Pengaruh Moving Average dan Transaction Volume pada Return Saham Perbankan Indonesia

Main Article Content

Valdi Sayoga Majiah
Said Kelana

Abstract

This study investigates the impact of Moving Average (MA) Stock prices and Transaction Volume on Stock returns in the Indonesian banking sector, specifically focusing on the LQ45 index from 2020 to 2023. It delves into the concepts of Efficient Market Hypothesis (EMH), Behavioral Finance, and Technical Analysis. EMH posits that Stock prices reflect all available information and are hard to consistently beat, while Behavioral Finance suggests that psychological factors can lead to market inefficiencies exploitable by technical analysis. Employing a quantitative approach and data from the Indonesia Stock Exchange (BEI), the study finds that MA and Transaction Volume positively and significantly influence Stock returns in the banking sector. However, variations exist in individual bank samples due to factors like fundamentals and unique behaviors, which cannot be solely determined from historical Stock prices. Thus, the research underscores the importance of considering additional factors beyond past price trends when analyzing the Stock performance of individual banks.

Article Details

How to Cite
Majiah, V. S., & Kelana, S. (2024). Pengaruh Moving Average dan Transaction Volume pada Return Saham Perbankan Indonesia. Jurnal Manajemen, 13(1), 1–15. https://doi.org/10.46806/jm.v13i1.1032
Section
Artikel Riset

References

Aguirre, A.A.A., Medina, R.A.R., & Méndez, N.D.D.(2020). Machine learning applied in the stock market through the Moving Average Convergence Divergence (MACD) indicator. Investment Management & Financial Innovations, 17(4), p.44. http://dx.doi.org/10.21511/imfi.17(4).2020.05.

Asnawi, S.K., Siagian, D., Alzah, S.F., & Halim, I. (2022). Does Disposition Effect Appear on Investor Decision During the COVID-19 Pandemic Era: Empirical Evidence from Indonesia. Journal of Asian Finance, Economics and Business, 9(4), pp.53-62. https://doi.org/10.13106/jafeb.2022.vol9.no4.0053.

Balaban, E., Bayar, A., & Faff, R.W. (2006). Forecasting stock market volatility: Further international evidence. The European Journal of Finance, 12(2), 71-188. https://doi.org/10.1080/13518470500146082.

Hatemi-J, A., Singh, H., & Nandha, M. (2011). An empirical investigation between oil prices and the stock price in China and India. Corporate Ownership and Control, 8(2 D), 163-169. Retrieved from http://www.virtusinterpress.org/IMG/pdf/COC__Volume_8_Issue_2_Winter_2011_Continued1_.pdf#page=3.

Hájek, P. (2018). Combining bag-of-words and sentiment features of annual reports to predict abnormal stock returns. Neural Computing and Applications, 29, 343-358. https://doi.org/10.1007/s00521-017-3194-2.

Hsieh, S.F., Chan, C.Y., & Wang, M.C. (2020). Retail investor attention and herding behavior. Journal of Empirical Finance, 59, 109-132.: https://doi.org/10.1016/j.jempfin.2020.09.005.

Jain, R., Jain, P., & Jain, C. (2015). Behavioral Biases in the Decision Making of Individual Investors. IUP Journal of Knowledge Management, 13(3). Available at: UP Journal of Knowledge Management, 13(3), 7-27.

Jain, J., Walia, N., & Gupta, S. (2020). Evaluation of behavioral biases affecting investment decision making of individual equity investors by fuzzy analytic hierarchy process. Review of Behavioral Finance, 12(3), pp.297-314. https://doi.org/10.1108/RBF-03-2019-0044.

Jose Alvarez-Ramirez, Rodriguez, E., & Echeverria, J.C. (2009). A DFA approach for assessing asymmetric correlations. 2263-2270. https://doi.org/10.1016/j.physa.2009.03.007.

Lo, A.W., & Wang, J. (2000). Trading volume: definitions, data analysis, and implications of portfolio theory. The Review of Financial Studies, 13(2), 257-300. https://doi.org/10.1093/rfs/13.2.257

Sandarsari, W.T. (2010). Analisis pengaruh volume perdagangan, frekuensi perdagangan, dan order imbalance terhadap volatilitas harga saham pada perusahaan go public di bursa efek Indonesia. Retrieved from https://digilib.uns.ac.id/dokumen/detail/13555

Sapienza, P., & Zingales, L. (2013). Economic experts versus average Americans. American Economic Review, 103(3), 636-642. https://doi.org/10.1257/aer.103.3.636

Statman, M. (2014). Behavioral finance: Finance with normal people. Borsa Istanbul Review, 14(2), 65-73. https://doi.org/10.1016/j.bir.2014.03.001

Wang, Y. & Guo, Y. (2020). Forecasting method of stock market volatility in time series data based on mixed model of ARIMA and XGBoost. China Communications, 17(3), 205-221. https://doi.org/10.23919/JCC.2020.03.017.

Yao, S., He, H., Chen, S., & Ou, J. (2018). Financial liberalization and cross-border market integration: Evidence from China's stock market. International Review of Economics & Finance, 58, 220-245. https://doi.org/10.1016/j.iref.2018.03.023.