IDEAS home Printed from https://ideas.repec.org/r/eee/ecofch/2-328.html
   My bibliography  Save this item

Forecasting Stock Returns

In: Handbook of Economic Forecasting

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Taofeek O. AYINDE & Farouq A. ADEYEMI, 2023. "Global Evidence of Oil Supply Shocks and Climate Risk a GARCH-MIDAS Approach," Energy RESEARCH LETTERS, Asia-Pacific Applied Economics Association, vol. 4(2), pages 1-7.
  2. Wang, Yudong & Hao, Xianfeng, 2023. "Forecasting the real prices of crude oil: What is the role of parameter instability?," Energy Economics, Elsevier, vol. 117(C).
  3. Leopoldo Catania & Nima Nonejad, 2016. "Density Forecasts and the Leverage Effect: Some Evidence from Observation and Parameter-Driven Volatility Models," Papers 1605.00230, arXiv.org, revised Nov 2016.
  4. Mehmet Balcilar & Rangan Gupta & Christian Pierdzioch & Mark E. Wohar, 2018. "Terror attacks and stock-market fluctuations: evidence based on a nonparametric causality-in-quantiles test for the G7 countries," The European Journal of Finance, Taylor & Francis Journals, vol. 24(4), pages 333-346, March.
  5. Tiwari, Aviral K. & Dar, Arif B. & Bhanja, Niyati & Gupta, Rangan, 2016. "A historical analysis of the US stock price index using empirical mode decomposition over 1791-2015," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 10, pages 1-15.
  6. Ayinde, Taofeek O. & Olaniran, Abeeb O. & Abolade, Onomeabure C. & Ogbonna, Ahamuefula Ephraim, 2023. "Technology shocks - Gold market connection: Is the effect episodic to business cycle behaviour?," Resources Policy, Elsevier, vol. 84(C).
  7. Rangan Gupta & Patrick Kanda & Mark E. Wohar, 2021. "Predicting Stock Market Movements in the United States: The Role of Presidential Approval Ratings," International Review of Finance, International Review of Finance Ltd., vol. 21(1), pages 324-335, March.
  8. Christou, Christina & Gupta, Rangan, 2020. "Forecasting equity premium in a panel of OECD countries: The role of economic policy uncertainty," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 243-248.
  9. Yin, Anwen, 2015. "Forecasting and model averaging with structural breaks," ISU General Staff Papers 201501010800005727, Iowa State University, Department of Economics.
  10. Snowberg, Erik & Wolfers, Justin & Zitzewitz, Eric, 2013. "Prediction Markets for Economic Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 657-687, Elsevier.
  11. Zhao, Albert Bo & Cheng, Tingting, 2022. "Stock return prediction: Stacking a variety of models," Journal of Empirical Finance, Elsevier, vol. 67(C), pages 288-317.
  12. Wang, Yudong & Hao, Xianfeng & Wu, Chongfeng, 2021. "Forecasting stock returns: A time-dependent weighted least squares approach," Journal of Financial Markets, Elsevier, vol. 53(C).
  13. Xi Dong & Yan Li & David E. Rapach & Guofu Zhou, 2022. "Anomalies and the Expected Market Return," Journal of Finance, American Finance Association, vol. 77(1), pages 639-681, February.
  14. Massimo Guidolin & Alexei G. Orlov, 2022. "Can Investors Benefit from Hedge Fund Strategies? Utility-Based, Out-of-Sample Evidence," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 12(03), pages 1-61, September.
  15. Elie Bouri & Riza Demirer & Rangan Gupta & Hardik A. Marfatia, 2019. "Geopolitical Risks and Movements in Islamic Bond and Equity Markets: A Note," Defence and Peace Economics, Taylor & Francis Journals, vol. 30(3), pages 367-379, April.
  16. Jondeau, Eric & Zhang, Qunzi & Zhu, Xiaoneng, 2019. "Average skewness matters," Journal of Financial Economics, Elsevier, vol. 134(1), pages 29-47.
  17. Møller, Stig V. & Nørholm, Henrik & Rangvid, Jesper, 2014. "Consumer confidence or the business cycle: What matters more for European expected returns?," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 230-248.
  18. Plakandaras, Vasilios & Gupta, Rangan & Wong, Wing-Keung, 2019. "Point and density forecasts of oil returns: The role of geopolitical risks," Resources Policy, Elsevier, vol. 62(C), pages 580-587.
  19. Adrian Fernandez-Perez & Ana-Maria Fuertes & Joelle Miffre, 2017. "Commodity Markets, Long-Run Predictability, and Intertemporal Pricing," Review of Finance, European Finance Association, vol. 21(3), pages 1159-1188.
  20. Gupta, Rangan & Risse, Marian & Volkman, David A. & Wohar, Mark E., 2019. "The role of term spread and pattern changes in predicting stock returns and volatility of the United Kingdom: Evidence from a nonparametric causality-in-quantiles test using over 250 years of data," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 391-405.
  21. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2023. "Gold risk premium estimation with machine learning methods," Journal of Commodity Markets, Elsevier, vol. 31(C).
  22. 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.
  23. Christou, Christina & Gupta, Rangan & Hassapis, Christis, 2017. "Does economic policy uncertainty forecast real housing returns in a panel of OECD countries? A Bayesian approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 50-60.
  24. Gonçalo Faria & Fabio Verona, 2016. "Forecasting the equity risk premium with frequency-decomposed predictors," Working Papers de Economia (Economics Working Papers) 06, Católica Porto Business School, Universidade Católica Portuguesa.
  25. Jiang, Yuexiang & Fu, Tao & Long, Huaigang & Zaremba, Adam & Zhou, Wenyu, 2022. "Real estate climate index and aggregate stock returns: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 75(C).
  26. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2020. "Longer-Term Forecasting of Excess Stock Returns—The Five-Year Case," Mathematics, MDPI, vol. 8(6), pages 1-20, June.
  27. Dunbar, Kwamie & Owusu-Amoako, Johnson, 2022. "Cryptocurrency returns under empirical asset pricing," International Review of Financial Analysis, Elsevier, vol. 82(C).
  28. Hillebrand, Eric & Lukas, Manuel & Wei, Wei, 2021. "Bagging weak predictors," International Journal of Forecasting, Elsevier, vol. 37(1), pages 237-254.
  29. Kuntz, Laura-Chloé, 2020. "Beta dispersion and market timing," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 235-256.
