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題名 模糊性與資產定價：台灣加權指數的實證研究與機器學習應用
Ambiguity and asset pricing: Empirical study and machine learning applications using the Taiwan Weighted Stock Index
作者 林晉毅
Lin, Chin-I
貢獻者 廖四郎
Liao, Szu-Lang
林晉毅
Lin, Chin-I
關鍵詞 模糊性
Knightian uncertainty
Ambiguity
神經網路
LSTM
台灣加權指數
Knightian uncertainty
Ambiguity
Neural network
LSTM
Taiwan Weighted Stock Index
日期 2024
上傳時間 5-Aug-2024 12:18:55 (UTC+8)
摘要 傳統資產定價模型主要考慮風險，而忽略了機率本身的不確定性，即模糊性。本研究採用台灣加權指數作為研究對象，參考了Brenner and Izhakian (2018)提出的實證方法來測量台灣市場中的模糊性程度，並從台灣的市場數據中評估投資者對模糊性的態度。實證結果顯示，模糊性在股票市場中具有價格，且當預期的有利報酬機率較高時，投資者對模糊性表現出厭惡態度。此外，本研究嘗試將模糊性作為一種新指標，並將其引入神經網路與長短期記憶(LSTM)機器學習模型，以觀察其對股票價格預測的影響。結果顯示，加入模糊性後，機器學習模型的預測準確度有提升的趨勢。
Traditional asset pricing models primarily focus on risk, often neglecting the uncertainty inherent in probabilities, known as ambiguity. This research examines the Taiwan Weighted Index, using the empirical method by Brenner and Izhakian (2018) to quantify the level of ambiguity in the Taiwanese market. By analyzing market data from Taiwan, the study evaluates investor attitudes towards ambiguity. The findings indicate that ambiguity is indeed reflected in stock market prices, and investors show aversion to ambiguity when the likelihood of favorable returns is high. Furthermore, this study explores incorporating ambiguity as a new indicator into neural network and long short-term memory (LSTM) machine learning models to assess its impact on stock price predictions. The results indicate an improvement in prediction accuracy for machine learning models with the inclusion of ambiguity.
參考文獻 參考文獻 Augustin, P., & Izhakian, Y. (2020). Ambiguity, volatility, and credit risk. The review of financial studies, 33(4), 1618-1672. Black, F. (1976). Studies of stock market volatility changes. Proceedings of the American Statistical Association, Business & Economic Statistics Section, 1976. Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of political economy, 81(3), 637-654. Brenner, M., & Izhakian, Y. (2012). Asset Pricing and Ambiguity: Empirical Evidence, working paper. Brenner, M., & Izhakian, Y. (2018). Asset pricing and ambiguity: Empirical evidence. Journal of Financial Economics, 130(3), 503-531. Campbell, J. Y., & Hentschel, L. (1992). No news is good news: An asymmetric model of changing volatility in stock returns. Journal of Financial Economics, 31(3), 281-318. https://doi.org/https://doi.org/10.1016/0304-405X(92)90037-X Drechsler, I. (2013). Uncertainty, time‐varying fear, and asset prices. The Journal of Finance, 68(5), 1843-1889. Epstein, L. G., & Schneider, M. (2003). Recursive multiple-priors. Journal of Economic Theory, 113(1), 1-31. Epstein, L. G., & Schneider, M. (2008). Ambiguity, Information Quality, and Asset Pricing. The Journal of Finance, 63(1), 197-228. https://doi.org/https://doi.org/10.1111/j.1540-6261.2008.01314.x Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European journal of operational research, 270(2), 654-669. French, K. R., Schwert, G. W., & Stambaugh, R. F. (1987). Expected stock returns and volatility. Journal of Financial Economics, 19(1), 3-29. https://doi.org/https://doi.org/10.1016/0304-405X(87)90026-2 Gagliardini, P., Porchia, P., & Trojani, F. (2008). Ambiguity aversion and the term structure of interest rates. The review of financial studies, 22(10), 4157-4188. Ghysels, E., Santa-Clara, P., & Valkanov, R. (2005). There is a risk-return trade-off after all. Journal of Financial Economics, 76(3), 509-548. https://doi.org/https://doi.org/10.1016/j.jfineco.2004.03.008 Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48(5), 1779-1801. Guo, H., & Whitelaw, R. F. (2006). Uncovering the Risk–Return Relation in the Stock Market. The Journal of Finance, 61(3), 1433-1463. https://doi.org/https://doi.org/10.1111/j.1540-6261.2006.00877.x Harvey, C. R. (2001). The specification of conditional expectations. Journal of Empirical Finance, 8(5), 573-637. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. Izhakian, Y. Y. (2012). Ambiguity Measurement. Available at SSRN 1938628. Jahan-Parvar, M. R., & Liu, H. (2014). Ambiguity aversion and asset prices in production economies. The review of financial studies, 27(10), 3060-3097. Jeong, D., Kim, H., & Park, J. Y. (2015). Does ambiguity matter? Estimating asset pricing models with a multiple-priors recursive utility. Journal of Financial Economics, 115(2), 361-382. Ju, N., & Miao, J. (2012). Ambiguity, learning, and asset returns. Econometrica, 80(2), 559-591. Kahneman, D., & Tversky, A. (2013). Prospect theory: An analysis of decision under risk. In Handbook of the fundamentals of financial decision making: Part I (pp. 99-127). World Scientific. Klibanoff, P., Marinacci, M., & Mukerji, S. (2005). A smooth model of decision making under ambiguity. Econometrica, 73(6), 1849-1892. Kumbure, M. M., Lohrmann, C., Luukka, P., & Porras, J. (2022). Machine learning techniques and data for stock market forecasting: A literature review. Expert Systems with Applications, 197, 116659. Lintner, J. (1965). The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets. The Review of Economics and Statistics, 47(1), 13. Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91. https://doi.org/10.2307/2975974 McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5, 115-133. Merton, R. C. (1980). On estimating the expected return on the market: An exploratory investigation. Journal of Financial Economics, 8(4), 323-361. https://doi.org/https://doi.org/10.1016/0304-405X(80)90007-0 Minsky, M., & Papert, S. (1969). An introduction to computational geometry. Cambridge tiass., HIT, 479(480), 104. Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the econometric society, 347-370. Pástor, Ľ., Sinha, M., & Swaminathan, B. (2008). Estimating the Intertemporal Risk–Return Tradeoff Using the Implied Cost of Capital. The Journal of Finance, 63(6), 2859-2897. https://doi.org/https://doi.org/10.1111/j.1540-6261.2008.01415.x Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6), 386. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536. https://doi.org/10.1038/323533a0 Scholes, M., & Williams, J. (1977). Estimating betas from nonsynchronous data. Journal of Financial Economics, 5(3), 309-327. Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425-442. Welch, I., & Goyal, A. (2007). A Comprehensive Look at The Empirical Performance of Equity Premium Prediction. The review of financial studies, 21(4), 1455-1508. https://doi.org/10.1093/rfs/hhm014 Yu, J., & Yuan, Y. (2011). Investor sentiment and the mean–variance relation. Journal of Financial Economics, 100(2), 367-381. https://doi.org/https://doi.org/10.1016/j.jfineco.2010.10.011
描述 碩士
國立政治大學
金融學系
111352034
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111352034
資料類型 thesis
dc.contributor.advisor 廖四郎zh_TW
dc.contributor.advisor Liao, Szu-Langen_US
dc.contributor.author (Authors) 林晉毅zh_TW
dc.contributor.author (Authors) Lin, Chin-Ien_US
dc.creator (作者) 林晉毅zh_TW
dc.creator (作者) Lin, Chin-Ien_US
dc.date (日期) 2024en_US
dc.date.accessioned 5-Aug-2024 12:18:55 (UTC+8)-
dc.date.available 5-Aug-2024 12:18:55 (UTC+8)-
dc.date.issued (上傳時間) 5-Aug-2024 12:18:55 (UTC+8)-
dc.identifier (Other Identifiers) G0111352034en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/152473-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 金融學系zh_TW
dc.description (描述) 111352034zh_TW
dc.description.abstract (摘要) 傳統資產定價模型主要考慮風險，而忽略了機率本身的不確定性，即模糊性。本研究採用台灣加權指數作為研究對象，參考了Brenner and Izhakian (2018)提出的實證方法來測量台灣市場中的模糊性程度，並從台灣的市場數據中評估投資者對模糊性的態度。實證結果顯示，模糊性在股票市場中具有價格，且當預期的有利報酬機率較高時，投資者對模糊性表現出厭惡態度。此外，本研究嘗試將模糊性作為一種新指標，並將其引入神經網路與長短期記憶(LSTM)機器學習模型，以觀察其對股票價格預測的影響。結果顯示，加入模糊性後，機器學習模型的預測準確度有提升的趨勢。zh_TW
