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Harri Pönkä
(Harri Ponka)

Personal Details

First Name:Harri
Middle Name:
Last Name:Ponka
Suffix:
RePEc Short-ID:ppn4
[This author has chosen not to make the email address public]
http://blogs.helsinki.fi/hponka/
Terminal Degree:2016 Politiikan ja Talouden Tutkimuksen Laitos; Valtiotieteellinen tiedekunta; Helsingin Yliopisto (from RePEc Genealogy)

Affiliation

(99%) Suomen Pankki

Helsinki, Finland
https://www.bof.fi/
RePEc:edi:bofgvfi (more details at EDIRC)

(1%) Politiikan ja Talouden Tutkimuksen Laitos
Valtiotieteellinen tiedekunta
Helsingin Yliopisto

Helsinki, Finland
http://www.helsinki.fi/politiikkajatalous/
RePEc:edi:valhefi (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Gόrnicka, Lucyna & Koester, Gerrit & Radowski, Daniel & Gautier, Erwan & Peinado, Mario Izquierdo & Stiglbauer, Alfred & Wittekopf, David & Puente, Sergio & Duarte, Claudia & Martins, Fernando & Basso, 2024. "A forward-looking tracker of negotiated wages in the euro area," Occasional Paper Series 338, European Central Bank.
  2. Obstbaum, Meri & Oinonen, Sami & Pönkä, Harri & Vanhala, Juuso & Vilmi, Lauri, 2023. "Transmission of recent shocks in a labour-DSGE model with wage rigidity," BoF Economics Review 1/2023, Bank of Finland.
  3. Pönkä, Harri & Sariola, Mikko, 2021. "Output gaps and cyclical indicators: Finnish evidence," BoF Economics Review 6/2021, Bank of Finland.
  4. Baumann, Ursel & Darracq Pariès, Matthieu & Westermann, Thomas & Riggi, Marianna & Bobeica, Elena & Meyler, Aidan & Böninghausen, Benjamin & Fritzer, Friedrich & Trezzi, Riccardo & Jonckheere, Jana & , 2021. "Inflation expectations and their role in Eurosystem forecasting," Occasional Paper Series 264, European Central Bank.
  5. Pönkä, Harri & Stenborg, Markku, 2018. "Forecasting the state of the Finnish business cycle," MPRA Paper 91226, University Library of Munich, Germany.
  6. Pönkä, Harri, 2017. "Sentiment and sign predictability of stock returns," MPRA Paper 81861, University Library of Munich, Germany.
  7. Pönkä, Harri, 2015. "Real oil prices and the international sign predictability of stock returns," MPRA Paper 68330, University Library of Munich, Germany.
  8. Harri Pönkä, 2015. "The Role of Credit in Predicting US Recessions," CREATES Research Papers 2015-48, Department of Economics and Business Economics, Aarhus University.
  9. Henri Nyberg & Harri Pönkä, 2015. "International Sign Predictability of Stock Returns: The Role of the United States," CREATES Research Papers 2015-20, Department of Economics and Business Economics, Aarhus University.
  10. Pönkä, Harri, 2014. "Predicting the direction of US stock markets using industry returns," MPRA Paper 62942, University Library of Munich, Germany.

Articles

  1. Harri Pönkä & Markku Stenborg, 2020. "Forecasting the state of the Finnish business cycle," Finnish Economic Papers, Finnish Economic Association, vol. 29(1), pages 81-99, Spring.
  2. Pönkä, Harri & Zheng, Yi, 2019. "The role of oil prices on the Russian business cycle," Research in International Business and Finance, Elsevier, vol. 50(C), pages 70-78.
  3. Harri Pönkä, 2018. "Sentiment and sign predictability of stock returns," Economics Bulletin, AccessEcon, vol. 38(3), pages 1676-1684.
  4. Harri Ponka, 2017. "The Role of Credit in Predicting US Recessions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(5), pages 469-482, August.
  5. Harri Pönkä, 2017. "Predicting the direction of US stock markets using industry returns," Empirical Economics, Springer, vol. 52(4), pages 1451-1480, June.
  6. Pönkä, Harri, 2016. "Real oil prices and the international sign predictability of stock returns," Finance Research Letters, Elsevier, vol. 17(C), pages 79-87.
  7. 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.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Baumann, Ursel & Darracq Pariès, Matthieu & Westermann, Thomas & Riggi, Marianna & Bobeica, Elena & Meyler, Aidan & Böninghausen, Benjamin & Fritzer, Friedrich & Trezzi, Riccardo & Jonckheere, Jana & , 2021. "Inflation expectations and their role in Eurosystem forecasting," Occasional Paper Series 264, European Central Bank.

