Large-cap Stocks Research Articles

Capital Resilience: Dynamics Movements of Large Cap and Small-Mid Cap Stock in Global Macroeconomic Uncertainty

This research was conducted to examine how large and small-medium cap stocks in Indonesia respond to movements in global macroeconomic variables (Federal funds rate, M2 of Dollar, S&P 500, CBOE Volatility Index) and to determine the extent of the contribution of each variable in influencing both of stock prices. The study period spans from January 2022 to July 2023, during a period of global economic instability due to excessive quantitative easing post-Covid-19 pandemic. This research is quantitative, and the Vector Error Correction Model (VECM) was chosen as the method for analysis. The findings of the study, analysed using Impulse Response Functions (IRF), indicate that small-medium cap stocks represented by DBX are more significantly impacted than large-cap stocks represented by IDX30 when there is a shock at a particular moment; it can be seen from standard deviation values in the IRF output, suggesting a higher level of volatility in small-medium cap stocks. Further analysis using Forecast Error Variance Decomposition (FEVD) reveals that the movements of both IDX30 and DBX in the short run are predominantly influenced by their past values at t-1, indicating inefficient market conditions based on the Efficient Market Hypothesi. However, these conditions are expected to shift gradually towards more efficiency in the long run. Regarding the examined macroeconomic variables, it was found that the Federal funds rate and S&P 500 have a more significant impact on large-cap stocks than small-medium-cap stocks. Conversely, the M2 of Dollar and CBOE Volatility Index is more dominant in small-medium-cap stocks than large-cap stocks. This study offers valuable insights and considerations for investors seeking to diversify their stock portfolios according to their objectives and risk profiles.

Separating the predictable part of returns with CNN-GRU-attention from inputs to predict stock returns

The noise and high randomness of the stock market are primary obstacles to profitability. These factors cause stock returns to consist of short-term predictable and stock-specific residual parts. Therefore, it is beneficial to separate and model the two items for forecasting returns in the randomness environment. On this basis, we propose a novel Temporal Return Separation and Restore Forecasting (TRSRF) method. TRSRF first extracts price- and relation-based features from historical stock inputs and uses them to decompose and restore predictable and residual elements of historical returns , respectively. Then it forecasts these components based on historical values. We conducted experiments on the Chinese small and mid-cap, blue-chip, and large-cap indexes over ten years to compare with seven proposed baselines. On the three Chinese indexes our method separately exceeds 6%, 16%, and 22% absolute annualized returns than our baseline. The ablation study also proves the necessity of return decomposition and inter-stock relation modeling. We find that modeling inter-stock relations on small-cap stocks works better than on large-cap stocks. Furthermore, we prove our method might be an implicit ensemble model of the predictable and residual parts, which is why it works stability under various market conditions.

A three-level nested portfolio optimization model with position allocation

Existing portfolio optimization models cannot well capture the real position allocation requirement, leading to limited impact in practice. To overcome this challenge, we propose a three-level nested portfolio optimization model with position allocation. Within this model, the inner and middle levels collaboratively determine the optimal portfolio, while the outer level focuses on optimizing the holding proportion of each stock in the optimal portfolio. Compared with existing models with portfolio weights, the proposed model imposes the position allocation constraint that precisely characterizes the limitations on holding each stock. This constraint is crucial for investors to obey securities trading regulations involving position limitations and to mitigate the potential impact of market risks. To address the nonlinear and nonconvex nature of the novel model, we develop an intelligent optimization algorithm by effectively hybridizing the support vector regression and the enhanced grey wolf optimizer. We comprehensively evaluate its performance using eight metrics, including accumulative return, annual return, Sharpe ratio, maximum drawdown, absolute and relative win ratios, predictive precision and accuracy. The experimental results indicate that (i) the proposed model can achieve more excess returns than those stock selection models not considering position allocation, especially for the large-cap stocks; (ii) compared with other state-of-the-art meta-heuristics, the enhanced grey wolf optimizer can yield better portfolio in conjunction with the support vector regression; (iii) in the context of the Chinese A-share stock market, specific financial indicators such as return on equity, inventory turnover rate, net income growth rate, and debt-to-equity ratio should be given greater consideration compared to other financial metrics.

