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The purpose of this study is to identify and analyze the key factors influencing stock market movements, using a multifactor hierarchical approach. By applying interpretive structural modeling (ISM) and Matrice d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) techniques, this study aims to uncover the interrelationships between these factors and provide a clearer understanding of their role in shaping market dynamics, with practical implications for investors and policymakers.

This study uses ISM and MICMAC analysis to explore the hierarchical relationships among key factors driving stock market movements. A panel of 25 financial market experts was used to develop the structural self-interaction matrix, and ISM was applied to structure the relationships between these factors. MICMAC analysis categorized the factors based on their driving power and dependence. The combined use of ISM and MICMAC provides a structured and quantitative approach to understanding the complexities of stock market dynamics.

The research identifies behavioral biases, corporate governance, interest rates, global events, investor sentiment and market volatility as pivotal factors influencing stock market movements. The hierarchical ISM model reveals that behavioral biases strongly drive investor sentiment, while global events and interest rates heavily impact market volatility. The MICMAC analysis categorizes these variables into autonomous, dependent and independent factors, providing a nuanced understanding of their influence on stock prices.

This study is limited by its reliance on expert judgments, which may introduce bias, and the sample size of 25 experts may not fully capture the diversity of financial market perspectives. In addition, the scope of the study is limited to generalized stock market factors, excluding regional or sector-specific analyses. These limitations affect the generalizability of the findings.

The findings of this research offer practical implications for investors, financial analysts and portfolio managers seeking to navigate the complexities of stock market behavior. By identifying key factors such as behavioral biases, corporate governance, currency fluctuations and regulatory changes, stakeholders can gain a deeper understanding of the dynamics driving stock prices. This structured approach can inform investment strategies, risk management practices and decision-making processes, enabling stakeholders to adapt to market fluctuations and make informed choices that align with their financial goals.

This study’s exploration of factors influencing stock market movements carries social implications that extend beyond financial markets. Understanding how global events, political stability and regulatory changes impact stock prices can shed light on the broader socio-economic landscape. By recognizing the interplay between these factors and their influence on investment decisions, policymakers, regulators and society at large can gain insights into the interconnectedness of financial markets with social and political dynamics. This awareness can inform policy decisions, economic strategies and initiatives aimed at fostering market stability and sustainable economic growth.

By using ISM and expert judgment, this research developed a comprehensive model that unveils the key factors influencing stock market movements. This model can potentially be used to inform investment decision-making and improve investment strategies, providing a structured approach for stakeholders to analyze and adapt to the complexities of stock market behavior.

This study aims to systematically review the literature on how various factors influence investor sentiment and affect financial markets. This study also sought to present an overview of explored contexts and research foci, identifying gaps in the literature and setting an agenda for future research.

The systematic literature investigation yielded 555 journal articles, with few other exceptional inclusions. The data have been extracted from the two databases, i.e. Scopus and Web of Science. For bibliometric analysis, VOSviewer and Biblioshiny by R have been used. The period of investigation is from 1985 to July 2023.

This systematic literature review helped us identify factors influencing investor sentiment and financial markets. This study has broadly classified these factors into two categories: rational and irrational. Rational factors include – economics and monetary policy, exchange rate, interest rates, inflation, government mandatory regulations, earning announcements, stock-split, dividend decisions, audit quality, environmental, social and governance aspects and ratings. Irrational factors include – behavioural and psychological factors, social media and online talk, news and entertainment, geopolitical and war events, calendar anomalies, environmental, natural disasters, religious events and festivals, irrationality caused due to government/supervisory body regulations, and corporate events. Using these factors, this study has developed an investor sentiment model. In addition, this review identified research trends, methodology, data and techniques used by researchers.

This review comprehensively explains how various factors affect investor sentiment and the stock market using the investor sentiment model. It further proposes an extensive future research agenda. This study has implications for stock market participants.

We examine explicitly priced financial distress risk in post-1990 equity markets. We add a financial distress risk factor to Fama and French's (1993) three-factor model, based on Griffin and Lemmon's (2002) findings that financial distress is not fully captured by the book-to-market factor. We test three-factor and four-factor capital asset pricing models using both annual buy-and-hold analysis and monthly time series analysis across portfolios adjusted for common book-to-market, size, and financial distress factors. We find empirical support for an Ohlson (1980) O-score-based financial distress risk four-factor asset pricing model in the U.S. and Japanese markets.

