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The purpose of this paper is to analyze the hedging capacity of Bitcoin in relation to the S&P 500 index during the COVID-19 pandemic.

In order to investigate the hedging features of Bitcoin in relation to the S&P 500 index during the COVID-19 pandemic, the authors use the Granger causality applied on a daily sample of observations ranging from January 1st, 2019 to December 31st, 2020. As robustness checks, the authors use autoregressive models to test the validity of the findings.

Using time series of daily data from 1st January 2019 to 31st December 2020, the results show that Bitcoin is not considered as a safe haven because it moves at the same pace as the S&P 500. As a robustness check, the authors use the exponential GARCH model and confirm our previous findings. Overall, the study contributes to the debate on both COVID-19's impact on financial systems and the hypothesis of Bitcoin being a safe haven during extreme global crises.

The study contributes to the debate on both COVID-19's impact on financial systems and the hypothesis of Bitcoin being a safe haven during extreme global crises.

This paper aims to examine and compare the dividend ratios’ statistical and economic ability to predict the equity premium in the UK and US markets and two US sub-indices (S&P 500 Growth and S&P 500 Value).

In this paper, the authors use the linear regression models to examine the dividend ratios’ statistical ability to predict the equity premium. The in-sample and out-of-sample approaches, including Diebold and Mariano (1995) statistics, and Goyal and Welch’s (2003) graphical approach, are used. Also, the mean-variance analysis is used to test the economic significance.

The paper findings indicate that the dividend ratios have in-sample and out-of-sample predictive abilities in both UK and US markets and both US sub-indices. However, the results show that the dividend ratios have a less impressive predictive ability in the US market compared to the UK market and less in the US value index than the US growth index. This could indicate that there is no relation between the number of companies that distribute dividends in each index and the informativeness of dividends ratios. Furthermore, the tests show the dividend ratios’ predictive ability departure during particular periods and in some indices.

Results and implications of this research are exclusively applied to the US and UK markets. These results can also be applied with caution to other markets, taking into consideration the distinctive characteristics of these markets.

Results revealed in this paper imply that the investors in any of the indices may experience economic gain by adopting a dynamic trading strategy using the information content of the dividend ratios prediction models instead of the benchmark model, which is the prevailing simple moving average model.

This paper adds value through testing the prediction models’ economic significance in two well-developed markets, in addition to exploring the relationship between the number of companies distributing cash dividends and the dividends ratio prediction ability. Unlike most of the previous studies in which dividend ratios’ prediction ability is attributed to the number of companies that distribute dividends in the market, this paper denied this interpretation by studying two S&P 500 sub-indices. To the best of the authors’ knowledge, this is the first study to test the prediction models’ ability for these sub-indices.

The purpose of this paper is to explore the impact of S&P 500 index fund money flow on the valuations of companies that are constituents of the index and those that are not.

To examine the impact of passive investing on corporate valuations, the authors run panel regressions of price‐to‐earnings ratio on aggregate money flow into S&P 500 index funds and control for various accounting variables that impact price‐to‐earnings ratio. These regressions involve two samples of stocks. The first sample consists of S&P 500 constituents. The second consists of large‐cap stocks that are not constituents of the S&P 500. The authors also run a set of separate regressions with price‐to‐book ratio rather than price‐to‐earnings ratio as the dependent variable.

It is found that the valuations of S&P 500 constituents increased by 139 to 167 basis points relative to nonconstituents, depending on valuation metric, due to S&P 500 index fund money flow when evaluated at mean values of money flow and valuation metrics. The valuations of firms within the S&P 500 index respond positively to changes in S&P 500 index fund money flow while the valuations of firms outside the index do not. Additionally, the impact of money flow on valuations persists the month after the flow occurs, suggesting that the impact does not dissipate over time.

Mispricings among individual stocks arising from index fund investing may reduce the allocative efficiency of the stock market and distort investors' performance evaluations of actively managed funds.

The paper is the first to explore the long‐run relationship between S&P 500 index fund money flow and corporate valuations.