Macroeconomic shocks, market uncertainty and speculative bubbles: a decomposition-based predictive model of Indian stock markets

PurposeStock markets are essential for households for wealth creation and for firms for raising financial resources for capacity expansion and growth. Market participants, therefore, need an understanding of stock price movements. Stock market indices and individual stock prices reflect the macroeconomic environment and are subject to external and internal shocks. It is important to disentangle the impact of macroeconomic shocks, market uncertainty and speculative elements and examine them separately for prediction. To aid households, firms and policymakers, the paper proposes a granular decomposition-based prediction framework for different time periods in India, characterized by different market states with varying degrees of uncertainty.Design/methodology/approachEnsemble empirical mode decomposition (EEMD) and fuzzy-C-means (FCM) clustering algorithms are used to decompose stock prices into short, medium and long-run components. Multiverse optimization (MVO) is used to combine extreme gradient boosting regression (XGBR), Facebook Prophet and support vector regression (SVR) for forecasting. Application of explainable artificial intelligence (XAI) helps identify feature contributions.FindingsWe find that historic volatility, expected market uncertainty, oscillators and macroeconomic variables explain different components of stock prices and their impact varies with the industry and the market state. The proposed framework yields efficient predictions even during the COVID-19 pandemic and the Russia–Ukraine war period. Efficiency measures indicate the robustness of the approach. Findings suggest that large-cap stocks are relatively more predictable.Research limitations/implicationsThe paper is on Indian stock markets. Future work will extend it to other stock markets and other financial products.Practical implicationsThe proposed methodology will be of practical use for traders, fund managers and financial advisors. Policymakers may find it useful for assessing the impact of macroeconomic shocks and reducing market volatility.Originality/valueDevelopment of a granular decomposition-based forecasting framework and separating the effects of explanatory variables in different time scales and macroeconomic periods.

Examining the Distributional Characteristics of Daily Returns of Nifty 50: Normality Assessment and Implications

This paper investigates the distributional characteristics of daily returns of the Nifty 50 index, a benchmark index comprising 50 large-cap stocks traded on the National Stock Exchange of India. Utilizing historical data spanning a specified time period, we conduct normality testing to assess the adequacy of the normal distribution assumption underlying many financial models. Our analysis provides insights into the departure from normality. As a result of departure from Normality, it may affect the tail behaviour, and volatility dynamics of Nifty 50 daily returns, offering implications for risk management, option pricing, portfolio management, and market efficiency. By synthesizing empirical findings with theoretical considerations, this research contributes to a deeper understanding of the statistical properties of Nifty 50 daily returns and informs practitioners in finance about the challenges associated with Modeling and analysing stock market data.

Can Investor Sentiment Explain the Abnormal Returns of Volatility-Managed Portfolio Strategy? Evidence from the Chinese Stock Market

ABSTRACT This study develops a theoretical model to capture volatility-managed portfolios’ risk-adjusted returns affected by investor sentiment, and uses Chinese A-share market data to analyze the volatility-managed effect based on the small-minus-big (SMB) factor and explain the abnormal returns of volatility-managed portfolios from the investor sentiment perspective. Our results show that the Sharpe ratio of the SMB factor significantly increases after volatility management, especially in low-sentiment periods. Moreover, the mechanism analysis shows that in low-sentiment periods, small-cap stock investors overreact to volatility compared to large-cap stock investors, making the SMB factor significantly positively correlated with lagged volatility. Additional analyses show that volatility-managed portfolios constructed through stocks with gambling characteristics can achieve higher abnormal returns, and the abnormal returns of volatility-managed portfolios cannot be corrected by arbitrage traders as arbitrage is ineffective, reflecting the immaturity of China’s emerging capital market.

Can the Sell in May effect be enhanced by a size tilt?

PurposeThe authors evaluate the Sell in May effect in the Canadian context to comprehensively explore the Sell in May effect as well as its interactions with the size effect and risk and with multiple indices.Design/methodology/approachThe authors use ordinary least squares (OLS) regressions to examine the Sell in May effect and Huber M-estimation to handle potential outliers. They also use the generalized autoregressive conditional heteroskedasticity (GARCH) models to explore the role of risk in the Sell in May effect.FindingsThe results demonstrate that the Sell in May effect is present in all three main Canadian stock market indices. More telling, the anomaly is strongest in small cap indices and in indices that give equal weighting to small and large cap stocks. They do not find that the effect is driven by risk.Originality/valueWhile several papers have explored the Sell in May phenomenon in several countries, little scholarly attention has been paid to this effect in Canada and to its interaction with the size effect. The authors contribute to the literature by examining of the interactions between Sell in May and the size effect in Canada. They examine the Sell in May effect using CFMRC value-weighted and equally weighted indices of all Canadian companies. They also incorporate in their analysis the role of risk.

