Journal of Network Theory in Finance

Technical indicator selection and trading signal forecasting: varying input window length and forecast horizon for the Pakistan Stock Exchange

Beenish Bashir and Faheem Aslam

  • A total of 34 technical indicators (TIs) are used. The random forest algorithm is used as a filter for the selection of market specific TIs, which reduces the list of TIs to 15.
  • The precision score of the artificial neural network is better than that of the support vector machine, for varying input window lengths and forecast horizons.
  • The precision score is highest when the forecast horizon is 15 days or more.
  • The marginal increase in precision is greater when increasing from 10 to 15 days than from 20 to 25 days.

The development of a predictive system that correctly forecasts trading signals is crucial for algorithmic trading and investment management. Technical analysis has been used by many researchers for financial market prediction. Numerous technical indicators (TIs) are computed by setting a time-frame parameter called the input window length. This paper therefore investigates how the input window length and forecast horizon together affect the predictive performance of the model. Market-specific TIs are extracted through a random forest technique. These TIs are used as inputs for an artificial neural network and a support vector machine to forecast the future direction of trading signals. The data set consists of 22 years of daily prices for the Pakistan Stock Exchange. This research finds the 15 most relevant features for the Pakistan Stock Exchange from a list of 34 TIs. The prediction system performs best when the forecast horizon is more than 15 days, which shows the dependency of the input variable parameter selection and the forecast horizon. This unique pattern is studied using multiple confusion metrics. The findings of this study may improve the prediction accuracy of a trading strategy based on technical analysis.

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