    Cited by:

    1. Glas, Alexander & Heinisch, Katja, 2023. "Conditional macroeconomic survey forecasts: Revisions and errors," Journal of International Money and Finance, Elsevier, vol. 138(C).
    2. Michael Ehrmann & Sarah Holton & Danielle Kedan & Gillian Phelan, 2024. "Monetary Policy Communication: Perspectives from Former Policymakers at the ECB," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 56(4), pages 837-864, June.
    3. Andrejs Zlobins, 2022. "Into the Universe of Unconventional Monetary Policy: State-dependence, Interaction and Complementarities," Working Papers 2022/05, Latvijas Banka.
    4. Bobeica, Elena & Hartwig, Benny, 2023. "The COVID-19 shock and challenges for inflation modelling," International Journal of Forecasting, Elsevier, vol. 39(1), pages 519-539.
    5. Baumann, Ursel & Ferrando, Annalisa & Georgarakos, Dimitris & Gorodnichenko, Yuriy & Reinelt, Timo, 2024. "SAFE to Update Inflation Expectations? New Survey Evidence on Euro Area Firms," IZA Discussion Papers 17015, Institute of Labor Economics (IZA).
    6. Yury Perevyshin, 2024. "Analysts' Inflation Expectations vs Univariate Models of Inflation Forecasting in the Russian Economy," Russian Journal of Money and Finance, Bank of Russia, vol. 83(2), pages 54-76, June.
    7. Koester, Gerrit & Lis, Eliza & Nickel, Christiane & Osbat, Chiara & Smets, Frank, 2021. "Understanding low inflation in the euro area from 2013 to 2019: cyclical and structural drivers," Occasional Paper Series 280, European Central Bank.

  2. Pönkä, Harri & Stenborg, Markku, 2018. "Forecasting the state of the Finnish business cycle," MPRA Paper 91226, University Library of Munich, Germany.

    Cited by:

    1. Nissilä, Wilma, 2020. "Probit based time series models in recession forecasting – A survey with an empirical illustration for Finland," BoF Economics Review 7/2020, Bank of Finland.

  3. Pönkä, Harri, 2015. "Real oil prices and the international sign predictability of stock returns," MPRA Paper 68330, University Library of Munich, Germany.

    Cited by:

