The aim of the study is to discover the validity of the Exponential Moving Average (EMA) for short term investments. Here we collected the closing prices data of HDFC Bank, ICICI Bank, Kotak Bank, State Bank of India and Punjab National Bank which was listed in National Stock Exchange (NSE) for the period of 1st April 2019 to 1st October 2019. Here we identified that the Exponential Moving Average (EMA) technical tool will able to generate the reliable and valid results which can make to choose right equity stock for short term investments. The study concludes that exponential Moving Average (EMA) will competent to generate the valid results but investor should not only depends Exponential Moving Average (EMA) and also verifies with other technical indicators
Table of Contents
1. Introduction
2. Exponential Moving Average
3. Research Methodology
4. Trading Strategies
4.1 Strategy 1
4.2 Strategy 2
4.3 Strategy 3
4.4 Strategy 4
5. Empirical Results
6. Findings
7. Conclusions
Research Objectives and Focus
This study aims to evaluate the validity and reliability of the Exponential Moving Average (EMA) as a technical analysis tool for making informed investment decisions regarding short-term equity stocks listed on the National Stock Exchange (NSE).
- Analysis of stock price movements using EMA-based trading strategies.
- Evaluation of five major Indian banking stocks: HDFC Bank, ICICI Bank, Kotak Bank, State Bank of India, and Punjab National Bank.
- Examination of the "golden cross" and "dead signal" phenomena in technical trading.
- Assessment of stock market performance for the period spanning April 2019 to October 2019.
- Formulation of practical guidelines for investors to mitigate risk in volatile markets.
Excerpt from the Book
Exponential Moving Average
The exponential moving average is a type of moving average that gives more weight to recent prices in an attempt to give the more responsive information. That helps investors to choose right investments and to make profitable returns. The following the equations of exponential moving average:
EMA=Price (t) × k + EMA(y) x (1−k)
Price (t) = current price
K (smoothing factor) = 2/ (n+1)
N = Number of time periods
EMA (y) = previous EMA
Initially while applying the formula to calculate the first point of the EMA, there is no value available to use as the previous EMA. To overcome this small problem can be solved by starting the calculation with a simple moving average and continuing on with the above formula from there.
Summary of Chapters
Introduction: This chapter highlights the inherent risks of stock market investments and introduces technical analysis, specifically the moving average, as a method to predict price movements.
Exponential Moving Average: This section defines the EMA, explains its responsiveness to recent price data, and provides the mathematical formula used for its calculation.
Research Methodology: This chapter outlines the data collection process, identifying the selection of top-performing stocks from the NSE between April and October 2019 for analysis.
Trading Strategies: This section details four specific strategies based on EMA crossovers, defining conditions for buying, selling, and identifying trend reversals.
Empirical Results: This chapter presents the comparative analysis of stock line data against 20-day and 50-day EMAs across various banking institutions.
Findings: This section synthesizes the results, confirming that EMA crossovers provide reliable signals for determining positive and negative market trends.
Conclusions: This chapter summarizes the study, advising investors to use EMA as a supportive tool alongside other indicators to navigate global economic uncertainties.
Keywords
Exponential Moving Average, Technical Analysis, Trading Strategy, Indian Stock Market, NSE, Equity Stocks, Short-term Investment, Market Trends, Golden Cross, Dead Signal, HDFC Bank, ICICI Bank, Financial Indicators, Risk Management, Investment Decision
Frequently Asked Questions
What is the primary focus of this study?
The study investigates the reliability of the Exponential Moving Average (EMA) as a technical analysis tool for predicting stock price movements for short-term investments in the Indian stock market.
Which financial institutions were analyzed?
The research examines five major Indian banking stocks: HDFC Bank, ICICI Bank, Kotak Bank, State Bank of India, and Punjab National Bank.
What is the core objective of the research?
The goal is to determine if EMA can effectively help investors generate valid and reliable signals to choose the right equity stocks for short-term profitability.
Which scientific method is utilized in this research?
The study employs empirical analysis of historical closing price data from the NSE, utilizing mathematical EMA formulas and comparative charting of 20-day and 50-day averages.
What topics are covered in the main section of the book?
The main sections cover the technical definitions of EMA, the methodology of data selection, four specific trading strategies based on EMA crossovers, and empirical performance results for the selected banks.
Which keywords characterize this work?
Key terms include Exponential Moving Average, Technical Analysis, Trading Strategy, Indian Stock Market, Market Trends, and Risk Management.
What is a 'golden cross' according to the text?
A golden cross occurs when a shorter EMA crosses above a longer EMA, serving as an indicator for a buy signal and the commencement of an uptrend.
How is a 'dead signal' defined?
A dead signal occurs when a shorter EMA crosses below a longer EMA, indicating a sell signal and the beginning of a downtrend.
Should an investor rely solely on EMA for decisions?
No, the author explicitly concludes that investors should not depend solely on EMA, but should verify findings with other indicators like the Relative Strength Index (RSI) and candle stick charts.
Why are fluctuations considered risky for ICICI Bank?
The study notes that despite positive trends, frequent quick price fluctuations in the ICICI Bank stock make it a high-risk asset for taking positions.
- Quote paper
- Viswanatha Reddy Pedirappagari (Author), 2019, Predicting the movements of equity stocks using exponential moving average. The sample of banking stocks in NSE, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/507070