  30. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2021. "Short-Term Exuberance and Long-Term Stability: A Simultaneous Optimization of Stock Return Predictions for Short and Long Horizons," Mathematics, MDPI, vol. 9(6), pages 1-19, March.
  31. 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.
  32. Haibin Xie & Yuying Sun & Pengying Fan, 2023. "Return direction forecasting: a conditional autoregressive shape model with beta density," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-16, December.
  33. Bacchetta, Philippe & Tièche, Simon & van Wincoop, Eric, 2020. "International Portfolio Choice with Frictions: Evidence from Mutual Funds," CEPR Discussion Papers 14898, C.E.P.R. Discussion Papers.
  34. Bekiros, Stelios & Gupta, Rangan & Majumdar, Anandamayee, 2016. "Incorporating economic policy uncertainty in US equity premium models: A nonlinear predictability analysis," Finance Research Letters, Elsevier, vol. 18(C), pages 291-296.
  35. Edson VENGESAI & Adefemi A. OBALADE & Paul-Francois MUZINDUTSI, 2021. "Country Risk Dynamics and Stock Market Volatility: Evidence from the JSE Cross-Sector Analysis," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 5(2), pages 63-84.
  36. Wenbo Wu & Jiaqi Chen & Liang Xu & Qingyun He & Michael L. Tindall, 2019. "A statistical learning approach for stock selection in the Chinese stock market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-18, December.
  37. Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2023. "Climate Risks and Forecasting Stock Market Returns in Advanced Economies over a Century," Mathematics, MDPI, vol. 11(9), pages 1-21, April.
  38. 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.
  39. Eric Jondeau & Xuewu Wang & Zhipeng Yan & Qunzi Zhang, 2020. "Skewness and index futures return," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(11), pages 1648-1664, November.
  40. 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).
  41. Goodness C. Aye & Frederick W. Deale & Rangan Gupta, 2016. "Does Debt Ceiling and Government Shutdown Help in Forecasting the US Equity Risk Premium?," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 63(3), pages 273-291.
  42. Narayan, Paresh Kumar & Gupta, Rangan, 2015. "Has oil price predicted stock returns for over a century?," Energy Economics, Elsevier, vol. 48(C), pages 18-23.
  43. Suleman, Tahir & Gupta, Rangan & Balcilar, Mehmet, 2017. "Does country risks predict stock returns and volatility? Evidence from a nonparametric approach," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1173-1195.
  44. Cotter, John & Eyiah-Donkor, Emmanuel & Potì, Valerio, 2017. "Predictability and diversification benefits of investing in commodity and currency futures," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 52-66.
  45. Tsiakas, Ilias & Li, Jiahan & Zhang, Haibin, 2020. "Equity premium prediction and the state of the economy," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 75-95.
  46. Christou, Christina & Gupta, Rangan & Jawadi, Fredj, 2021. "Does inequality help in forecasting equity premium in a panel of G7 countries?," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
  47. Stig V. Møller & Jesper Rangvid, 2018. "Global Economic Growth and Expected Returns Around the World: The End-of-the-Year Effect," Management Science, INFORMS, vol. 64(2), pages 573-591, February.
  48. Li-Xin Wang, 2014. "Dynamical Models of Stock Prices Based on Technical Trading Rules Part II: Analysis of the Models," Papers 1401.1891, arXiv.org, revised Feb 2016.
  49. Gupta, Rangan & Pierdzioch, Christian & Vivian, Andrew J. & Wohar, Mark E., 2019. "The predictive value of inequality measures for stock returns: An analysis of long-span UK data using quantile random forests," Finance Research Letters, Elsevier, vol. 29(C), pages 315-322.
  50. Theologos Dergiades & Panos K. Pouliasis, 2023. "Should stock returns predictability be ‘hooked on’ long‐horizon regressions?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 718-732, January.
  51. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2014. "Forecasting stock returns under economic constraints," Journal of Financial Economics, Elsevier, vol. 114(3), pages 517-553.
  52. Richard K. Crump & Domenico Giannone & Sean Hundtofte, 2018. "Changing Risk-Return Profiles," Liberty Street Economics 20181004, Federal Reserve Bank of New York.
  53. 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.
  54. Massimo Guidolin & Manuela Pedio, 2021. "Forecasting commodity futures returns with stepwise regressions: Do commodity-specific factors help?," Annals of Operations Research, Springer, vol. 299(1), pages 1317-1356, April.
  55. Odendahl, Florens & Rossi, Barbara & Sekhposyan, Tatevik, 2023. "Evaluating forecast performance with state dependence," Journal of Econometrics, Elsevier, vol. 237(2).
  56. Cenedese, Gino & Mallucci, Enrico, 2016. "What moves international stock and bond markets?," Journal of International Money and Finance, Elsevier, vol. 60(C), pages 94-113.
  57. Salisu, Afees A. & Bouri, Elie & Gupta, Rangan, 2022. "Out-of-sample predictability of gold market volatility: The role of US Nonfarm Payroll," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 482-488.
  58. Nikolaos Antonakakis & Rangan Gupta & Aviral K. Tiwari, 2016. "Time-Varying Correlations between Inflation and Stock Prices in the United States over the Last Two Centuries," Working Papers 201605, University of Pretoria, Department of Economics.
  59. Narayan, Paresh Kumar & Bannigidadmath, Deepa, 2015. "Are Indian stock returns predictable?," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 506-531.
  60. Rangan Gupta & Hardik A. Marfatia & Eric Olson, 2020. "Effect of uncertainty on U.S. stock returns and volatility: evidence from over eighty years of high-frequency data," Applied Economics Letters, Taylor & Francis Journals, vol. 27(16), pages 1305-1311, September.
  61. Rangan Gupta & Chi Keung Marco Lau & Wendy Nyakabawo, 2018. "Predicting Aggregate and State-Level US House Price Volatility: The Role of Sentiment," Working Papers 201866, University of Pretoria, Department of Economics.
  62. Bouri, Elie & Gupta, Rangan & Majumdar, Anandamayee & Subramaniam, Sowmya, 2021. "Time-varying risk aversion and forecastability of the US term structure of interest rates," Finance Research Letters, Elsevier, vol. 42(C).
  63. Bekiros, Stelios & Gupta, Rangan, 2015. "Predicting stock returns and volatility using consumption-aggregate wealth ratios: A nonlinear approach," Economics Letters, Elsevier, vol. 131(C), pages 83-85.
  64. Wang, Yudong & Liu, Li & Ma, Feng & Diao, Xundi, 2018. "Momentum of return predictability," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 141-156.