    1. Fredj Jawadi & Wael Louhichi & Hachmi Ben Ameur & Abdoulkarim Idi Cheffou, 2017. "On Oil-US Exchange Rate Volatility Relationships: an Intradaily Analysis," Working Papers hal-04141662, HAL.
    2. Canh P. Nguyen & Christophe Schinckus & Thanh D. Su & Felicia H. L. Chong, 2022. "Determinants of stock market returns in emerging markets: The linkage between institutional quality and macro liquidity," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4472-4486, October.
    3. Salisu, Afees A. & Raheem, Ibrahim D. & Ndako, Umar B., 2019. "A sectoral analysis of asymmetric nexus between oil price and stock returns," International Review of Economics & Finance, Elsevier, vol. 61(C), pages 241-259.
    4. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
    5. Ayedi Ahmed & Marjène Gana & Stéphane Goutte & Khaled Guesmi, 2023. "Managing Portfolio Risk During the BREXIT Crisis: A Cross-Quantilogram Analysis of Stock Markets and Commodities Across European Countries, the US, and BRICS," Working Papers halshs-04068651, HAL.
    6. 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.
    7. Pönkä, Harri, 2014. "Predicting the direction of US stock markets using industry returns," MPRA Paper 62942, University Library of Munich, Germany.
    8. Lu Yang & Lei Yang & Kung-Cheng Ho & Shigeyuki Hamori, 2019. "Determinants of the Long-Term Correlation between Crude Oil and Stock Markets," Energies, MDPI, vol. 12(21), pages 1-15, October.
    9. Licheng Sun & Liang Meng & Mohammad Najand, 2017. "The Role of U.S. Market on International Risk-Return Tradeoff Relations," The Financial Review, Eastern Finance Association, vol. 52(3), pages 499-526, August.
    10. Wen, Zhuzhu & Gong, Xu & Ma, Diandian & Xu, Yahua, 2021. "Intraday momentum and return predictability: Evidence from the crude oil market," Economic Modelling, Elsevier, vol. 95(C), pages 374-384.
    11. Nonejad, Nima, 2021. "Predicting equity premium by conditioning on macroeconomic variables: A prediction selection strategy using the price of crude oil," Finance Research Letters, Elsevier, vol. 41(C).
    12. Alqahtani, Faisal & Samargandi, Nahla & Kutan, Ali M., 2020. "The influence of oil prices on the banking sector in oil-exporting economies: Is there a psychological barrier?," International Review of Financial Analysis, Elsevier, vol. 69(C).
    13. Hadhri, Sinda & Ftiti, Zied, 2017. "Stock return predictability in emerging markets: Does the choice of predictors and models matter across countries?," Research in International Business and Finance, Elsevier, vol. 42(C), pages 39-60.
    14. Perry Sadorsky, 2021. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers," JRFM, MDPI, vol. 14(5), pages 1-21, April.
    15. Narayan, Paresh Kumar, 2018. "Profitability of technology-investing Islamic and non-Islamic stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 52(C), pages 70-81.
    16. Sadorsky, Perry, 2022. "Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices?," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
    17. Nguyen, Dat Thanh & Phan, Dinh Hoang Bach & Anglingkusumo, Reza & Sasongko, Aryo, 2021. "US government shutdowns and Indonesian stock market," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    18. Gogolin, Fabian & Kearney, Fearghal & Lucey, Brian M. & Peat, Maurice & Vigne, Samuel A., 2018. "Uncovering long term relationships between oil prices and the economy: A time-varying cointegration analysis," Energy Economics, Elsevier, vol. 76(C), pages 584-593.
    19. Mouna Aloui & Jarboui Anis, 2023. "The Dynamic Relation between the Oil Price Volatility, Stock Market, Exchange and Interest Rate in GCC Countries: Panel Vector Autoregressive (PVAR) Model," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(3), pages 114-128.
    20. Xiaolan Jia & Xinfeng Ruan & Jin E. Zhang, 2021. "The implied volatility smirk of commodity options," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(1), pages 72-104, January.
    21. Tissaoui, Kais & Azibi, Jamel, 2019. "International implied volatility risk indexes and Saudi stock return-volatility predictabilities," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 65-84.
    22. Pönkä, Harri & Zheng, Yi, 2019. "The role of oil prices on the Russian business cycle," Research in International Business and Finance, Elsevier, vol. 50(C), pages 70-78.
    23. Narayan, Paresh Kumar & Liu, Ruipeng, 2018. "A new GARCH model with higher moments for stock return predictability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 56(C), pages 93-103.
    24. Luo, Xingguo & Qin, Shihua, 2017. "Oil price uncertainty and Chinese stock returns: New evidence from the oil volatility index," Finance Research Letters, Elsevier, vol. 20(C), pages 29-34.
    25. Alqahtani, Abdullah & Bouri, Elie & Vo, Xuan Vinh, 2020. "Predictability of GCC stock returns: The role of geopolitical risk and crude oil returns," Economic Analysis and Policy, Elsevier, vol. 68(C), pages 239-249.
    26. Riza Erdugan & Nada Kulendran & Riccardo Natoli, 2019. "Incorporating financial market volatility to improve forecasts of directional changes in Australian share market returns," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 33(4), pages 417-445, December.
    27. Syed Abul, Basher & Perry, Sadorsky, 2022. "Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?," MPRA Paper 113293, University Library of Munich, Germany.
    28. Diaz, Elena Maria & de Gracia, Fernando Perez, 2017. "Oil price shocks and stock returns of oil and gas corporations," Finance Research Letters, Elsevier, vol. 20(C), pages 75-80.
    29. Erhard Reschenhofer & Manveer Kaur Mangat & Christian Zwatz & Sándor Guzmics, 2020. "Evaluation of current research on stock return predictability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 334-351, March.
    30. Liu, Jingzhen & Kemp, Alexander, 2019. "Forecasting the sign of U.S. oil and gas industry stock index excess returns employing macroeconomic variables," Energy Economics, Elsevier, vol. 81(C), pages 672-686.
    31. Latife Ghalayini, 2017. "Modeling and forecasting spot oil price," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 7(3), pages 355-373, December.
    32. Perry Sadorsky, 2021. "A Random Forests Approach to Predicting Clean Energy Stock Prices," JRFM, MDPI, vol. 14(2), pages 1-20, January.