Monetary momentum and risk management in stock market

Purpose- This study aims to investigate the relationship between monetary interest rate decisions, liquidity mechanisms and risk management issues. As a core interest the significance of a structural change in the data around the FOMC meetings is analyzed. By the help of continuous time models we analyze the kind of dynamics, which can be observed in the stock returns, i.e. conditional volatilities and jumps. A further central interest is given to investment decisions and risk management issues. This encompasses the elaboration of the hedging strategies to achieve higher performance. Methodology- The study employs GARCH-Ito and GARCH-Ito-Jump models to analyze the stock market returns and their related volatilities on the day of a FED interest decision announcement. The continuous time GARCH model setting allows to model stock market returns with a high flexibility, therefore these models are abled to capture jump dynamics in the stock returns. Findings- The analysis reveals that persistence in conditional volatilities change according to alternative stocks. These stocks can be classified according to alternative market capitalization sizes. Mega market capitalization stocks are better governed by no jump GARCH-Ito models regardless the monetary policy changes, that is, changes in interest rates or not. Conclusion- Based upon the analysis, it may be concluded that risk management applications effectively might perform under the consideration of stock types in terms of market sizes. The persistence in the conditional volatility massively decreases if a jump component is introduced into the model. Since most of the mega market cap stocks perform better without a jump part component, it might be conjectured that persistence in the conditional volatility for mega cap stocks play a more important role compared to large cap stocks. Regardless the case whether there is an interest rate change or not, the persistence in conditional volatility remains in mega cap stocks, and thus, these stocks are prone to the involvement of prices jumps. Keywords: Monetary policy, risk management, jump detection, investment decisions JEL Codes: C22, E49, G11

Small-Cap and Large-Cap Stocks and the Herding Phenomenon: Insights from a Leading Social Trading Platform (STP)

Small-Cap and Large-Cap Stocks and the Herding Phenomenon: Insights from a Leading Social Trading Platform (STP)

Research on Optimizing Pension Planning Allocations

This article examines two ways of pension allocation, choosing the best opportunity mix based on Fama and the French three-factor model to maximize returns while satisfying each portfolio category and other investment conditions. The database used by the author is based on the excess returns of 8 different stocks from October 2018 to January 2022. This article studies the relationship between different stocks by calculating the variance, standard deviation and covariance of different stocks, and divides stocks into large-cap stocks, small-cap stocks, value stocks, and growth stocks. The author calculated the optimal Sharpe ratio. Empirical results show that the sharpe ratio value obtained from the investment portfolio can be concluded that when people live for more than 20 years after retirement, they can choose to receive pensions every year. If your life expectancy after retirement cannot reach 20 years, it is recommended to withdraw your pension in one lump sum. However, this article shows that results change when using different portfolio approaches because of data limitations.

Investment styles of islamic equity funds

We investigate the dynamics of investment styles of Islamic equity funds (IEFs), mainly through portfolio holdings. We rely on an unbiased survivorship sample of 224 active portfolios domiciled in 22 countries from 2004 to 2018 to shed new light on style concentration. IEFs are overwhelmingly skewed initially to value stocks in Islamic countries and growth stocks in non-Islamic countries. We find a subsequent shift from these styles to a more blended approach. Investments in Islamic countries shift from mid-cap to large-cap stocks, while those in non-Islamic countries remain in extremely large-cap stocks. The propensity of style shift is larger in asset type than in asset size. The style drift analyses show that most IEFs drift in style, with a more aggressive drift in Islamic countries than in their non-Islamic counterparts. They are more likely to alter their portfolio exposure in the sight of adverse outcomes. Implications of our results for faith-based investors and regulators are identified in the study.

Do actively managed equity funds add value in developing economies? – The case of ‘inverse Gruber puzzle’ in Pakistan