  65. Muzhao Jin & Fearghal Kearney & Youwei Li & Yung Chiang Yang, 2020. "Intraday time‐series momentum: Evidence from China," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(4), pages 632-650, April.
  66. Chen Zhang, 2022. "Asset Pricing and Deep Learning," Papers 2209.12014, arXiv.org.
  67. Balcilar, Mehmet & Bonato, Matteo & Demirer, Riza & Gupta, Rangan, 2018. "Geopolitical risks and stock market dynamics of the BRICS," Economic Systems, Elsevier, vol. 42(2), pages 295-306.
  68. Yue-Jun Zhang & Han Zhang & Rangan Gupta, 2021. "Forecasting the Artificial Intelligence Index Returns: A Hybrid Approach," Working Papers 202182, University of Pretoria, Department of Economics.
  69. Faria, Gonçalo & Verona, Fabio, 2020. "The yield curve and the stock market: Mind the long run," Journal of Financial Markets, Elsevier, vol. 50(C).
  70. Jamal Bouoiyour & Refk Selmi, 2017. "Are Trump and Bitcoin Good Partners?," Papers 1703.00308, arXiv.org.
  71. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Lasisi, Lukman & Olaniran, Abeeb, 2022. "Geopolitical risk and stock market volatility in emerging markets: A GARCH – MIDAS approach," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
  72. Oleg Rytchkov & Xun Zhong, 2020. "Information Aggregation and P-Hacking," Management Science, INFORMS, vol. 66(4), pages 1605-1626, April.
  73. Salisu, Afees A. & Ademuyiwa, Idris & Isah, Kazeem O., 2018. "Revisiting the forecasting accuracy of Phillips curve: The role of oil price," Energy Economics, Elsevier, vol. 70(C), pages 334-356.
  74. 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.
  75. Yi, Yongsheng & He, Mengxi & Zhang, Yaojie, 2022. "Out-of-sample prediction of Bitcoin realized volatility: Do other cryptocurrencies help?," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
  76. Wang, Yudong & Pan, Zhiyuan & Liu, Li & Wu, Chongfeng, 2019. "Oil price increases and the predictability of equity premium," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 43-58.
  77. Liu, Li & Zhang, Tao, 2015. "Economic policy uncertainty and stock market volatility," Finance Research Letters, Elsevier, vol. 15(C), pages 99-105.
  78. Møller, Stig V. & Rangvid, Jesper, 2015. "End-of-the-year economic growth and time-varying expected returns," Journal of Financial Economics, Elsevier, vol. 115(1), pages 136-154.
  79. Gupta, Rangan & Mwamba, John W. Muteba & Wohar, Mark E., 2018. "The role of partisan conflict in forecasting the U.S. equity premium: A nonparametric approach," Finance Research Letters, Elsevier, vol. 25(C), pages 131-136.
  80. Massimo Guidolin & Manuela Pedio, 2018. "Forecasting Commodity Futures Returns: An Economic Value Analysis of Macroeconomic vs. Specific Factors," BAFFI CAREFIN Working Papers 1886, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
  81. Baur, Dirk G. & Dichtl, Hubert & Drobetz, Wolfgang & Wendt, Viktoria-Sophie, 2020. "Investing in gold – Market timing or buy-and-hold?," International Review of Financial Analysis, Elsevier, vol. 71(C).
  82. Ferrer Fernández, María & Henry, Ólan & Pybis, Sam & Stamatogiannis, Michalis P., 2023. "Can we forecast better in periods of low uncertainty? The role of technical indicators," Journal of Empirical Finance, Elsevier, vol. 71(C), pages 1-12.
  83. Gupta, Rangan & Pierdzioch, Christian & Salisu, Afees A., 2022. "Oil-price uncertainty and the U.K. unemployment rate: A forecasting experiment with random forests using 150 years of data," Resources Policy, Elsevier, vol. 77(C).
  84. Afees A. Salisu & Abeeb Olaniran, 2022. "The U.S. Nonfarm Payroll and the out-of-sample predictability of output growth for over six decades," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4663-4673, December.
  85. Buncic, Daniel & Stern, Cord, 2019. "Forecast ranked tailored equity portfolios," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).
  86. Antonakakis, Nikolaos & Gupta, Rangan & Tiwari, Aviral K., 2017. "Has the correlation of inflation and stock prices changed in the United States over the last two centuries?," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1-8.
  87. Buncic, Daniel & Tischhauser, Martin, 2017. "Macroeconomic factors and equity premium predictability," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 621-644.
  88. Kenwin Maung, 2021. "Estimating high-dimensional Markov-switching VARs," Papers 2107.12552, arXiv.org.
  89. Rangan Gupta & Tahir Suleman & Mark E. Wohar, 2019. "The role of time‐varying rare disaster risks in predicting bond returns and volatility," Review of Financial Economics, John Wiley & Sons, vol. 37(3), pages 327-340, July.
  90. Carr, Peter & Wu, Liuren, 2016. "Analyzing volatility risk and risk premium in option contracts: A new theory," Journal of Financial Economics, Elsevier, vol. 120(1), pages 1-20.
  91. Salisu, Afees A. & Cuñado, Juncal & Gupta, Rangan, 2022. "Geopolitical risks and historical exchange rate volatility of the BRICS," International Review of Economics & Finance, Elsevier, vol. 77(C), pages 179-190.
  92. Guofu Zhou, 2018. "Measuring Investor Sentiment," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 239-259, November.
  93. Christou, Christina & Cunado, Juncal & Gupta, Rangan & Hassapis, Christis, 2017. "Economic policy uncertainty and stock market returns in PacificRim countries: Evidence based on a Bayesian panel VAR model," Journal of Multinational Financial Management, Elsevier, vol. 40(C), pages 92-102.
  94. Pönkä, Harri, 2016. "Real oil prices and the international sign predictability of stock returns," Finance Research Letters, Elsevier, vol. 17(C), pages 79-87.
  95. Gonçalo Faria & Fabio Verona, 2016. "Forecasting the equity risk premium with frequency-decomposed predictors," Working Papers de Economia (Economics Working Papers) 06, Católica Porto Business School, Universidade Católica Portuguesa.
  96. Ciner, Cetin, 2022. "Predicting the equity market risk premium: A model selection approach," Economics Letters, Elsevier, vol. 215(C).
  97. Nonejad, Nima, 2021. "Predicting equity premium using dynamic model averaging. Does the state–space representation matter?," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
  98. Li, Yi & Shen, Dehua & Wang, Pengfei & Zhang, Wei, 2020. "Does intraday time-series momentum exist in Chinese stock index futures market?," Finance Research Letters, Elsevier, vol. 35(C).
