Large-cap Index Research Articles

Nexus between foreign exchange rate and stock market: evidence from India

This study examines the impact of foreign exchange rate fluctuations on various NSE capitalized indices of India. Five exchange rates were chosen based on trading contracts in the currency derivative segment of NSE. These exchange rates are US Dollar-Indian Rupee (USD/INR), Euro-Indian Rupee (EUR/INR), Great Britain Pound-Indian Rupee (GBP/INR), Chinese Yuan-Indian Rupee (CNY/INR) and Japanese Yen-Indian Rupee (JPY/INR), which are used as a regressor in this study. The data of NSE Nifty large-cap 100, Nifty mid-cap 100 and Nifty small-cap from December 1, 2012 to December 1, 2022 was considered for the study. GARCH (1, 1) model was used to analyze the nexus between exchange rate fluctuations and capitalized indices, and it was further validated by DCC GARCH to evaluate the volatility spillover. The result shows that exchange rate fluctuations have a positive effect on stock market volatility along with a varying degree of incidence on small-cap, mid-cap, and large-cap. DCC α has been found to be significant in USD & GBP for small-cap, and GBP & CNY for mid-cap. On the other hand, USD, Euro, CNY and JPY have a significant impact on the large-cap index in the short-run. Further, it is found that there is long-run spillover effect (DCC β) of exchange rates on all capitalized indices of the Indian stock market, and it is highest in in the large-cap case.

A Two-Phase Optimal Portfolio Selection Using Sharpe Index Model Applied to the Athens Stock Exchange


 Although in literature numerous multivariate models have been applied for optimal portfolio selection based on either market or accounting stock characteristics, whereby plenty of technical and/or fundamental criteria have been proposed, the problem is yet to be solved. This paper enhances a two-phased analysis that combines both fundamental and technical criteria, to overcome the aforementioned shortcomings. Initially, the fundamental characteristics of 25 stocks from 11 industries/sectors with the largest market capitalization, are compared to the performance of the Athens Stock Exchange FTSE/XA Large CAP Index, and hence two scoring tables are formed using different benchmarks, where the best performing stocks are selected. Subsequently, for these stocks, based on weekly data covering a 3-year period, the Sharpe Index Model is applied, and the best performing portfolio is selected. The estimated Sharpe Index Model (SIM) reveals that there are several opportunities to optimize return and diversify risk in an efficient manner, outperforming the FTSE Large Cap Index.
 
 JEL Classifications: D53, G11, G12, G17, G23
 Key words: Risk & Return, Technical Analysis, Fundamental Analysis, Sharpe Index Model

2T-POT Hawkes model for left- and right-tail conditional quantile forecasts of financial log returns: Out-of-sample comparison of conditional EVT models

Conditional extreme value theory (EVT) methods promise enhanced forecasting of the extreme tail events that often dominate systemic risk. We present an improved two-tailed peaks-over-threshold (2T-POT) Hawkes model that is adapted for conditional quantile forecasting in both the left and right tails of a univariate time series. This is applied to the daily log returns of six large-cap indices. We also take the unique step of fitting the model at multiple exceedance thresholds (from the 1.25% to 25.00% mirrored quantiles). Quantitatively similar asymmetries in Hawkes parameters are found across all six indices, adding further empirical support to a temporal leverage effect in financial price time series in which the impact of losses is not only larger but also more immediate. Out-of-sample backtests find that our 2T-POT Hawkes model is more reliably accurate than the GARCH-EVT model when forecasting (mirrored) value at risk and expected shortfall at the 5% coverage level and below. This suggests that asymmetric Hawkes-type arrival dynamics are a better approximation of the true generating process for extreme daily log returns than GARCH-type conditional volatility. Our 2T-POT Hawkes model therefore presents a better-performing alternative for financial risk modelling.

Modelling Volatility of Size Indices Through Garch and Tgarch Models: Evidence from India

