TECHNICAL ANALYSIS IN INDIAN STOCK
SHARDA SINGH KAUR
Literature Review DATE:-30TH March, 2023
1. Rewarding
The profitability of moving average based trading rules in the Indian stock market using four stock index series. The study finds that most technical trading rules are able to capture the direction of market movements reasonably well and give significant positive returns both in long and short positions. But these returns cannot be exploited fully due to real world transaction costs. Although the transaction cost has come down over the years, various components of transaction costs due to the bid-ask spread, brokerage, etc. will never be zero. The trading rules based on short term moving averages may be able to detect trends in financial series very quickly, but those rules also generate a large number of trades causing higher transaction costs. Thus technical traders have to pay more attention to minimizing transaction costs while choosing a trading rule. Nevertheless, profit opportunities from technical analysis continue to remain an interesting and debatable issue in the Indian stock market. (Subrata Kumar Mitra, 2011).
2. Analyzes
This paper empirically estimates and analyzes various efficiency scores of Indian banks during 1997-2003 using data envelopment analysis (DEA). During the 1990s India’s financial sector underwent a process of gradual liberalization aimed at strengthening and improving the operational efficiency of the financial system. It is observed, none the less, that Indian banks are still not much differentiated in terms of input or output oriented technical efficiency and cost efficiency. However, they differ sharply in respect of revenue and profit efficiencies. The results provide interesting insight into the empirical correlates of efficiency scores of Indian banks. Bank size, ownership, and the fact of its being listed on the stock exchange are some of the factors that are found to have positive impact on the average profit efficiency and to some extent revenue efficiency scores are. Finally, we observe that the median efficiency scores of Indian banks in general and of bigger banks in particular have improved considerably during the post-reform period. (Abhiman et al (2004).
3. Challenges
The most significant challenges in the stock market is to predict the stock prices. The stock price data represents a financial time series data which becomes more difficult to predict due to its characteristics and dynamic nature. Case description Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are widely used for prediction of stock prices and its movements. Every algorithm has its way of learning patterns and then predicting. Artificial Neural Network (ANN) is a popular method which also incorporate technical analysis for making predictions in financial markets. Discussion and evaluation Most common techniques used in the forecasting of financial time series are Support Vector Machine (SVM), Support Vector Regression (SVR) and Back Propagation Neural Network (BPNN). In this article, we use neural networks based on three different learning algorithms, i.e., Levenberg-Marquardt, Scaled Conjugate Gradient and Bayesian Regularization for stock market prediction based on tick data as well as 15-min data of an Indian company and their results compared. Conclusion All three algorithms provide an accuracy of 99.9% using tick data. The accuracy over 15-min dataset drops to 96.2%, 97.0% and 98.9% for LM, SCG and Bayesian Regularization respectively which is significantly poor in comparison with that of results obtained using tick data.( Dharmaraja et al 2019).
4. Growth
This paper assesses the total factor productivity (TFP) growth and efficiency levels in the Indian dairy processing industry using the Tornqvist index and data envelopment analysis (DEA) models over the period 1980-2008. We utilize a different empirical approach and extend the data sets. To examine the nature of scale inefficiency, nonincreasing returns to scale DEA frontier is used. Our results suggest that total factor productivity in the Indian dairy processing industry has grown significantly. An average technical efficiency level of 72% which implies approximately a 38% inefficiency level is observed from the study. The decomposition of TFP growth indicates that growth is driven more by technical efficiency changes than by scale efficiency. Highest input slacks are observed for working capital. We note that a devaluation in terms of real effective exchange rate, profitability, export and import penetration and research stock play a significant role in explaining the productivity growth in the Indian dairy industry. The non-increasing returns to scale DEA frontier analysis suggests that on an average scale inefficiency is due to increasing returns to scale. Finally, it is noticed that in India, a high volume of milk does not reach to milk processing plants. It is suggested that for efficient utilization of existing processing capacity in dairy plants, a systematic investment is needed in logistics of raw milk collection and infrastructure development. The European model may be used as a benchmark in strengthening milk farmers for increasing farm size and building own processing capacity. (Ohlan, Ramphul, 2013).
