IDEAS home Printed from https://ideas.repec.org/a/spr/fininn/v6y2020i1d10.1186_s40854-020-00201-5.html
   My bibliography  Save this article

S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA

Author

Listed:
  • Madhavi Latha Challa

    (CMR College of Engineering & Technology)

  • Venkataramanaiah Malepati

    (SG Govt. Degree & PG College)

  • Siva Nageswara Rao Kolusu

    (Department of Management Studies, Vignan Foundation for Science, Technology & Research)

Abstract

This study forecasts the return and volatility dynamics of S&P BSE Sensex and S&P BSE IT indices of the Bombay Stock Exchange. To achieve the objectives, the study uses descriptive statistics; tests including variance ratio, Augmented Dickey-Fuller, Phillips-Perron, and Kwiatkowski Phillips Schmidt and Shin; and Autoregressive Integrated Moving Average (ARIMA). The analysis forecasts daily stock returns for the S&P BSE Sensex and S&P BSE IT time series, using the ARIMA model. The results reveal that the mean returns of both indices are positive but near zero. This is indicative of a regressive tendency in the long-term. The forecasted values of S&P BSE Sensex and S&P BSE IT are almost equal to their actual values, with few deviations. Hence, the ARIMA model is capable of predicting medium- or long-term horizons using historical values of S&P BSE Sensex and S&P BSE IT.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:fininn:v:6:y:2020:i:1:d:10.1186_s40854-020-00201-5
    DOI: 10.1186/s40854-020-00201-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s40854-020-00201-5
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1186/s40854-020-00201-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. Chow, K. Victor & Denning, Karen C., 1993. "A simple multiple variance ratio test," Journal of Econometrics, Elsevier, vol. 58(3), pages 385-401, August.
    3. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    4. M. Mallikarjuna & R. Prabhakara Rao, 2019. "Evaluation of forecasting methods from selected stock market returns," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-16, December.
    5. K. Siva Kiran Guptha & R. Prabhakar Rao, 2018. "The causal relationship between financial development and economic growth: an experience with BRICS economies," Journal of Social and Economic Development, Springer;Institute for Social and Economic Change, vol. 20(2), pages 308-326, October.
    6. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2014. "Forecasting stock returns under economic constraints," Journal of Financial Economics, Elsevier, vol. 114(3), pages 517-553.
    7. Lo, Andrew W. & MacKinlay, A. Craig, 1989. "The size and power of the variance ratio test in finite samples : A Monte Carlo investigation," Journal of Econometrics, Elsevier, vol. 40(2), pages 203-238, February.
    8. Zhu, Xiaoneng & Zhu, Jie, 2013. "Predicting stock returns: A regime-switching combination approach and economic links," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4120-4133.
    9. Martin J. Gerra, 1959. "An Econometric Model of the Egg Industry: A Correction," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 41(4), pages 803-804.
    10. D. Michael Long & Janet D. Payne & Chenyang Feng, 1999. "Information Transmission In The Shanghai Equity Market," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 22(1), pages 29-45, March.
    11. Wright, Jonathan H, 2000. "Alternative Variance-Ratio Tests Using Ranks and Signs," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(1), pages 1-9, January.
    12. Wang, Zijun & Qian, Yan & Wang, Shiwen, 2018. "Dynamic trading volume and stock return relation: Does it hold out of sample?," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 195-210.
    13. Benjamin Miranda Tabak, 2002. "The Random Walk Hypothesis and the Behavior of Foreign Capital Portfolio Flows: the Brazilian Stock Market Case," Working Papers Series 58, Central Bank of Brazil, Research Department.
    14. 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.
    15. Kim, Jae H., 2006. "Wild bootstrapping variance ratio tests," Economics Letters, Elsevier, vol. 92(1), pages 38-43, July.
    16. Aye, Goodness C. & Gil-Alana, Luis A. & Gupta, Rangan & Wohar, Mark E., 2017. "The efficiency of the art market: Evidence from variance ratio tests, linear and nonlinear fractional integration approaches," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 283-294.
    17. Martin Laurence & Francis Cai & Sun Qian, 1997. "Weak-form Efficiency and Causality Tests in Chinese Stock Markets," Multinational Finance Journal, Multinational Finance Journal, vol. 1(4), pages 291-307, December.
    18. Andrew Schmitz & Donald G. Watts, 1970. "Forecasting Wheat Yields: An Application of Parametric Time Series Modeling," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 52(2), pages 247-254.
