Research Article
BibTex RIS Cite
Year 2017, Volume: 01 Issue: 2, 66 - 71, 29.12.2017

Abstract

References

  • Bańbura, Marta, Domenico Giannone, and Lucrezia Reichlin. "Large Bayesian vector auto regressions." Journal of Applied Econometrics 25.1 (2010): 71-92.
  • Chen, An-Sing, Mark T. Leung, and Hazem Daouk. "Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index." Computers & Operations Research 30.6 (2003): 901-923.
  • Cremers, KJ Martijn. "Stock return predictability: A Bayesian model selection perspective." The Review of Financial Studies 15.4 (2002): 1223-1249.
  • Maitre, P. O., et al. "A three-step approach combining Bayesian regression and NONMEM population analysis: application to midazolam." Journal of pharmacokinetics and biopharmaceutics 19.4 (1991): 377-384.
  • Kajner, Lyle, Marian Kurlanda, and Gordon Sparks. "Development of Bayesian regression model to predict hot-mix asphalt concrete overlay roughness." Transportation Research Record: Journal of the Transportation Research Board1539 (1996): 125-131.
  • Hossain, Mustaque, and Tanveer Chowdhury. Use of falling weight deflectometer data for assessing pavement structural evaluation values. No. K-TRAN: KSU-96-4,. NTIS, 1999.

BIST 100 Index Estimation Using Bayesian Regression Modelling

Year 2017, Volume: 01 Issue: 2, 66 - 71, 29.12.2017

Abstract

Identification of factors, determining the fluctuations of stock indices in the market, possesses great importance for the capital market actors. Not only specifying the factors and market but also explaining the relationship between them correctly, will reduce the amount of exposure financial risk and bring this topic into the limelight of market actors. The most important indicator of good progress in the economic cycle can be observed as the stock market index which is also used as macroeconomic indicators for developed economies. It is generally observed that all the stock prices rise or fall in the same period and this question gives us the impression that there are some factors that have an influence in this period. Having the properties of dynamic, complex and non-linear structure makes this analysis tough and solving the problem requires very complex and difficult processes. Due to high uncertainty and volatility, while estimating the stock price behaviour, stock investments carry more risk than any other investment. Moreover, BIST index possesses very high chaotic structure, it is not possible to conclude the long-term predictability. Our study will address the portion of the macro factors affecting the stock index. Interest rates, exchange rates, money supply, inflation, gold, oil prices are investigated among the macro factors. For the study data was obtained using CBT from the Electronic Data Dissemination System monthly frequencies. The factors influencing the stock index was determined by the method of regression relationship between them, but the Bayesian method is used with regression for the estimation.

References

  • Bańbura, Marta, Domenico Giannone, and Lucrezia Reichlin. "Large Bayesian vector auto regressions." Journal of Applied Econometrics 25.1 (2010): 71-92.
  • Chen, An-Sing, Mark T. Leung, and Hazem Daouk. "Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index." Computers & Operations Research 30.6 (2003): 901-923.
  • Cremers, KJ Martijn. "Stock return predictability: A Bayesian model selection perspective." The Review of Financial Studies 15.4 (2002): 1223-1249.
  • Maitre, P. O., et al. "A three-step approach combining Bayesian regression and NONMEM population analysis: application to midazolam." Journal of pharmacokinetics and biopharmaceutics 19.4 (1991): 377-384.
  • Kajner, Lyle, Marian Kurlanda, and Gordon Sparks. "Development of Bayesian regression model to predict hot-mix asphalt concrete overlay roughness." Transportation Research Record: Journal of the Transportation Research Board1539 (1996): 125-131.
  • Hossain, Mustaque, and Tanveer Chowdhury. Use of falling weight deflectometer data for assessing pavement structural evaluation values. No. K-TRAN: KSU-96-4,. NTIS, 1999.
There are 6 citations in total.

Details

Primary Language English
Subjects Mathematical Sciences
Journal Section Articles
Authors

Cihat Öztürk

Deniz Efendioğlu

Nurullah Güleç This is me

Publication Date December 29, 2017
Submission Date October 4, 2017
Acceptance Date December 25, 2017
Published in Issue Year 2017 Volume: 01 Issue: 2

Cite

APA Öztürk, C., Efendioğlu, D., & Güleç, N. (2017). BIST 100 Index Estimation Using Bayesian Regression Modelling. Turkish Journal of Forecasting, 01(2), 66-71.
AMA Öztürk C, Efendioğlu D, Güleç N. BIST 100 Index Estimation Using Bayesian Regression Modelling. TJF. December 2017;01(2):66-71.
Chicago Öztürk, Cihat, Deniz Efendioğlu, and Nurullah Güleç. “BIST 100 Index Estimation Using Bayesian Regression Modelling”. Turkish Journal of Forecasting 01, no. 2 (December 2017): 66-71.
EndNote Öztürk C, Efendioğlu D, Güleç N (December 1, 2017) BIST 100 Index Estimation Using Bayesian Regression Modelling. Turkish Journal of Forecasting 01 2 66–71.
IEEE C. Öztürk, D. Efendioğlu, and N. Güleç, “BIST 100 Index Estimation Using Bayesian Regression Modelling”, TJF, vol. 01, no. 2, pp. 66–71, 2017.
ISNAD Öztürk, Cihat et al. “BIST 100 Index Estimation Using Bayesian Regression Modelling”. Turkish Journal of Forecasting 01/2 (December 2017), 66-71.
JAMA Öztürk C, Efendioğlu D, Güleç N. BIST 100 Index Estimation Using Bayesian Regression Modelling. TJF. 2017;01:66–71.
MLA Öztürk, Cihat et al. “BIST 100 Index Estimation Using Bayesian Regression Modelling”. Turkish Journal of Forecasting, vol. 01, no. 2, 2017, pp. 66-71.
Vancouver Öztürk C, Efendioğlu D, Güleç N. BIST 100 Index Estimation Using Bayesian Regression Modelling. TJF. 2017;01(2):66-71.

INDEXING

   16153                        16126   

  16127                       16128                       16129