandlung. Annalen der Naturphilosophische XIV (3/4).Google Scholar
- (2018) Stock movement prediction from tweets and historical prices. Proc. 56th Annual Meeting Assoc. Comput. Linguistics (Association for Computational Linguistics, Kerrville, TX), 1970–1979.Google Scholar
- (2022) Analyzing firm reports for volatility prediction: A knowledge-driven text-embedding approach. INFORMS J. Comput. 34(1):522–540.Link, Google Scholar
- (2024) Let the laser beam connect the dots: Forecasting and narrating stock market volatility. https://dx.doi.org/10.1287/ijoc.2022.0055.cd, https://github.com/INFORMSJoC/2022.0055.Google Scholar