  4. Harri Pönkä, 2015. "The Role of Credit in Predicting US Recessions," CREATES Research Papers 2015-48, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Hashmat Khan & Santosh Upadhayaya, 2017. "Does Business Confidence Matter for Investment?," Carleton Economic Papers 17-13, Carleton University, Department of Economics, revised 20 Mar 2019.
    2. Jean-Baptiste Hasse & Quentin Lajaunie, 2020. "Does the Yield Curve Signal Recessions? New Evidence from an International Panel Data Analysis," AMSE Working Papers 2013, Aix-Marseille School of Economics, France.
    3. Christiansen, Charlotte & Eriksen, Jonas N. & Møller, Stig V., 2019. "Negative house price co-movements and US recessions," Regional Science and Urban Economics, Elsevier, vol. 77(C), pages 382-394.
    4. Claudio Borio & Mathias Drehmann & Dora Xia, 2018. "The financial cycle and recession risk," BIS Quarterly Review, Bank for International Settlements, December.
    5. Guender, Alfred V, 2018. "Credit prices vs. credit quantities as predictors of economic activity in Europe: Which tell a better story?," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 380-399.
    6. Marius M. Mihai, 2020. "Do credit booms predict US recessions?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 887-910, September.
    7. Pönkä, Harri & Stenborg, Markku, 2018. "Forecasting the state of the Finnish business cycle," MPRA Paper 91226, University Library of Munich, Germany.
    8. Nissilä, Wilma, 2020. "Probit based time series models in recession forecasting – A survey with an empirical illustration for Finland," BoF Economics Review 7/2020, Bank of Finland.
    9. Ying Zhou & Xia Lin & Guotai Chi & Peng Jin & Mengtong Li, 2024. "EWT‐SMOTE to improve default prediction performance in imbalanced data: Analysis of Chinese data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 615-643, April.
    10. Anastasios Evgenidis & Anastasios G. Malliaris, 2022. "Monetary policy, financial shocks and economic activity," Review of Quantitative Finance and Accounting, Springer, vol. 59(2), pages 429-456, August.
    11. Hwang, Youngjin, 2019. "Forecasting recessions with time-varying models," Journal of Macroeconomics, Elsevier, vol. 62(C).
    12. Claudio Morana, 2021. "A new macro-financial condition index for the euro area," Working Paper series 21-07, Rimini Centre for Economic Analysis, revised Sep 2021.
    13. Mönch, Emanuel & Stein, Tobias, 2021. "Equity premium predictability over the business cycle," Discussion Papers 25/2021, Deutsche Bundesbank.
    14. Knut Lehre Seip & Dan Zhang, 2021. "The Yield Curve as a Leading Indicator: Accuracy and Timing of a Parsimonious Forecasting Model," Forecasting, MDPI, vol. 3(2), pages 1-16, May.
    15. Borio, Claudio & Drehmann, Mathias & Xia, Fan Dora, 2020. "Forecasting recessions: the importance of the financial cycle," Journal of Macroeconomics, Elsevier, vol. 66(C).
    16. Claudio Borio & Mathias Drehmann & Dora Xia Author-X-Name_First: Dora, 2019. "Predicting recessions: financial cycle versus term spread," BIS Working Papers 818, Bank for International Settlements.

