scholarly journals A Trading Strategy for the Indian Stock Market: Analysis and Implications

2000 ◽  
Vol 25 (4) ◽  
pp. 27-38 ◽  
Author(s):  
Madhusudan Karmakar ◽  
Madhumita Chakraborty
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Stock Market ◽  
Trading Strategy ◽  
Market Analysis ◽  
Trading Strategies ◽  
Daily Return ◽  
The Mean ◽  
Indian Stock Market

A curious seasonality reported in finance is the monthly effect which implies that the mean daily return for stock is positive and higher during the first half of the month than the second half. Another related anomaly is the turn-of-the-month effect which is said to exist when the average daily return at the turn of the month is significantly higher than the daily return on the remain ing days of the month. This paper examines both the monthly effect and the turn-of-themonth effect in the Indian stock market by applying two different approaches: calendar day approach and trading day approach. The results of both the approaches reveal significantly higher return at the first half of the month than that of the second half and abnormally high returns at the turn of the month. Various explanations for the ob served anomalies have been considered including the problem of ‘data mining,’ proxy of other anomalies, etc., but none could provide adequate explanations for the observed intra-month return regularities. However, based on the findings, the study tries to evolve certain trading strategies which would benefit in the decision making of the investors concerned with timing of stock purchases and sales.

A Data Warehouse Based Modelling Technique for Stock Market Analysis

2018 ◽  
Vol 7 (3.13) ◽  
pp. 165
Author(s):  
Debomita Mondal ◽  
Giridhar Maji ◽  
Takaaki Goto ◽  
Narayan C. Debnath ◽  
Soumya Sen
Keyword(s):  
Decision Making ◽  
Stock Market ◽  
Data Warehouse ◽  
Analytical Model ◽  
Market Performance ◽  
Analytical Framework ◽  
Market Analysis ◽  
External Factors ◽  
Stock Market Performance ◽  
Indian Stock Market

The objective of this paper is identifying a warehouse model to build an analytical framework and analyze different important parameters which directly impact the changes of share market. We identify parameters that represent different viewing windows and perspectives towards stock market performance and movement trends. We categorize and define many intrinsic as well as external factors that may affect stock market as a whole. Sensex and Nifty are used as the pulse of Indian stock market. In this paper, we focus on defining a suitable OLAP model which can cater all the parameters that affect share market. We also identify different applications of this analytical model for forecasting information to help decision making.  

A Data Warehouse Based Modelling Technique for Stock Market Analysis

2018 ◽  
Vol 7 (3.13) ◽  
pp. 165
Author(s):  
Debomita Mondal ◽  
Giridhar Maji ◽  
Takaaki Goto ◽  
Narayan C. Debnath ◽  
Soumya Sen
Keyword(s):  
Decision Making ◽  
Stock Market ◽  
Data Warehouse ◽  
Analytical Model ◽  
Market Performance ◽  
Analytical Framework ◽  
Market Analysis ◽  
External Factors ◽  
Stock Market Performance ◽  
Indian Stock Market

The objective of this paper is identifying a warehouse model to build an analytical framework and analyze different important parameters which directly impact the changes of share market. We identify parameters that represent different viewing windows and perspectives towards stock market performance and movement trends. We categorize and define many intrinsic as well as external factors that may affect stock market as a whole. Sensex and Nifty are used as the pulse of Indian stock market. In this paper, we focus on defining a suitable OLAP model which can cater all the parameters that affect share market. We also identify different applications of this analytical model for forecasting information to help decision making.  

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 Risks of Traders on the World Stock Market on the Example of Ukraine

2019 ◽  
Vol 67 ◽  
pp. 06001 ◽  
Author(s):  
George Abuselidze ◽  
Olga Mohylevska ◽  
Nina Merezhko ◽  
Nadiia Reznik ◽  
Anna Slobodianyk
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
World Market ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Practical Significance ◽  
External Shocks ◽  
Domino Effect ◽  
The World ◽  
The Impact

The article reveals the essence and features of the development of the stock market in Ukraine. It was established that the vigorous activity of countries in the world financial markets means that they also face a risk of global financial turmoil (the so-called “domino effect”). It is determined that the impact of global financial instability on the country depends on the openness of its economy that will lead to significant external “shocks”. The possibility of providing effective influence on domestic stock market activity with taking into account the changing world situation, development of perfect trading strategies for each participant is substantiated. The conducted analysis of the world market conditions of stock markets in recent years has made it possible to assess the real risks for new participants in the stock market and become the basis for the development of an appropriate effective trading strategy. The practical significance of the results is that they allow for a measurable approach to assessing the existing risk when choosing one or another trading strategy to move to the world stock market.

scholarly journals Optimizing the Pairs-Trading Strategy Using Deep Reinforcement Learning with Trading and Stop-Loss Boundaries

