{"id":10670,"date":"2025-11-07T07:44:55","date_gmt":"2025-11-07T07:44:55","guid":{"rendered":"https:\/\/revista.isfin.ro\/?p=10670"},"modified":"2025-12-18T16:07:03","modified_gmt":"2025-12-18T16:07:03","slug":"bayesian-learning-and-autoregressive-persistence-adaptive-forecasting-of-the-dow%e2%80%afjones%e2%80%afindustrial%e2%80%afaverage-2022-2025","status":"publish","type":"post","link":"https:\/\/revista.isfin.ro\/en\/2025\/11\/07\/bayesian-learning-and-autoregressive-persistence-adaptive-forecasting-of-the-dow%e2%80%afjones%e2%80%afindustrial%e2%80%afaverage-2022-2025\/","title":{"rendered":"BAYESIAN LEARNING AND AUTOREGRESSIVE PERSISTENCE: ADAPTIVE FORECASTING OF THE DOW\u202fJONES\u202fINDUSTRIAL\u202fAVERAGE (2022\u20132025)"},"content":{"rendered":"
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Authors: George Cristian Gruia, Ioana Duca, Anca Postole<\/p>

Vol. 10 \u2022 Special Issue \u2022 2025<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Abstract<\/strong><\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Financial markets operate as dynamic systems in which information and volatility interact continuously, challenging traditional forecasting models based on fixed historical patterns.\u202fThis study compares Bayesian and Autoregressive (AR (1)) models for forecasting daily returns of the Dow Jones Industrial Average (DJIA) between October 2022 and October 2025, using daily open-close data (approx. 750 trading days).\u202fUsing the EViews\u202f12\u202fStudent\u202fVersion, the analysis constructs daily percentage returns and evaluates each model through key econometric indicators:\u202fMean\u202fAbsolute\u202fError\u202f(MAE),\u202fForecast\u202fBias and a newly introduced\u202fAdaptability\u202fmetric.\u202f
Empirical results show that the AR (1)\u202fmodel achieves lower error and negligible bias, confirming its stability and persistence, whereas the Bayesian model exhibits higher MAE and modest positive bias but significantly greater adaptability.\u202fThe Bayesian approach updates prior beliefs (0.55) into posterior probabilities (0.92) in response to market evidence, illustrating how information assimilation enhances real\u2011time responsiveness.\u202fAlthough less smooth, the Bayesian forecasts better capture short\u2011term uncertainty and regime shift typical of modern equity markets.\u202fThe findings demonstrate that Bayesian learning provides a flexible alternative to autoregressive persistence, offering a richer representation of how market expectations evolve under uncertainty.\u202fThis research contributes to the econometric literature by operationalizing Bayesian inference within a standard forecasting environment and proposing adaptability as a quantitative measure of model responsiveness.\u202fThe results have implications for both academic forecasting and practical investment decision\u2011making in volatile market conditions.<\/p>

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Keywords: <\/strong>Bayes\u2019 theorem; Bayesian inference; capital markets; financial forecasting; market uncertainty; adaptive decision-making; probabilistic modelling.<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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JEL Classification:<\/strong> C11, C58, G11, G17<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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DOI: <\/strong>10.55654\/JFS.2025.10.SP.31<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Authors: George Cristian Gruia, Ioana Duca, Anca Postole Vol. 10 \u2022 Special Issue \u2022 2025 View abstract PDF View article PDF Abstract Financial markets operate as dynamic systems in which information and volatility interact continuously, challenging traditional forecasting models based on fixed historical patterns.\u202fThis study compares Bayesian and Autoregressive (AR (1)) models for forecasting daily …<\/p>\n

BAYESIAN LEARNING AND AUTOREGRESSIVE PERSISTENCE: ADAPTIVE FORECASTING OF THE DOW\u202fJONES\u202fINDUSTRIAL\u202fAVERAGE (2022\u20132025)<\/span> Read More »<\/a><\/p>","protected":false},"author":6,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[185],"tags":[],"yoast_head":"BAYESIAN LEARNING AND AUTOREGRESSIVE PERSISTENCE: ADAPTIVE FORECASTING OF THE DOW\u202fJONES\u202fINDUSTRIAL\u202fAVERAGE (2022\u20132025) - Revista de Studii Financiare<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/revista.isfin.ro\/en\/2025\/11\/07\/bayesian-learning-and-autoregressive-persistence-adaptive-forecasting-of-the-dow\u202fjones\u202findustrial\u202faverage-2022-2025\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"BAYESIAN LEARNING AND AUTOREGRESSIVE PERSISTENCE: ADAPTIVE FORECASTING OF THE DOW\u202fJONES\u202fINDUSTRIAL\u202fAVERAGE (2022\u20132025) - 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