  5. Henri Nyberg & Harri Pönkä, 2015. "International Sign Predictability of Stock Returns: The Role of the United States," CREATES Research Papers 2015-20, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. James Nguyen & Wei-Xuan Li & Clara Chia-Sheng Chen, 2022. "Mean Reversions in Major Developed Stock Markets: Recent Evidence from Unit Root, Spectral and Abnormal Return Studies," JRFM, MDPI, vol. 15(4), pages 1-20, April.
    2. António R. Antunes & Diana Bonfim & Nuno Monteiro & Paulo M.M. Rodrigues, 2016. "Forecasting banking crises with dynamic panel probit models," Working Papers w201613, Banco de Portugal, Economics and Research Department.
    3. Yi-Chieh Wen & Bin Li, 2020. "Lagged country returns and international stock return predictability during business cycle recession periods," Applied Economics, Taylor & Francis Journals, vol. 52(46), pages 5005-5019, October.
    4. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
    5. Ayedi Ahmed & Marjène Gana & Stéphane Goutte & Khaled Guesmi, 2023. "Managing Portfolio Risk During the BREXIT Crisis: A Cross-Quantilogram Analysis of Stock Markets and Commodities Across European Countries, the US, and BRICS," Working Papers halshs-04068651, HAL.
    6. Licheng Sun & Liang Meng & Mohammad Najand, 2017. "The Role of U.S. Market on International Risk-Return Tradeoff Relations," The Financial Review, Eastern Finance Association, vol. 52(3), pages 499-526, August.
    7. Henri Nyberg, 2018. "Forecasting US interest rates and business cycle with a nonlinear regime switching VAR model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(1), pages 1-15, January.
    8. Hadhri, Sinda & Ftiti, Zied, 2017. "Stock return predictability in emerging markets: Does the choice of predictors and models matter across countries?," Research in International Business and Finance, Elsevier, vol. 42(C), pages 39-60.
    9. Ahmed, Walid M.A. & Sleem, Mohamed A.E., 2023. "Short- and long-run determinants of the price behavior of US clean energy stocks: A dynamic ARDL simulations approach," Energy Economics, Elsevier, vol. 124(C).
    10. Narayan, Paresh Kumar & Phan, Dinh Hoang Bach & Narayan, Seema, 2018. "Technology-investing countries and stock return predictability," Emerging Markets Review, Elsevier, vol. 36(C), pages 159-179.
    11. Perry Sadorsky, 2021. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers," JRFM, MDPI, vol. 14(5), pages 1-21, April.
    12. Narayan, Paresh Kumar, 2018. "Profitability of technology-investing Islamic and non-Islamic stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 52(C), pages 70-81.
    13. Kang, Yong Joo & Park, Dojoon & Eom, Young Ho, 2024. "Global contagion of US COVID-19 panic news," Emerging Markets Review, Elsevier, vol. 59(C).
    14. Sadorsky, Perry, 2022. "Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices?," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
    15. Lauri Nevasalmi, 2022. "Recession forecasting with high‐dimensional data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 752-764, July.
    16. Salisu, Afees A. & Tchankam, Jean Paul, 2022. "US Stock return predictability with high dimensional models," Finance Research Letters, Elsevier, vol. 45(C).
    17. Nguyen, Dat Thanh & Phan, Dinh Hoang Bach & Anglingkusumo, Reza & Sasongko, Aryo, 2021. "US government shutdowns and Indonesian stock market," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    18. Jonathan Iworiso & Spyridon Vrontos, 2020. "On the directional predictability of equity premium using machine learning techniques," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 449-469, April.
    19. 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.
    20. Harri Pönkä, 2018. "Sentiment and sign predictability of stock returns," Economics Bulletin, AccessEcon, vol. 38(3), pages 1676-1684.
    21. Pönkä, Harri, 2015. "Real oil prices and the international sign predictability of stock returns," MPRA Paper 68330, University Library of Munich, Germany.
    22. Narayan, Paresh Kumar & Liu, Ruipeng, 2018. "A new GARCH model with higher moments for stock return predictability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 56(C), pages 93-103.
    23. Riza Erdugan & Nada Kulendran & Riccardo Natoli, 2019. "Incorporating financial market volatility to improve forecasts of directional changes in Australian share market returns," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 33(4), pages 417-445, December.
    24. Syed Abul, Basher & Perry, Sadorsky, 2022. "Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?," MPRA Paper 113293, University Library of Munich, Germany.
    25. Liu, Jingzhen & Kemp, Alexander, 2019. "Forecasting the sign of U.S. oil and gas industry stock index excess returns employing macroeconomic variables," Energy Economics, Elsevier, vol. 81(C), pages 672-686.
    26. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting NFT coin prices using machine learning: Insights into feature significance and portfolio strategies," Global Finance Journal, Elsevier, vol. 58(C).
    27. Ana Monteiro & Nuno Silva & Helder Sebastião, 2023. "Industry return lead-lag relationships between the US and other major countries," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-48, December.
    28. Becker, Janis & Leschinski, Christian, 2018. "Directional Predictability of Daily Stock Returns," Hannover Economic Papers (HEP) dp-624, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    29. Perry Sadorsky, 2021. "A Random Forests Approach to Predicting Clean Energy Stock Prices," JRFM, MDPI, vol. 14(2), pages 1-20, January.