  99. Xidonas, Panos & Doukas, Haris & Hassapis, Christis, 2021. "Grouped data, investment committees & multicriteria portfolio selection," Journal of Business Research, Elsevier, vol. 129(C), pages 205-222.
  100. Chuliá, Helena & Gupta, Rangan & Uribe, Jorge M. & Wohar, Mark E., 2017. "Impact of US uncertainties on emerging and mature markets: Evidence from a quantile-vector autoregressive approach," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 48(C), pages 178-191.
  101. Mobeen Ur Rehman & Wafa Ghardallou & Nasir Ahmad & Xuan Vinh Vo & Sang Hoon Kang, 2024. "Does effect of risk and uncertainties on US sectoral returns differ across different investment horizons and market conditions," Risk Management, Palgrave Macmillan, vol. 26(1), pages 1-49, February.
  102. Gu, Ailing & Viens, Frederi G. & Yao, Haixiang, 2018. "Optimal robust reinsurance-investment strategies for insurers with mean reversion and mispricing," Insurance: Mathematics and Economics, Elsevier, vol. 80(C), pages 93-109.
  103. Ghysels, Eric & Plazzi, Alberto & Valkanov, Rossen & Torous, Walter, 2013. "Forecasting Real Estate Prices," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 509-580, Elsevier.
  104. Bahloul, Walid & Balcilar, Mehmet & Cunado, Juncal & Gupta, Rangan, 2018. "The role of economic and financial uncertainties in predicting commodity futures returns and volatility: Evidence from a nonparametric causality-in-quantiles test," Journal of Multinational Financial Management, Elsevier, vol. 45(C), pages 52-71.
  105. Wang, Yudong & Pan, Zhiyuan & Wu, Chongfeng & Wu, Wenfeng, 2020. "Industry equi-correlation: A powerful predictor of stock returns," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 1-24.
  106. João F. Caldeira & Rangan Gupta & Hudson S. Torrent, 2020. "Forecasting U.S. Aggregate Stock Market Excess Return: Do Functional Data Analysis Add Economic Value?," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
  107. Nyberg, Henri & Pönkä, Harri, 2016. "International sign predictability of stock returns: The role of the United States," Economic Modelling, Elsevier, vol. 58(C), pages 323-338.
  108. Rangan Gupta & Shawkat Hammoudeh & Beatrice D. Simo-Kengne & Soodabeh Sarafrazi, 2014. "Can the Sharia-based Islamic stock market returns be forecasted using large number of predictors and models?," Applied Financial Economics, Taylor & Francis Journals, vol. 24(17), pages 1147-1157, September.
  109. Harri Pönkä, 2017. "Predicting the direction of US stock markets using industry returns," Empirical Economics, Springer, vol. 52(4), pages 1451-1480, June.
  110. 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.
  111. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
  112. Mehmet Balcilar & Rangan Gupta & Christian Pierdzioch, 2022. "Oil-Price Uncertainty and International Stock Returns: Dissecting Quantile-Based Predictability and Spillover Effects Using More than a Century of Data," Energies, MDPI, vol. 15(22), pages 1-26, November.
  113. 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.
  114. Rangan Gupta & Hardik A. Marfatia & Christian Pierdzioch & Afees A. Salisu, 2022. "Machine Learning Predictions of Housing Market Synchronization across US States: The Role of Uncertainty," The Journal of Real Estate Finance and Economics, Springer, vol. 64(4), pages 523-545, May.
  115. Zuzanna Karolak, 2021. "Energy prices forecasting using nonlinear univariate models," Bank i Kredyt, Narodowy Bank Polski, vol. 52(6), pages 577-598.
  116. Oktay Ozkan, 2020. "Time-varying return predictability and adaptive markets hypothesis: Evidence on MIST countries from a novel wild bootstrap likelihood ratio approach," Bogazici Journal, Review of Social, Economic and Administrative Studies, Bogazici University, Department of Economics, vol. 34(2), pages 101-113.
  117. Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2018. "Forecasting (Good and Bad) Realized Exchange-Rate Volatility: Is there a Role for Realized Skewness and Kurtosis?," Working Papers 201879, University of Pretoria, Department of Economics.
  118. 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.
  119. Imen Dakhlaoui & Chaker Aloui, 2016. "The Interactive Relationship Between the US Economic Policy Uncertainty and BRIC Stock Markets," International Economics, CEPII research center, issue 146, pages 141-157.
  120. 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).
  121. Dai, Zhifeng & Zhu, Huan, 2021. "Indicator selection and stock return predictability," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
  122. Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
  123. Hounyo, Ulrich & Lahiri, Kajal, 2023. "Estimating the variance of a combined forecast: Bootstrap-based approach," Journal of Econometrics, Elsevier, vol. 232(2), pages 445-468.
  124. Chronopoulos, Dimitris K. & Papadimitriou, Fotios I. & Vlastakis, Nikolaos, 2018. "Information demand and stock return predictability," Journal of International Money and Finance, Elsevier, vol. 80(C), pages 59-74.
  125. Lee, Chien-Chiang & Chen, Mei-Ping, 2020. "Do natural disasters and geopolitical risks matter for cross-border country exchange-traded fund returns?," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
  126. Nonejad, Nima, 2021. "The price of crude oil and (conditional) out-of-sample predictability of world industrial production," Journal of Commodity Markets, Elsevier, vol. 23(C).
  127. Yue-Jun Zhang & Han Zhang & Rangan Gupta, 2023. "A new hybrid method with data-characteristic-driven analysis for artificial intelligence and robotics index return forecasting," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
  128. Yin, Anwen, 2019. "Out-of-sample equity premium prediction in the presence of structural breaks," International Review of Financial Analysis, Elsevier, vol. 65(C).
  129. Spierdijk, Laura & Umar, Zaghum, 2014. "Stocks for the long run? Evidence from emerging markets," Journal of International Money and Finance, Elsevier, vol. 47(C), pages 217-238.
  130. Gupta, Rangan & Huber, Florian & Piribauer, Philipp, 2020. "Predicting international equity returns: Evidence from time-varying parameter vector autoregressive models," International Review of Financial Analysis, Elsevier, vol. 68(C).
  131. Jones, Clive, 2015. "Predictability of the daily high and low of the S&P 500 index," MPRA Paper 62664, University Library of Munich, Germany.
