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Picking Buy-Sell Signals: A Practitioner’S Perspective On Key Technical Indicators For Selected Indian Firms

Author

Listed:
  • TALWAR Shalini

    (K J Somaiya Institute of Management Studies and Research, Vidyanagar, India)

  • SHAH Pranav

    (K J Somaiya Institute of Management Studies and Research, Vidyanagar, India)

  • SHAH Utkarsh

    (K J Somaiya Institute of Management Studies and Research, Vidyanagar, India)

Abstract

The purpose of this study is to undertake technical analysis of selected companies included in the S&P CNX Nifty 50, a leading stock market index in India. We have used the stock price data of twenty leading listed firms in India for a period from January 1, 2012 through December 31, 2017. We have applied Guppy Multiple Moving Average (GMMA), Moving Average Convergence Divergence (MACD), Stochastic Relative Strength Index (Stoch RSI) and Average Directional Index (ADX) to Heikin Ashi charts to back test and provide entry and exit points for the players in the stock market. Analysis of the price information has revealed that the GMMA and ADX are effective indicators for most of the stocks under the study but they give late signals as compared to RSI and MACD. Further, the study has shown that though RSI and MACD give early signals, yet they are risky as the number of false signals generated by them is also found out to be quite high. The study is important as the findings can be used by investors, option traders and portfolio managers to get generate profitable trading signals and obtain good risk to reward ratios.

Suggested Citation

  • TALWAR Shalini & SHAH Pranav & SHAH Utkarsh, 2019. "Picking Buy-Sell Signals: A Practitioner’S Perspective On Key Technical Indicators For Selected Indian Firms," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 14(3), pages 205-219, December.
    • Handle: RePEc:blg:journl:v:14:y:2019:i:3:p:205-219
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    File URL: http://eccsf.ulbsibiu.ro/RePEc/blg/journl/14316talwar.pdf
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    Cited by:

    1. Orte, Francisco & Mira, José & Sánchez, María Jesús & Solana, Pablo, 2023. "A random forest-based model for crypto asset forecasts in futures markets with out-of-sample prediction," Research in International Business and Finance, Elsevier, vol. 64(C).

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