  6. Pönkä, Harri, 2014. "Predicting the direction of US stock markets using industry returns," MPRA Paper 62942, University Library of Munich, Germany.

    Cited by:

    1. Hashmat Khan & Santosh Upadhayaya, 2017. "Does Business Confidence Matter for Investment?," Carleton Economic Papers 17-13, Carleton University, Department of Economics, revised 20 Mar 2019.
    2. 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.
    3. Garcia, M.M. & Machado Pereira, A.C. & Acebal, J.L. & Bosco de Magalhães, A.R., 2020. "Forecast model for financial time series: An approach based on harmonic oscillators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    4. Chung, Chune Young & Liu, Chang & Wang, Kainan, 2021. "The big picture: The industry effect of short interest," International Review of Financial Analysis, Elsevier, vol. 76(C).
    5. Liu, Jiadong & Papailias, Fotis & Quinn, Barry, 2021. "Direction-of-change forecasting in commodity futures markets," International Review of Financial Analysis, Elsevier, vol. 74(C).
    6. Harri Pönkä, 2018. "Sentiment and sign predictability of stock returns," Economics Bulletin, AccessEcon, vol. 38(3), pages 1676-1684.
    7. Pönkä, Harri, 2015. "Real oil prices and the international sign predictability of stock returns," MPRA Paper 68330, University Library of Munich, Germany.
    8. de Resende, Charlene C. & Pereira, Adriano C.M. & Cardoso, Rodrigo T.N. & de Magalhães, A.R. Bosco, 2017. "Investigating market efficiency through a forecasting model based on differential equations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 199-212.
    9. Liu, Jingzhen & Kemp, Alexander, 2019. "Forecasting the sign of U.S. oil and gas industry stock index excess returns employing macroeconomic variables," Energy Economics, Elsevier, vol. 81(C), pages 672-686.
    10. Becker, Janis & Leschinski, Christian, 2018. "Directional Predictability of Daily Stock Returns," Hannover Economic Papers (HEP) dp-624, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    11. Djoumbissie David Romain, 2020. "Predicting S&P500 Index direction with Transfer Learning and a Causal Graph as main Input," Papers 2011.13113, arXiv.org, revised Apr 2022.

Articles

  1. Harri Pönkä & Markku Stenborg, 2020. "Forecasting the state of the Finnish business cycle," Finnish Economic Papers, Finnish Economic Association, vol. 29(1), pages 81-99, Spring.
    See citations under working paper version above.
  2. Pönkä, Harri & Zheng, Yi, 2019. "The role of oil prices on the Russian business cycle," Research in International Business and Finance, Elsevier, vol. 50(C), pages 70-78.

