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Out-of-sample equity premium prediction: economic fundamentals vs. moving-average rules

Citations

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Cited by:

  1. Amélie Charles & Olivier Darné & Jae H. Kim, 2022. "Stock return predictability: Evaluation based on interval forecasts," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 363-385, April.
  2. De Simone, Francisco Nadal, 2024. "The transmission of U.S. monetary policy to small open economies," Journal of International Money and Finance, Elsevier, vol. 142(C).
  3. Pan, Zhiyuan & Zhong, Hao & Wang, Yudong & Huang, Juan, 2024. "Forecasting oil futures returns with news," Energy Economics, Elsevier, vol. 134(C).
  4. Massacci, Daniele & Kapetanios, George, 2024. "Forecasting in factor augmented regressions under structural change," International Journal of Forecasting, Elsevier, vol. 40(1), pages 62-76.
  5. Baetje, Fabian & Menkhoff, Lukas, 2016. "Equity premium prediction: Are economic and technical indicators unstable?," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1193-1207.
  6. Liya Chu & Xue-Zhong He & Kai Li & Jun Tu, 2022. "Investor Sentiment and Paradigm Shifts in Equity Return Forecasting," Management Science, INFORMS, vol. 68(6), pages 4301-4325, June.
  7. Goodness C. Aye & Rangan Gupta & Mampho P. Modise, 2012. "Structural Breaks and Predictive Regressions Models of South African Equity Premium," Working Papers 201209, University of Pretoria, Department of Economics.
  8. Dai, Zhifeng & Kang, Jie & Hu, Yangli, 2021. "Efficient predictability of oil price: The role of number of IPOs and U.S. dollar index," Resources Policy, Elsevier, vol. 74(C).
  9. Day, Min-Yuh & Ni, Yensen & Huang, Paoyu, 2019. "Trading as sharp movements in oil prices and technical trading signals emitted with big data concerns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 349-372.
  10. Li, Jiahan & Tsiakas, Ilias, 2017. "Equity premium prediction: The role of economic and statistical constraints," Journal of Financial Markets, Elsevier, vol. 36(C), pages 56-75.
  11. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2020. "Forecasting commodity prices out-of-sample: Can technical indicators help?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 666-683.
  12. Ruan, Qingsong & Yang, Haiquan & Lv, Dayong & Zhang, Shuhua, 2018. "Cross-correlations between individual investor sentiment and Chinese stock market return: New perspective based on MF-DCCA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 243-256.
  13. Kuntz, Laura-Chloé, 2020. "Beta dispersion and market timing," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 235-256.
  14. Li, Chenchen & Wang, Yudong & Wu, Chongfeng, 2022. "Oil implied volatility and expected stock returns along the worldwide supply chain," Energy Economics, Elsevier, vol. 114(C).
  15. Yingying Xu & Jichang Zhao, 2022. "Can sentiments on macroeconomic news explain stock returns? Evidence form social network data," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2073-2088, April.
  16. Libo Yin & Qingyuan Yang & Zhi Su, 2017. "Predictability of structural co-movement in commodity prices: the role of technical indicators," Quantitative Finance, Taylor & Francis Journals, vol. 17(5), pages 795-812, May.
  17. Mengxi He & Xianfeng Hao & Yaojie Zhang & Fanyi Meng, 2021. "Forecasting stock return volatility using a robust regression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1463-1478, December.
  18. Hardik A. Marfatia & Qiang Ji & Jiawen Luo, 2022. "Forecasting the volatility of agricultural commodity futures: The role of co‐volatility and oil volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 383-404, March.
  19. Faria, Gonçalo & Verona, Fabio, 2023. "Forecast combination in the frequency domain," Bank of Finland Research Discussion Papers 1/2023, Bank of Finland.
  20. Lansing, Kevin J. & LeRoy, Stephen F. & Ma, Jun, 2022. "Examining the sources of excess return predictability: Stochastic volatility or market inefficiency?," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 50-72.
  21. Giovannelli, Alessandro & Massacci, Daniele & Soccorsi, Stefano, 2021. "Forecasting stock returns with large dimensional factor models," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 252-269.
  22. Nonejad, Nima, 2021. "Predicting equity premium using news-based economic policy uncertainty: Not all uncertainty changes are equally important," International Review of Financial Analysis, Elsevier, vol. 77(C).
  23. Ding Du & Ronald J Gunderson & Xiaobing Zhao, 2016. "Investor sentiment and oil prices," Journal of Asset Management, Palgrave Macmillan, vol. 17(2), pages 73-88, March.
