Tests of Technical Analysis in India

2007 ◽  
Vol 11 (3) ◽  
pp. 11-23 ◽  
Author(s):  
Sanjay Sehgal ◽  
Meenakshi Gupta
Keyword(s):  
Stock Market ◽  
Technical Information ◽  
Technical Analysis ◽  
Efficient Market Hypothesis ◽  
Economic Feasibility ◽  
Trading Strategies ◽  
Technical Indicators ◽  
Value Stocks ◽  
Indian Stock Market ◽  
Passive Strategy

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-à-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.

Relationship between Foreign Portfolio Investments (FPI), Domestic Institutional Investors, and Stock Market Returns in India

GIS Business ◽  
2017 ◽  
Vol 12 (6) ◽  
pp. 1-9
Author(s):  
Dhananjaya Kadanda ◽  
Krishna Raj
Keyword(s):  
Stock Market ◽  
Institutional Investors ◽  
Negative Relationship ◽  
Trading Strategies ◽  
Market Returns ◽  
Stock Market Returns ◽  
Foreign Portfolio Investment ◽  
Stock Market Return ◽  
Indian Stock Market ◽  
Feedback Trading

The present article attempts to understand the relationship between foreign portfolio investment (FPI), domestic institutional investors (DIIs), and stock market returns in India using high frequency data. The study analyses the trading strategies of FPIs, DIIs and its impact on the stock market return. We found that the trading strategies of FIIs and DIIs differ in Indian stock market. While FIIs follow positive feedback trading strategy, DIIs pursue the strategy of negative feedback trading which was more pronounced during the crisis. Further, there is negative relationship between FPI flows and DII flows. The results indicate the importance of developing strong domestic institutional investors to counteract the destabilising nature FIIs, particularly during turbulent times.

Institutional Herding: Causality and Persistence

2021 ◽  
pp. 227797522110402
Author(s):  
S S S Kumar
Keyword(s):  
Stock Market ◽  
Market Efficiency ◽  
Mutual Funds ◽  
Institutional Investors ◽  
International Markets ◽  
Jel Classification ◽  
Trading Strategies ◽  
Trading Activity ◽  
Indian Stock Market ◽  
Institutional Herding

We investigate the causality in herding between foreign portfolio investors (FPIs) and domestic mutual funds (MFs) in the Indian stock market. The estimated herding levels are considerably higher than those observed in other international markets, and herding is prevalent in small stocks. We find that institutional investors follow contrarian-trading strategies, unlike what was documented in most other markets. Analysis of the aggregate herding measure shows a bi-directional causality between FPIs and MFs. Further analysis using directional herding measures indicate no evidence of causality between institutional herds on the sell-side. But we find causality on the buy-side and it is running in both directions between FPIs and MFs, implying a feedback of information. Given the tendency of institutions for herding in small stocks, adopting contrarian-trading strategies, the observed sell-side causality is perhaps having a salubrious effect. As institutional investors are contrarians, their trading activity will lead to price corrections in small stocks aligning with the fundamentals, thereby contributing to market efficiency. JEL Classification: C23, C58, G23, G15, G40

scholarly journals Value and growth stocks in Brazil: risks and returns for one - and two-dimensional portfolios under different economic conditions

2011 ◽  
Vol 22 (56) ◽  
pp. 189-202 ◽  
Author(s):  
Leandro da Rocha Santos ◽  
Roberto Marcos da Silva Montezano
Keyword(s):  
Risk Analysis ◽  
Stock Market ◽  
Stock Prices ◽  
Risk Measure ◽  
Academic Research ◽  
Efficient Market Hypothesis ◽  
Economic Conditions ◽  
High Price ◽  
Value Stocks ◽  
Growth Stocks

For empirical purposes, value stocks are usually defined as those traded at low price-to-earnings ratios (stock prices divided by earnings per share), low price-to-book ratios (stock prices divided by book value per share) or high dividend yields (dividends per share divided by stock prices). Growth stocks, on the other hand, are traded at high price-to-earnings ratios, high price-to-book ratios or low dividend yields. Academic research so far produced, international and Brazilian alike, shows that value stocks outperform growth stocks, challenging the Efficient Market Hypothesis, which states that the market prices of traded stocks are the best estimate of their intrinsic values. Most studies use a single ratio to sort stocks on percentiles; risks (generally defined as beta or standard deviations) and returns are then calculated for the resulting value and growth portfolios. In the present paper, we aim to further contribute to the growing literature on the field by applying a method not previously tested on the Brazilian market. We build portfolios sorted by the price-to-earnings and price-to-book ratios alone and by a combination of both in order to assess value and growth stocks' risks and returns on the Brazilian stock market between 1989 and 2009. Furthermore, our risk analysis may be regarded as the paper's main contribution, since its approach departs from conventional risk concepts, as we not only test for beta: portfolios' returns are measured under different economic conditions. Results support a pervasive value premium in the Brazilian stock market. Risk analysis shows that this premium holds under every economic condition analyzed, suggesting that value stocks are indeed less risky. Beta proved not to be a satisfactory risk measure. Portfolios sorted by the price-to-earnings ratio yielded the best results.