5. Technical Indicators
The study evaluates the economic feasibility of technical analysis in the Indian stock market. It discusses that technical indicators do not outperform Simple Buy and Hold strategy on net return basis for individual stocks. Technical indicators seem to do better during market upturns compared to market downturns. However, technical based trading strategies are not feasible vis-Ã -vis passive strategy irrespective of market cycle conditions. Technical indicators also do not provide economically significant profit for industry as well as economy based data. Combining fundamentals with technical information, we find, that technical indicators are more profitable for small stocks compared to big stocks and for high value stocks compared to low value stocks. However, the economic feasibility of fundamentals’ based technical strategies is still questionable. Our results seem to confirm with the efficient market hypothesis. (Sanjay Sehgal & Meenakshi Gupta, 2007)
6. Government Restrictions
Over the last decade, numerous factors including robust economic growth, population pressure, and the mounting need for office space among growth sectors such as information technology have placed significant upward pressure on Indian realty prices. The easing of government restrictions on foreign investments and venture capital into Indian real estate have provided an additional fillip to the real estate market in the country, and the confluence of such factors appears to have contributed to a speculative bubble in Indian real estate equities in the latter part of the decade. By using this bubble period as a case study, we test for the existence of long memory among real estate equities. For the January 2006-December 2008 period, we employ three self-affine fractal analysis techniques (classical rescaled range, roughness-length, and the variogram/structure function methods) to estimate the Hurst exponent, and find significant evidence of long memory in the Bombay Stock Exchange (BSE) Realty Index. Return persistence is further confirmed by the more powerful Lo¡¦s modified rescaled range analysis (MRSA), which is robust to short-term dependence. In addition to potential regulatory policy implications for this emerging market, our results have ramifications for modeling and forecasting returns, as well as for technical trading rules. (Sanjay, Hays, 2012).
7. Forecasting
The rapid development and evolution of sophisticated algorithms for statistical analysis of time series data, the research community has started spending considerable effort in technical analysis of such data. Forecasting is also an area which has witnessed a paradigm shift in its approach. In this work, we have used the time series of the index values of the Auto sector in India during January 2010 to December 2015 for a deeper understanding of the behavior of its three constituent components, e.g., the Trend, the Seasonal component, and the Random component. Based on this structural analysis, we have also designed three approaches for forecasting and also computed their accuracy in prediction using suitably chosen training and test data sets. The results clearly demonstrate the accuracy of our decomposition results and efficiency of our forecasting techniques, even in presence of a dominant Random component in the time series. (Jaydip Sen & Tamal Datta Chaudhuri, 2016).
8. Indicator
Moving Average Convergence Divergence (MACD) indicator is the most widely used technical analysis tool to identify trading signals based on the trends in security prices. On testing some of the standard and refined MACD indicators on the movements of BSE Sensex and NSE Nifty during 1997-2010, it was found that the returns of refined MACD indicators outperformed the benchmark market returns. Thus, the findings of this paper challenge the Efficient Market Hypothesis, which rules out the possibility of earning excess returns.
(V Subramanian & K P Balakrishnan, 2014).
9. Future Trends
The stock market patterns are non-linear in nature therefore it is difficult to forecast the future trends of the market. In this paper we have used different macro-economic factors of Indian stock market. Macro-economic factors include technical indicators. These technical indicators help to decide the patterns of the market at a particular time. There are hundreds of technical indicators are available, but all technical indicators are not useful. So we have obtained most effective technical indicators by applying Principal Component Analysis (PCA). Selected technical indicators are taken as input variable. Future prices are found through Hidden Markov Model (HMM). Hidden Markov Model is a very powerful stochastic model. In literature survey it was found that HMM gives better accuracy than other models. On the basis of experiment it was found that HMM with PCA performed well and gives Mean Absolute Percentage Error (MAPE) 1.77%. (Jyoti Badge, 2012).
10. Different Models
This paper analyses the performance of non-banking finance companies (NBFC) in the Indian context using data envelopment analysis (DEA). The underlying objective of this study is to fill the void in the domain of NBFC, although a lot of research has been done on the banking industry in the context of the application of DEA, but none on NBFCs. The paper takes the panel data of the last 5 years (2014–2018) to calculate super-efficiencies in the first stage and then regresses the same on exogenous factors in stage-2. Descriptive statistics are used to estimate the efficiency by carrying out the calculations using both the traditional models (OTE, PTE and SE) and super-efficiency model. A comparison is made by categorizing NBFC’s based on the size of total assets and using non-parametric statistic tests to find whether the efficiency scores are significantly different across different categories. The second stage DEA analysis uses Tobit regression to find the exogenous factors which affect the model significantly. Based on traditional models, the total number of efficient DMUs are 8 out of 43 while there are 15 after considering the super-efficiency algorithm. Malmquist Indices are used to study the productivity indices of NBFCs over the last 5 years, and it gives us a maximum productivity growth of 8.53%. It was noticed that there is a significant difference in the mean efficiency values of different sized NBFC’s which can be explained by the lack of standardization in the NBFC domain and the few companies which are listed on the stock market. The managers should not consider ROE as a significant indicator of efficiency and should instead focus on aspects such as ROA and income diversity. Through the Malmquist analysis, the managers can break down the productivity change into technical and efficiency shifts for further investigation.( Pankaj Dutta & Aayush Jain & Asish Gupta, 2020).