  24. He, Mengxi & Zhang, Yaojie, 2022. "Climate policy uncertainty and the stock return predictability of the oil industry," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
  25. Lu Wang & Feng Ma & Guoshan Liu & Qiaoqi Lang, 2023. "Do extreme shocks help forecast oil price volatility? The augmented GARCH‐MIDAS approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 2056-2073, April.
  26. Charles, Amelie & Darne, Olivier & Kim, Jae, 2016. "Stock Return Predictability: Evaluation based on Prediction Intervals," MPRA Paper 70143, University Library of Munich, Germany.
  27. Kenan Qiao & Haibin Xie, 2024. "Time‐varying risk preference and equity risk premium forecasting: The role of the disposition effect," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2659-2674, November.
  28. Chen, Kuan-Hau & Su, Xuan-Qi & Lin, Li-Feng & Shih, Yi-Cheng, 2021. "Profitability of moving-average technical analysis over the firm life cycle: Evidence from Taiwan," Pacific-Basin Finance Journal, Elsevier, vol. 69(C).
  29. He, Zhongzhi (Lawrence) & Zhu, Jie & Zhu, Xiaoneng, 2015. "Multi-factor volatility and stock returns," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 132-149.
  30. Buncic, Daniel & Piras, Gion Donat, 2016. "Heterogeneous agents, the financial crisis and exchange rate predictability," Journal of International Money and Finance, Elsevier, vol. 60(C), pages 313-359.
  31. Faria, Gonçalo & Verona, Fabio, 2018. "Forecasting stock market returns by summing the frequency-decomposed parts," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 228-242.
  32. Guglielmo Maria Caporale & Luis A. Gil-Alana & Miguel Martin-Valmayor, 2021. "Persistence in the market risk premium: evidence across countries," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 45(3), pages 413-427, July.
  33. Odendahl, Florens & Rossi, Barbara & Sekhposyan, Tatevik, 2023. "Evaluating forecast performance with state dependence," Journal of Econometrics, Elsevier, vol. 237(2).
  34. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Long Run Returns Predictability and Volatility with Moving Averages," Risks, MDPI, vol. 6(4), pages 1-18, September.
  35. Su, Yuandong & Lu, Xinjie & Zeng, Qing & Huang, Dengshi, 2022. "Good air quality and stock market returns," Research in International Business and Finance, Elsevier, vol. 62(C).
  36. Ma, Feng & Wang, Ruoxin & Lu, Xinjie & Wahab, M.I.M., 2021. "A comprehensive look at stock return predictability by oil prices using economic constraint approaches," International Review of Financial Analysis, Elsevier, vol. 78(C).
  37. Han, Yufeng & Zhou, Guofu & Zhu, Yingzi, 2016. "A trend factor: Any economic gains from using information over investment horizons?," Journal of Financial Economics, Elsevier, vol. 122(2), pages 352-375.
  38. Tsiakas, Ilias & Zhang, Haibin, 2021. "Economic fundamentals and the long-run correlation between exchange rates and commodities," Global Finance Journal, Elsevier, vol. 49(C).
  39. Wen, Danyan & He, Mengxi & Wang, Yudong & Zhang, Yaojie, 2024. "Forecasting crude oil market volatility: A comprehensive look at uncertainty variables," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1022-1041.
  40. Eom, Cheoljun & Park, Jong Won, 2023. "Price behavior of small-cap stocks and momentum: A study using principal component momentum," Research in International Business and Finance, Elsevier, vol. 65(C).
  41. Smith, Simon C., 2017. "Equity premium estimates from economic fundamentals under structural breaks," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 49-61.
  42. Lu, Xinjie & Ma, Feng & Wang, Tianyang & Wen, Fenghua, 2023. "International stock market volatility: A data-rich environment based on oil shocks," Journal of Economic Behavior & Organization, Elsevier, vol. 214(C), pages 184-215.
  43. Faria, Gonçalo & Verona, Fabio, 2020. "The yield curve and the stock market: Mind the long run," Journal of Financial Markets, Elsevier, vol. 50(C).
  44. Nuno Silva, 2015. "Time-Varying Stock Return Predictability: The Eurozone Case," Notas Económicas, Faculty of Economics, University of Coimbra, issue 41, pages 28-38, June.
  45. Oleg Rytchkov & Xun Zhong, 2020. "Information Aggregation and P-Hacking," Management Science, INFORMS, vol. 66(4), pages 1605-1626, April.