dc.description.abstract (摘要) Traditional asset pricing models primarily focus on risk, often neglecting the uncertainty inherent in probabilities, known as ambiguity. This research examines the Taiwan Weighted Index, using the empirical method by Brenner and Izhakian (2018) to quantify the level of ambiguity in the Taiwanese market. By analyzing market data from Taiwan, the study evaluates investor attitudes towards ambiguity. The findings indicate that ambiguity is indeed reflected in stock market prices, and investors show aversion to ambiguity when the likelihood of favorable returns is high. Furthermore, this study explores incorporating ambiguity as a new indicator into neural network and long short-term memory (LSTM) machine learning models to assess its impact on stock price predictions. The results indicate an improvement in prediction accuracy for machine learning models with the inclusion of ambiguity.en_US
dc.description.tableofcontents 第一章 緒論 1 第一節 研究背景與研究動機 1 第二節 研究目的 2 一、研究目的 2 二、研究貢獻 2 第二章 文獻回顧 4 第一節 資產定價模型的發展 4 一、傳統資產定價模型 4 二、傳統模型的實證檢驗與挑戰 4 三、從資產定價到模糊性 5 第二節 模糊性的相關研究 5 一、模糊性的早期研究 5 二、模糊性在資產市場中的應用 6 三、模糊性厭惡與經濟決策 6 四、近期的模糊性研究 6 五、模糊性與宏觀經濟不確定性 7 第三節 神經網絡和長短期記憶 7 一、早期神經網絡的發展與挑戰 7 二、長短期記憶的引入 8 三、LSTM於金融時間序列預測的應用 8 四、小結 8 第三章 研究方法 10 第一節 模糊性(Ambiguity) 10 一、模糊性 10 二、模糊性的量化 11 三、投資者對模糊性的態度 12 第二節 神經網路(Neural network, NN) 13 一、神經元 13 二、層(Layers) 13 三、激活函數(Activation Functions) 14 四、神經網路的工作原理 14 第三節 長短期記憶(Long Short-Term Memory, LSTM) 15 一、LSTM的結構及原理 15 二、LSTM的優勢 16 第四節 模型過擬合(Overfitting)的處理 17 一、Elastic Net正則化(Elastic Net Regularization) 17 二、Dropout 18 第四章 實證結果 19 第一節 模糊性於台灣市場之實證 19 一、資料描述及預處理 19 二、統計結果 21 第二節 模糊性應用於機器學習模型 24 一、資料集描述 24 二、模型績效表現 26 三、與過去的模糊性指標之比較 29 第五章 結論與展望 31 第一節 結論 31 第二節 未來展望 31 參考文獻 33zh_TW
dc.format.extent 1780898 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111352034en_US
dc.subject (關鍵詞) 模糊性zh_TW
dc.subject (關鍵詞) Knightian uncertaintyzh_TW
dc.subject (關鍵詞) Ambiguityzh_TW
dc.subject (關鍵詞) 神經網路zh_TW
dc.subject (關鍵詞) LSTMzh_TW
dc.subject (關鍵詞) 台灣加權指數zh_TW
dc.subject (關鍵詞) Knightian uncertaintyen_US
dc.subject (關鍵詞) Ambiguityen_US
dc.subject (關鍵詞) Neural networken_US
dc.subject (關鍵詞) LSTMen_US
dc.subject (關鍵詞) Taiwan Weighted Stock Indexen_US
dc.title (題名) 模糊性與資產定價：台灣加權指數的實證研究與機器學習應用zh_TW
dc.title (題名) Ambiguity and asset pricing: Empirical study and machine learning applications using the Taiwan Weighted Stock Indexen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 參考文獻 Augustin, P., & Izhakian, Y. (2020). Ambiguity, volatility, and credit risk. The review of financial studies, 33(4), 1618-1672. Black, F. (1976). Studies of stock market volatility changes. Proceedings of the American Statistical Association, Business & Economic Statistics Section, 1976. Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of political economy, 81(3), 637-654. Brenner, M., & Izhakian, Y. (2012). Asset Pricing and Ambiguity: Empirical Evidence, working paper. Brenner, M., & Izhakian, Y. (2018). Asset pricing and ambiguity: Empirical evidence. Journal of Financial Economics, 130(3), 503-531. Campbell, J. Y., & Hentschel, L. (1992). No news is good news: An asymmetric model of changing volatility in stock returns. Journal of Financial Economics, 31(3), 281-318. https://doi.org/https://doi.org/10.1016/0304-405X(92)90037-X Drechsler, I. (2013). Uncertainty, time‐varying fear, and asset prices. The Journal of Finance, 68(5), 1843-1889. Epstein, L. G., & Schneider, M. (2003). Recursive multiple-priors. Journal of Economic Theory, 113(1), 1-31. Epstein, L. G., & Schneider, M. (2008). Ambiguity, Information Quality, and Asset Pricing. The Journal of Finance, 63(1), 197-228. https://doi.org/https://doi.org/10.1111/j.1540-6261.2008.01314.x Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European