  132. Shi, Qi, 2023. "The RP-PCA factors and stock return predictability: An aligned approach," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
  133. Weilun Zhou & Jiti Gao & David Harris & Hsein Kew, 2019. "Semiparametric Single-index Predictive Regression," Monash Econometrics and Business Statistics Working Papers 25/19, Monash University, Department of Econometrics and Business Statistics.
  134. Nonejad, Nima, 2022. "Predicting equity premium out-of-sample by conditioning on newspaper-based uncertainty measures: A comparative study," International Review of Financial Analysis, Elsevier, vol. 83(C).
  135. Gupta, Rangan & Kanda, Patrick & Tiwari, Aviral Kumar & Wohar, Mark E., 2019. "Time-varying predictability of oil market movements over a century of data: The role of US financial stress," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
  136. Møller, Stig V. & Sander, Magnus, 2017. "Dividends, earnings, and predictability," Journal of Banking & Finance, Elsevier, vol. 78(C), pages 153-163.
  137. Shamsi Zamenjani, Azam, 2021. "Do financial variables help predict the conditional distribution of the market portfolio?," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 327-345.
  138. Adnen Ben Nasr & Ahdi N. Ajmi & Rangan Gupta, 2013. "Modeling the Volatility of the Dow Jones Islamic Market World Index Using a Fractionally Integrated Time Varying GARCH (FITVGARCH) Model," Working Papers 201357, University of Pretoria, Department of Economics.
  139. Leland E. Farmer & Lawrence Schmidt & Allan Timmermann, 2023. "Pockets of Predictability," Journal of Finance, American Finance Association, vol. 78(3), pages 1279-1341, June.
  140. 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.
  141. Massimo Guidolin & Manuela Pedio, 2022. "Switching Coefficients or Automatic Variable Selection: An Application in Forecasting Commodity Returns," Forecasting, MDPI, vol. 4(1), pages 1-32, February.
  142. Cotter, John & Eyiah-Donkor, Emmanuel & Potì, Valerio, 2023. "Commodity futures return predictability and intertemporal asset pricing," Journal of Commodity Markets, Elsevier, vol. 31(C).
  143. Liu, Li & Ma, Feng & Wang, Yudong, 2015. "Forecasting excess stock returns with crude oil market data," Energy Economics, Elsevier, vol. 48(C), pages 316-324.
  144. Oğuzhan Çepni & Rangan Gupta & Mark E. Wohar, 2021. "Variants of consumption‐wealth ratios and predictability of U.S. government bond risk premia," International Review of Finance, International Review of Finance Ltd., vol. 21(2), pages 661-674, June.
  145. Qunzi Zhang, 2021. "One hundred years of rare disaster concerns and commodity prices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(12), pages 1891-1915, December.
  146. Dunbar, Kwamie & Owusu-Amoako, Johnson, 2023. "Role of hedging on crypto returns predictability: A new habit-based explanation," Finance Research Letters, Elsevier, vol. 55(PB).
  147. Goodness C. Aye & Mehmet Balcilar & Rangan Gupta, 2017. "International stock return predictability: Is the role of U.S. time-varying?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 44(1), pages 121-146, February.
  148. Gino Cenedese & Richard Payne & Lucio Sarno & Giorgio Valente, 2016. "What Do Stock Markets Tell Us about Exchange Rates?," Review of Finance, European Finance Association, vol. 20(3), pages 1045-1080.
  149. Gebka, Bartosz & Wohar, Mark E., 2019. "Stock return distribution and predictability: Evidence from over a century of daily data on the DJIA index," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 1-25.
  150. Balcilar, Mehmet & Gupta, Rangan & Kim, Won Joong & Kyei, Clement, 2019. "The role of economic policy uncertainties in predicting stock returns and their volatility for Hong Kong, Malaysia and South Korea," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 150-163.
  151. Eriksen, Jonas N., 2017. "Expected Business Conditions and Bond Risk Premia," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(4), pages 1667-1703, August.
  152. repec:zbw:bofrdp:2016_029 is not listed on IDEAS
  153. Yu, Deshui & Huang, Difang & Chen, Li & Li, Luyang, 2023. "Forecasting dividend growth: The role of adjusted earnings yield," Economic Modelling, Elsevier, vol. 120(C).
  154. 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.
  155. Nicholas Apergis & Matteo Bonato & Rangan Gupta & Clement Kyei, 2016. "Does Geopolitical Risks Predict Stock Returns and Volatility of Leading Defense Companies? Evidence from a Nonparametric Approach," Working Papers 201671, University of Pretoria, Department of Economics.
  156. Dai, Zhifeng & Dong, Xiaodi & Kang, Jie & Hong, Lianying, 2020. "Forecasting stock market returns: New technical indicators and two-step economic constraint method," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
  157. Gupta, Rangan & Wohar, Mark, 2017. "Forecasting oil and stock returns with a Qual VAR using over 150years off data," Energy Economics, Elsevier, vol. 62(C), pages 181-186.
  158. Christoffersen, Peter & Fournier, Mathieu & Jacobs, Kris & Karoui, Mehdi, 2021. "Option-Based Estimation of the Price of Coskewness and Cokurtosis Risk," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 56(1), pages 65-91, February.
  159. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
  160. Dai, Zhifeng & Zhu, Huan, 2020. "Stock return predictability from a mixed model perspective," Pacific-Basin Finance Journal, Elsevier, vol. 60(C).
  161. Gao, Lei & Han, Yufeng & Zhengzi Li, Sophia & Zhou, Guofu, 2018. "Market intraday momentum," Journal of Financial Economics, Elsevier, vol. 129(2), pages 394-414.
  162. 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.
  163. Rangan Gupta & Christian Pierdzioch & Refk Selmi & Mark E. Wohar, 2017. "Does Partisan Conflict Predict a Reduction in US Stock Market (Realized) Volatility? Evidence from a Quantile-on-Quantile Regression Model," Working Papers 201744, University of Pretoria, Department of Economics.
  164. Ikhlaas Gurrib & Firuz Kamalov & Elgilani E. Alshareif, 2022. "High Frequency Return and Risk Patterns in U.S. Sector ETFs during COVID-19," International Journal of Energy Economics and Policy, Econjournals, vol. 12(5), pages 441-456, September.
  165. Brückbauer, Frank, 2022. "Do financial market experts know their theory? New evidence from survey data," ZEW Discussion Papers 20-092, ZEW - Leibniz Centre for European Economic Research, revised 2022.