  46. Gupta, Rangan & Hammoudeh, Shawkat & Modise, Mampho P. & Nguyen, Duc Khuong, 2014. "Can economic uncertainty, financial stress and consumer sentiments predict U.S. equity premium?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 33(C), pages 367-378.
  47. Ma, Feng & Cao, Jiawei, 2023. "The Chinese equity premium predictability: Evidence from a long historical data," Finance Research Letters, Elsevier, vol. 53(C).
  48. Czudaj, Robert L., 2019. "Crude oil futures trading and uncertainty," Energy Economics, Elsevier, vol. 80(C), pages 793-811.
  49. Scholz, Michael & Nielsen, Jens Perch & Sperlich, Stefan, 2015. "Nonparametric prediction of stock returns based on yearly data: The long-term view," Insurance: Mathematics and Economics, Elsevier, vol. 65(C), pages 143-155.
  50. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
  51. Paskalis Glabadanidis, 2015. "Market Timing With Moving Averages," International Review of Finance, International Review of Finance Ltd., vol. 15(3), pages 387-425, September.
  52. Liu, Jingzhen, 2019. "Impacts of lagged returns on the risk-return relationship of Chinese aggregate stock market: Evidence from different data frequencies," Research in International Business and Finance, Elsevier, vol. 48(C), pages 243-257.
  53. Ikhlaas Gurrib & Mohammad Nourani & Rajesh Kumar Bhaskaran, 2022. "Energy crypto currencies and leading U.S. energy stock prices: are Fibonacci retracements profitable?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-27, December.
  54. Thorsten Lehnert & Gildas Blanchard & Dennis Bams, 2014. "Evaluating Option Pricing Model Performance Using Model Uncertainty," LSF Research Working Paper Series 14-06, Luxembourg School of Finance, University of Luxembourg.
  55. Vortelinos, Dimitrios I., 2017. "Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 824-839.
  56. Lin, Qi, 2018. "Technical analysis and stock return predictability: An aligned approach," Journal of Financial Markets, Elsevier, vol. 38(C), pages 103-123.
  57. Qingxiang Han & Mengxi He & Yaojie Zhang & Muhammad Umar, 2023. "Default return spread: A powerful predictor of crude oil price returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1786-1804, November.
  58. Fernando M. Duarte & Carlo Rosa, 2015. "The equity risk premium: a review of models," Economic Policy Review, Federal Reserve Bank of New York, issue 2, pages 39-57.
  59. Guofu Zhou, 2018. "Measuring Investor Sentiment," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 239-259, November.
  60. Xue Gong & Weiguo Zhang & Yuan Zhao & Xin Ye, 2023. "Forecasting stock volatility with a large set of predictors: A new forecast combination method," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1622-1647, November.
  61. Hoang, Khoa & Cannavan, Damien & Huang, Ronghong & Peng, Xiaowen, 2021. "Predicting stock returns with implied cost of capital: A partial least squares approach," Journal of Financial Markets, Elsevier, vol. 53(C).
  62. Shi Yafeng & Tao Xiangxing & Shi Yanlong & Zhu Nenghui & Ying Tingting & Peng Xun, 2020. "Can Technical Indicators Provide Information for Future Volatility: International Evidence," Journal of Systems Science and Information, De Gruyter, vol. 8(1), pages 53-66, February.
  63. Smith, Simon C., 2021. "International stock return predictability," International Review of Financial Analysis, Elsevier, vol. 78(C).
  64. Demetrescu, Matei & Georgiev, Iliyan & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2022. "Testing for episodic predictability in stock returns," Journal of Econometrics, Elsevier, vol. 227(1), pages 85-113.
  65. Xing, Li-Min & Zhang, Yue-Jun, 2022. "Forecasting crude oil prices with shrinkage methods: Can nonconvex penalty and Huber loss help?," Energy Economics, Elsevier, vol. 110(C).
  66. Qian, Lihua & Zeng, Qing & Li, Tao, 2022. "Geopolitical risk and oil price volatility: Evidence from Markov-switching model," International Review of Economics & Finance, Elsevier, vol. 81(C), pages 29-38.
  67. Li, Hongchang & Strauss, Jack & Shunxiang, Hu & Lui, Lu, 2018. "Do high-speed railways lead to urban economic growth in China? A panel data study of China’s cities," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 70-89.