scholarly journals Sequel outcome of automobile sector on account of covid – 19 through technical analysis

2021 ◽  
Vol 8 (2) ◽  
pp. 39-43
Author(s):  
Vivek Prabu M ◽  
Dharani K S
Keyword(s):  
Economic Development ◽  
Stock Market ◽  
Free Fall ◽  
Technical Analysis ◽  
Domestic Demand ◽  
Automotive Sector ◽  
Market Growth ◽  
Travel Restrictions ◽  
Private Transportation ◽  
Indian Stock Market

The COVID – 19 pandemic has deteriorated multiple facets of the stable functioning of economies of most countries. Social restrictions associated with the immediate response to the pandemic has curtailed dynamic functioning of many industries that buttress the economic development of countries. Performance of automotive industries was expected to nosedive following the travel restrictions. One of the major sources of profit for the automotive industries in India is their consumer base in countries like U. K, Germany, and China etc. Severity of the pandemic in these countries entailed trade regulations that propelled a negative trend in the market growth of Indian automotive industries. But the economy of automotive sector of India was saved from a free fall by the countering effect of the domestic demand in private transportation. This paper presents the technical analysis on the Maruti Suzuki Private Limited to measure the stock movement of the Automobile sector in the Indian Stock Market.

Stock Market Prediction Using Machine Learning

2020 ◽  
Vol 17 (4) ◽  
pp. 1584-1589
Author(s):  
J. Shiva Nandhini ◽  
Chitrak Bari ◽  
Gareja Pradip
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Learning Algorithm ◽  
Market Price ◽  
Technical Analysis ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Machine Learning Techniques ◽  
Fundamental Analysis ◽  
Stock Market Prediction

The basic tool aimed at increasing the rate of investor’s interest in stock markets is by developing a vibrant application for analyzing and predicting stock market prices. In this report we explain, the development and implementation of a stock market price prediction application using machine learning algorithm. In this report, we try to analyze existing and new methods of stock market prediction. We take three different approaches for solving the problem: Fundamental analysis, Technical Analysis and The application of Machine Learning. We found evidence in support of the weak form of the Efficient Market Hypothesis. We can use Fundamental Analysis and Machine Learning to guide an investor’s decisions. We demonstrate a common flaw in Technical Analysis methodology to show that it produces limited useful information. Based on our findings, algorithmic trading programs are developed and simulated using Quant. During the past few decades, various machine learning techniques have been applied to study the highly theoretical and speculative nature of stock market by capturing and using repetitive patterns. Different companies use different types of analysis tools for forecasting and the main aim is the accuracy, with which they predict which set of stocks would yield the maximum amount of profit.

scholarly journals Stock Price Prediction using Technical Indicators: A Predictive Model using Optimal Deep Learning

2019 ◽  
Vol 8 (2) ◽  
pp. 2297-2305
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Stock Price ◽  
Short Term Memory ◽  
Technical Analysis ◽  
Learning Task ◽  
Correlation Tensor ◽  
Technical Indicators ◽  
Stock Price Prediction ◽  
Proposed Model

The stock market is highly volatile and complex in nature. Technical analysts often apply Technical Analysis (TA) on historical price data, which is an exhaustive task and might produce incorrect predictions. The machine learning coupled with fundamental and / or Technical Analysis also yields satisfactory results for stock market prediction. In this work an effort is made to predict the price and price trend of stocks by applying optimal Long Short Term Memory (O-LSTM) deep learning and adaptive Stock Technical Indicators (STIs). We also evaluated the model for taking buy-sell decision at the end of day. To optimize the deep learning task we utilized the concept of Correlation-Tensor built with appropriate STIs. The tensor with adaptive indicators is passed to the model for better and accurate prediction. The results are analyzed using popular metrics and compared with two benchmark ML classifiers and a recent classifier based on deep learning. The mean prediction accuracy achieved using proposed model is 59.25%, over number of stocks, which is much higher than benchmark approaches.

Predicting Stock Market Price Using Neural Network Model

2018 ◽  
Vol 9 (3) ◽  
pp. 84-94 ◽  
Author(s):  
Naliniprava Tripathy
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Network Model ◽  
Neural Network Model ◽  
Market Price ◽  
Efficient Market Hypothesis ◽  
Feedforward Neural Network ◽  
Neural Network Models ◽  
Daily Movement ◽  
Indian Stock Market

The present article predicts the movement of daily Indian stock market (S&P CNX Nifty) price by using Feedforward Neural Network Model over a period of eight years from January 1st 2008 to April 8th 2016. The prediction accuracy of the model is accessed by normalized mean square error (NMSE) and sign correctness percentage (SCP) measure. The study indicates that the predicted output is very close to actual data since the normalized error of one-day lag is 0.02. The analysis further shows that 60 percent accuracy found in the prediction of the direction of daily movement of Indian stock market price after the financial crises period 2008. The study indicates that the predictive power of the feedforward neural network models reasonably influenced by one-day lag stock market price. Hence, the validity of an efficient market hypothesis does not hold in practice in the Indian stock market. This article is quite useful to the investors, professional traders and regulators for understanding the effectiveness of Indian stock market to take appropriate investment decision in the stock market.

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