    Cited by:

    1. Bilal Ahmed Memon & Rabia Tahir, 2021. "Examining Network Structures and Dynamics of World Energy Companies in Stock Markets: A Complex Network Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 329-344.
    2. Lin, Boqiang & Bai, Rui, 2021. "Oil prices and economic policy uncertainty: Evidence from global, oil importers, and exporters’ perspective," Research in International Business and Finance, Elsevier, vol. 56(C).
    3. Aviral Kumar Tiwari & Muhammad Tahir Suleman & Subhan Ullah & Muhammad Shahbaz, 2023. "Analyzing the connectedness between crude oil and petroleum products: Evidence from USA," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 2278-2347, July.
    4. Khan, Faridoon & Muhammadullah, Sara & Sharif, Arshian & Lee, Chien-Chiang, 2024. "The role of green energy stock market in forecasting China's crude oil market: An application of IIS approach and sparse regression models," Energy Economics, Elsevier, vol. 130(C).
    5. Su, Zhi & Mo, Xuan & Yin, Libo, 2021. "Oil market uncertainty and excess returns on currency carry trade," Research in International Business and Finance, Elsevier, vol. 56(C).
    6. Köse, Nezir & Ünal, Emre, 2020. "The impact of oil price shocks on stock exchanges in Caspian Basin countries," Energy, Elsevier, vol. 190(C).
    7. Yang, Jinxuan & Rizvi, Syed Kumail Abbas & Tan, Zhixiong & Umar, Muhammad & Koondhar, Mansoor Ahmed, 2021. "The competing role of natural gas and oil as fossil fuel and the non-linear dynamics of resource curse in Russia," Resources Policy, Elsevier, vol. 72(C).
    8. Anastasiya Lisina & Philippe Van Kerm, 2022. "Understanding Twenty Years of Inequality and Poverty Trends in Russia," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(S1), pages 108-130, April.

  3. Harri Ponka, 2017. "The Role of Credit in Predicting US Recessions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(5), pages 469-482, August.
    See citations under working paper version above.
  4. Harri Pönkä, 2017. "Predicting the direction of US stock markets using industry returns," Empirical Economics, Springer, vol. 52(4), pages 1451-1480, June.
    See citations under working paper version above.
  5. Pönkä, Harri, 2016. "Real oil prices and the international sign predictability of stock returns," Finance Research Letters, Elsevier, vol. 17(C), pages 79-87.
    See citations under working paper version above.
  6. 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.
    See citations under working paper version above.

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Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 10 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-FOR: Forecasting (6) 2015-04-02 2015-05-22 2015-11-21 2017-10-15 2019-01-28 2021-09-27. Author is listed
  2. NEP-MAC: Macroeconomics (4) 2015-11-21 2019-01-28 2021-09-27 2021-10-25
  3. NEP-EEC: European Economics (3) 2021-09-27 2023-06-19 2024-03-04
  4. NEP-BAN: Banking (1) 2015-11-21
  5. NEP-BEC: Business Economics (1) 2015-11-21
  6. NEP-CBA: Central Banking (1) 2021-09-27
  7. NEP-CFN: Corporate Finance (1) 2015-05-22
  8. NEP-DGE: Dynamic General Equilibrium (1) 2023-06-19
  9. NEP-ECM: Econometrics (1) 2015-05-22
  10. NEP-ENE: Energy Economics (1) 2015-12-20
  11. NEP-EUR: Microeconomic European Issues (1) 2024-03-04
  12. NEP-FMK: Financial Markets (1) 2015-04-02
  13. NEP-INV: Investment (1) 2024-03-04
  14. NEP-ISF: Islamic Finance (1) 2021-09-27
  15. NEP-LAB: Labour Economics (1) 2024-03-04
  16. NEP-MON: Monetary Economics (1) 2021-09-27
  17. NEP-OPM: Open Economy Macroeconomics (1) 2023-06-19
  18. NEP-RMG: Risk Management (1) 2015-05-22

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