  68. Wen, Chufu & Zhu, Haoyang & Dai, Zhifeng, 2023. "Forecasting commodity prices returns: The role of partial least squares approach," Energy Economics, Elsevier, vol. 125(C).
  69. Haase, Felix & Neuenkirch, Matthias, 2023. "Predictability of bull and bear markets: A new look at forecasting stock market regimes (and returns) in the US," International Journal of Forecasting, Elsevier, vol. 39(2), pages 587-605.
  70. Liu, Jing & Ma, Feng & Zhang, Yaojie, 2019. "Forecasting the Chinese stock volatility across global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 466-477.
  71. Yaojie Zhang & Qingxiang Han & Mengxi He, 2024. "Forecasting stock market returns with a lottery index: Evidence from China," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1595-1606, August.
  72. Yu, Jing-Rung & Paul Chiou, Wan-Jiun & Lee, Wen-Yi & Lin, Shun-Ji, 2020. "Portfolio models with return forecasting and transaction costs," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 118-130.
  73. Gupta, Rangan & Majumdar, Anandamayee & Pierdzioch, Christian & Wohar, Mark E., 2017. "Do terror attacks predict gold returns? Evidence from a quantile-predictive-regression approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 276-284.
  74. Yaojie Zhang & Mengxi He & Zhikai Zhang, 2024. "Forecasting stock returns with industry volatility concentration," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2705-2730, November.
  75. Chenchen Li & Chongfeng Wu & Chunyang Zhou, 2021. "Forecasting equity returns: The role of commodity futures along the supply chain," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(1), pages 46-71, January.
  76. Luo, Qin & Ma, Feng & Wang, Jiqian & Wu, You, 2024. "Changing determinant driver and oil volatility forecasting: A comprehensive analysis," Energy Economics, Elsevier, vol. 129(C).
  77. Qianjie Geng & Xianfeng Hao & Yudong Wang, 2024. "Forecasting the volatility of crude oil futures: A time‐dependent weighted least squares with regularization constraint," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 309-325, March.
  78. Pan, Zhiyuan & Pettenuzzo, Davide & Wang, Yudong, 2020. "Forecasting stock returns: A predictor-constrained approach," Journal of Empirical Finance, Elsevier, vol. 55(C), pages 200-217.
  79. Ma, Feng & Lu, Fei & Tao, Ying, 2022. "Geopolitical risk and excess stock returns predictability: New evidence from a century of data," Finance Research Letters, Elsevier, vol. 50(C).
  80. Bätje, Fabian & Menkhoff, Lukas, 2016. "Predicting the equity premium via its components," VfS Annual Conference 2016 (Augsburg): Demographic Change 145789, Verein für Socialpolitik / German Economic Association.
  81. Lycheva, Maria & Mironenkov, Alexey & Kurbatskii, Alexey & Fantazzini, Dean, 2022. "Forecasting oil prices with penalized regressions, variance risk premia and Google data," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 68, pages 28-49.
  82. Yi, Yongsheng & Ma, Feng & Zhang, Yaojie & Huang, Dengshi, 2019. "Forecasting stock returns with cycle-decomposed predictors," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 250-261.
  83. Han, Yufeng & Hu, Ting & Yang, Jian, 2016. "Are there exploitable trends in commodity futures prices?," Journal of Banking & Finance, Elsevier, vol. 70(C), pages 214-234.
  84. Zhang, Yaojie & Zeng, Qing & Ma, Feng & Shi, Benshan, 2019. "Forecasting stock returns: Do less powerful predictors help?," Economic Modelling, Elsevier, vol. 78(C), pages 32-39.
  85. Timmermann, Allan, 2018. "Forecasting Methods in Finance," CEPR Discussion Papers 12692, C.E.P.R. Discussion Papers.
  86. Lyu, Zhichong & Ma, Feng & Zhang, Jixiang, 2023. "Oil futures volatility prediction: Bagging or combination?," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 457-467.
  87. Shi, Qi & Li, Bin, 2022. "Further evidence on financial information and economic activity forecasts in the United States," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
  88. Mei, Dexiang & Zeng, Qing & Zhang, Yaojie & Hou, Wenjing, 2018. "Does US Economic Policy Uncertainty matter for European stock markets volatility?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 215-221.
  89. Kenechukwu E. Anadu & James Bohn & Lina Lu & Matthew Pritsker & Andrei Zlate, 2019. "Reach for Yield by U.S. Public Pension Funds," Supervisory Research and Analysis Working Papers RPA 19-2, Federal Reserve Bank of Boston.