PurposeThis paper evaluates the performance of actively managed conventional and Islamic equity funds in a developing economy with a focus to assess the performance-growth puzzle posited by Gruber (1993) (a.k.a Gruber’s puzzle). Under the context of an emerging market of Pakistan, this study explores if actively managed equity fund (AMEF) managers have been able to add value by outperforming the market in terms of stock-selection and market-timing abilities; and the comparative performance analysis of Islamic versus conventional AMEFs is also carried out.Design/methodology/approachWe employ Sharpe and Treynor ratios, Capital asset pricing model, Fama–French three factors model (1993), Carhart four-factor model (1997) and Hendrickson (1981) market timing models on 45 equity funds comprising of 23 conventional and 22 Islamic equity funds operating in Pakistan for a period of 10 years. The overall sample period (2008–2018) is divided into two 5 years sub-periods (i.e. 2009–2013 and 2014–2018) and three 3 years sub-periods (2009–2011, 2012–2014 and 2015–2017) to be viewed in conjunction with the country's macro-economic condition.FindingsWe report that the actively managed equity funds (AMEFs) were unable to beat the market index with their stock selection or market timing capabilities. However, AMEFs depicted improved performance in the post-global financial crisis period where both conventional and Islamic AMEFs generated substantial rewards for the given amount of risk. Also, conventional AMEFs outperformed Islamic AMEFs potentially due to their holdings in highly leveraged value and large-cap stocks, while Islamic AMEFS invest more cautiously in small-cap and value firms. Analysis of market timing skills revealed that the funds have not been able to select the undervalued stocks and adopted a defensive strategy in the post-global financial crisis recovery period.Practical implicationsOur findings shed some interesting insights and raise some pertinent questions for research, policy, and practice – specifically for developing countries’ context. The no ‘return-growth’ configuration defies its fit with the ‘Gruber puzzle’ and somewhat presents a case of what we call the ‘Inverse Grubber puzzle’. This novel notion of the ‘Inverse Grubber puzzle’ should inform policy and practice to reflect on their practices, institutional arrangement, regulatory framework and policy design in developing economies characterized by lacklustre performance and growth of AMEFs. For example, the regulatory design may consider focusing on stimulating financial inclusion and deepening by motivating low-cost Index tracker funds (ITFs) – with lower fund management costs, while allocating the avoided cost to flow towards effective marketing campaigns driving greater awareness, financial deepening, and investor base diversification. For future research, financial development researchers may explore the implications and appropriateness of AMEFs versus ITFs in other developing economies.Originality/valueThe work reported in this paper is original and constitutes a valuable asset for ethno-religious-sensitive investors. The research has not been published in any capacity and is not under consideration for publication elsewhere.

Portfolio Optimization under Real Constraints

How to balance risk and return is a question that every investor cares about. Since there is little research on portfolio management in specific industries under special constraints, this paper aims to conduct asset allocation analysis on consumer defensive, technology, financial service and healthcare industries. This paper selects ten representative companies from these industries and combine them with generalized indices and use Markowitz model and index model for analysis. Portfolio optimization is based on investor preferences and five constraints of financial market regulations. The results show that, first, disallowing shorting has the strongest binding power. Second, the S&P 500, as the best single measure of U.S. large-cap stocks, is a good choice for balancing risk and reward. These findings may be of great significance to the research on the optimal allocation of financial assets in the industry and to investors with similar requirements to make their own investment decisions.

IMPACT OF FIRM PERFORMANCE ON STOCK RETURNS: EVIDENCE FROM LARGE-CAP, MID-CAP AND SMALL-CAP STOCKS

In this article, we analyse the impact of firm specific factors, namely, total income, net profit, and earnings per share (EPS) on stock returns of companies from the large cap, mid-cap and small- cap (LMS) categories using the panel quantile regression. We also use the ordinary least square method to compare our panel regression results. We select companies from NSE Large Cap100, NSE Mid Cap 100 and NSE Small Cap 100 Indices and each index is composed of 100 companies. These indices reflect the overall state of the Indian stock market. Our empirical analysis based on quarterly data from June 2010 to March 2022 shows some important findings. First, net sales significantly and negatively affect stock returns across the large, mid, and small cap stocks. Second, net profit and EPS have a substantial and positive impact on companies from all the categories. However, the coefficients are not significant across all quantiles. In short, the impact of firm specific factors on stock returns is not homogeneous across the LMS stocks implying that these factors do not influence stocks in a uniform way. The plausible reason may be that large, mid, and small cap companies share differences in terms of market capitalisation, growth potential and volatility.

It Depends On When You Search

Existing studies have found that online search is a revealed measure for investor attention and a useful predictor of stock returns. We study the heterogeneity in retail investor attention by comparing search conducted on weekdays vs. weekends and investigate the price pressure channel and information processing channel for stock return predictability. According to the information processing channel, weekends afford retail investors more time for the intensive cognitive analysis necessary to make better predictions. Alternatively, weekend search might better capture the price pressure from retail investors’ trading activities. We provide empirical results that support the information processing channel. We first show that weekend search, rather than weekday search, predicts large-cap stock returns in both the cross-section and time series. Additionally, our findings on retail trading activity contradict the price pressure channel in that weekday search, rather than weekend search, leads to a subsequent retail order imbalance. Overall, our study contributes to the literature on the predictive power of online search on stock returns, which has mainly focused on the price pressure channel, which yields significant results for small-cap stocks only.