journal of operational research, 270(2), 654-669. French, K. R., Schwert, G. W., & Stambaugh, R. F. (1987). Expected stock returns and volatility. Journal of Financial Economics, 19(1), 3-29. https://doi.org/https://doi.org/10.1016/0304-405X(87)90026-2 Gagliardini, P., Porchia, P., & Trojani, F. (2008). Ambiguity aversion and the term structure of interest rates. The review of financial studies, 22(10), 4157-4188. Ghysels, E., Santa-Clara, P., & Valkanov, R. (2005). There is a risk-return trade-off after all. Journal of Financial Economics, 76(3), 509-548. https://doi.org/https://doi.org/10.1016/j.jfineco.2004.03.008 Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48(5), 1779-1801. Guo, H., & Whitelaw, R. F. (2006). Uncovering the Risk–Return Relation in the Stock Market. The Journal of Finance, 61(3), 1433-1463. https://doi.org/https://doi.org/10.1111/j.1540-6261.2006.00877.x Harvey, C. R. (2001). The specification of conditional expectations. Journal of Empirical Finance, 8(5), 573-637. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. Izhakian, Y. Y. (2012). Ambiguity Measurement. Available at SSRN 1938628. Jahan-Parvar, M. R., & Liu, H. (2014). Ambiguity aversion and asset prices in production economies. The review of financial studies, 27(10), 3060-3097. Jeong, D., Kim, H., & Park, J. Y. (2015). Does ambiguity matter? Estimating asset pricing models with a multiple-priors recursive utility. Journal of Financial Economics, 115(2), 361-382. Ju, N., & Miao, J. (2012). Ambiguity, learning, and asset returns. Econometrica, 80(2), 559-591. Kahneman, D., & Tversky, A. (2013). Prospect theory: An analysis of decision under risk. In Handbook of the fundamentals of financial decision making: Part I (pp. 99-127). World Scientific. Klibanoff, P., Marinacci, M., & Mukerji, S. (2005). A smooth model of decision making under ambiguity. Econometrica, 73(6), 1849-1892. Kumbure, M. M., Lohrmann, C., Luukka, P., & Porras, J. (2022). Machine learning techniques and data for stock market forecasting: A literature review. Expert Systems with Applications, 197, 116659. Lintner, J. (1965). The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets. The Review of Economics and Statistics, 47(1), 13. Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91. https://doi.org/10.2307/2975974 McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5, 115-133. Merton, R. C. (1980). On estimating the expected return on the market: An exploratory investigation. Journal of Financial Economics, 8(4), 323-361. https://doi.org/https://doi.org/10.1016/0304-405X(80)90007-0 Minsky, M., & Papert, S. (1969). An introduction to computational geometry. Cambridge tiass., HIT, 479(480), 104. Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the econometric society, 347-370. Pástor, Ľ., Sinha, M., & Swaminathan, B. (2008). Estimating the Intertemporal Risk–Return Tradeoff Using the Implied Cost of Capital. The Journal of Finance, 63(6), 2859-2897. https://doi.org/https://doi.org/10.1111/j.1540-6261.2008.01415.x Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6), 386. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536. https://doi.org/10.1038/323533a0 Scholes, M., & Williams, J. (1977). Estimating betas from nonsynchronous data. Journal of Financial Economics, 5(3), 309-327. Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425-442. Welch, I., & Goyal, A. (2007). A Comprehensive Look at The Empirical Performance of Equity Premium Prediction. The review of financial studies, 21(4), 1455-1508. https://doi.org/10.1093/rfs/hhm014 Yu, J., & Yuan, Y. (2011). Investor sentiment and the mean–variance relation. Journal of Financial Economics, 100(2), 367-381. https://doi.org/https://doi.org/10.1016/j.jfineco.2010.10.011zh_TW