  90. Yin, Libo & Su, Zhi & Lu, Man, 2022. "Is oil risk important for commodity-related currency returns?," Research in International Business and Finance, Elsevier, vol. 60(C).
  91. Wang, Yudong & Hao, Xianfeng, 2022. "Forecasting the real prices of crude oil: A robust weighted least squares approach," Energy Economics, Elsevier, vol. 116(C).
  92. Yulin Liu & Luyao Zhang, 2022. "Cryptocurrency Valuation: An Explainable AI Approach," Papers 2201.12893, arXiv.org, revised Jul 2023.
  93. Paskalis Glabadanidis, 2017. "Timing the Market with a Combination of Moving Averages," International Review of Finance, International Review of Finance Ltd., vol. 17(3), pages 353-394, September.
  94. Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Market Timing with Moving Averages," Sustainability, MDPI, vol. 10(7), pages 1-25, June.
  95. Xu, Yongan & Liang, Chao & Wang, Jianqiong, 2023. "Financial stress and returns predictability: Fresh evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
  96. Zhang, Yaojie & Wang, Yudong, 2023. "Forecasting crude oil futures market returns: A principal component analysis combination approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 659-673.
  97. Chen, Guojin & Hong, Zhiwu & Ren, Yu, 2016. "Durable consumption and asset returns: Cointegration analysis," Economic Modelling, Elsevier, vol. 53(C), pages 231-244.
  98. Li, Zhao-Chen & Xie, Chi & Zeng, Zhi-Jian & Wang, Gang-Jin & Zhang, Ting, 2023. "Forecasting global stock market volatilities in an uncertain world," International Review of Financial Analysis, Elsevier, vol. 85(C).
  99. Sakkas, Athanasios & Tessaromatis, Nikolaos, 2020. "Factor based commodity investing," Journal of Banking & Finance, Elsevier, vol. 115(C).
  100. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Working Papers 202111, Geary Institute, University College Dublin.
  101. Hsu, Po-Hsuan & Taylor, Mark P. & Wang, Zigan & Li, Yan, 2024. "The out-of-sample performance of carry trades," Journal of International Money and Finance, Elsevier, vol. 143(C).
  102. Wang, Jiqian & Lu, Xinjie & He, Feng & Ma, Feng, 2020. "Which popular predictor is more useful to forecast international stock markets during the coronavirus pandemic: VIX vs EPU?," International Review of Financial Analysis, Elsevier, vol. 72(C).
  103. Matthew Lorig & Zhou Zhou & Bin Zou, 2017. "A Mathematical Analysis of Technical Analysis," Papers 1710.09476, arXiv.org, revised Feb 2019.
  104. Cotter, John & Eyiah-Donkor, Emmanuel & Potì, Valerio, 2023. "Commodity futures return predictability and intertemporal asset pricing," Journal of Commodity Markets, Elsevier, vol. 31(C).
  105. Mingwei Sun & Paskalis Glabadanidis, 2022. "Can technical indicators predict the Chinese equity risk premium?," International Review of Finance, International Review of Finance Ltd., vol. 22(1), pages 114-142, March.
  106. Wang Pu & Yixiang Chen & Feng Ma, 2016. "Forecasting the realized volatility in the Chinese stock market: further evidence," Applied Economics, Taylor & Francis Journals, vol. 48(33), pages 3116-3130, July.
  107. Chen, Juan & Ma, Feng & Qiu, Xuemei & Li, Tao, 2023. "The role of categorical EPU indices in predicting stock-market returns," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 365-378.
  108. Baltas, Nick & Karyampas, Dimitrios, 2018. "Forecasting the equity risk premium: The importance of regime-dependent evaluation," Journal of Financial Markets, Elsevier, vol. 38(C), pages 83-102.
  109. Dong, Ming & Tremblay, Andréanne, 2022. "Global weather-based trading strategies," Journal of Banking & Finance, Elsevier, vol. 143(C).
  110. Jurdi, Doureige & Kim, Jae, 2019. "Predicting the U.S. Stock Market Return: Evidence from the Improved Augmented Regression Method," MPRA Paper 94028, University Library of Munich, Germany.
  111. Dai, Zhifeng & Zhou, Huiting & Wen, Fenghua & He, Shaoyi, 2020. "Efficient predictability of stock return volatility: The role of stock market implied volatility," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
  112. Zarrabi, Nima & Snaith, Stuart & Coakley, Jerry, 2017. "FX technical trading rules can be profitable sometimes!," International Review of Financial Analysis, Elsevier, vol. 49(C), pages 113-127.
