AUTHOR:- MANAV S. PAWAR , ROLL NO. :-44 , DIV:-A , KBS
The study examines the co-movement of the Pakistan Stock Exchange (KSE-100) with the Indian (BSE 30), U.S. (S&P 500), and Japanese (Nikkei 225) stock markets from 1998 to 2013 using wavelet analysis. It finds a strong correlation between Pakistan and India, with Pakistan’s market generally lagging behind. While Pakistan has a moderate correlation with the U.S. and weak negative correlation with Japan, its long-run relationship with these developed markets is stronger. The 2008–09 financial crisis increased market integration, highlighting the impact of global shocks. The findings suggest that regional investors have limited diversification opportunities, while long-term investors should consider co-movement trends. The study’s insights are valuable for portfolio optimization and policymaking in emerging markets.
The study explores stock market networks and portfolio selection for intraday traders using high-frequency data from the Indian stock market (NSE) in 2014. It compares correlation-based and mutual information-based methods to identify stock relationships, revealing that mutual information captures both linear and non-linear dependencies more effectively. The study constructs minimum spanning trees (MSTs) to analyze market structure, particularly during India’s general elections, showing that market co-movement increases during major events. A key practical application is portfolio selection: peripheral stocks in mutual information-based networks provide better diversification and returns for intraday traders than those in correlation-based models. The findings suggest mutual information is a superior tool for short-term trading strategies and market risk assessment
The study investigates the causal relationship between Foreign Institutional Investors (FIIs) and the Indian stock market (BSE National Index) from 1992 to 2010 using the Granger Causality Test. It finds a bidirectional relationship, indicating that FII flows influence stock market movements, and vice versa. The research also highlights that FIIs react to market trends, often withdrawing during downturns, contributing to volatility. Using variance decomposition and impulse response functions, the study confirms that BSE movements have a stronger impact on FII flows than the reverse. The findings suggest that FIIs play a significant role in India’s financial markets, but their behavior is influenced by domestic stock trends.
The study examines optimal stock market returns in India during the COVID-19 pandemic by analyzing a portfolio composed of five sectors—Pharmaceuticals, Petroleum, Banking, Software (IT), and Metal—using historical stock data and regression models. The research extends a previous study by validating the portfolio’s post-pandemic performance (January–October 2021). Findings show that the portfolio significantly outperformed major indices (NIFTY 50, SENSEX) and mutual funds, achieving an absolute return of 48.18%. A paired t-test comparing 2019 and 2021 results confirms that applying the stock selection methodology enhanced returns. The study highlights sector-wise fund allocation and risk management strategies as key factors in achieving higher returns during market uncertainty.
The study proposes an algorithm for constructing an optimal stock portfolio using Treynor’s ratio, which measures excess return relative to market risk. The method selects stocks from an index (e.g., S&P CNX 500) based on historical data and assigns investment weights without allowing short selling. The algorithm ranks stocks by Treynor’s ratio, determines a cut-off rate (C)*, and includes only stocks exceeding this threshold. Applied to Indian market data (2010–2012), the optimized portfolio achieved a 67.22% annual return with strong risk-adjusted performance. The findings suggest that Treynor’s ratio is an effective tool for portfolio optimization, offering superior returns while managing risk.
The study examines the causal relationship between macroeconomic variables and Indian stock prices (NIFTY 200) from 2010 to 2020 using the ARDL model. It finds that in the long run, macroeconomic factors (inflation, money supply, interest rates, exchange rates, and foreign institutional investments) have an insignificant impact on stock prices. However, in the short run, inflation and foreign portfolio investments positively influence stock prices, while exchange rates have a negative impact. The findings suggest that policy measures should focus on stabilizing inflation and foreign investments to support the stock market, while investors should consider these short-term influences in their decision-making.
The study presents a hybrid portfolio selection model for the Indian stock market using a multi-criteria approach that integrates fundamental, technical, and risk-based factors to optimize portfolio performance. By applying machine learning techniques and financial indicators, the model identifies high-performing stocks and allocates assets dynamically to maximize returns while managing risk. Empirical analysis on NIFTY-listed stocks demonstrates that the proposed approach outperforms traditional portfolio selection methods in terms of risk-adjusted returns. The findings suggest that a hybrid strategy enhances investment decision-making, offering improved diversification and profitability for market participants.
