Random Regression Forest Model using Technical Analysis Variables

Senol Emir · Hasan Dincer · Umit Hacioglu · Serhat Yuksel

International Journal of Finance & Banking Studies · 2016 · Vol. 5 / Issue 3 · 85-102

Research reach

Publication metrics

Dimensions ↗OpenAlex search ↗Mendeley search ↗Google Scholar ↗

External links open provider records or DOI searches. Provider counts are not combined. Mendeley readership measures saves, not citations.

Dimensions

Live provider badge. If unavailable, use the Dimensions link above.

CROSSREF CITATIONS2

Observed 21 Oct 2026, 00:21 UTC

OpenAlex · Citations3Cached provider observation

Observed 21 Oct 2026 00:22 UTC

Mendeley · Readers—No verified count available

OpenAlex and Mendeley observations refresh on article visits, at most once per 24 hours. Unavailable data is not zero. Google Scholar opens a DOI search.

OpenAlex / Citation year

Citations received by this article, grouped by the year of the citing work. Crossref and OpenAlex lifetime totals remain separate in Publication metrics.

2017–2026 Annual history last updated: 21 Oct 2026, 00:22 UTC

Citations received

OpenAlex · year of the citing work

OpenAlex

2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.

View data & coverage

OpenAlex · Citations received by year

View openalex · citations received by year data & coverage

Citation year	Journal / scope	OpenAlex · Citations received by year	Publications	Annual history known
2017	This article	0	1	1/1
2018	This article	0	1	1/1
2019	This article	2	1	1/1
2020	This article	0	1	1/1
2021	This article	0	1	1/1
2022	This article	0	1	1/1
2023	This article	0	1	1/1
2024	This article	0	1	1/1
2025	This article	1	1	1/1
2026	This article	0	1	1/1

2026 is incomplete. Within a successfully fetched ten-year window, omitted years mean zero. Years beyond a saved observation’s window remain unknown (—). Older citations are excluded, so this chart’s sum may differ from the OpenAlex lifetime total. The date above belongs to this annual history; an unsuccessful refresh keeps the previous observation.

Overview

Abstract

The purpose of this study is to explore the importance and ranking of technical analysis variables in Turkish banking sector. Random Forest method is used for determining importance scores of inputs for eight banks in Borsa Istanbul. Then two predictive models utilizing Random Forest (RF) and Artificial Neural Networks (ANN) are built for predicting BIST-100 index and bank closing prices. Results of the models are compared by three metrics namely Mean Absolute Error (MAE), Mean Square Error (MSE), Median Absolute Error (MedAE). Findings show that moving average (MAV-100) is the most important variable for both BIST -100 index and bank closing prices. Therefore, investors should follow this technical indicator with respect to Turkish banks. In addition ANN shows better performance for all metrics.

Authors & affiliations

Senol Emir
Affiliation not supplied

Hasan Dincer
Affiliation not supplied

Umit Hacioglu
Affiliation not supplied

Serhat Yuksel
Affiliation not supplied

Deposited references

Supplied references, not a list of publications citing this article.

DOI: 10.2307/2491464

DOI: 10.5539/ijbm.v7n15p25

DOI: 10.1108/17554171311308959

Bauer, R. J., & Dahlquist, J. R. (1999). Technical Markets Indicators: Analysis & Performance (Vol. 64). John Wiley & Sons.

DOI: 10.1016/j.eswa.2012.04.016

DOI: 10.1007/978-1-84882-260-3

DOI: 10.1007/s10994-014-5480-x

DOI: 10.1111/j.1540-6261.1994.tb04424.x

DOI: 10.1016/j.eswa.2013.12.009

DOI: 10.1002/9781118835081.ch12

DOI: 10.2307/1911513

DOI: 10.1023/A:1010933404324

DOI: 10.1111/j.1540-6261.1992.tb04681.x

DOI: 10.1111/0022-1082.00054

DOI: 10.1016/j.eswa.2008.05.027

DOI: 10.1016/j.eswa.2015.03.017

DOI: 10.1111/1468-0297.00466

DOI: 10.1016/j.eswa.2006.10.028

DOI: 10.1080/13504850600993598

DOI: 10.2307/1956249

DOI: 10.1016/j.eswa.2008.07.021

Davies, D. W. (1995). Defining the Commodity Channel Index. Technical Analysis of Stocks and Commodities, 13, 95-101.

de Castro, P. A. L., & Sichman, J. S. (2007). Towards Cooperation Among Competitive Trader Agents. In ICEIS (4) (pp. 138-143).

Edwards, R. D., Magee, J., & Bassetti, W. H. C. (2012). Technical analysis of stock trends. CRC Press.

DOI: 10.1016/S0304-405X(98)00026-9

Fernandes, R. G. (2015). The Effect of Stock Characteristics on Technical Analysis Profitability: the Portuguese Stock Exchange case.