The cornerstone of Modern Portfolio Theory with implications for many aspects of corporate finance is that reduced correlation among assets and reduced standard deviation are key elements in portfolio risk reduction. The purpose of this paper is to analyze the conditional correlation and standard deviation of a broad set of indices with the S & P 500 conditioned on market performance.

The authors examined volatility and correlation for a set of indices for a 19-year period based on weekly data from July 2, 1993 to June 30, 2012. These included the NASDAQ, MSCI EAFE, Russell 1000, Russell 2000, Russell 3000, Russell 1000 Growth, Russell 1000 Value, Gold, MSCI EM and Dow Jones UBS Commodity. The data for the Wilshire US REIT, Barclays Multiverse, Multiverse 1-3, Multiverse 3-5 and Multiverse 10+ became available starting July 2, 2002. For these indices the authors used weekly data from July 1, 2002 through June 30, 2012. For the iBarclays TIPS, the authors used weekly data from the time of availability, namely, for the period December 12, 2003 through June 29, 2012.

The findings demonstrate that both the conditional correlations and standard deviations vary as a function of market performance. Moreover, the authors obtain a U-shape distribution of correlations conditioned on market performance for equity indices, such as NASDAQ, as well as for the Wilshire REIT. Namely, correlations tend to be high when market returns are at low or high extremes. For more typical market performance, correlations tend to be low. A modified U-shape is found for bond indices and the Dow Jones UBS Commodity Index. Interestingly, the correlation between gold and the S & P 500 is unrelated to the return on the S & P.

While it has been observed that asset classes move together, this paper is the first to systematically analyze the nature of these asset class correlations.

The purpose of this paper is to propose two hybrid forecasting models which integrate available ones. A hybrid contaminated normal distribution (CND) model accurately reflects the non‐normal features of monthly S&P 500 index returns, and a hybrid GARCH model captures a serial correlation with respect to volatility. The hybrid GARCH model potentially enables financial institutions to evaluate long‐term investment risks in the S&P 500 index more accurately than current models.

The probability distribution of an expected investment outcome is generated with a Monte Carlo simulation. A taller peak and fatter tails (kurtosis), which the probability distribution of monthly S&P 500 index returns contains, is produced by integrating a CND model and a bootstrapping model. The serial correlation of volatilities is simulated by applying a GARCH model.

The hybrid CND model can simulate the non‐normality of monthly S&P 500 index returns, while avoiding the influence of discrete observations. The hybrid GARCH model, by contrast, can simulate the serial correlation of S&P 500 index volatilities, while generating fatter tails. Long‐term investment risks in the S&P 500 index are affected by the serial correlation of volatilities, not the non‐normality of returns.

The hybrid models are applied only to the S&P 500 index. Cross‐sectional correlations among different asset groups are not examined.

The proposed hybrid models are unique because they combine available ones with a decision tree algorithm. In addition, the paper clearly explains the strengths and weaknesses of existing forecasting models.

Many papers have argued that there are long‐run downward‐sloping demand curves (LRDDC) for stocks. The purpose of this paper is to analyze this hypothesis using a new, unique, and ostensibly information‐free event: the re‐weighting of the Standard & Poor (S&P) 500 index from market based to free‐float based, which involves a significant shift in supply that, under the LRDDC, should result in significant and permanent price movements.

Event study methodology is used to examine abnormal returns and trading activity around the free‐float weight implementation dates for S&P 500 firms with various investable weight factors.

As a result of S&P 500 index re‐weighting, affected stocks experience statistically significant excess returns of −1.54 percent during the event week. This return is reversed during the following 30 days as trading volume returns to normal levels. These results are contrary to previous studies that analyze ostensibly informational events and/or different exchanges.

Results of this study indicate that arbitrage appears to be effective in eliminating a long‐term mispricing, which challenges the validity of the LRDDC hypothesis.

This study contributes to the body of literature on the S&P 500 index firms by providing supporting evidence for the price‐pressure hypothesis.

