Browsing by Author "Karabudak, Burak"

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    Master ThesisPublicationMetadata only
    Effectiveness of technical indicators in predicting BIST100 index returns
    Karabudak, Burak; Gökçen, Umut; Gökçen, Umut; Ahi, Emrah; Ertan, A. S.; Department of Financial Engineering
    This paper presents a logistic regression-based approach for predicting the direction of the Borsa Istanbul (BIST) index's price movements using technical indicators. Historical price data from 2005 to 2021 is utilized, and five thresholds are determined from -2% to 2% based on the daily or weekly returns of the BIST index. Binary variables which utilized in logistic regression as dependent variable are defined according to the thresholds, and logistic regression is applied using annual or semiannual training data. Daily models perform better than weekly models in logistic regression, and On Balance Volume (OBV) and Average Directional Index (ADX) are the indicators within the best statistical results. 0% threshold models are best in accuracy of prediction (approximately half of prediction is accurate). Weekly models are better than daily models, and annual models are better than semiannual models in accuracy. Some of the models outperformed the BIST index in cumulative return (daily cumulative return is 4,84; weekly is 4,48), with two models having approximately three times the cumulative return of the BIST index namely Daily-1% threshold-6 month's cumulative return is 13,35; Weekly-2% threshold-12 month's is 12,96. In summary, the results show that technical indicators can be successful in predicting stock or index returns depending on the training period, time period, and the combination of technical indicators used.