    19. Darrat, Ali F & Zhong, Maosen, 2000. "On Testing the Random-Walk Hypothesis: A Model-Comparison Approach," The Financial Review, Eastern Finance Association, vol. 35(3), pages 105-124, August.
    20. 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.
    21. Fama, Eugene F. & French, Kenneth R., 1988. "Dividend yields and expected stock returns," Journal of Financial Economics, Elsevier, vol. 22(1), pages 3-25, October.
    22. Groenewold, Nicolaas & Tang, Sam Hak Kan & Wu, Yanrui, 2003. "The efficiency of the Chinese stock market and the role of the banks," Journal of Asian Economics, Elsevier, vol. 14(4), pages 593-609, August.
    23. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
    24. Martin J. Gerra, 1959. "An Econometric Model of the Egg Industry," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 41(2), pages 284-301.
    25. Xiaming Liu & Haiyan Song & Peter Romilly, 1997. "Are Chinese stock markets efficient? A cointegration and causality analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 4(8), pages 511-515.
    26. Madhavi Latha Challa & Venkataramanaiah Malepati & Siva Nageswara Rao Kolusu, 2018. "Forecasting risk using auto regressive integrated moving average approach: an evidence from S&P BSE Sensex," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 4(1), pages 1-17, December.
    27. Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Narayan, Paresh Kumar, 2015. "Stock return forecasting: Some new evidence," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 38-51.
    28. Whang, Yoon-Jae & Kim, Jinho, 2003. "A multiple variance ratio test using subsampling," Economics Letters, Elsevier, vol. 79(2), pages 225-230, May.
    29. 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.
    30. Richardson, Matthew & Smith, Tom, 1991. "Tests of Financial Models in the Presence of Overlapping Observations," The Review of Financial Studies, Society for Financial Studies, vol. 4(2), pages 227-254.
    31. George Kapetanios & Yongcheol Shin, 2011. "Testing the Null Hypothesis of Nonstationary Long Memory Against the Alternative Hypothesis of a Nonlinear Ergodic Model," Econometric Reviews, Taylor & Francis Journals, vol. 30(6), pages 620-645.
    32. Kim, Jae H. & Shamsuddin, Abul & Lim, Kian-Ping, 2011. "Stock return predictability and the adaptive markets hypothesis: Evidence from century-long U.S. data," Journal of Empirical Finance, Elsevier, vol. 18(5), pages 868-879.
    33. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2013. "International Stock Return Predictability: What Is the Role of the United States?," Journal of Finance, American Finance Association, vol. 68(4), pages 1633-1662, August.
    34. Eduardo Jose Araujo Lima & Benjamin Miranda Tabak, 2004. "Tests of the random walk hypothesis for equity markets: evidence from China, Hong Kong and Singapore," Applied Economics Letters, Taylor & Francis Journals, vol. 11(4), pages 255-258.
    35. Fifield, Suzanne G.M. & Jetty, Juliana, 2008. "Further evidence on the efficiency of the Chinese stock markets: A note," Research in International Business and Finance, Elsevier, vol. 22(3), pages 351-361, September.
    36. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    37. Wen, Fenghua & Xu, Longhao & Ouyang, Guangda & Kou, Gang, 2019. "Retail investor attention and stock price crash risk: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 65(C).
    38. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2010. "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy," The Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 821-862, February.
    39. Zotteri, Giulio & Kalchschmidt, Matteo & Caniato, Federico, 2005. "The impact of aggregation level on forecasting performance," International Journal of Production Economics, Elsevier, vol. 93(1), pages 479-491, January.
    40. 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.
    41. Mookerjee, Rajen & Yu, Qiao, 1999. "An empirical analysis of the equity markets in China," Review of Financial Economics, Elsevier, vol. 8(1), pages 41-60, June.
    42. Deo, Rohit S. & Richardson, Matthew, 2003. "On The Asymptotic Power Of The Variance Ratio Test," Econometric Theory, Cambridge University Press, vol. 19(2), pages 231-239, April.
    43. James A. Turner, 2015. "Casting Doubt on the Predictability of Stock Returns in Real Time: Bayesian Model Averaging using Realistic Priors," Review of Finance, European Finance Association, vol. 19(2), pages 785-821.
    44. H. R. Seddighi & W. Nian, 2004. "The Chinese stock exchange market: operations and efficiency," Applied Financial Economics, Taylor & Francis Journals, vol. 14(11), pages 785-797.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Sabyasachi Mohapatra & Rohan Mukherjee & Arindam Roy & Anirban Sengupta & Amit Puniyani, 2022. "Can Ensemble Machine Learning Methods Predict Stock Returns for Indian Banks Using Technical Indicators?," JRFM, MDPI, vol. 15(8), pages 1-16, August.