  166. Zhang, Yaojie & Ma, Feng & Zhu, Bo, 2019. "Intraday momentum and stock return predictability: Evidence from China," Economic Modelling, Elsevier, vol. 76(C), pages 319-329.
  167. Afees A. Salisu & Rangan Gupta, 2021. "Commodity Prices and Forecastability of South African Stock Returns Over a Century: Sentiments versus Fundamentals," Working Papers 202144, University of Pretoria, Department of Economics.
  168. Mehmet Balcilar & Deven Bathia & Riza Demirer & Rangan Gupta, 2017. "Credit Ratings and Predictability of Stock Returns and Volatility of the BRICS and the PIIGS: Evidence from a Nonparametric Causality-in-Quantiles Approach," Working Papers 201719, University of Pretoria, Department of Economics.
  169. Yu, Deshui & Huang, Difang, 2023. "Cross-sectional uncertainty and expected stock returns," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 321-340.
  170. 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.
  171. Nonejad, Nima, 2023. "Conditional out-of-sample predictability of aggregate equity returns and aggregate equity return volatility using economic variables," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 91-122.
  172. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2021. "Economic drivers of commodity volatility: The case of copper," Resources Policy, Elsevier, vol. 73(C).
  173. Dichtl, Hubert, 2020. "Forecasting excess returns of the gold market: Can we learn from stock market predictions?," Journal of Commodity Markets, Elsevier, vol. 19(C).
  174. Philippe Goulet Coulombe, 2021. "To Bag is to Prune," Working Papers 21-03, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Jun 2021.
  175. Bekiros, Stelios & Gupta, Rangan & Kyei, Clement, 2016. "On economic uncertainty, stock market predictability and nonlinear spillover effects," The North American Journal of Economics and Finance, Elsevier, vol. 36(C), pages 184-191.
  176. Balcilar, Mehmet & Gupta, Rangan & Sousa, Ricardo M. & Wohar, Mark E., 2017. "Do cay and cayMS predict stock and housing returns? Evidence from a nonparametric causality test," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 269-279.
  177. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
  178. Abdul RASHID & Aamir JAVED & Zainab JEHAN & Uzma IQBAL, 2022. "Time-Varying Impacts of Macroeconomic Variables on Stock Market Returns and Volatility : Evidence from Pakistan," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 144-166, October.
  179. Hansen, Erwin, 2022. "Economic evaluation of asset pricing models under predictability," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 50-66.
  180. Nima Nonejad, 2021. "Bayesian model averaging and the conditional volatility process: an application to predicting aggregate equity returns by conditioning on economic variables," Quantitative Finance, Taylor & Francis Journals, vol. 21(8), pages 1387-1411, August.
  181. Wolfgang Drobetz & Tizian Otto, 2021. "Empirical asset pricing via machine learning: evidence from the European stock market," Journal of Asset Management, Palgrave Macmillan, vol. 22(7), pages 507-538, December.
  182. Spierdijk, Laura & Umar, Zaghum, 2015. "Stocks, bonds, T-bills and inflation hedging: From great moderation to great recession," Journal of Economics and Business, Elsevier, vol. 79(C), pages 1-37.
  183. Hammerschmid, Regina & Lohre, Harald, 2018. "Regime shifts and stock return predictability," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 138-160.
  184. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2023. "Tail risks and forecastability of stock returns of advanced economies: evidence from centuries of data," The European Journal of Finance, Taylor & Francis Journals, vol. 29(4), pages 466-481, March.
  185. Patrick Bielstein, 2018. "International asset allocation using the market implied cost of capital," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(1), pages 17-51, February.
  186. Li Liu & Zhiyuan Pan & Yudong Wang, 2022. "Shrinking return forecasts," The Financial Review, Eastern Finance Association, vol. 57(3), pages 641-661, August.
  187. Enno Mammen & Jens Perch Nielsen & Michael Scholz & Stefan Sperlich, 2019. "Conditional Variance Forecasts for Long-Term Stock Returns," Risks, MDPI, vol. 7(4), pages 1-22, November.
  188. Rangan Gupta & Sayar Karmakar & Christian Pierdzioch, 2022. "Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data," Working Papers 202201, University of Pretoria, Department of Economics.
  189. Yin, Anwen, 2020. "Equity premium prediction and optimal portfolio decision with Bagging," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
  190. Dunbar, Kwamie & Owusu-Amoako, Johnson, 2022. "Hedging the extreme risk of cryptocurrency," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
  191. Afees A. Salisu & Rangan Gupta & Idris A. Adediran, 2021. "The Effect of US Uncertainty Shock on International Equity Markets: The Role of the Global Financial Cycle," Working Papers 202136, University of Pretoria, Department of Economics.
  192. Kothari, Pratik & O’Doherty, Michael S., 2023. "Job postings and aggregate stock returns," Journal of Financial Markets, Elsevier, vol. 64(C).
  193. Nima Nonejad, 2021. "Using the conditional volatility channel to improve the accuracy of aggregate equity return predictions," Empirical Economics, Springer, vol. 61(2), pages 973-1009, August.
  194. Andreas Gruener & Christian Finke, 2018. "Lead-Lag Relationships in International Stock Markets Revisited: Are They Exploitable?," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 9(1), pages 8-30, January.
  195. Dbouk, Wassim & Moussawi-Haidar, Lama & Jaber, Mohamad Y., 2020. "The effect of economic uncertainty on inventory and working capital for manufacturing firms," International Journal of Production Economics, Elsevier, vol. 230(C).
  196. Hammami, Yacine & Zhu, Jie, 2020. "Understanding time-varying short-horizon predictability✰," Finance Research Letters, Elsevier, vol. 32(C).
  197. Gang Chu & John W. Goodell & Dehua Shen & Yongjie Zhang, 2022. "Machine learning to establish proxies for investor attention: evidence of improved stock-return prediction," Annals of Operations Research, Springer, vol. 318(1), pages 103-128, November.
  198. Gagnon, Marie-Hélène & Power, Gabriel J. & Toupin, Dominique, 2023. "The sum of all fears: Forecasting international returns using option-implied risk measures," Journal of Banking & Finance, Elsevier, vol. 146(C).
  199. Dominik Wolff & Ulrich Neugebauer, 2019. "Tree-based machine learning approaches for equity market predictions," Journal of Asset Management, Palgrave Macmillan, vol. 20(4), pages 273-288, July.
  200. Bonato, Matteo & Cepni, Oguzhan & Gupta, Rangan & Pierdzioch, Christian, 2023. "Climate risks and realized volatility of major commodity currency exchange rates," Journal of Financial Markets, Elsevier, vol. 62(C).
