學術產出-Theses
Article View/Open
Publication Export
-
題名 以長短期記憶模型分析及預測房價指數
Long Short-Term Memory Analyses of House Price Index作者 洪丞佑
Hong, Cheng-You貢獻者 何靜嫺
洪丞佑
Hong, Cheng-You關鍵詞 LSTM
機器學習
理論啟發的機器學習
房價指數
股票加權指數的交互作用
房地產市場上的情緒指標
LSTM
Machine Learning
Theory-inspired Machine Learning
House Price Index
Stock Index’s Interaction
Emotional Indicator On The Real Estate Market日期 2023 上傳時間 1-Sep-2023 15:34:30 (UTC+8) 摘要 房市一直是個很熱門的話題，在預測房價指數上多數是傳統理論與機器學習各自分開進行的。在台灣，很少有將傳統理論與機器學習結合使用，進行房價指數的預測。本篇論文探討了LSTM中特徵縮放的選擇，並建構理論啟發的LSTM，將其與LSTM與VAR模型進行比較。另外，在資料的選擇上，我們也考慮了一般民眾、投資客等對房市的情緒指標，並使其成為其中一個解釋變數。我們的研究表明，第一，在我們的數據集中，當出現異常值是之前的房價指數，預測上將會出現延遲問題，此時選擇StandardScaler可能是一個不錯的選擇。第二，在理論啟發的LSTM中，我們透過更清晰的區分短期與長期影響，可以達到類似StandardScaler的效果，使得使用MinmaxScaler的LSTM的延遲問題與準確度將得到部分改善。第三，我們的結果表明，我們的情緒指標會有效的影響房價指數，因此應該作為衡量房地產市場情緒的重要指標。
The housing market has always been a hot topic, and when it comes to predicting house price index, most approaches involve separate applications of traditional theories and machine learning. In Taiwan, there are few attempts to combine traditional theories and machine learning to predict house price index. This paper explores the choice of feature scaling in LSTM and constructs a theory-inspired LSTM, which is compared with LSTM and VAR models in predicting house price index. In addition, in data selection, we also considered sentiment or emotional indicators for the housing market among the general public, investors, and others, and included it as one of the explanatory variables. Our results first show that, in our dataset, when there are outliers in previous house price index, there may be a delay problem in prediction. In such cases, choosing StandardScaler may be a good option. Second, in the theory-inspired LSTM, we achieve a similar effect to StandardScaler by clearly distinguishing between short-term and long-term influences. This can partially improve the delay issue and accuracy of using MinmaxScaler in LSTM. Third, our results indicate that our emotional indicator has a significant impact on house price index and should be considered an important measure of sentimental or emotional motivation in the real estate market.參考文獻 陳明吉與曾琬婷 (2008)，「台灣不動產市場從眾行為之檢視」，《管理與系統》，15，591-615。黃偉德（2021）。台灣房地產市場輿論與從眾行為之房價泡沫分析。碩士論文。國立清華大學﹞臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/g5vmyd。楊長霖（2017）。深度學習於台灣房價指數趨勢預測模式建立之研究-應用NNLSTM演算法。﹝碩士論文。國立臺灣科技大學臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/4v83d4。戴梓栩（2016）。總體經濟變數對臺灣房地產市場之影響。﹝碩士論文。國立中山大學﹞臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/w2j863。Algieri, B. (2013). House price determinants: Fundamentals and underlying factors. Comparative Economic Studies, 55, 315-341.Alshaher, H. (2021). Studying the effects of feature scaling in machine learning (Doctoral dissertation, North Carolina Agricultural and Technical State University).Altché, F., & de La Fortelle, A. (2017, October). An LSTM network for highway trajectory prediction. In 2017 IEEE 20th international conference on intelligent transportation systems (ITSC) (pp. 353-359). IEEE.Amarasinghe, A. A. (2015). Dynamic relationship between interest rate and stock price: Empirical evidence from colombo stock exchange. International Journal of Business and Social Science, 6(4).Bai, Y., Xie, J., Liu, C., Tao, Y., Zeng, B., & Li, C. (2021). Regression modeling for enterprise electricity consumption: A comparison of recurrent neural network and its variants. International Journal of Electrical Power & Energy Systems, 126, 106612.Bakalli, G., Guerrier, S., & Scaillet, O. (2023). A penalized two-pass regression to predict stock returns with time-varying risk premia. Journal of Econometrics.Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of economic perspectives, 21(2), 129-151.Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of political Economy, 100(5), 992-1026.Bojer, C. S. (2022). Understanding machine learning-based forecasting methods: A decomposition framework and research opportunities. International Journal of Forecasting, 38(4), 1555-1561.Bzdok, D., Altman, N. & Krzywinski, M. Statistics versus machine learning. Nat Methods 15, 233–234 (2018). https://doi.org/10.1038/nmeth.4642Case, K. E., & Shiller, R. J. (1987). Prices of single family homes since 1970: New indexes for four cities.Case, K. E., & Shiller, R. J. (1989). The efficiency of the market for single-family homes.Chaieb, I., Langlois, H., & Scaillet, O. (2021). Factors and risk premia in individual international stock returns. Journal of Financial Economics, 141(2), 669-692.Clayton, J., Ling, D. C., & Naranjo, A. (2009). Commercial real estate valuation: Fundamentals versus investor sentiment. The Journal of Real Estate Finance and Economics, 38, 5-37.Conrad, C. (2021). The effects of money supply and interest rates on stock prices, evidence from two behavioral experiments. Applied Economics and Finance, 8(2).de Koning, K., Filatova, T., & Bin, O. (2018). Improved methods for predicting property prices in hazard prone dynamic markets. Environmental and resource economics, 69, 247-263.De Veaux, R. D., & Ungar, L. H. (1994). Multicollinearity: A tale of two nonparametric regressions. In Selecting models from data: artificial intelligence and statistics IV (pp. 393-402). New York, NY: Springer New York.Du, S., Guo, H., & Simpson, A. (2019). Self-driving car steering angle prediction based on image recognition. arXiv preprint arXiv:1912.05440.Eichler, M. (2012). Causal inference in time series analysis. Causality: Statistical perspectives and applications, 327-354.Elhag, A. A., & Abu-Zinadah, H. (2020). Forecasting under applying machine learning and statistical models. Thermal Science, 24(Suppl. 1), 131-137.Elman, J. L. (1990). Finding structure in time. Cognitive science, 14(2), 179-211.Fan, J., & Yao, Q. (2003). Nonlinear time series: nonparametric and parametric methods (Vol. 20). New York: Springer.Fan, J., Ke, Z. T., Liao, Y., & Neuhierl, A. (2022). Structural deep learning in conditional asset pricing. Available at SSRN 4117882.Gagliardini, P., Ossola, E., & Scaillet, O. (2016). Time‐varying risk premium in large cross‐sectional equity data sets. Econometrica, 84(3), 985-1046.Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. " O`Reilly Media, Inc.".Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: Continual prediction with LSTM. Neural computation, 12(10), 2451-2471.Gevrey, M., Dimopoulos, I., & Lek, S. (2003). Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological modelling, 160(3), 249-264.Ghysels, E., Plazzi, A., Valkanov, R., & Torous, W. (2013). Forecasting real estate prices. Handbook of economic forecasting, 2, 509-580.Goodhart, C., & Hofmann, B. (2008). House prices, money, credit, and the macroeconomy. Oxford review of economic policy, 24(1), 180-205.Guirguis, H. S., Giannikos, C. I., & Anderson, R. I. (2005). The US housing market: Asset pricing forecasts using time varying coefficients. The Journal of real estate finance and economics, 30, 33-53.Harvey, A. (1997). Trends, cycles and autoregressions. The Economic Journal, 107(440), 192-201.Harvey, A. C. (1990). Forecasting, structural time series models and the Kalman filter.Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.Hoffer, J. G., Ofner, A. B., Rohrhofer, F. M., Lovrić, M., Kern, R., Lindstaedt, S., & Geiger, B. C. (2022). Theory-inspired machine learning—towards a synergy between knowledge and data. Welding in the World, 66(7), 1291-1304.Hoffmann, J., Navarro, O., Kastner, F., Janßen, B., & Hubner, M. (2017). A survey on CNN and RNN implementations. In PESARO 2017: The Seventh International Conference on Performance, Safety and Robustness in Complex Systems and Applications (No. 3).Hohenstatt, R., & Kaesbauer, M. (2014). GECO`s Weather Forecast for the UK Housing Market: To What Extent Can We Rely on Google Econometrics?. Journal of Real Estate Research, 36(2), 253-282.Iacoviello, M. (2000). House prices and the macroeconomy in Europe: results from a structural VAR analysis.Kelly, B. T., Pruitt, S., & Su, Y. (2019). Characteristics are covariances: A unified model of risk and return. Journal of Financial Economics, 134(3), 501-524.Kennedy, P. (2008). A guide to econometrics. John Wiley & Sons.Khashei, M., & Bijari, M. (2010). An artificial neural network (p, d, q) model for timeseries forecasting. Expert Systems with applications, 37(1), 479-489.Kim Lum, S. (2004). Property price indices in the Commonwealth: Construction methodologies and problems. Journal of Property Investment & Finance, 22(1), 25-54.Ley, C., Martin, R. K., Pareek, A., Groll, A., Seil, R., & Tischer, T. (2022). Machine learning and conventional statistics: making sense of the differences. Knee Surgery, Sports Traumatology, Arthroscopy, 30(3), 753-757.Li, M. W., & Wu, P. C. (2008, June). The relationship between money supply and stock prices. In 2008 3rd International Conference on Innovative Computing Information and Control (pp. 598-598). IEEE.Marcato, G., & Nanda, A. (2016). Information content and forecasting ability of sentiment indicators: Case of real estate market. Journal of Real Estate Research, 38(2), 165-204.Marcato, G., & Nanda, A. (2016). Information content and forecasting ability of sentiment indicators: Case of real estate market. Journal of Real Estate Research, 38(2), 165-204.Moriya, Y., & Jones, G. J. (2018, December). LSTM language model adaptation with images and titles for multimedia automatic speech recognition. In 2018 IEEE Spoken Language Technology Workshop (SLT) (pp. 219-226). IEEE.Nelson, D. M., Pereira, A. C., & De Oliveira, R. A. (2017, May). Stock market`s price movement prediction with LSTM neural networks. In 2017 International joint conference on neural networks (IJCNN) (pp. 1419-1426). Ieee.Okunev, J., Wilson, P., & Zurbruegg, R. (2000). The causal relationship between real estate and stock markets. The Journal of Real Estate Finance and Economics, 21, 251-261.Olden, J. D., Joy, M. K., & Death, R. G. (2004). An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological modelling, 178(3-4), 389-397.Otrok, C., & Terrones, M. E. (2005). House prices, interest rates and macroeconomic fluctuations: international evidence. International Monetary Fund, mimeo.Patel, M. M., Tanwar, S., Gupta, R., & Kumar, N. (2020). A deep learning-based cryptocurrency price prediction scheme for financial institutions. Journal of information security and applications, 55, 102583.Pettinger, T. (2022). Factors that affect the housing market. Economics Help Website.Pillaiyan, S. (2015). Macroeconomic drivers of house prices in Malaysia. Canadian Social Science, 11(9), 119-130.Plakandaras, V., Gupta, R., Gogas, P., & Papadimitriou, T. (2015). Forecasting the US real house price index. Economic Modelling, 45, 259-267.Potrawa, T., & Tetereva, A. (2022). How much is the view from the window worth? Machine learning-driven hedonic pricing model of