ESG performance, herding behavior and stock market returns: evidence from Europe

This paper tests how financial performance indicators and combined ESG score for large-cap stocks impact on stock return. In particular, we examine how market capitalization, price to book value, Sharpe ratio and ESG score of large-cap firms in Europe are related to their stock performance. We consider a panel data consisting of six European countries—Portugal, Italy, Greece, Spain, France and Germany—for the period 2010–2020. For Greek and French firms, a firm’s size tends to negatively affect its stock returns. The investors in European countries (except Italy) do not jeopardize their returns by investing in highly ESG scoring firms. We argue that the benefit of not investing in highly ESG-scoring firms might lead investors to select smaller size companies with a higher price to book value and higher Sharpe ratio, as it is more likely to generate higher returns. Moreover, Italian firms are more susceptible to ESG issues, as ESG performance and stock return seem to have a significant negative correlation. This valuable result was confirmed by conducting a robustness test for Europe as a whole using the Euronext100 index. Finally, we find no evidence that ESG motivates herding in our selected sample (this is not the case for Greece and France), while we report evidence of ESG herding behavior during the Covid-19 outbreak in Portugal, Italy and Greece.

Betting against beta with intraday and overnight signals

The abnormal returns of the Betting Against Beta (BAB) strategy have attracted much interest among researchers and practitioners. Based on a market anomaly related to the Capital Asset Pricing Model, this strategy uses daily beta as a signal for portfolio construction. However, recent literature shows how some financial quantities, including beta, change between trading and non-trading periods. For this reason, we decided to compare the performance of the original BAB strategy with two BAB variants, where the signal for portfolio construction is given by intraday and overnight beta, respectively. Despite all strategies exhibiting positive cumulative returns, using the intraday beta signal leads to significantly higher performances. Further analyses show that the abnormal intraday BAB returns are mainly due to nano and micro-cap stocks which tend to outperform large-cap stocks, as well known from the literature.

10 important financial investments for orthopods in India

10 important financial investments for orthopods in India

Understanding the dynamics of investment factors and exchange-traded funds performance in the U.S. market 2018-2022

The increasing popularity of exchange-traded funds (ETFs) among retail and professional investors necessitates a deeper understanding of their value-creation process. Recognizing inconsistencies between stated investment strategies and portfolio exposures is crucial for appropriate rebalancing in accordance with investment policy statements. Against the backdrop of evolving investment factors during the pandemic and changing geopolitical circumstances, the performance of ETFs has undergone significant shifts. Analyzing the directional changes of prevailing investment factors within specific macro environments is essential for optimizing portfolios composed out of ETFs. This study has a dual objective: firstly, to comprehend the dominant investment factors and their dynamics in the U.S. market, and secondly, to evaluate the performance of ETFs that adhere to specific investment philosophies and strategies. To achieve these objectives, the Fama-French three and five-factor models were employed to analyze a dataset comprising 72 U.S. ETFs. These ETFs were then categorized into four portfolios based on investment style and size. Performance appraisal measures were utilized to compare portfolios on a risk-adjusted basis relative to the benchmark. The bear market that commenced in early 2022 had a universally negative impact on observed ETFs due to their longonly exposures. This inflection point also marked a shift in the relative performance between value and growth styles, as well as the outperformance of more conservative investing approaches, underscoring the importance of adapting to changing market conditions. Additionally, the absence of a size premium throughout the observed period confirms investors' preference for large-cap stocks as a resilient factor. Furthermore, the size effect exerted a universal negative influence due to the size drift of ETFs employing a stated large-size investment strategy. During the observed period, the value style experienced a significant recovery, characterized by higher book-to-price ratios, operating profits, and more conservative investment policies that produced superior results compared to the previous longer period. The findings of this research enhance our understanding of the influence of investment factors on U.S. ETF performance, providing valuable insights for investors and portfolio managers who may need to adjust their strategies in response to observed changes in market dynamics.

Construction and Analysis of Individual Stock Sentiment Anomalies in the A-share Market

Behavioral finance heralds investor sentiment as an anomaly in traditional asset pricing models, and the financing and financing business is an important indicator of individual stock sentiment. In this paper, this paper try to use the growth rate of monthly balance of financing and financing securities as a factor SEN to explain stock returns in the context of Chinese A-share market. This paper finds that market capitalization and SEN have a significant inverse monotonicity, and their information crosses to a large extent to make SEN redundant. This paper argues that the reason for the redundancy of SEN due to market capitalization is that the A-share stock market has set a high margin for financing and financing business, which leads to a weaker liquidity of market transactions and limits the investment choices of rational investors to stocks with relatively small market capitalization. Based on the conclusion, this paper suggests that small investors adopt a strategy of going long on small-cap stocks and short on large-cap stocks.