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Taewook Kim ◽  
Ha Young Kim
Keyword(s):  
Reinforcement Learning ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Cointegration Test ◽  
Optimum Level ◽  
Pairs Trading ◽  
Proposed Model ◽  
The Mean ◽  
Stop Loss ◽  
The Given

Many researchers have tried to optimize pairs trading as the numbers of opportunities for arbitrage profit have gradually decreased. Pairs trading is a market-neutral strategy; it profits if the given condition is satisfied within a given trading window, and if not, there is a risk of loss. In this study, we propose an optimized pairs-trading strategy using deep reinforcement learning—particularly with the deep Q-network—utilizing various trading and stop-loss boundaries. More specifically, if spreads hit trading thresholds and reverse to the mean, the agent receives a positive reward. However, if spreads hit stop-loss thresholds or fail to reverse to the mean after hitting the trading thresholds, the agent receives a negative reward. The agent is trained to select the optimum level of discretized trading and stop-loss boundaries given a spread to maximize the expected sum of discounted future profits. Pairs are selected from stocks on the S&P 500 Index using a cointegration test. We compared our proposed method with traditional pairs-trading strategies which use constant trading and stop-loss boundaries. We find that our proposed model is trained well and outperforms traditional pairs-trading strategies.

scholarly journals RISK-RETURN TRADEOFFS IN INDIAN STOCK MARKET: EVIDENCE FROM GARCH MODEL APPROACH

2021 ◽  
pp. 556-566
Author(s):  
Riteshbhai Patel
Keyword(s):  
High Risk ◽  
Stock Market ◽  
Garch Model ◽  
Sample Period ◽  
Asymmetric Volatility ◽  
Empirical Results ◽  
Daily Return ◽  
Risk Return ◽  
Indian Stock Market ◽  
Model Approach

The objective is to examine the risk-return tradeoff in the Indian stock market. The sample period of study is from January 4, 2000 to December 31, 2020. The empirical results shows existence of risk-return tradeoff in the BSE. A positive risk-return tradeoff is found for monthly & annual return series. The market has weak risk-return relationship in daily return series. The CGARCH (1,1) captures the asymmetric volatility effect for all the different frequency based returns. The study has implications for the investors. The riskreturn relationship is stronger and significant in longer duration of investment. The market gives higher return when there is a high risk.

scholarly journals Understanding the Investors’ Investment Decisions through Some Data Mining Tools- A Study in Dhaka Stock Exchange (DSE)

2018 ◽  
pp. 9-19
Author(s):  
Md. Khashrul Alam ◽  
S. M. Towhidur Rahman ◽  
Afifa Khanom
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Stock Market ◽  
Stock Exchange ◽  
Investment Decision ◽  
Scientific Data ◽  
Wrong Decision ◽  
Secondary Sources ◽  
Money Flow ◽  
Mining Tools

Purpose: Decision making is the process of choosing a particular alternative from a number of alternatives. Decision making is very much important in investment in the stock market. As it is enormously sensitive, a wrong decision may put the investor back to the street. Modern scientific data mining tools can play important role in making investment decision in the stock market. The purpose of the study is to find out the effectiveness of investors’ decision in buying and selling stock and the efficiency of some data mining tools in aiding investor’s decision. Methodology: This paper used several data mining techniques such as beta, Chaikin money flow indicator (CMI) and Bollinger band to analyze investors’ decision in buying and selling stocks. Data for the study were taken both from primary and secondary sources specially, from website of Dhaka Stock Exchange. Findings: The result shows that in most cases majority of investors failed to take right decision in right time in terms of the estimation derived from data mining tools used in the study. It was also found that Bollinger band was found to be more efficient than CMI in making prediction.

scholarly journals Impact of Lockdown in the FMCG sector of the Indian Stock Market – Analysis using Statistical Methods

2021 ◽  
Vol 12 (1S) ◽  
pp. 647-654
Author(s):  
M. Vivek Prabu, Et. al.
Keyword(s):  
Stock Market ◽  
Statistical Methods ◽  
Global Economy ◽  
Market Analysis ◽  
Negative Trend ◽  
High Demand ◽  
Statistical Tools ◽  
Indian Economy ◽  
Indian Stock Market ◽  
The Impact

The Covid19 outbreak has shattered the Global economy and Indian economy too had got no exemption from it. Despite the GDP of India moving in the negative trend, very few sectors like Pharmaceutical and FMCG have shown some positive signs because of this pandemic and the lockdown followed by it. Consumer staples will always remain essential irrespective of the economical movement. In particular, during the tougher times, whenever there arises an unprecedented scenario, the humankind will always try to safeguard itself and in turn that will certainly cause a high demand in the FMCG sector. In this paper, we will be analysing the impact of lockdown in the movement of the FMCG sector using some of the Statistical tools

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