11. Conclusion:-
Technical analysis can conclude after above literature review that technical analysis is a popular approach used by traders and investors in the Indian stock market to make informed decisions regarding their investment portfolio. It involves analyzing past market data, mainly price and volume, to identify patterns and trends that may help predict future market movements.
Some key conclusions drawn from the use of technical analysis in the Indian stock market include:
a) Technical analysis can provide useful insights into short-term market movements: Technical analysis is most effective in predicting short-term market movements. By analyzing price and volume patterns over short time frames, technical analysts can identify potential buying and selling opportunities in the market.
b) Technical analysis cannot predict all market movements: While technical analysis can be a useful tool for predicting short-term market movements, it is not foolproof. It cannot predict all market movements, and traders and investors should use other forms of analysis, such as fundamental analysis, to supplement their technical analysis.
c) Technical analysis requires careful interpretation: Technical analysis can be complex, and requires careful interpretation of data to avoid making incorrect investment decisions. It is important for traders and investors to have a solid understanding of technical analysis concepts and techniques before relying on them for investment decisions.
d) Technical analysis can be combined with other forms of analysis: Technical analysis is often used in combination with other forms of analysis, such as fundamental analysis, to gain a more complete understanding of the market. By using a variety of analytical approaches, traders and investors can reduce their risk and make more informed investment decisions.
In conclusion, technical analysis is a valuable tool for traders and investors in the Indian stock market. While it has its limitations, it can provide useful insights into short-term market movements and help investors make informed investment decisions.
References:-
1) Abhiman Das & Ashok Nag & Subhash Ray, 2004. “Liberalization, Ownership, and Efficiency in Indian Banking: A Nonparametric Approach,” Working papers 2004-29, University of Connecticut, Department of Economics.
2) Dharmaraja Selvamuthu & Vineet Kumar & Abhishek Mishra, 2019. “Indian stock market prediction using artificial neural networks on tick data,” Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-12, December.
3) Jaydip Sen & Tamal Datta Chaudhuri, 2016. “Decomposition of Time Series Data of Stock Markets and its Implications for Prediction: An Application for the Indian Auto Sector,” Papers 1601.02407, arXiv.org.
4) Jyoti Badge, 2012. “Forecasting of Indian Stock Market by Effective Macro- Economic Factors and Stochastic Model,” Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 1(2), pages 1-4.
5) Ohlan, Ramphul, 2013. “Efficiency and Total Factor Productivity Growth in Indian Dairy Sector,” Quarterly Journal of International Agriculture, Humboldt-Universitaat zu Berlin, vol. 52(1), pages 1-27, February
6) Pankaj Dutta & Aayush Jain & Asish Gupta, 2020. “Performance analysis of non-banking finance companies using two-stage data envelopment analysis,” Annals of Operations Research, Springer, vol. 295(1), pages 91-116, December.
7) Sanjay Rajagopal & Patrick Hays, 2012. “Return Persistence in the Indian Real Estate Market,” International Real Estate Review, Global Social Science Institute, vol. 15(3), pages 283-305.
8) Sanjay Sehgal & Meenakshi Gupta, 2007. “Tests of Technical Analysis in India,” Vision, , vol. 11(3), pages 11-23, July.
9) Subrata Kumar Mitra, 2011. “How rewarding is technical analysis in the Indian stock market?,” Quantitative Finance, Taylor & Francis Journals, vol. 11(2), pages 287-297.
10) V Subramanian & K P Balakrishnan, 2014. “Efficacy of Refined Macd Indicators: Evidence from Indian Stock Markets,” The IUP Journal of Applied Finance, IUP Publications, vol. 20(1), pages 76-91, January.