The purpose of this study is to compare market reactions to the change in the demand by index funds between large and small company stocks by examining the transition of the S&P 500, S&P 400 MidCap and S&P 600 SmallCap indexes from market capitalization to free-float weighting. This unique information-free event allows not only avoiding confounding information signaling and investor awareness effects but also comparing the effect of the decrease in demand on stocks of different sizes.

This study uses the event study methodology to calculate abnormal returns and trading volume around the full-float adjustment day. It also tests for significant changes in institutional ownership and liquidity. Multivariate regressions are used to examine the relation of liquidity changes and price elasticity of demand to the cumulative abnormal returns around the full-float adjustment day.

This study finds significant decreases in stock price accompanied with significant increases in trading volume on the full-float adjustment day, and significant gains in quasi-indexer institutional ownership and liquidity. The main finding is that cumulative abnormal returns around the event period are related to changes in the number of quasi-indexer and transient institutional shareholders, not to changes in liquidity or price elasticity of demand.

This study provides the first comprehensive comparison analysis of stock market reactions to the decline in demand between large and small company stocks. As an important implication for future studies of the index effect, changes in institutional ownership should be considered in the analysis.

There is empirical evidence that a firm's addition to S&P 500 results in significant abnormal returns and an increase in a stock's liquidity. The purpose of this paper is to argue that changes in the information environment after the year 2000 due to the implementation of Regulation Fair Disclosure (FD), decimalization and Sarbanes Oxley Act, should result in reduced abnormal returns in the post‐2000 period.

The authors compare the abnormal returns and liquidity changes around the announcement day of firm's addition to S&P 500 in the pre‐ and post‐2000 periods. Univariate and multivariate tests are used to control for factors that research shows affect the abnormal returns around additions to S&P 500.

It is found that the reduction in informational asymmetry in the post‐2000 period has resulted in a significant decrease in the abnormal return on the announcement day of additions to S&P 500 index and changes in the stock's liquidity in the post announcement period are now marginal.

Existing literature related to changes in the abnormal returns around additions to S&P 500 does not account for changes in the information environment in the two sub periods, pre‐ and post‐2000. The results may have implications for studies related to additions to S&P 500 where the sample period spans over the two sub periods.

When a stock is added into the S&P 500 Index, it in effect becomes cross-listed in the Index derivative markets. When index-based trading strategies such as index arbitrage are executed, the component stocks are directly affected by such trading. We find increased volatility of daily returns, plus increased trading volume for the underlying stocks. Utilizing a list of S&P 500 Index composition changes over the period September 1976 to December 2005, we study the market-adjusted volume turnover and return variance of the stocks added to and deleted from the Index. The results indicate that after the introduction of the S&P 500 Index futures and options contracts, stocks added to the S&P 500 experience statistically significant increase in both trading volume and return volatility. Both daily and monthly return variances increase following index inclusion. When stocks are removed from the index, though, neither volatility of returns nor trading volume experiences any significant change. So, we have new evidence showing that Index inclusion changes a firm's return volatility, and supporting the destabilization hypothesis.

The purpose of this paper is to investigate the initial public offerings (IPOs) of the firms that are eventually included in one of the S&P 400, the S&P 500, or the S&P 600 Indices. Do these firms have very different IPO features than the rest of the IPOs?

The control sample is formed of IPOs that are not included in the corresponding index, and the IPOs that end up in each S&P index are compared to this control sample. Logistic regressions are utilized to estimate the odds of inclusion into one of these indices.

The author finds that the IPO features, such as underpricing, offer price, underwriter's reputation, venture capital presence, and so on, are found to be substantially different for the index samples. The index firms are found to be “superstars” that deliver extremely high long‐run returns between their IPO date and their index inclusion date. The above results suggest that the quality of index firms has a persistent component to it that can be detected even during the IPO process. After estimating the determinants of the index inclusion, the author discovers that factors implying lower asymmetric information about firm's business (such as, the firm being a spinoff, or being certified by a venture capitalist or a prestigious underwriter, etc.) increase its odds of inclusion.

The paper proposes and tests two new hypotheses related to inclusion into an S&P index. Discoveries made in this paper can help someone recognize which IPOs could become “superstars” that end up in an S&P index.