Purpose: The objective of this study was to select the best model for modelling the volatility of log return series of four size indices taken from the Indian stock markets. Theoretical framework: Stock markets throughout the world have been designing various kinds of indices depending upon the requirements of the investors. Accordingly, BSE launched some indices, categorized as size indices, depending upon the market capitalization of the stocks listed on their exchange. Such categorization helps the investors in appropriate decision making. Research on these specific indices is scarce and therefore the aim of the present study. Design/methodology/approach: We analyzed the log return series of four indices, namely S&P BSE Sensex, S&P BSE Large cap index, S&P BSE Mid cap Select index and the S&P BSE Small cap index, by fitting the GARCH (1,1) and the TGARCH (1,1) models for the period from 1 Januray 2018 to 31 December 2022. Findings: The research on modelling volatility is voluminous with varied results as per the various assets and datasets utilized for the purpose. Present study confirms the results from previous study that to model the volatility of size indices a GARCH (1,1) model should suffice and there is no betterment achieved by implementing a TAGARCH (1,1) model. Research, Practical & Social implications: We suggest the implementation of a simple GARCH (1,1) model for all those investors who want to invest in Indian size indices in order to model volatility of these indices. Originality/value: Present study is one of its kinds, investigating size indices specifically, through the two of the most popular GARCH-family models in the Indian context.

Is There Value in Large Cap Index Selection?

The rise of passive investing is shining a new light on the world of Indexing. Indexing is all around us today, whether being discussed by a media outlet showing the movements of the day or a newly launched passive ETF that tracks a recently created index. But there is little research on whether picking one index over another provides value to the investor when the indexes all share very similar index construction methodologies. Is there value in selecting one over the other or should investors choose the one that is the most accessible or cheapest to access? This article aims to fill that gap by conducting a comparative analysis of US Large Cap indexes that are substantially similar in their construction methodologies. As expected, the indexes selected perform very similar to one another based on the quantitative methods used within the analysis. Other than number of securities, the largest difference among the indexes is their exposure to Mid-Cap stocks. The decision of which index to select is dependent on the application being selected. This article focuses on the four purposes outlined by DiBartolomeo (2019) which are: journalistic, representation of an asset class, a fair benchmark, and basis of passive investment. In most cases, each index is a reasonable substitution for the others. Given that risk and return profiles are similar, factors such as number of securities, licensing costs, and brand value are the most significant to consider. This is especially true when selecting an index as the basis of a passive product, where those factors affect the product cost and capacity that can determine the success or failure of a passive product.

Survival Analysis of Ichimoku Cloud Indicator Benchmarked on the S&P 500 Index

This paper implements a genetic algorithm of 20 input variables (technical indicators) outlined by the Bank of St. Louis Fed’s research department updated to include newly adopted technical indicators Ichimoku cloud. The research is tested over the 1980-2016 period and benchmarked on the S&P 500 Large Cap Index. The hypothesis is to see if investors may gain additional information from using technical indicators in their asset allocation strategy. The results show through stepwise regression that moving averages, and Ichimoku cloud indicators may convey information to investors although there may be additional macro￾economic information not picked up by the technical signals that should be included in the system of equations. The results show from 1980-2016 the genetic algorithm strategy produces total return of 3308.31 percent versus the S&P 500 1909.90 percent. The result is .16608 with p-value of 0.000 for the Moving Average 3,12 and 0.24 for Ichimoku Cloud Indicator based on the 1,26 period. For Ichimoku Indicator 26,52 it is 0.000 and for 1,52 0.009.

Does commitment to environment and society pays? Evidence from COVID-19 impact on stock volatility

Purpose This study aims to analyse whether investment in green and sustainable stocks provide some cushion during current precarious time. To compare the impact of COVID-19 on the volatility of sustainable and market-capitalisation-based stocks, daily returns from Greenex, Carbonex, Large-Cap, Mid-Cap and Small-Cap index have been analysed over a period of six years from 2015 to 2021. Design/methodology/approach At the outset, logarithmic return of all selected indices has been tested for possible unit root and heteroscedastic. On confirmation of stationarity and heteroscedasticity of data, auto-regressive conditional heteroscedastic models have been applied. Thereafter, volatility is modelled through best suitable model as suggested by Akaike and Schwarz information criterions. Findings The findings indicate the positive impact of COVID-19 on the volatility of the indices. Asymmetric power ARCH model indicates highest significant impact of COVID-19 over the volatility of Large-Cap index, whereas exponential GARCH model detected highest significant impact of COVID-19 over the volatility of Mid-Cap Index. Originality/value To the best of the authors’ knowledge, the present study is original in the sense that it aimed at comparing the possible impact of COVID-19 over sustainable and market-capitalisation-based indices.