This study aims to compare the risk‐return characteristics and performance of real estate investment trust (REIT) hotel companies (hotel REITs hereafter) with those of C‐corporation hotel companies (hotel C‐corps hereafter).

The risk‐return characteristics and performance of hotel REITs and C‐corps were examined by estimating single‐factor and Fama‐French three‐factor asset pricing models for each portfolio. Differences between the hotel REIT and C‐corp estimations were tested using Wald test statistics.

Little evidence was found that hotel REITs have significantly different risk‐return characteristics and performance than hotel C‐corps, which suggests that hotel REITs and C‐corps are not significantly different in terms of market risk‐return characteristics and performance. The market portfolio had a significantly positive effect on the returns of both hotel REITs and C‐corps. The size and book‐to‐market factors of common stock also had a significant explanatory power for the returns of hotel REITs and C‐corps. Both hotel REITs and C‐corps performed similarly to the market portfolio, on a risk‐adjusted basis, during the 2000s.

Despite the fact that the three‐factor asset pricing model explains a significantly greater proportion of the variation in the hotel firms' returns than the single‐factor asset pricing model, approximately 30 percent of the total variation still remains unexplained.

The risk‐return characteristics and performance of hotel REITs and C‐corps revealed by this study will render hotel investors' decisions between the two organizational structures less complicated. In addition, the findings can be used by portfolio managers to construct a well‐diversified portfolio.

A multifactor asset pricing model was used for the first time in this article to examine the risk‐return characteristics and performance of hotel companies. In addition, the importance of understanding differences between REIT and C‐corp structures in the lodging industry is emphasized.

The purpose of this paper is to examine whether there is a systematic real estate risk factor in retail firms' common stock returns and whether this risk is priced in the stock market. In addition, whether the real estate risk sensitivities of retail stocks are linked to each firm's real estate intensity is investigated.

With a sample of 556 retail firms from 15 countries and a three‐index model with a domestic stock market and a retail market factor, as well as a real estate risk factor as the three explanatory variables, the paper appeals to the maximum likelihood methodology of Gibbons which estimates factor sensitivity coefficients and factor risk premia simultaneously using an iterative seemingly unrelated regression (ITSUR) technique, as well as the generalized method of moments (GMM) procedure. In addition, the paper investigates whether the individual retail firms' real estate βs are affected by the firms' CRER levels and other financial characteristics, using instrumental variables estimation technique via three‐stage least squares (3SLS).

The paper finds property market risks carry positive risk premia after controlling for sensitivities to general market and retail market risks, implying that real estate is an important factor priced in the stock market value of the sample retail firms. However, higher real estate concentration does not necessarily cause higher real estate exposure after controlling for firm size, leverage and growth, implying that stock market investors are unwilling or unable to understand and capture the full risk real estate ownership risk in corporate valuation.

From the corporate management viewpoint, those retail firms with a significant real estate portfolio should always consider the “real estate exposure” factor in their overall corporate strategy. Their high real estate exposure renders them vulnerable to shocks in the real estate market.

The paper offers insights into whether real estate is an important factor in corporate valuation

The purpose of this study is to examine the performances of liquidity factors in the stock market cycle. It aims to investigate whether the contribution of liquidity factors changes with stock market trends.

Six liquidity proxies and two-factor construction methods are compared in this study. The spanning regression method was applied to examine the contribution of liquidity factors to the asset pricing model, while the Fama and MacBeth regression method was used for examining the pricing power of liquidity factors.

The result shows that liquidity factors are accretive to models explaining returns in bull markets but not accretive to models in bear markets. The most appropriate method of constructing liquidity factors in the Japanese stock market has also been clarified.

In the Japanese stock market, there has never been a comprehensive test of the role of the liquidity risk factor in different market trends using the long-run data. This study helps with identifying the importance of liquidity pricing risk in different market trends. It also fills the gaps by comparing liquidity factors that are constructed through different methods and proxies and provides evidence for further confirming the correct asset pricing model in the future.