  113. Liya Chu & Xue-Zhong He & Kai Li & Jun Tu, 2015. "Market Sentiment and Paradigm Shifts," Research Paper Series 356, Quantitative Finance Research Centre, University of Technology, Sydney.
  114. Zhang, Yaojie & Ma, Feng & Shi, Benshan & Huang, Dengshi, 2018. "Forecasting the prices of crude oil: An iterated combination approach," Energy Economics, Elsevier, vol. 70(C), pages 472-483.
  115. Niu, Zibo & Ma, Feng & Zhang, Hongwei, 2022. "The role of uncertainty measures in volatility forecasting of the crude oil futures market before and during the COVID-19 pandemic," Energy Economics, Elsevier, vol. 112(C).
  116. Chang, C-L. & Hsu, S.-H. & McAleer, M.J., 2018. "Asymmetric Risk Impacts of Chinese Tourists to Taiwan," Econometric Institute Research Papers EI2018-18, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  117. repec:zbw:bofrdp:2016_029 is not listed on IDEAS
  118. Caginalp, Gunduz & DeSantis, Mark, 2020. "Nonlinear price dynamics of S&P 100 stocks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
  119. Apergis, Nicholas & Gupta, Rangan, 2017. "Can (unusual) weather conditions in New York predict South African stock returns?," Research in International Business and Finance, Elsevier, vol. 41(C), pages 377-386.
  120. Madhavi Latha Challa & Venkataramanaiah Malepati & Siva Nageswara Rao Kolusu, 2020. "S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-19, December.
  121. Farias Nazário, Rodolfo Toríbio & e Silva, Jéssica Lima & Sobreiro, Vinicius Amorim & Kimura, Herbert, 2017. "A literature review of technical analysis on stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 115-126.
  122. Zakamulin, Valeriy & Giner, Javier, 2022. "Time series momentum in the US stock market: Empirical evidence and theoretical analysis," International Review of Financial Analysis, Elsevier, vol. 82(C).
  123. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
  124. Yaojie Zhang & Yudong Wang & Feng Ma, 2021. "Forecasting US stock market volatility: How to use international volatility information," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 733-768, August.
  125. Gao, Lei & Han, Yufeng & Zhengzi Li, Sophia & Zhou, Guofu, 2018. "Market intraday momentum," Journal of Financial Economics, Elsevier, vol. 129(2), pages 394-414.
  126. Urquhart, Andrew & Zhang, Hanxiong, 2019. "The performance of technical trading rules in Socially Responsible Investments," International Review of Economics & Finance, Elsevier, vol. 63(C), pages 397-411.
  127. Koo, Bonsoo & Anderson, Heather M. & Seo, Myung Hwan & Yao, Wenying, 2020. "High-dimensional predictive regression in the presence of cointegration," Journal of Econometrics, Elsevier, vol. 219(2), pages 456-477.
  128. Aladesanmi, Olalekan & Casalin, Fabrizio & Metcalf, Hugh, 2019. "Stock market integration between the UK and the US: Evidence over eight decades," Global Finance Journal, Elsevier, vol. 41(C), pages 32-43.
  129. Jixiang, Zhang & Feng, Ma, 2024. "Video apps user engagement and stock market volatility: Evidence from China," Finance Research Letters, Elsevier, vol. 64(C).
  130. Chuliá, Helena & Guillén, Montserrat & Uribe, Jorge M., 2017. "Measuring uncertainty in the stock market," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 18-33.
  131. Thomadakis, Apostolos, 2016. "Do Combination Forecasts Outperform the Historical Average? Economic and Statistical Evidence," MPRA Paper 71589, University Library of Munich, Germany.
  132. Vecchi, Edoardo & Berra, Gabriele & Albrecht, Steffen & Gagliardini, Patrick & Horenko, Illia, 2023. "Entropic approximate learning for financial decision-making in the small data regime," Research in International Business and Finance, Elsevier, vol. 65(C).
  133. Zhifeng Dai & Tingyu Li & Mi Yang, 2022. "Forecasting stock return volatility: The role of shrinkage approaches in a data‐rich environment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 980-996, August.
  134. Yafeng Qin & Guoyao Pan & Min Bai, 2020. "Improving market timing of time series momentum in the Chinese stock market," Applied Economics, Taylor & Francis Journals, vol. 52(43), pages 4711-4725, September.
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