MARKET SPECIFIC TECHNICAL INDICATORS COMBINING THE WISDOM OF CROWD

Authors

  • Beenish Bashir COMSATS University Islamabad, Pakistan
  • Faheem Aslam COMSATS University Islamabad, Pakistan
  • Aneel Salman COMSATS University Islamabad, Pakistan

DOI:

https://doi.org/10.56536/ijmres.v9i1.42

Keywords:

Technical Indicators, Random Forest, Feature Selection, Financial Forecasting

Abstract

To improve the profitability and predictability of financial markets we highlight the issue of relevant technical indicators (TI) for different markets. In previous studies, not much attention has been given to feature selection problem by counting on the most popular technical indicators or following the footsteps of related literature. This research work is focusing on the importance of feature selection problem. There is no specific set of TI that is good enough to predict every market price movement. TI which are good predictors of developed markets are not suitable for emerging or frontier markets. The analysis is based on a well diversified sample of nine countries representing developed, emerging and frontier markets as per the categorization of Morgan Stanley Capital International (MSCI). We use random forest (RF) technique for feature selection from a group of 90 technical indicators. The results show that top five technical indicators are different for each market according to Gini index. Even within the three categories of market, these indicators and their ranking varies. Hence, appropriate feature selection according to market eventually improves the accuracy of predictive model and profitability.

Purpose: Finding the relevant technical indicators for financial market is crucial for financial market prediction. There is no particular set of technical indicators that fits all financial market since every market has its own dynamics and it is important to first select the technical indicators that reflects the market conditions rather than using the most commonly employed technical indicators.

Design/Methodology/Approach: Random forest technique is used to find the relevant technical indicator. And each indicator is ranked according to Gini index. Findings: The analysis suggest that the set of technical indicators is not the same for every financial market. Further with in the categories of developed markets, emerging markets and frontier markets the set of technical indicators changes.

Implications/Originality/Value: Hence, it is summarized that to improve the forecasting ability of the predictive model it is important to find the market specific technical indicators that will end up in higher profit for investors.

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Published

2019-01-01

How to Cite

Beenish Bashir, Faheem Aslam, & Aneel Salman. (2019). MARKET SPECIFIC TECHNICAL INDICATORS COMBINING THE WISDOM OF CROWD. International Journal of Management Research and Emerging Sciences, 9(1). https://doi.org/10.56536/ijmres.v9i1.42