  201. Berardi, Michele, 2021. "Uncertainty, sentiments and time-varying risk premia," MPRA Paper 106922, University Library of Munich, Germany.
  202. Bouri, Elie & Gupta, Rangan & Hosseini, Seyedmehdi & Lau, Chi Keung Marco, 2018. "Does global fear predict fear in BRICS stock markets? Evidence from a Bayesian Graphical Structural VAR model," Emerging Markets Review, Elsevier, vol. 34(C), pages 124-142.
  203. Fletcher, Jonathan & Basu, Devraj, 2016. "An examination of the benefits of dynamic trading strategies in U.K. closed-end funds," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 109-118.
  204. Oguzhan Cepni & Rangan Gupta & Qiang Ji, 2023. "Sentiment Regimes and Reaction of Stock Markets to Conventional and Unconventional Monetary Policies: Evidence from OECD Countries," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 24(3), pages 365-381, July.
  205. Sun, Yuying & Hong, Yongmiao & Lee, Tae-Hwy & Wang, Shouyang & Zhang, Xinyu, 2021. "Time-varying model averaging," Journal of Econometrics, Elsevier, vol. 222(2), pages 974-992.
  206. Rangan Gupta & Jacobus Nel & Joshua Nielsen & Christian Pierdzioch, 2023. "Stock Market Volatility and Multi-Scale Positive and Negative Bubbles," Working Papers 202310, University of Pretoria, Department of Economics.
  207. Dunbar, Kwamie & Owusu-Amoako, Johnson, 2023. "Predicting inflation expectations: A habit-based explanation under hedging," International Review of Financial Analysis, Elsevier, vol. 89(C).
  208. Cao, Zhen & Han, Liyan & Wei, Xinbei & Zhang, Qunzi, 2022. "Fear in commodity return prediction," Finance Research Letters, Elsevier, vol. 46(PB).
  209. Kuntz, Laura-Chloé, 2020. "Beta dispersion and market timing," Discussion Papers 46/2020, Deutsche Bundesbank.
  210. Xu Chong Bo & Jianlei Han & Yin Liao & Jing Shi & Wu Yan, 2021. "Do outliers matter? The predictive ability of average skewness on market returns using robust skewness measures," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(3), pages 3977-4006, September.
  211. Afsaneh Bahrami & Abul Shamsuddin & Katherine Uylangco, 2018. "Out‐of‐sample stock return predictability in emerging markets," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(3), pages 727-750, September.
  212. Dichtl, Hubert & Drobetz, Wolfgang & Neuhierl, Andreas & Wendt, Viktoria-Sophie, 2021. "Data snooping in equity premium prediction," International Journal of Forecasting, Elsevier, vol. 37(1), pages 72-94.
  213. Amit Goyal & Narasimhan Jegadeesh, 2018. "Cross-Sectional and Time-Series Tests of Return Predictability: What Is the Difference?," The Review of Financial Studies, Society for Financial Studies, vol. 31(5), pages 1784-1824.
  214. Yu, Deshui & Huang, Difang & Chen, Li, 2023. "Stock return predictability and cyclical movements in valuation ratios," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 36-53.
  215. Haibin Xie & Shouyang Wang, 2015. "Risk-return trade-off, information diffusion, and U.S. stock market predictability," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 2(04), pages 1-20, December.
  216. Chang, Tsangyao & Gupta, Rangan & Majumdar, Anandamayee & Pierdzioch, Christian, 2019. "Predicting stock market movements with a time-varying consumption-aggregate wealth ratio," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 458-467.
  217. Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2020. "Forecasting Realized Stock-Market Volatility: Do Industry Returns have Predictive Value?," Working Papers 2020107, University of Pretoria, Department of Economics.
  218. Hong, Yanran & Yu, Jize & Su, Yuquan & Wang, Lu, 2023. "Southern oscillation: Great value of its trends for forecasting crude oil spot price volatility," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 358-368.
  219. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2019. "Machine Learning for Forecasting Excess Stock Returns – The Five-Year-View," Graz Economics Papers 2019-06, University of Graz, Department of Economics.
  220. Liu, Li & Wang, Yudong & Yang, Li, 2018. "Predictability of crude oil prices: An investor perspective," Energy Economics, Elsevier, vol. 75(C), pages 193-205.
  221. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2020. "Short-Term Exuberance and long-term stability: A simultaneous optimization of stock return predictions for short and long horizons," Graz Economics Papers 2020-20, University of Graz, Department of Economics.
  222. Bouri, Elie & Gupta, Rangan, 2021. "Predicting Bitcoin returns: Comparing the roles of newspaper- and internet search-based measures of uncertainty," Finance Research Letters, Elsevier, vol. 38(C).
  223. Bin Chen & Kenwin Maung, 2020. "Time-varying Forecast Combination for High-Dimensional Data," Papers 2010.10435, arXiv.org.
  224. Rangan Gupta & Anandamayee Majumdar & Mark E. Wohar, 2017. "The Role of Current Account Balance in Forecasting the US Equity Premium: Evidence From a Quantile Predictive Regression Approach," Open Economies Review, Springer, vol. 28(1), pages 47-59, February.
  225. Chen, Yong & Da, Zhi & Huang, Dayong, 2022. "Short selling efficiency," Journal of Financial Economics, Elsevier, vol. 145(2), pages 387-408.
  226. Anwen Yin, 2021. "Forecasting the Market Equity Premium: Does Nonlinearity Matter?," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 13(5), pages 1-9, May.
  227. Nonejad, Nima, 2018. "Déjà vol oil? Predicting S&P 500 equity premium using crude oil price volatility: Evidence from old and recent time-series data," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 260-270.
  228. Giulia Dal Pra & Massimo Guidolin & Manuela Pedio & Fabiola Vasile, 2016. "Do Regimes in Excess Stock Return Predictability Create Economic Value? An Out-of-Sample Portfolio Analysis," BAFFI CAREFIN Working Papers 1637, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
  229. Zhifeng Dai & Huiting Zhou, 2020. "Prediction of Stock Returns: Sum-of-the-Parts Method and Economic Constraint Method," Sustainability, MDPI, vol. 12(2), pages 1-13, January.
  230. repec:zbw:bofrdp:2017_001 is not listed on IDEAS
  231. Gupta, Rangan & Sheng, Xin & Pierdzioch, Christian & Ji, Qiang, 2021. "Disaggregated oil shocks and stock-market tail risks: Evidence from a panel of 48 economics," Research in International Business and Finance, Elsevier, vol. 58(C).
