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A Comparative Analysis of Risk due to Rolling Average Strategies using Bayesian Classifier


Affiliations
1 Department of Statistics in Government Arts College (Autonomous), Salem-7, India
     

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Any trading strategy which is a combination of technical indicators will have rolling averages as a natural choice. While it is true that the convergence of short and long rolling average is a widely used concept to analyze the pattern of price changes, the methodology of computing the rolling averages has always been a subject of discussion. In this study, the credibility of simple and weighted rolling averages in predicting the price patterns is analyzed using Bayesian classifier and the accuracy of the strategy which is assessed with back testing is used as a proxy to analyze the risk. The empirical analysis is carried out using the five stocks from NIFTY 50 selected at random. The study proposes two strategies which are: 1. SMA-RSI-ATR which is a combination of MACD computed with simple moving averages along with Relative Strength Index (RSI) and Average True Range (ATR). 2. EMA-RSI-ATR which is a combination of MACD computed with Exponential moving averages along with Relative Strength Index(RSI) and Average True Range(ATR) Among these two strategies, the study concludes that the strategy SMA-RSI-ATR is associated with larger returns as compared with EMA-RSI-ATR. But the risk associated with SMA-RSI-ATR is higher than that of EMA-RSI-ATR as revealed by the accuracy of the model.

Keywords

Average True Range (ATR), Confusion Matrix, Momentum Indicator, Moving Average Convergence and Divergence (MACD), Naïve Bayes Classifier, Relative Strength Index (RSI), Sensitivity, Specificity, Trend Indicator, Volatility Indicator.
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  • Brock, W, Lakonishok, J and LeBaron, B. 1992. “Simple technical trading rules and the stochastic properties of stock returns”. Journal of Finance. Vol. 47. pp. 1731−1764.

  • Bessembinder, H and Chan, K. 1998. “The profitability of technical trading rules in the Asian stock markets”. Pacific-Basin Finance Journal. Vol. 3. pp. 257−284.

  • Craig, A and Parbery, S. 2005. “Is smarter better? A comparison of adaptive, and simple moving average trading strategies”. Research in International Business and Finance. Vol. 19. pp. 399−411.

  • Fama, E & Blume, M. 1966. “Filter rules and stock market trading profits”. Journal of Business. Vol. 39. pp. 226−341.

  • Fama, F. 1965. “The Behavior of Stock-Market Prices”. The Journal of Business. Vol. 38. pp. 34-105 [6] Fama, E. 1970. “Efficient Capital Markets: A Review of Theory and Empirical Work”. The Journal of Finance. Vol. 25. pp. 383-417


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  • A Comparative Analysis of Risk due to Rolling Average Strategies using Bayesian Classifier

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Authors

R Subathra
Department of Statistics in Government Arts College (Autonomous), Salem-7, India

Abstract


Any trading strategy which is a combination of technical indicators will have rolling averages as a natural choice. While it is true that the convergence of short and long rolling average is a widely used concept to analyze the pattern of price changes, the methodology of computing the rolling averages has always been a subject of discussion. In this study, the credibility of simple and weighted rolling averages in predicting the price patterns is analyzed using Bayesian classifier and the accuracy of the strategy which is assessed with back testing is used as a proxy to analyze the risk. The empirical analysis is carried out using the five stocks from NIFTY 50 selected at random. The study proposes two strategies which are: 1. SMA-RSI-ATR which is a combination of MACD computed with simple moving averages along with Relative Strength Index (RSI) and Average True Range (ATR). 2. EMA-RSI-ATR which is a combination of MACD computed with Exponential moving averages along with Relative Strength Index(RSI) and Average True Range(ATR) Among these two strategies, the study concludes that the strategy SMA-RSI-ATR is associated with larger returns as compared with EMA-RSI-ATR. But the risk associated with SMA-RSI-ATR is higher than that of EMA-RSI-ATR as revealed by the accuracy of the model.

Keywords


Average True Range (ATR), Confusion Matrix, Momentum Indicator, Moving Average Convergence and Divergence (MACD), Naïve Bayes Classifier, Relative Strength Index (RSI), Sensitivity, Specificity, Trend Indicator, Volatility Indicator.

References