    2. Rupel Nargunam & William W. S. Wei & N. Anuradha, 2021. "Investigating seasonality, policy intervention and forecasting in the Indian gold futures market: a comparison based on modeling non-constant variance using two different methods," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-15, December.
    3. Alam, Md Shabbir & Murshed, Muntasir & Manigandan, Palanisamy & Pachiyappan, Duraisamy & Abduvaxitovna, Shamansurova Zilola, 2023. "Forecasting oil, coal, and natural gas prices in the pre-and post-COVID scenarios: Contextual evidence from India using time series forecasting tools," Resources Policy, Elsevier, vol. 81(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Charles, Amélie & Darné, Olivier, 2009. "The random walk hypothesis for Chinese stock markets: Evidence from variance ratio tests," Economic Systems, Elsevier, vol. 33(2), pages 117-126, June.
    2. Hung, Jui-Cheng, 2009. "Deregulation and liberalization of the Chinese stock market and the improvement of market efficiency," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(3), pages 843-857, August.
    3. Hoque, Hafiz A.A.B. & Kim, Jae H. & Pyun, Chong Soo, 2007. "A comparison of variance ratio tests of random walk: A case of Asian emerging stock markets," International Review of Economics & Finance, Elsevier, vol. 16(4), pages 488-502.
    4. Zhang, Yaojie & Wei, Yu & Ma, Feng & Yi, Yongsheng, 2019. "Economic constraints and stock return predictability: A new approach," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 1-9.
    5. Amélie Charles & Olivier Darné, 2009. "Variance‐Ratio Tests Of Random Walk: An Overview," Journal of Economic Surveys, Wiley Blackwell, vol. 23(3), pages 503-527, July.
    6. Kim, Jae H. & Shamsuddin, Abul, 2008. "Are Asian stock markets efficient? Evidence from new multiple variance ratio tests," Journal of Empirical Finance, Elsevier, vol. 15(3), pages 518-532, June.
    7. Charles, Amélie & Darné, Olivier & Kim, Jae H., 2012. "Exchange-rate return predictability and the adaptive markets hypothesis: Evidence from major foreign exchange rates," Journal of International Money and Finance, Elsevier, vol. 31(6), pages 1607-1626.
    8. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    9. Kian-Ping Lim & Muzafar Shah Habibullah & Melvin J. Hinich, 2009. "The Weak-form Efficiency of Chinese Stock Markets," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 8(2), pages 133-163, May.
    10. Aye, Goodness C. & Gil-Alana, Luis A. & Gupta, Rangan & Wohar, Mark E., 2017. "The efficiency of the art market: Evidence from variance ratio tests, linear and nonlinear fractional integration approaches," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 283-294.
    11. Charles, Amélie & Darné, Olivier, 2009. "The efficiency of the crude oil markets: Evidence from variance ratio tests," Energy Policy, Elsevier, vol. 37(11), pages 4267-4272, November.
    12. Amelie Charles & Olivier Darne, 2009. "Testing for Random Walk Behavior in Euro Exchange Rates," Economie Internationale, CEPII research center, issue 119, pages 25-45.
    13. Mamdouh Abdulaziz Saleh Al-Faryan & Everton Dockery, 2021. "Testing for efficiency in the Saudi stock market: does corporate governance change matter?," Review of Quantitative Finance and Accounting, Springer, vol. 57(1), pages 61-90, July.
    14. Dai, Zhifeng & Zhu, Huan, 2020. "Stock return predictability from a mixed model perspective," Pacific-Basin Finance Journal, Elsevier, vol. 60(C).
    15. Dai, Zhifeng & Zhu, Huan, 2021. "Indicator selection and stock return predictability," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    16. Roberto Ortiz & Mauricio Contreras & Marcelo Villena, 2015. "On the Efficient Market Hypothesis of Stock Market Indexes: The Role of Non-synchronous Trading and Portfolio Effects," Papers 1510.03926, arXiv.org.
    17. Wang, Yunqi & Zhou, Ti, 2023. "Out-of-sample equity premium prediction: The role of option-implied constraints," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 199-226.
    18. Yin, Anwen, 2020. "Equity premium prediction and optimal portfolio decision with Bagging," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    19. Kian-Ping Lim & Robert Brooks, 2009. "Are Chinese stock markets efficient? Further evidence from a battery of nonlinearity tests," Applied Financial Economics, Taylor & Francis Journals, vol. 19(2), pages 147-155.
    20. Katusiime, Lorna & Shamsuddin, Abul & Agbola, Frank W., 2015. "Foreign exchange market efficiency and profitability of trading rules: Evidence from a developing country," International Review of Economics & Finance, Elsevier, vol. 35(C), pages 315-332.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:fininn:v:6:y:2020:i:1:d:10.1186_s40854-020-00201-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.