  232. Sander, Magnus, 2018. "Market timing over the business cycle," Journal of Empirical Finance, Elsevier, vol. 46(C), pages 130-145.
  233. Daniele Bianchi & Massimo Guidolin & Manuela Pedio, 2020. "Dissecting Time-Varying Risk Exposures in Cryptocurrency Markets," BAFFI CAREFIN Working Papers 20143, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
  234. 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.
  235. Oguzhan Cepni & Rangan Gupta & Mark E. Wohar, 2019. "Variants of Consumption-Wealth Ratios and Predictability of U.S. Government Bond Risk Premia: Old is still Gold," Working Papers 201912, University of Pretoria, Department of Economics.
  236. Balcilar, Mehmet & Bathia, Deven & Demirer, Riza & Gupta, Rangan, 2021. "Credit ratings and predictability of stock return dynamics of the BRICS and the PIIGS: Evidence from a nonparametric causality-in-quantiles approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 290-302.
  237. Naser, Hanan & Alaali, Fatema, 2015. "Can Oil Prices Help Predict US Stock Market Returns: An Evidence Using a DMA Approach," MPRA Paper 65295, University Library of Munich, Germany, revised 25 Jun 2015.
  238. Bing Han & Gang Li, 2021. "Information Content of Aggregate Implied Volatility Spread," Management Science, INFORMS, vol. 67(2), pages 1249-1269, February.
  239. Dierkes, Maik & Germer, Stephan & Sejdiu, Vulnet, 2020. "Probability distortion, asset prices, and economic growth," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 84(C).
  240. Massimo Guidolin & Manuela Pedio, 2020. "Distilling Large Information Sets to Forecast Commodity Returns: Automatic Variable Selection or HiddenMarkov Models?," BAFFI CAREFIN Working Papers 20140, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
  241. Rapach, David E. & Ringgenberg, Matthew C. & Zhou, Guofu, 2016. "Short interest and aggregate stock returns," Journal of Financial Economics, Elsevier, vol. 121(1), pages 46-65.
  242. Umar, Zaghum, 2017. "Islamic vs conventional equities in a strategic asset allocation framework," Pacific-Basin Finance Journal, Elsevier, vol. 42(C), pages 1-10.
  243. Nonejad, Nima, 2022. "Equity premium prediction using the price of crude oil: Uncovering the nonlinear predictive impact," Energy Economics, Elsevier, vol. 115(C).
  244. Li, Jun & Wang, Huijun & Yu, Jianfeng, 2021. "Aggregate expected investment growth and stock market returns," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 618-638.
  245. Kyoung‐Hun Bae & Peter Dixon, 2018. "Do investors use options and futures to trade on different types of information? Evidence from an aggregate stock index," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(2), pages 175-198, February.
  246. Bo Yi & Frederi Viens & Baron Law & Zhongfei Li, 2015. "Dynamic portfolio selection with mispricing and model ambiguity," Annals of Finance, Springer, vol. 11(1), pages 37-75, February.
  247. Hanan Naser & Fatema Alaali, 2018. "Can oil prices help predict US stock market returns? Evidence using a dynamic model averaging (DMA) approach," Empirical Economics, Springer, vol. 55(4), pages 1757-1777, December.
  248. Boudoukh, Jacob & Israel, Ronen & Richardson, Matthew, 2022. "Biases in long-horizon predictive regressions," Journal of Financial Economics, Elsevier, vol. 145(3), pages 937-969.
  249. Cunha, Ronan & Pereira, Pedro L. Valls, 2015. "Automatic model selection for forecasting Brazilian stock returns," Textos para discussão 398, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
  250. Dahlquist, Magnus & Hasseltoft, Henrik, 2020. "Economic momentum and currency returns," Journal of Financial Economics, Elsevier, vol. 136(1), pages 152-167.
  251. Dong, Dayong & Yue, Sishi & Cao, Jiawei, 2020. "Site visit information content and return predictability: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
  252. Gupta, Rangan & Pierdzioch, Christian & Selmi, Refk & Wohar, Mark E., 2018. "Does partisan conflict predict a reduction in US stock market (realized) volatility? Evidence from a quantile-on-quantile regression model☆," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 87-96.
  253. Zhang, Yue-Jun & Li, Zhao-Chen, 2021. "Forecasting the stock returns of Chinese oil companies: Can investor attention help?," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 531-555.
  254. Philippe Goulet Coulombe, 2020. "To Bag is to Prune," Papers 2008.07063, arXiv.org, revised Jun 2021.
  255. Nonejad, Nima, 2021. "Predicting the return on the spot price of crude oil out-of-sample by conditioning on news-based uncertainty measures: Some new empirical results," Energy Economics, Elsevier, vol. 104(C).
  256. Wu, Shue-Jen & Lee, Wei-Ming, 2015. "Predicting severe simultaneous bear stock markets using macroeconomic variables as leading indicators," Finance Research Letters, Elsevier, vol. 13(C), pages 196-204.
  257. Nicholas Apergis & Rangan Gupta, 2016. "Can Weather Conditions in New York Predict South African Stock Returns?," Working Papers 201634, University of Pretoria, Department of Economics.
  258. Nonejad, Nima, 2020. "Crude oil price volatility and equity return predictability: A comparative out-of-sample study," International Review of Financial Analysis, Elsevier, vol. 71(C).
  259. William J. Procasky & Anwen Yin, 2022. "Forecasting high‐yield equity and CDS index returns: Does observed cross‐market informational flow have predictive power?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(8), pages 1466-1490, August.
  260. Lawrenz, Jochen & Zorn, Josef, 2017. "Predicting international stock returns with conditional price-to-fundamental ratios," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 159-184.
  261. Rangan Gupta & Xin Sheng & Christian Pierdzioch & Qiang Ji, 2021. "Disaggregated Oil Shocks and Stock-Market Tail Risks: Evidence from a Panel of 48 Countries," Working Papers 202106, University of Pretoria, Department of Economics.
  262. Lin, Qi & Lin, Xi, 2021. "Cash conversion cycle and aggregate stock returns," Journal of Financial Markets, Elsevier, vol. 52(C).
  263. Parastoo Mousavi, 2021. "Debt-by-Price Ratio, End-of-Year Economic Growth, and Long-Term Prediction of Stock Returns," Mathematics, MDPI, vol. 9(13), pages 1-18, July.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.