the real estate market. Journal of Business Research, 144, 50-65.Potrawa, T., & Tetereva, A. (2022). How much is the view from the window worth? Machine learning-driven hedonic pricing model of the real estate market. Journal of Business Research, 144, 50-65.Raju, V. G., Lakshmi, K. P., Jain, V. M., Kalidindi, A., & Padma, V. (2020, August). Study the influence of normalization/transformation process on the accuracy of supervised classification. In 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 729-735). IEEE.Scardi, M., & Harding Jr, L. W. (1999). Developing an empirical model of phytoplankton primary production: a neural network case study. Ecological modelling, 120(2-3), 213-223.Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. The Journal of finance, 40(3), 777-790.Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2018, December). A comparison of ARIMA and LSTM in forecasting time series. In 2018 17th IEEE international conference on machine learning and applications (ICMLA) (pp. 1394-1401). IEEE.Sims, C. A. (1980). Macroeconomics and reality. Econometrica: journal of the Econometric Society, 1-48.Soo, C. K. (2018). Quantifying sentiment with news media across local housing markets. The Review of Financial Studies, 31(10), 3689-3719.Thara, D. K., PremaSudha, B. G., & Xiong, F. (2019). Auto-detection of epileptic seizure events using deep neural network with different feature scaling techniques. Pattern Recognition Letters, 128, 544-550.Torres, J. F., Hadjout, D., Sebaa, A., Martínez-Álvarez, F., & Troncoso, A. (2021). Deep learning for time series forecasting: a survey. Big Data, 9(1), 3-21.Tsatsaronis, K., & Zhu, H. (2004). What drives housing price dynamics: cross-country evidence. BIS Quarterly Review, March.Wan, X. (2019, June). Influence of feature scaling on convergence of gradient iterative algorithm. In Journal of physics: Conference series (Vol. 1213, No. 3, p. 032021). IOP Publishing.Wu, L., & Brynjolfsson, E. (2015). The future of prediction: How Google searches foreshadow housing prices and sales. In Economic analysis of the digital economy (pp. 89-118). University of Chicago Press.Xu, X. E., & Fung, H. G. (2005). What moves the mortgage‐backed securities market?. Real Estate Economics, 33(2), 397-426.Yu, Y., Lu, J., Shen, D., & Chen, B. (2021). Research on real estate pricing methods based on data mining and machine learning. Neural Computing and Applications, 33, 3925-3937 描述 碩士
國立政治大學
經濟學系
110258042資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110258042 資料類型 thesis dc.contributor.advisor 何靜嫺 zh_TW dc.contributor.author (Authors) 洪丞佑 zh_TW dc.contributor.author (Authors) Hong, Cheng-You en_US dc.creator (作者) 洪丞佑 zh_TW dc.creator (作者) Hong, Cheng-You en_US dc.date (日期) 2023 en_US dc.date.accessioned 1-Sep-2023 15:34:30 (UTC+8) - dc.date.available 1-Sep-2023 15:34:30 (UTC+8) - dc.date.issued (上傳時間) 1-Sep-2023 15:34:30 (UTC+8) - dc.identifier (Other Identifiers) G0110258042 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/147073 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 經濟學系 zh_TW dc.description (描述) 110258042 zh_TW dc.description.abstract (摘要) 房市一直是個很熱門的話題，在預測房價指數上多數是傳統理論與機器學習各自分開進行的。在台灣，很少有將傳統理論與機器學習結合使用，進行房價指數的預測。本篇論文探討了LSTM中特徵縮放的選擇，並建構理論啟發的LSTM，將其與LSTM與VAR模型進行比較。另外，在資料的選擇上，我們也考慮了一般民眾、投資客等對房市的情緒指標，並使其成為其中一個解釋變數。我們的研究表明，第一，在我們的數據集中，當出現異常值是之前的房價指數，預測上將會出現延遲問題，此時選擇StandardScaler可能是一個不錯的選擇。第二，在理論啟發的LSTM中，我們透過更清晰的區分短期與長期影響，可以達到類似StandardScaler的效果，使得使用MinmaxScaler的LSTM的延遲問題與準確度將得到部分改善。第三，我們的結果表明，我們的情緒指標會有效的影響房價指數，因此應該作為衡量房地產市場情緒的重要指標。 