The study analyzes value and contrarian investment strategies in the Indian stock market (BSE) from 1990 to 2019, investigating whether these strategies select the same stocks and how they perform in different market efficiency forms. The findings reveal that value and contrarian strategies largely pick different stocks, despite both focusing on underpriced or distressed assets. However, their returns are positively correlated, suggesting that both strategies can function within the same market efficiency framework. This challenges conventional beliefs that contrarian strategies work best in semi-strong markets while value strategies thrive in weak-form markets. The study provides valuable insights for investors, questioning the efficiency of the Indian stock market and emphasizing the importance of behavioral and fundamental factors in stock selection.
The study examines the market efficiency of the Indian stock market by analyzing nine Bombay Stock Exchange (BSE) broad market indices from 2011 to 2020 using statistical tests like unit root tests, autocorrelation, and runs tests. The findings reveal that BSE indices do not follow a random walk, indicating that the Indian stock market is weak-form inefficient. This suggests that past stock price movements can help predict future prices, allowing investors to potentially achieve abnormal returns. The study highlights implications for investment strategies, financial literacy, and policy-making, while also recommending further research on investor behavior and market anomalies.
The prime objective of this research is to examine the impact of the selected macroeconomic variables on S&P BSE SME IPO index. Correlation, Multiple regression and Granger casualty tests have been used to estimate the relationship and impact. The variables selected for this study is average monthly closing price of S&P BSE SME IPO and macroeconomic indicators like; Index of Industrial production (IIP), Gross Domestic Product (GDP), Interest Rate (IR), Foreign Direct Investment (FDI), Inflation Rate (IF), Exchange Rate (ER), and Crude oil Price (CP). An average monthly data over a period of three years are taken, starting from January 2013 to December 2015. The result shows that, Interest rate and Inflation rate have a significant positive impact on stock market.
Conclusion
The published research provides in-depth analysis on stock markets, investment practices, and macro factors affecting stock markets, especially in emerging markets like India and Pakistan. A few common findings are:
1. Stock Market Co-Movement and Integration
The KSE-100 is strongly correlated with India’s BSE 30, but with a lag, moderately integrated with the U. S., and weakly negatively correlated with Japan.
Global financial crises, such as the 2008–09 crisis, deepen market integration and reduce the scope for regional diversification.
2. Market Structure and Portfolio Selection
Mutual information-based network analysis offers superior information to intraday traders compared to correlation-based methods, allowing better diversification.
Applying Treynor’s ratio and hybrid portfolio selection methods improves portfolio optimization and generates higher risk-adjusted returns.
Sector-based portfolio strategies have outperformed broad market indices during economic downturns (e. g., COVID-19)1.
3. Role of Institutional Investors and Macroeconomic Factors
FIIs influence and are influenced by the Indian stock market but also react to the market movement and thus increase the volatility.
Macroeconomic factors like inflation, exchange rates for currencies, and foreign investments have a major short-term effect on share prices but little long-term effect.
Both interest rates and inflation are positive influences on the S&P BSE SME IPO index and are indicators of emerging markets.
4. Market Efficiency and Investment Strategies
The Indian stock market is weak-form inefficient, in the sense that the past movements in prices can be used to forecast the future, thus going against the efficient market hypothesis.
The value and contrarian approaches select different stocks but exhibit correlated returns. Thus, both approaches can coexist within the same market efficiency framework.
Implications
For Investors: Macroeconomic indicators, stock market co-movements, and portfolio optimization help you make smarter investment decisions.
For policymakers: Enhancing financial stability, monitoring institutional investor behavior, and enhancing market efficiency can improve market resilience.
For Researchers: Additional research on investor psychology, market anomalies, and sectoral investment strategies can shed more light on stock market predictability and performance.
Overall, these studies provide useful information to market participants, suggesting that adaptive investment strategies, efficient risk management, and policy measures are needed to maintain market stability and growth.
REFERENCE –
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Sharneet Singh Jagirdar & Pradeep Kumar Gupta, 2023. “Value and Contrarian Investment Strategies: Evidence from Indian Stock Market,” JRFM, MDPI, vol. 16(2), pages 1-19, February.
Sinha, Pankaj & Goyal, Lavleen, 2012. “Algorithm for construction of portfolio of stocks using Treynor’s ratio,” MPRA Paper 40134, University Library of Munich, Germany
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