Volatility Spillover Dynamics between Large-, Mid-, and Small-Cap Stocks in the Time-Frequency Domain: Implications for Portfolio Management

The connectedness dynamics between large-, mid-, and small-cap stocks is investigated using the forecasted error variance decomposition (FEVD) spillover framework of Diebold and Yilmaz in the time-frequency domain. Total volatility spillover (i.e., connectedness) is elevated between large-, mid-, and small-cap stocks during the study period. This high level of spillover exists in the short run only, and declines gradually in the medium to long run, thus providing opportunities for portfolio diversification (hedging) in multi-cap investing during the medium-to-long run (short run) only. Like total connectedness, a similar pattern of bilateral connectedness is observed between either of the two indices, thus providing a similar opportunity in the short and long runs. The mid-cap index emerges as the major contributor to total volatility in the system, followed by the small- and large-cap indices, during the analyzed period. The volatility spillover is time-varying in both the time and frequency domains.

On the predictive power of CAPE or Shiller’s PE ratio: the case of the Greek stock market

This paper examines the capability of the Cyclically Adjusted Price to Earnings (CAPE) or Shiller’s P/E ratio, along with other relative valuation ratios such as the P/E and the P/BV, to predict future returns of the FTSE/ASE Large Cap Index, starting from the development of the index (1997) to December 2018. We have herein used several regression models in order to examine the relationship between the above ratios and the future returns of 1, 3, 5 and 10 years. We show that, while P/E and P/BV ratios are not correlated to future returns, the CAPE ratio and its variation CAPE 5, which uses real 5 year earnings, are efficient estimators of future returns. Our results imply the informational inefficiency of the Greek Stock Market.

Does passive investment have a positive governance effect? Evidence from index funds ownership and corporate innovation

Using Chinese listed companies' data from 2007 to 2017, this study finds that companies with a higher proportion of index fund ownership are more innovative, implying that passive institutional investors positively influence company governance. The effect is stronger in private firms, high-tech firms, and firms in high marketization regions and prominent mainly in large-cap index funds. Index fund ownership reduces the stock turnover rate, stabilizes shareholder structure, and lowers managers turnover rates, mitigating managers’ myopic behavior and boosting innovation. This study documents empirical evidence on the positive effect of passive investors on corporate governance from the perspective of corporate innovation.

Trading rules and excess returns: evidence from Turkey

PurposeThis paper aims to investigate popular technical trading rules (TTRs) applied to the FTSE Turkish all-cap and small-cap indexes from September 23, 2003 to August 9, 2019 to determine rules that produce net excess returns over the Buy-and-Hold strategy (B&H).Design/methodology/approachFive TTRs, namely, simple moving average, relative strength index, moving average convergence divergence, momentum, and rate of change, are applied, singly (one indicator) and in combination (two indicators) for multiple time periods.FindingsFor the small-cap index, some TTRs – including the famous Golden Cross, when the 50-day moving average rises above 200-day moving average – produced net annual excess returns (NAERs) over the B&H strategy, for the entire period and each sub-period, after accounting for risk and transaction costs. Results were mixed for the large-cap index. The results support Cakici and Topyan (2013).Research limitations/implicationsThis study investigates several indicators, but future studies should examine others, especially based on volume and price.Practical implicationsInvestors in the FTSE Turkish small-cap index may use some trading rules to earn NAERs over the B&H strategy.Originality/valueThis research is important because it addresses a gap in the research by examining numerous TTRs in the Turkish stock market. Studies of TTRs in Turkey are scarce.

Diversification Within Large Cap Equity Mutual Funds in India

We ask whether a portfolio of large-cap mutual funds in India generates any diversification benefits as compared to holding a single large-cap index tracking exchange-traded fund. Using a mix of traditional measures like correlation and covariance of excess returns, and measures like tracking error, information ratio, and active share, we show that there are limited diversification benefits in a portfolio of two or more large-cap funds. A majority of the funds have similar risk return characteristics and negative information ratios versus a NIFTY100 tracking ETF. A cross-sectional analysis of active shares of the funds at the end of June 2020 shows that underlying holdings of funds are crowded together and funds do not have significant active share.

Small versus Large Caps—Empirical Performance Analyses of Stock Market Indices in Germany, EU & US since Global Financial Crisis

This academic paper applied different models in order to analyse the performance of several small cap and large cap indices for the German-, European- and US-market since the financial crisis in 2008. Thus, the period under consideration amounts to approx. 12 years. The research period starts in July 2008 and ends in August 2020. It was found, that an investment in the US large cap index outperformed the German as well as the European indices with regard to the Sharpe Ratio and Sortino Ratio. Furthermore, it has been proven that small cap indices in Germany and Europe outperformed their counterparts in terms of return, the large cap indices in terms of their risk/return profiles. For the US-market, this relationship turns. Thus, the large cap index represents the better investment compared to the small cap index. It can therefore be said that the small cap anomaly could only be detected on a country-specific basis. With regard to the maximum drawdown, it is evident that the German market implies a very similar risk to the American market. The European market again clearly beats the German and the US-market in terms of maximum drawdown and is therefore less risky.