zh_TW dc.description.abstract (摘要) The housing market has always been a hot topic, and when it comes to predicting house price index, most approaches involve separate applications of traditional theories and machine learning. In Taiwan, there are few attempts to combine traditional theories and machine learning to predict house price index. This paper explores the choice of feature scaling in LSTM and constructs a theory-inspired LSTM, which is compared with LSTM and VAR models in predicting house price index. In addition, in data selection, we also considered sentiment or emotional indicators for the housing market among the general public, investors, and others, and included it as one of the explanatory variables. Our results first show that, in our dataset, when there are outliers in previous house price index, there may be a delay problem in prediction. In such cases, choosing StandardScaler may be a good option. Second, in the theory-inspired LSTM, we achieve a similar effect to StandardScaler by clearly distinguishing between short-term and long-term influences. This can partially improve the delay issue and accuracy of using MinmaxScaler in LSTM. Third, our results indicate that our emotional indicator has a significant impact on house price index and should be considered an important measure of sentimental or emotional motivation in the real estate market. en_US dc.description.tableofcontents List of Tables iList of Figures ii1. Introduction 11.1 Related Literature 32. Data & Variables 62.1 House price index (HPI) 72.2 Other Explanatory Variables 83. LSTM (Long Short-Term Memory) Model 93.1 Feature Scaling: Two Setups 123.1.1 Min-Max Scaler 123.1.2 StandardScaler 133.2 LSTM Results 133.2.1 MinMaxScaler 153.2.2 StandardScaler 214. Traditional VAR Model 264.1 The VAR Model 274.2 Results 274.2.1 In-sample Estimation and Out-of-sample Forecasting 284.2.2 Forward Pattern Analysis 304.2.3 Future Trend Analysis 315. Theory-Inspired LSTM (Long Short-Term Memory) Model 335.1 Model Building 345.1.1 Estimating Short Term Factor 355.2 Results 375.2.1 Training Results 375.2.2 Testing results 405.2.3 Forward Pattern Analysis 435.2.4 Future Trend Analysis 436. Theory-Inspired LSTM (part II): Stock Index’s Interaction 466.1 Results 476.1.1 Training Results 476.1.2 Testing results 497. Effects of Emotional Indicator 517.1 Coefficient and feature importance of Google trend: A Summary 517.2 Comparison between the Testing Results Of LSTM With and Without Including the Google Trend. 527.3 Comparison between the Feature Importance of Theory-inspired LSTM with and without Including the Google Trend. 548. Concluding Remarks 55References 57Appendix 62 zh_TW dc.format.extent 2614219 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110258042 en_US dc.subject (關鍵詞) LSTM zh_TW dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) 理論啟發的機器學習 zh_TW dc.subject (關鍵詞) 房價指數 zh_TW dc.subject (關鍵詞) 股票加權指數的交互作用 zh_TW dc.subject (關鍵詞) 房地產市場上的情緒指標 zh_TW dc.subject (關鍵詞) LSTM en_US dc.subject (關鍵詞) Machine Learning en_US dc.subject (關鍵詞) Theory-inspired Machine Learning en_US dc.subject (關鍵詞) House Price Index en_US dc.subject (關鍵詞) Stock Index’s Interaction en_US dc.subject (關鍵詞) Emotional Indicator On The Real Estate Market en_US dc.title (題名) 以長短期記憶模型分析及預測房價指數 zh_TW dc.title (題名) Long Short-Term Memory Analyses of House Price Index en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 陳明吉與曾琬婷 (2008)，「台灣不動產市場從眾行為之檢視」，《管理與系統》，15，591-615。黃偉德（2021）。台灣房地產市場輿論與從眾行為之房價泡沫分析。碩士論文。國立清華大學﹞臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/g5vmyd。楊長霖（2017）。深度學習於台灣房價指數趨勢預測模式建立之研究-應用NNLSTM演算法。﹝碩士論文。國立臺灣科技大學臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/4v83d4。戴梓栩（2016）。