Rebalancing of Exchange Traded Funds in Stock Market Using Option Trading Strategies

• Autorzy: Vinaykumar Elegeti
• Języki publikacji: EN

Abstrakty

EN

Motivation: The finance and academic industries are highly discussed in the stock market trading domain. The increase in economic globalization shows the connection among stock markets in different countries, which produces the effect of risk conduction in the market. Forecasting the direction of every day's stock market return is important and challenging. The growing complexity and dynamic features in stock markets are difficult in the financial industry. The inflexible trading method developed by financial practitioners utilized a larger amount of stock market features and is failed to achieve a satisfactory result in every condition of the market. Further, the existing data mining approaches are incomplete and inefficient. Aim: To overcome the issues in stock and problem of existing methods, proposed option trading strategies for rebalancing Exchange Traded Fund (ETF) in the stock market. Rebalancing-ETF measure the volatility of the stock to track the error of model and rebalance the threshold quality to improve the trade. The proposed method increases the order of threshold quantity to rebalance the trade. Results: The result showed that the minimum orders increases in rebalancing trade, which reduces the impact of price formations in market. The tracking error occurs when the larger quantity of threshold value reduces the quantity. Then, the markets are changed significantly when the Net Asset Values (NAV) of rebalancing ETF increases. (original abstract)

Słowa kluczowe

EN

Asset value; Stock markets; Exchange Traded Fund (ETF)

PL

Wartość aktywów; Rynki giełdowe; Fundusze ETF

• Czasopismo: Ekonomia i Prawo
• Rocznik: 2021
• Tom: 20
• Numer: nr 3
• Strony: 513--527

Twórcy

autor

Vinaykumar Elegeti

• Shri Jagdishprasad Jhabarmal Tibrewala University

Bibliografia

• Cao, G., Han, Y., Li, Q., & Xu, W. (2017). Asymmetric MF-DCCA method based on risk conduction and its application in the Chinese and foreign stock markets. Physica A: Statistical Mechanics and its Applications, 468, 119-130. https://doi.org/10.1016/j.physa.2016.10.002.
• Chang, Y.H., & Lee, M.S. (2017). Incorporating Markov decision process on genetic algorithms to formulate trading strategies for stock markets. Applied Soft Computing, 52, 1143-1153. https://doi.org/10.1016/j.asoc.2016.09.016.
• Chen, Y., & Hao, Y. (2017). A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction. Expert Systems with Applications, 80, 340-355. https://doi.org/10.1016/j.eswa.2017.02.044.
• Dai, Z., Dong, X., Kang, J., & Hong, L. (2020). Forecasting stock market returns: new technical indicators and two-step economic constraint method. The North American Journal of Economics and Finance, 53, 101216. https://doi.org/10.1016/j.najef.2020.101216.
• Das, S.P., & Padhy, S. (2017). A new hybrid parametric and machine learning model with homogeneity hint for European-style index option pricing. Neural Computing and Applications, 28(12), 4061-4077. https://doi.org/10.1007/s00521-016-2303-y.
• Gambaro, A.M., Kyriakou, I., & Fusai, G. (2020). General lattice methods for arithmetic Asian options. European Journal of Operational Research, 282(3), 1185-1199. https://doi.org/10.1016/j.ejor.2019.10.026.
• Golbabai, A., & Nikan, O. (2020). A computational method based on the moving least-squares approach for pricing double barrier options in a time-fractional Black-Scholes model. Computational Economics, 55(1), 119-141. https://doi.org/10.1007/s10614-019-09880-4.
• Gudelek, M.U., Boluk, S.A., & Ozbayoglu, A.M. (2017). A deep learning based stock trading model with 2-D CNN trend detection. In 2017 IEEE Symposium Series on Computational Intelligence, 1-8. https://doi.org/10.1109/ssci.2017.8285188.
• Huang, C.F., & Li, H.C. (2017). An evolutionary method for financial forecasting in microscopic high-speed trading environment. Computational Intelligence and Neuroscience, 1-18. https://doi.org/10.1155/2017/9580815.
• Li, X., & Wei, Y. (2018). The dependence and risk spillover between crude oil market and China stock market: new evidence from a variational mode decomposition-based copula method. Energy Economics, 74, 565-581. https://doi.org/10.1016/j.eneco.2018.07.011.
• Oscar, V., Aguilasocho-Montoya, D., Álvarez-García, J., & Simonetti, B. (2020). Using Markov-switching models with Markov chain Monte Carlo inference methods in agricultural commodities trading. Soft Computing, 24(18), 13823-13836. https://doi.org/10.1007/s00500-019-04629-5.
• Pimenta, A., Nametala, C.A., Guimarães, F.G., & Carrano, E.G. (2018). An automated investing method for stock market based on multiobjective genetic programming. Computational Economics, 52(1), 125-144. https://doi.org/10.1007/s10614-017-9665-9.
• Wu, X., Chen, H., Wang, J., Troiano, L., Loia, V., & Fujita, H. (2020). Adaptive stock trading strategies with deep reinforcement learning methods. Information Sciences, 538, 142-158. https://doi.org/10.1016/j.ins.2020.05.066.
• Yagi, I., Maruyama, S., & Mizuta, T. (2020). Trading strategies of a leveraged ETF in a continuous double auction market using an agent-based simulation. Complexity, 1-7. https://doi.org/10.1155/2020/3497689.
• Zhang, X.D., Li, A., & Pan, R. (2016). Stock trend prediction based on a new status box method and AdaBoost probabilistic support vector machine. Applied Soft Computing, 49, 385-398. https://doi.org/10.1016/j.asoc.2016.08.026.

Identyfikatory

DOI

https://doi.org/10.12775/EiP.2021.031