The purpose of this paper is to consider the economic information content within several popular stock market factors and to the extent to which their movements are both explained by economic variables and can explain future output growth.

Using US stock portfolios from 1964 to 2019, the authors undertake three related exercises: whether a set of common factors contain independent predictive ability for stock returns, what economic and market variables explain movements in the factors and whether stock market factors have predictive power for future output growth.

The results show that several of the considered factors do not contain independent information for stock returns. Further, most of these factors are neither explained by economic conditions nor they provide any predictive power for future output growth. Thus, they appear to contain very little economic content. However, the results suggest that the impact of these factors is more prominent with higher macroeconomic risk (contractionary regime).

The stock market factors are more likely to reflect existing market conditions and exhibit a weaker relation with economic conditions and do not act as a window on future behavior.

Fama and French three-factor model still have better explanations for stock returns and economic information more than any other models.

This paper contributes to the literature by examining whether a selection of factors provides unique information when modelling stock returns data. It also investigates what variables can predict movements in the stock market factors. Third, it examines whether the factors exhibit a link with subsequent economic output. This should establish whether the stock market factors contain useful information for stock returns and the macroeconomy or whether the significance of the factor is a result of chance. The results in this paper should advance our understanding of asset price movement and the links between the macroeconomy and financial markets and, thus, be of interest to academics, investors and policy-makers.

This paper investigates the relative importance of local macroeconomic and global factors in the explanation of twelve MENA (Middle East and North Africa) stock market returns across the different quantiles in order to determine their degree of international financial integration.

The authors use both ordinary least squares and quantile regressions from January 2007 to January 2018. Quantile regression permits to know how the effects of explanatory variables vary across the different states of the market.

The results of this paper indicate that the impact of local macroeconomic and global factors differs across the quantiles and markets. Generally, there are wide ranges in degree of international integration and most of MENA stock markets appear to be weakly integrated. This reveals that the portfolio diversification within the stock markets in this region is still beneficial.

This paper is original for two reasons. First, it emphasizes, over a fairly long period, the impact of a large number of macroeconomic and global variables on the MENA stock market returns. Second, it examines if the relative effects of these factors on MENA stock returns vary or not across the market states and MENA countries.

This paper explores the relationship between size, book-to-market, beta, and expected stock returns in the U.S. Information Technology sector over the July 1990–June 2001 period. Two models, the multivariate model and the three-factor model, are employed to test these relationships. The risk-return tests confirm the relationship between size, book-to-market, beta and stock returns in IT stocks is different from that in other non-financial stocks. However, the sub-period results (the periods before and after the technology crash in April 2000) show that the nature of the relationship between stock returns, size, book-to-market, and market factors, or the magnitude of the size, book-to-market, and market premiums, is on average unchanged for both sub-periods. This result suggests the technology stock crash in April 2000 was not a correction of stock prices.

The determinants of investor sentiment based on stock market proxies are found in numbers in empirical studies. However, investor sentiment antecedents developed from primary survey measures by constructing an investor sentiment index (ISI) are not done till date. The purpose of this paper is to fill this research gap by first developing an ISI for the Indian retail investors and then examining the investor-specific, stock market-specific, macroeconomic and policy-specific factors’ individual impact on the investor sentiment.

First, the authors develop the ISI by using the mean scores of six statements as formulated based on popular direct investor sentiment surveys undertaken throughout the world. Then, the authors employ the structural equation modeling approach on the responses of 576 respondents on 40 statements (representing the index and four study hypotheses) collected in 2016 across the country.

The results show that investor- and stock market-specific factors are the major antecedents of investor sentiment for these investors. However, interestingly macroeconomic fundamentals and policy-specific factors have no role to play in driving their sentiment to invest in the stock market.

The major implication of the results is that the Indian retail investors are showing a mixed approach of Bayesian and behavioral finance decision making. So, these implications can guide the investment consultants, regulators, other stakeholders in markets and overwhelmingly the retail investors to introspect their investment decision making across time horizons.

The formulation of ISI in an emerging market context and thereafter examining possible antecedents to influence retail investors in their investment decision making are not done till date. So, the study is unique in its research issue and findings and will have significant implication for the retail investors at least in emerging market contexts.