Pinning in the S&P 500 futures

We show that Standard & Poor's (S&P) 500 futures are pulled toward the at-the-money strike price on days when serial options on the S&P 500 futures expire (pinning) and are pushed away from the cost-of-carry adjusted at-the-money strike price right before the expiration of options on the S&P 500 index (anti-cross-pinning). These effects are driven by the interplay of market makers' rebalancing of delta hedges due to the time decay of those hedges as well as in response to reselling (and early exercise) of in-the-money options by individual investors. The associated shift in notional futures value is at least $115 million per expiration day.     

Exploiting social media with higher-order Factorization Machines: statistical arbitrage on high-frequency data of the S&P 500

Over the past 15 years, there have been a number of studies using text mining for predicting stock market data. Two recent publications employed support vector machines and second-order Factorization Machines, respectively, to this end. However, these approaches either completely neglect interactions between the features extracted from the text, or they only account for second-order interactions. In this paper, we apply higher-order Factorization Machines, for which efficient training algorithms have only been available since 2016. As Factorization Machines require hyperparameters to be specified, we also introduce a novel adaptive-order algorithm for automatically determining them. Our study is the first one to make use of social media data for predicting minute-by-minute stock returns, namely the ones of the S&P 500 stock constituents. We show that, unlike a trading strategy employing support vector machines, Factorization-Machine-based strategies attain positive returns after transactions costs for the years 2014 and 2015. Especially the approach applying the adaptive-order algorithm outperforms classical approaches with respect to a multitude of criteria, and it features very favorable characteristics.     

S&P 500 Index Additions and Earnings Expectations

Stock price increases associated with addition to the S&P 500 Index have been interpreted as evidence that demand curves for stocks slope downward. A key premise underlying this interpretation is that Index inclusion provides no new information about companies' future prospects. We examine this premise by analyzing analysts' earnings per share (eps) forecasts around Index inclusion and by comparing postinclusion realized earnings to preinclusion forecasts. Relative to benchmark companies, companies newly added to the Index experience significant increases in eps forecasts and significant improvements in realized earnings. These results indicate that S&P Index inclusion is not an information‐free event.     

Founding-Family Ownership and Firm Performance: Evidence from the S&P 500

We investigate the relation between founding‐family ownership and firm performance. We find that family ownership is both prevalent and substantial; families are present in one‐third of the S&P 500 and account for 18 percent of outstanding equity. Contrary to our conjecture, we find family firms perform better than nonfamily firms. Additional analysis reveals that the relation between family holdings and firm performance is nonlinear and that when family members serve as CEO, performance is better than with outside CEOs. Overall, our results are inconsistent with the hypothesis that minority shareholders are adversely affected by family ownership, suggesting that family ownership is an effective organizational structure.     

Changes in cash holdings around the S&P 500 additions

We analyze the changes in cash holding policies of S&P 500 firms from before to after their inclusion in the index. One year after inclusion, their mean industry-adjusted cash holdings decline by nearly 32% from the year before inclusion. Several factors explain this decline. The precautionary motive for cash subsides due to these firms becoming more visible, less uncertain, and less constrained to raise cheap external capital. Corporate governance deteriorates after inclusion due to increased managerial entrenchment, which leads to a reduction in cash as suggested by the free cash flow hypothesis. Most index firms face diminishing investment opportunities and decreasing capital expenditures, which implies a lesser need for cash holdings related to the transaction motive.     

Disclosure strategies among S&P 500 firms: Evidence on the disclosure of non-GAAP financial measures and financial statements in earnings press releases

This paper examines the prominence of non-GAAP financial measures in press releases, testing whether managers emphasize these adjusted performance measures relative to GAAP numbers in four different settings where their disclosure helps managers reach strategic earnings benchmarks on a pro forma basis when they would otherwise fall short using GAAP numbers. Moreover, this research investigates the information content of disclosures reconciling non-GAAP to GAAP earnings (and other financial statements). The data is hand collected from quarterly earnings press releases of a sample of S&P 500 firms during the 2001–2003 period. In this particular sample, the disclosure of non-GAAP financial measures is frequent. The results suggest that managers strategically give more prominence to non-GAAP measures than to GAAP figures when the GAAP earnings number falls short of a benchmark but the non-GAAP earnings number does not. This disclosure strategy may influence the perception of the firm's financial results. Furthermore, the results suggest that both the reconciliation and the non-GAAP income statement contain information useful for users.     