Does herding behavior exist in cryptocurrency market?

This paper investigates the existence of herding behavior in cryptocurrencies market. Using data of the 20 large cryptocurrencies and MV Index Solution Crypto Compare Digital Assets for large cap index, we found no evidence of herding behavior using cross-sectional absolute standard deviation estimation. However, by applying a rolling window analysis, the results show significant herding behavior, which varies over time. Finally, we find an inverse relationship between herding behavior and the Bloomberg consumer comfort index which means that when traders are less comfortable they prefer to ignore their expectations and follow the market performance.

Implementing a Systematic Long-only Quality Strategy in the Indian Market

We believe investors should be willing to pay a higher price for higher quality companies. We build a composite quality score, including one which incorporates publicly available ESG scores, using 'off-the-shelf' criteria and publicly available financial data and show that a quarterly-rebalanced, long-only portfolio of top-decile stocks selected using our score in Hong Kong significantly outperforms the Hang Seng Composite LargeCap Index (HSLI) - both in terms of absolute returns (by 2.87% pa) and risk adjusted returns - while having an moderate annual turnover (a mean turnover of 42.15%). We show that our quality score predicts the persistence of quality for up to 3 years and there is a weak relationship between the price multiple and the quality score. Furthermore, we demonstrate that quality needs to be reviewed regularly - and the moderate turnover ensures this. In the absence of cheap ETFs to get systematic exposure to quality, the systematic long-only strategy using 'off-the-shelf' criteria provides a practical, executable systematic investment methodology that exposes an investor to quality in the Hong Kong market. We show that socially conscious investors can generate risk adjusted returns in excess of the HSLI in the Hong Kong market using a systematic strategy.

Implementing a Systematic Long-only Quality Strategy in the Hong Kong Market

We believe investors should be willing to pay a higher price for higher quality companies. We build a composite quality score, including one which incorporates publicly available ESG scores, using 'off-the-shelf' criteria and publicly available financial data and show that a quarterly-rebalanced, long-only portfolio of top-decile stocks selected using our score in Hong Kong significantly outperforms the Hang Seng Composite LargeCap Index (HSLI) - both in terms of absolute returns (by 2.87% pa) and risk adjusted returns - while having an moderate annual turnover (a mean turnover of 42.15%). We show that our quality score predicts the persistence of quality for up to 3 years and there is a weak relationship between the price multiple and the quality score. Furthermore, we demonstrate that quality needs to be reviewed regularly - and the moderate turnover ensures this. In the absence of cheap ETFs to get systematic exposure to quality, the systematic long-only strategy using 'off-the-shelf' criteria provides a practical, executable systematic investment methodology that exposes an investor to quality in the Hong Kong market. We show that socially conscious investors can generate risk adjusted returns in excess of the HSLI in the Hong Kong market using a systematic strategy.

Demystifying Illiquid Assets: Expected Returns for Private Equity

The growing interest in private equity means that allocators must carefully evaluate its risk and return. The challenge is that modeling private equity is not straightforward, due to a lack of good quality data and artificially smooth returns. We try to demystify the subject, considering theoretical arguments, historical average returns, and a forward-looking analysis. For institutional investors trying to calibrate their asset allocation decisions for private equity, we lay out a framework for expected returns, albeit one hampered by data limitations, that is based on a discounted cash-flow framework similar to what we use for public stocks and bonds. In particular, we attempt to assess private equity’s realized and estimated expected return edges over lower-cost public equity counterparts. Our estimates display a decreasing trend over time, which does not seem to have slowed the institutional demand for private equity. We conjecture that this is due to investors’ preference for the return-smoothing properties of illiquid assets in general. <b>TOPICS:</b>Private equity, performance measurement <b>Key Findings</b> • A leveraged small-cap public equity index may be a better benchmark for the performance of private equity than the large-cap indices generally used. Further, internal-rates-of-return (IRRs) can be especially misleading if they are compared against the time-weighted returns used for public market indices. • The smoothed returns of private equity understate the true economic risk and are an artifact of the lack of mark-to-market for illiquid assets. • The richening valuations of PE may be a headwind for future returns for the asset class, suggesting a slimmer edge over public equity than long-term averages.

