Testing the Profitability of Technical Trading Rules across Market Cycles: Evidence from India

Muruganandan, S

doi:10.4038/cbj.v11i1.56

Cited by 7 publications

(6 citation statements)

References 11 publications

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“…The Artificial Neural Networks (ANN) based hybrid models performed significantly better than the Wavelet-ARIMA-based (Autoregressive Integrated Moving Average) models (90% of the time) when first-ranked models are considered. Muruganandan (2020) tested the profitability of technical indicators, i.e., RSI and MACD, on different market cycles of BSE Sensex, where the findings showed that, even before accounting for trading expenses, the RSI trading rule could not produce a positive return. Yet, throughout the Bear market period, the sell signal produced by MACD trading rules beat both the mean return and the unconditional mean return of the buy signal, but it could neither help with market timing nor a reduction in losing trades.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of Technical Analysis in the Nepalese Stock Market: Implications for Investment Strategies

Karki,

Dahal,

Bhattarai

et al. 2023

QJMSS

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Purpose: This study aims to examine the performance of five widely used technical indicators on Nepal Stock Exchange (NEPSE) indexes. Specifically, the study evaluates indicators such as the simple moving average, moving average convergence and divergence, relative strength index, stochastic oscillator, and Bollinger Band. Design/methodology/approach: The study utilizes a descriptive and quantitative research design. Daily closing index data of NEPSE and its six sub-indices are analyzed for a substantial period, ranging from 1st September 2012 to 31st August 2022. The performance of the technical indicators is examined through modeling, backtesting, and statistical analysis. The technical trading rules of each indicator are incorporated into the modeling process, and the results are analyzed using different performance metrics. Furthermore, the predictability of the indicators is tested through standard statistical analysis and bootstrap techniques utilizing a random walk model. Findings: The results of the study reveal that the technical strategies, as represented by the analyzed indicators, generally support the effectiveness of technical analysis in the NEPSE. However, the relative strength index stands out as an exception, generating negative returns. The second stage of the analysis indicates that the simple moving average, relative strength, and Bollinger band fail to generate significant returns in certain indices. The findings from the bootstrap techniques further contradict the forecasting ability of technical strategies in the NEPSE, raising questions about their real performance in the Nepalese stock market. Practical Implications: This research provides valuable insights for investors, traders, and market participants in understanding the nuances and limitations of technical analysis in the context of the NEPSE. The findings suggest that technical indicators, notably the relative strength index, should be interpreted cautiously and that investing decisions should take into account additional factors. The study contributes to the ongoing discussion on the effectiveness of technical analysis in the Nepalese stock market, assisting practitioners to make informed decisions and better understand market dynamics.

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Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of Technical Analysis in the Nepalese Stock Market: Implications for Investment Strategies

Karki,

Dahal,

Bhattarai

et al. 2023

QJMSS

View full text Add to dashboard Cite

show abstract

“…Muruganandana exhibited results that showed the failure of the relative strength index trading rule to deliver the positive return even before deducting transaction cost. However, by moving the average convergence and divergence trading rules, sell signal outperformed the unconditional mean return and buy signal mean return, during the Bear market period before deducting transaction cost [26]. Besides that, Chong and Ng examined the effectiveness of technical analysis to produce excess return in the London stock exchange by using Moving Average Convergence-Divergence (MACD) and Relative Strength Index (RSI) as shown in Table 3.2.…”

Section: Global Combination Of Effective Multi Technical Strategy Dev...mentioning

confidence: 99%

Untitled

2022

IJMIT

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It is important for investors to understand stock trends and market conditions before trading stocks. Both these capabilities are very important for an investor in order to obtain maximized profit and minimized losses. Without this capability, investors will suffer losses due to their ignorance regarding stock trends and market conditions. Technical analysis helps to understand stock prices behavior with regards to past trends, the signals given by indicators and the major turning points of the market price. This paper reviews the stock trend predictions with a combination of the effective multi technical indicator strategy to increase investment performance by taking into account the global performance and the proposed combination of effective multi technical indicator strategy model.

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“…(5) Selection of good financial status EPS: From Figures 2 and 3, this study discovered important rules for the positive/negative status of listed stock, which can help investors to manage effective investment strategy. (6) The best practice of enablers: From the study results, it was found that the best classifier is the decision tree C4.5 algorithm for the TEJ dataset, and the best model is Models D and E in terms of average accuracy. (7) Interpretation of two data-split methods: It was found that the cross-validation data approach makes more appropriate use of the TEJ dataset than the percentage-split data approach.…”

Section: Helpful Research Findings and Management Implicationsmentioning

confidence: 99%

“…Second, market price trends or movements are not random but specific trends or movements of an identified pattern. Over the past decade, commonly used indicators include price trend indices, such as varied types of the moving average (MA) [5] or momentum indices, such as moving average convergence and divergence (MACD) [6]. MA has an average of closing prices of a specific number of time periods, such as a 7-period MA for seven days, which defines a flowing correlation between the time period and the money rate [5].…”

Section: Introductionmentioning

confidence: 99%

Applied Identification of Industry Data Science Using an Advanced Multi-Componential Discretization Model

et al. 2020

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Applied human large-scale data are collected from heterogeneous science or industry databases for the purposes of achieving data utilization in complex application environments, such as in financial applications. This has posed great opportunities and challenges to all kinds of scientific data researchers. Thus, finding an intelligent hybrid model that solves financial application problems of the stock market is an important issue for financial analysts. In practice, classification applications that focus on the earnings per share (EPS) with financial ratios from an industry database often demonstrate that the data meet the abovementioned standards and have particularly high application value. This study proposes several advanced multicomponential discretization models, named Models A–E, where each model identifies and presents a positive/negative diagnosis based on the experiences of the latest financial statements from six different industries. The varied components of the model test performance measurements comparatively by using data-preprocessing, data-discretization, feature-selection, two data split methods, machine learning, rule-based decision tree knowledge, time-lag effects, different times of running experiments, and two different class types. The experimental dataset had 24 condition features and a decision feature EPS that was used to classify the data into two and three classes for comparison. Empirically, the analytical results of this study showed that three main determinants were identified: total asset growth rate, operating income per share, and times interest earned. The core components of the following techniques are as follows: data-discretization and feature-selection, with some noted classifiers that had significantly better accuracy. Total solution results demonstrated the following key points: (1) The highest accuracy, 92.46%, occurred in Model C from the use of decision tree learning with a percentage-split method for two classes in one run; (2) the highest accuracy mean, 91.44%, occurred in Models D and E from the use of naïve Bayes learning for cross-validation and percentage-split methods for each class for 10 runs; (3) the highest average accuracy mean, 87.53%, occurred in Models D and E with a cross-validation method for each class; (4) the highest accuracy, 92.46%, occurred in Model C from the use of decision tree learning-C4.5 with the percentage-split method and no time-lag for each class. This study concludes that its contribution is regarded as managerial implication and technical direction for practical finance in which a multicomponential discretization model has limited use and is rarely seen as applied by scientific industry data due to various restrictions.

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Testing the Profitability of Technical Trading Rules across Market Cycles: Evidence from India

Cited by 7 publications

References 11 publications

Performance Evaluation of Technical Analysis in the Nepalese Stock Market: Implications for Investment Strategies

Performance Evaluation of Technical Analysis in the Nepalese Stock Market: Implications for Investment Strategies

Untitled

Applied Identification of Industry Data Science Using an Advanced Multi-Componential Discretization Model

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