Analysis of DJIA,S&P 500, S&P 400, NASDAQ 100 and Russell 2000 ETFs and their influence on price discovery

This study examines the temporal behavior of price discovery in the spot, ETF and futures markets of the DJIA, S&P 500, S&P 400, NASDAQ 100 and Russell 2000. We document an increasing trend in the price discovery metrics of exchange traded funds for all indexes but the DJIA. Contrary to past studies, our findings show that the spot market rather than the futures market leads the price discovery. The arbitrage process that links exchange traded funds to spot prices, and not the futures prices might explain the results. This daily arbitrage that ensures exchange traded funds prices equal net asset values appear to promote spot market price discovery especially with the popularity of exchange traded funds in more recent years. We additionally document that the temporal behavior of the exchange traded funds price discovery metric affects differently price discovery in the spot and futures markets across indexes.     

Decision trees unearth return sign predictability in the S&P 500

Technical trading rules and linear regression models are often used by practitioners to find trends in asset returns. However, these models typically neglect interaction terms between the lagged daily directional movements. We propose a decision tree forecasting model that has the flexibility to capture arbitrary interaction patterns. To study the importance of interaction terms, we construct a binary Markov process with a deterministic component that cannot be predicted without interaction terms between the lagged directional movements. We show that some tree based strategies achieve trading performance significant at the 99% confidence level on the S&P 500 over the past 20 years, after adjusting for multiple testing. The best strategy breaks even with the buy-and-hold strategy at 21 bps in transaction costs per round trip. A four-factor regression analysis shows significant intercept, and correlation with the market. The directional predictability is strongest during the bursts of the dotcom bubble, financial crisis, and European debt crisis. The return sign predictability during these periods confirms the necessity of interaction terms to model daily returns.     

Non-linearities in financial bubbles: Theory and Bayesian evidence from S&P500

The modeling process of bubbles, using advanced mathematical and econometric techniques, is a young field of research. In this context, significant model misspecification could result from ignoring potential non-linearities. More precisely, the present paper attempts to detect and date non-linear bubble episodes. To do so, we use Neural Networks to capture the neglected non-linearities. Also, we provide a recursive dating procedure for bubble episodes. When using data on stock price-dividend ratio S&P500 (1871.1–2014.6), employing Bayesian techniques, the proposed approach identifies more episodes than other bubble tests in the literature, while the common episodes are, in general, found to have a longer duration, which is evidence of an early warning mechanism (EWM) that could have important policy implications.     

Market uncertainty,expected volatility and the mispricing of S&P 500 index futures

An association between increased index futures mispricing and concurrent index volatility has been reported within several prior studies; in the present study, we argue that expected volatility over an arbitrage horizon also has an adverse effect on the ability and willingness of traders to engage in arbitrage, leading to greater and more persistent futures mispricing. Using the CBOE VIX and its innovation on the concurrent spot volatility as proxies for expected volatility, we present evidence of an increase in S&P 500 index futures mispricing with expected volatility. The impact of the VIX grows exponentially across the distribution of conditional mispricing levels, which suggests that the expectations of heightened future volatility become increasingly detrimental to arbitrage activities when the futures price deviations are enlarged; however, the influence of expected volatility is found to have been reduced during the global financial crisis period, a period during which concurrent volatility overwhelmingly dominated the magnitude of mispricing.     

Accounting for decarbonisation and reducing capital at risk in the S&P500

This article accounts for carbon emissions in the S&P 500 and explores the extent to which capital is at risk from decarbonising value chains. At a global level it is proving difficult to decouple carbon emissions from GDP growth. Top-down legal and regulatory arrangements envisaged by the Kyoto Protocol are practically redundant given inconsistent political commitment to mitigating global climate change and promoting sustainability. The United Nations Environment Programme (UNEP) and European Commission (EC) are promoting the role of financial markets and financial institutions as drivers of behavioural change mobilising capital allocations to decarbonise corporate activity.     

The jump component of S&P 500 volatility and the VIX index

Much research has investigated the differences between option implied volatilities and econometric model-based forecasts. Implied volatility is a market determined forecast, in contrast to model-based forecasts that employ some degree of smoothing of past volatility to generate forecasts. Implied volatility has the potential to reflect information that a model-based forecast could not. This paper considers two issues relating to the informational content of the S&P 500 VIX implied volatility index. First, whether it subsumes information on how historical jump activity contributed to the price volatility, followed by whether the VIX reflects any incremental information pertaining to future jump activity relative to model-based forecasts. It is found that the VIX index both subsumes information relating to past jump contributions to total volatility and reflects incremental information pertaining to future jump activity. This issue has not been examined previously and expands our understanding of how option markets form their volatility forecasts.     