The Index Effect Minute by Minute: Intraday Returns at NASDAQ-100 and MSCI U.S. Rebalancings

I use intraday data from 2013 to 2017 and a dataset of NASDAQ-100, MSCI USA and MSCI USA Small Cap Index constituent changes to investigate abnormal returns and trading volume around index rebalancings. The results show no pre-announcement speculation but a significantly positive (negative) post-announcement jump followed by an intraday drift for promotions (demotions). For small-cap changes, the post-announcement jump is smaller but abnormal returns with the same sign are also found on the rebalancing date and especially during the last 30 minutes of trading. Large-cap index changes have higher abnormal trading volume but much smaller abnormal returns on the rebalancing date. Consistent with the price pressure hypothesis, I find a reversal during the early trading hours on the day following the rebalancing date.

Predicting Short-term Market Returns and Volatility Using Index of Consumer Sentiment

This study examines the short-term predictive ability of Index of Consumer Sentiment (ICS) about Indian stock market returns. Monthly values of ICS, six broad market indices, and nine sectorial indices are collected. The paper finds significant contemporaneous co-movement between S&amp;PBSE500 and other indices. There is no contemporaneous co-movement between ICS and 15 indices. With one lag, ICS Granger cause two sectorial indices and four broad market indices. The one period ahead predictive regression model finds that sentiment has some predictive power of small cap, mid cap and BSE500 index returns. The effect is negative and statistically significant. The predictive regression result indicates that following month of high consumer sentiment, small cap, midcap, and BSE500 index returns decline and vice-versa. However, there is no association between ICS and large cap index and sentiment has no predictive ability of large cap index. The result of variance model indicate that ARCH term and GARCH term are insignificant indicating that the market has no long memory and new shocks will not persist to many future periods. The paper finds no volatility clustering and volatility persistence except in case of small cap index. This paper finds presence of noise trade and investors over-reaction in small cap stocks. EGARCH result supports for the presence of leverage effect, and confirms negative impact of consumer sentiment on small cap stocks.

Yield Spread Determinants &amp;amp; Reaction to Negative Event-shocks and Downgrades: The Case of Greece

The purpose of my study is to investigate the significance in the determination and worseness of ten year government bond yields spreads as well as the variation in the level of these spreads, factors like political risk and sovereign downgrades, considered as political and economic negative shocks in Greek economy during financial and public sector’s debt crisis. By extension to the main research, it is examined if shocks affected yields of two key sub-indexes of Athens Stock Exchange, ATHEX/FTSE large cap and ATHEX/Banks. The study extends from 1/1/2008 to 8/28/2015. My study is conducted in two stages, with similar goals. In this paper, I firstly investigate the role of significant, negatively signed political and economic events as well as sovereign downgrades, concerning Greece, in the determination of Greek ten-year bond spreads. In addition to the main research, I examine the influence of these negative shock-events in the yields of ATHEX/FTSE large cap index and ATHEX/Banks index. For this purpose, I make a series of regressions, since most studies analyzing the determinants of bond yield spreads rely on simple linear regression models and use OLS method of regression, as well. Secondly, I make an event-study, in Excel, on cumulative yields of the two latter indexes and on the yields of Greek bonds’ spreads and their movements ten days before and ten days after events and credit ratings that took place throughout the sample period. For the needs of the study, I reassess all models as well as the event study for three sub-time periods, namely “pre-crisis” period, from 1/1/2008 to 5/5/2010, “mid-crisis” period, from 5/6/2010 to 12/16/2014 and “new-crisis” period, from 12/17/2014 to 8/28/2015. Overall, the results that gave this empirical analysis seem to be very useful for politicians, for market makers and for market participants. It is mentioned that the evidence and conclusions occurred in the first part of this empirical analysis are confirmed, since they are in agreement with the findings of the event study that is in the second part of my research. Concluding, Greek government bond yield spreads are influenced from shock events and downgrades, mainly, during new crisis and pre-crisis period, respectively. Throughout the mid-crisis period neither yields of Athens stock Exchange indexes nor negatively signed shock events, including ratings, could cause a significant movement in spreads. This maybe means that, during this period, only other risk, fundamental or macroeconomic parameters could affect spreads. Especially from results of event study occurs the exact way that bond yield spreads and the yields of ATHEX/FTSE large cap and ATHEX/Banks indexes reacted ten days before and since an event-shock happened. The observed significant movements of spreads and the abnormal yields of indexes before the shock maybe indicates a leakage of information or a good prediction ability of market, while the noticed abnormal movements of spreads and yields after the event-day reflect the way of investors’ reaction in shocks.