總體經濟變數對臺灣房地產市場之影響。﹝碩士論文。國立中山大學﹞臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/w2j863。Algieri, B. (2013). House price determinants: Fundamentals and underlying factors. Comparative Economic Studies, 55, 315-341.Alshaher, H. (2021). Studying the effects of feature scaling in machine learning (Doctoral dissertation, North Carolina Agricultural and Technical State University).Altché, F., & de La Fortelle, A. (2017, October). An LSTM network for highway trajectory prediction. In 2017 IEEE 20th international conference on intelligent transportation systems (ITSC) (pp. 353-359). IEEE.Amarasinghe, A. A. (2015). Dynamic relationship between interest rate and stock price: Empirical evidence from colombo stock exchange. International Journal of Business and Social Science, 6(4).Bai, Y., Xie, J., Liu, C., Tao, Y., Zeng, B., & Li, C. (2021). Regression modeling for enterprise electricity consumption: A comparison of recurrent neural network and its variants. International Journal of Electrical Power & Energy Systems, 126, 106612.Bakalli, G., Guerrier, S., & Scaillet, O. (2023). A penalized two-pass regression to predict stock returns with time-varying risk premia. Journal of Econometrics.Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of economic perspectives, 21(2), 129-151.Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of political Economy, 100(5), 992-1026.Bojer, C. S. (2022). Understanding machine learning-based forecasting methods: A decomposition framework and research opportunities. International Journal of Forecasting, 38(4), 1555-1561.Bzdok, D., Altman, N. & Krzywinski, M. Statistics versus machine learning. Nat Methods 15, 233–234 (2018). https://doi.org/10.1038/nmeth.4642Case, K. E., & Shiller, R. J. (1987). Prices of single family homes since 1970: New indexes for four cities.Case, K. E., & Shiller, R. J. (1989). The efficiency of the market for single-family homes.Chaieb, I., Langlois, H., & Scaillet, O. (2021). Factors and risk premia in individual international stock returns. Journal of Financial Economics, 141(2), 669-692.Clayton, J., Ling, D. C., & Naranjo, A. (2009). Commercial real estate valuation: Fundamentals versus investor sentiment. The Journal of Real Estate Finance and Economics, 38, 5-37.Conrad, C. (2021). The effects of money supply and interest rates on stock prices, evidence from two behavioral experiments. Applied Economics and Finance, 8(2).de Koning, K., Filatova, T., & Bin, O. (2018). Improved methods for predicting property prices in hazard prone dynamic markets. Environmental and resource economics, 69, 247-263.De Veaux, R. D., & Ungar, L. H. (1994). Multicollinearity: A tale of two nonparametric regressions. In Selecting models from data: artificial intelligence and statistics IV (pp. 393-402). New York, NY: Springer New York.Du, S., Guo, H., & Simpson, A. (2019). Self-driving car steering angle prediction based on image recognition. arXiv preprint arXiv:1912.05440.Eichler, M. (2012). Causal inference in time series analysis. Causality: Statistical perspectives and applications, 327-354.Elhag, A. A., & Abu-Zinadah, H. (2020). Forecasting under applying machine learning and statistical models. Thermal Science, 24(Suppl. 1), 131-137.Elman, J. L. (1990). Finding structure in time. Cognitive science, 14(2), 179-211.Fan, J., & Yao, Q. (2003). Nonlinear time series: nonparametric and parametric methods (Vol. 20). New York: Springer.Fan, J., Ke, Z. T., Liao, Y., & Neuhierl, A. (2022). Structural deep learning in conditional asset pricing. Available at SSRN 4117882.Gagliardini, P., Ossola, E., & Scaillet, O. (2016). Time‐varying risk premium in large cross‐sectional equity data sets. Econometrica, 84(3), 985-1046.Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. " O`Reilly Media, Inc.".Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: Continual prediction with LSTM. Neural computation, 12(10), 2451-2471.Gevrey, M., Dimopoulos, I., & Lek, S. (2003). Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological modelling, 160(3), 249-264.Ghysels, E., Plazzi, A., Valkanov, R., & Torous, W. (2013). Forecasting real estate prices. Handbook of economic forecasting, 2, 509-580.Goodhart, C., & Hofmann, B. (2008). House prices, money, credit, and the macroeconomy. Oxford review of economic policy, 24(1), 180-205.Guirguis, H. S., Giannikos, C. I., & Anderson, R. I. (2005). The US housing market: Asset pricing forecasts using time varying coefficients. The Journal of real estate finance and economics, 30, 33-53.Harvey, A. (1997). Trends, cycles and autoregressions. The Economic Journal, 107(440), 192-201.Harvey, A. C. (1990). Forecasting, structural time series models and the Kalman filter.Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.Hoffer, J. G., Ofner, A. B., Rohrhofer, F. M., Lovrić, M., Kern, R., Lindstaedt, S., & Geiger, B. C. (2022). Theory-inspired machine learning—towards a synergy between knowledge and data. Welding in the World, 66(7), 1291-1304.Hoffmann, J., Navarro, O., Kastner, F., Janßen, B., & Hubner, M. (2017). A survey on CNN and RNN implementations. In PESARO 2017: The Seventh International Conference on Performance, Safety and Robustness in Complex Systems and Applications (No. 3).Hohenstatt, R., & Kaesbauer, M. (2014). GECO`s Weather Forecast for the UK Housing Market: To What Extent Can We Rely on Google Econometrics?. Journal of Real Estate Research, 36(2), 253-282.Iacoviello, M. (2000). House prices and the macroeconomy in Europe: results from a structural VAR analysis.Kelly, B. T., Pruitt, S., & Su, Y. (2019). Characteristics are covariances: A unified model of risk and return. Journal of Financial Economics, 134(3), 501-524.Kennedy, P. (2008). A guide to econometrics. John Wiley & Sons.Khashei, M., & Bijari, M. (2010). An artificial neural network (p, d, q) model for timeseries forecasting. Expert Systems with applications, 37(1), 479-489.Kim Lum, S. (2004). Property price indices in the Commonwealth: Construction methodologies and problems. Journal of Property Investment & Finance, 22(1), 25-54.Ley, C., Martin, R. K., Pareek, A., Groll, A., Seil, R., & Tischer, T. (2022). Machine learning and conventional statistics: making sense of the differences. Knee Surgery, Sports Traumatology, Arthroscopy, 30(3), 753-757.Li, M. W., & Wu, P. C. (2008, June). The relationship between money supply and stock prices. In 2008 3rd International Conference on Innovative Computing Information and Control (pp. 598-598). IEEE.Marcato, G., & Nanda, A. (2016). Information content and forecasting ability of sentiment indicators: Case of real estate market. Journal of Real Estate Research, 38(2), 165-204.Marcato, G., & Nanda, A. (2016). Information content and forecasting ability of sentiment indicators: Case of real estate market. Journal of Real Estate Research, 38(2), 165-204.Moriya, Y., & Jones, G. J. (2018, December). LSTM language model adaptation with images and titles for multimedia automatic speech recognition. In 2018 IEEE Spoken Language Technology Workshop (SLT) (pp. 219-226). IEEE.Nelson, D. M., Pereira, A. C., & De Oliveira, R. A. (2017, May). Stock market`s price movement prediction with LSTM neural networks. In 2017 International joint conference on neural networks (IJCNN) (pp. 1419-1426). Ieee.Okunev, J., Wilson, P., & Zurbruegg, R. (2000). The causal relationship between real estate and stock markets. The Journal of Real Estate Finance and Economics, 21, 251-261.Olden, J. D., Joy, M. K., & Death, R. G. (2004). An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological modelling, 178(3-4), 389-397.Otrok, C., & Terrones, M. E. (2005). House prices, interest rates and macroeconomic fluctuations: international evidence. International Monetary Fund, mimeo.Patel, M. M., Tanwar, S., Gupta, R., & Kumar, N. (2020). A deep learning-based cryptocurrency price prediction scheme for financial institutions. Journal of information security and applications, 55, 102583.Pettinger, T. (2022). Factors that affect the housing market. Economics Help Website.Pillaiyan, S. (2015). Macroeconomic drivers of house prices in Malaysia. Canadian Social Science, 11(9), 119-130.Plakandaras, V., Gupta, R., Gogas, P., & Papadimitriou, T. (2015). Forecasting the US real house price index. Economic Modelling, 45, 259-267.Potrawa, T., & Tetereva, A. (2022). How much is the view from the window worth? Machine learning-driven hedonic pricing model of the real estate market. Journal of Business Research, 144, 50-65.Potrawa, T., & Tetereva, A. (2022). How much is the view from the window worth? Machine learning-driven hedonic pricing model of the real estate market. Journal of Business Research, 144, 50-65.Raju, V. G., Lakshmi, K. P., Jain, V. M., Kalidindi, A., & Padma, V. (2020, August). Study the influence of normalization/transformation process on the accuracy of supervised classification. In 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 729-735). IEEE.Scardi, M., & Harding Jr, L. W. (1999). Developing an empirical model of phytoplankton primary production: a neural network case study. Ecological modelling, 120(2-3), 213-223.Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. The Journal of finance, 40(3), 777-790.Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2018, December). A comparison of ARIMA and LSTM in forecasting time series. In 2018 17th IEEE international conference on machine learning and applications (ICMLA) (pp. 1394-1401). IEEE.Sims, C. A. (1980). Macroeconomics and reality. Econometrica: journal of the Econometric Society, 1-48.Soo, C. K. (2018). Quantifying sentiment with news media across local housing markets. The Review of Financial Studies, 31(10), 3689-3719.Thara, D. K., PremaSudha, B. G., & Xiong, F. (2019). Auto-detection of epileptic seizure events using deep neural network with different feature scaling techniques. Pattern Recognition Letters, 128, 544-550.Torres, J. F., Hadjout, D., Sebaa, A., Martínez-Álvarez, F., & Troncoso, A. (2021). Deep learning for time series forecasting: a survey. Big Data, 9(1), 3-21.Tsatsaronis, K., & Zhu, H. (2004). What drives housing price dynamics: cross-country evidence. BIS Quarterly Review, March.Wan, X. (2019, June). Influence of feature scaling on convergence of gradient iterative algorithm. In Journal of physics: Conference series (Vol. 1213, No. 3, p. 032021). IOP Publishing.Wu, L., & Brynjolfsson, E. (2015). The future of prediction: How Google searches foreshadow housing prices and sales. In Economic analysis of the digital economy (pp. 89-118). University of Chicago Press.Xu, X. E., & Fung, H. G. (2005). What moves the mortgage‐backed securities market?. Real Estate Economics, 33(2), 397-426.Yu, Y., Lu, J., Shen, D., & Chen, B. (2021). Research on real estate pricing methods based on data mining and machine learning. Neural Computing and Applications, 33, 3925-3937 zh_TW