DOI: 10.1016/j.eswa.2015.02.042

Galloppo, G. (2009). Dynamic Asset Allocation Using a Combined Criteria Decision System. Accounting & Taxation, 1(1), 29-44.

DOI: 10.1016/j.patrec.2010.03.014

DOI: 10.1016/j.patrec.2005.08.011

DOI: 10.3905/jpm.9.3.28

DOI: 10.1111/j.1475-4932.1993.tb02121.x

Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques, Third Edition (3rd ed.). Morgan Kaufmann.

DOI: 10.1016/j.jebo.2014.04.002

DOI: 10.1016/0304-405X(78)90025-9

DOI: 10.1016/j.eswa.2013.03.031

DOI: 10.1016/j.eswa.2012.10.018

DOI: 10.1016/S0925-2312(03)00372-2

DOI: 10.1016/j.pacfin.2013.10.004

DOI: 10.1016/j.eswa.2013.03.019

DOI: 10.1016/S0167-9236(03)00088-5

DOI: 10.1016/j.eswa.2005.04.043

DOI: 10.1002/9781118596289

DOI: 10.1080/1350485022000041032

DOI: 10.1111/0022-1082.00265

DOI: 10.1007/978-1-349-21315-3_28

DOI: 10.14569/IJACSA.2011.020122

DOI: 10.1016/j.eswa.2012.03.033

DOI: 10.1257/jel.45.4.936

DOI: 10.5539/ijef.v4n1p90

DOI: 10.1002/(SICI)1099-1158(199710)2:4<319::AID-JFE53>3.0.CO;2-6

Murphy, J. J. (2012). Charting made easy (Vol. 149). John Wiley & Sons.

DOI: 10.1016/S0261-5606(01)00033-X

DOI: 10.2307/2331231

DOI: 10.1086/296551

Nisbet, R., Elder, J., & Miner, G. (2009). Handbook of Statistical Analysis and Data Mining Applications .Academic Press.

DOI: 10.1002/ijfe.145

DOI: 10.1111/1540-6261.00588

DOI: 10.1016/j.eswa.2014.07.040

Poshakwale, S. (1996). Evidence on weak form efficiency and day of the week effect in the Indian stock market. Finance India, 10(3), 605-616.

DOI: 10.2307/3594994

DOI: 10.1016/j.isprsjprs.2011.11.002

DOI: 10.1142/6604

DOI: 10.1080/00036846.2011.631894

DOI: 10.1016/S0261-5606(02)00009-8

DOI: 10.1016/j.rfe.2008.10.001

Sen, J., & Chaudhuri, T. D. (2016). Decomposition of Time Series Data of Stock Markets and its Implications for Prediction: An Application for the Indian Auto Sector. arXiv preprint arXiv:1601.02407.

DOI: 10.1007/s40815-015-0058-8

StatSoft, Inc. (2013). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/.

Stevens, L. (2002). Essential technical analysis: tools and techniques to spot market trends (Vol. 162). John Wiley & Sons.

Tan, P.-N., Steinbach, M., & Kumar, V. (2005). Introduction to Data Mining. Boston: Pearson.

DOI: 10.1016/0261-5606(92)90048-3

DOI: 10.1002/fut.3990140207

Thomsett, M. C. (2006). Getting started in fundamental analysis. John Wiley & Sons.

Tsang, W. W. H., & Chong, T. T. L. (2009). Profitability of the On-Balance Volume Indicator. Economics Bulletin, 29(3), 2424-2431.

DOI: 10.2139/ssrn.2652637

DOI: 10.7763/IJCCE.2015.V4.380

Vasileiou, E. (2014). Is technical analysis profitable even for an amateur investor? Evidence from the Greek stock market (2002-12). Forthcoming in (Copur, Z., Ed.), Behavioral Finance and Investment Strategies: Decision Making in the Financial Industry, IGI Global Publishers.

Vora, M. N. (2011). Genetic Algorithm for Trading Signal Generation. In International Conference on Business and Economics Research (Vol. 1).

Wafi, A. S., Hassan, H., & Mabrouk, A. (2015). Fundamental Analysis Vs Technical Analysis in the Egyptian Stock Exchange–Empirical Study. International Journal of Business and Management Study–IJBMS, 2(2).

Waworuntu, S. R., & Suryanto, H. (2015). The Complementary Nature Of Fundamental And Technical Analysis Evidence From Indonesia. International Research Journal of Business Studies, 3(2).

DOI: 10.1016/B978-0-12-374856-0.00001-8

DOI: 10.1080/0960310022000020906

DOI: 10.1016/j.eswa.2008.06.121

DOI: 10.1007/978-1-4419-9326-7

DOI: 10.1016/j.eswa.2014.12.028

Zumel, N., & Mount, J. (2014). Practical data science with R. Manning.