Bounds for VIX futures given S&P 500 smiles

We derive sharp bounds for the prices of VIX futures using the full information of S&P 500 smiles. To that end, we formulate the model-free sub/superreplication of the VIX by trading in the S&P 500 and its vanilla options as well as the forward-starting log-contracts. A dual problem of minimizing/maximizing certain risk-neutral expectations is introduced and shown to yield the same value.The classical bounds for VIX futures given the smiles only use a calendar spread of log-contracts on the S&P 500. We analyze for which smiles the classical bounds are sharp and how they can be improved when they are not. In particular, we introduce a family of functionally generated portfolios which often improves the classical bounds while still being tractable; more precisely, they are determined by a single concave/convex function on the line. Numerical experiments on market data and SABR smiles show that the classical lower bound can be improved dramatically, whereas the upper bound is often close to optimal.     

Idiosyncratic volatility and cash flow volatility: New evidence from S&P 500

Employing firm-level data of S&P 500 constituent companies from 1990 to 2016, we offer new evidence on the strong time series and cross-sectional relationships between Idiosyncratic stock return volatility (Ivol) and cash flow volatility even after controlling for illiquidity and firm size, which also vary by period of economic condition. Our results show that Ivol is well explained by the volatility of the three components of DuPont ROE. Aggregate asset turnover volatility alone explains 81.8% of the time series variation of aggregate Ivol, and all independent variables explain 94.7% of the aggregate Ivol. While profit margin volatility and asset turnover volatility have significant relationships with Ivol during the sample period, the volatility of equity multiplier shows significance during the two recession periods in early and late 2000s.     

Volatility dynamics for the S&P 500: Further evidence from non-affine,multi-factor jump diffusions

We apply Markov chain Monte Carlo methods to time series data on S&P 500 index returns, and to its option prices via a term structure of VIX indices, to estimate 18 different affine and non-affine stochastic volatility models with one or two variance factors, and where jumps are allowed in both the price and the instantaneous volatility. The in-sample fit to the VIX term structure shows that the second (stochastic long-term volatility) factor is required to fit the VIX term structure. Out-of-sample tests on the fit to individual option prices, as well as in-sample tests, show that the inclusion of jumps is less important than allowing for non-affine dynamics. The estimation and testing periods together cover more than 21 years of daily data.     

Trade Imbalances and Inventory Effects in Long-term S&P 500 Index Options

This article investigates how trade imbalances affect prices in the S&P 500 Long‐term Equity Anticipation Securities (LEAPS) market. From 1994 to 1996, put volume was 30 times higher than call volume, and public purchases of puts vastly outnumbered sales. We find that LEAPS put quotes are revised following trade imbalances by more than can be explained by information effects, suggesting that put prices are subject to price pressure or inventory effects. The results suggest market frictions are important in the pricing of options, at least in settings in which arbitrage is particularly costly and public demand leans toward one type of order.     

An Examination of Option-Implied S&P 500 Futures Price Distributions

Expected S&P 500 futures price distributions are derived using no-arbitrage option pricing models. These distributions are parameterized both as the lognormal and as a less restrictive three-parameter Burr-XII distribution. The resulting option-based probability assessments display some evidence of miscalibration very near to expiration and far from expiration, but are accurate over intermediate time ranges. The means of the implied price distributions correspond closely to the contemporaneous futures prices for both distributions, although marginally better with the Burr-XII. The Burr-XII distribution also performs better than the lognormal based on calibration statistics, and hence, is used to recalibrate estimated distributions.     

Hedging the black swan: Conditional heteroskedasticity and tail dependence in S&P500 and VIX

The recent financial crisis has accentuated the fact that extreme outcomes have been overlooked and not dealt with adequately. While extreme value theories have existed for a long time, the multivariate variant is difficult to handle in the financial markets due to the prevalent heteroskedasticity embedded in most financial time series, and the complex extremal dependence that cannot be conveniently captured by a single structure. Moreover, most of the existing approaches are based on a limiting argument in which all variables become large at the same rate. In this paper, we show how the conditional approach of Heffernan and Tawn (2004) can be implemented to model extremal dependence between financial time series. We use a hedging example based on VIX futures to demonstrate the flexibility and superiority of the conditional approach against the conventional OLS regression approach.