Academic literature on the topic 'Short-selling risk'

This paper aims to investigate short selling and stock price crash risk. The authors find that short selling is positively associated with one-month-ahead stock price crash risk, consistent with the literature showing that short sellers are informed traders. The authors attribute this prediction ability to the information short sellers receive from foreign investors with high levels of ownership in a firm. The results shed light on policy issues regarding short selling regulation.

Deleveraging risk is the risk attributable to investing in a security held by levered investors. When there is an aggregate negative shock to the availability of funding capital, securities with a greater presence of levered investors experience extreme return realizations as these investors unwind their positions. Using data on equity loans as a proxy for the degree of levered positions in a given stock, we find robust evidence of deleveraging risk. Stocks with a high degree of short selling experience large positive returns and a decrease in short selling around periods of funding capital scarcity.

This article analyzes the effectiveness of the short selling ban, and questions it with critiques from comparative empirical data. Authors have argued that the ban on short selling hit trading volumes but did not necessarily reduce market volatility. Today market regulators are seeking to rebuild a short selling policy that allows covered short selling while reducing the risk of market abuse. The reinforced framework must include rules and regulations that increase market efficiency, enhance the visibility of short selling to regulators and to investors, improve regulators? responsiveness to market failures and periods of extreme volatility, and enforce anti-abuse laws consistently and judiciously. Although most regulators have allowed their short sale bans to lapse and seem to be thinking constructively about the form of future regulation, the dust has not settled on the short sale debate. As the events of the year 2010 outline, short selling regulations tend to mirror the capital markets they oversee. The author questions if the capital market in Montenegro is ready to lift the short selling ban and to allow speculative trading again.

The use and effect of derivatives and short selling by US equity and bond open-end mutual funds are studied using a large and unique database. We find that the likelihood of their use is positively related to fund size, family size, and fund turnover for both fund types except for short selling by equity funds from larger families. Our findings suggest that funds that use derivatives exhibit significantly higher benchmark-adjusted performances based on both gross- and net-of-fees returns. This is done without adversely affecting market betas, net expense ratios (NERs), or brokerage fees as a proportion of total net assets (TNA). We find that for bond funds derivative use is negatively associated with non-systematic risk and short selling use is positively associated with total and systematic risk.

Most regulators around the world reacted to the 2007-09 crisis by imposing bans on short selling. Using data from seven equity markets, this study empirically examines the impact of the 2008 short-selling bans on financial stocks. Using panel and matching techniques, evidence indicates that bans on short-selling (i) on the whole widen volatility both in terms of High-Low spread and GARCH analysis, (ii) were not able to reduce systematic risk, (iii) overall failed to support prices. On the whole our results are in line with previous literature.

The goal of this thesis is to study the two key aspects of complexity of the financial system: interconnectedness and nonlinear relationships. In Chapter 1, I contribute to the literature that focuses on modelling the nonlinear relationship between variables at the extremes of their distribution. In particular, I study the nonlinear relationship between stock prices and short selling. Whereas most of the academic literature has focused on measuring the relationship between short selling and asset returns on average, in Chapter 1, I focus on studying the relationship that arises in the extremes of the two variables. I show that the association between financial stock prices and short selling can become extremely strong under exceptional circumstances, while at the same time being weak in normal times. The tail relationship is stronger for small cap firms, a result that is intuitively in line with the empirical findings that stocks with lower liquidity are more price-sensitive to short selling. Finally, results show that the adverse tail correlation between increases in short selling and declines in stock prices was not always lower during the ban periods, but had declined markedly towards the end of the analysis window. Such results cast doubts about the effectiveness of bans as a way to prevent self-reinforcing downward price spirals during the crisis. In Chapter 2, I propose a measure of interconnectedness that takes into account the time-varying nature of connections between financial institutions. Here, the parameters underlying comovement are allowed to evolve continually over time through permanent shifts at every period. The result is an extremely flexible measure of interconnectedness, which uncovers new dynamics of the US financial system and can be used to monitor financial stability for regulatory purposes. Various studies have combined statistical measures of association (e.g. correlation, Granger causality, tail dependence) with network techniques, in order to infer financial interconnectedness (Billio et al. 2012; Barigozzi and Brownlees, 2016; Hautsch et al. 2015). However, these standard statistical measures presuppose that the inferred relationships are time-invariant over the sample used for the estimation. To retrieve a dynamic measure of interconnectedness, the usual approach has been to divide the original sample period into multiple subsamples and calculate these statistical measures over rolling windows of data. I argue that this is potentially unsuitable if the system studied is time-varying. By relying on short subsamples, rolling windows lower the power of inference and induce dimensionality problems. Moreover, the rolling window approach is known to be susceptible to outliers because, in small subsamples, these have a larger impact on estimates (Zivot and Wang, 2006). On the other hand, choosing longer windows will lead to estimates that are less reactive to change, biasing results towards time-invariant connections. Thus, the rolling window approach requires the researcher to choose the window size, which involves a trade-off between precision and flexibility (Clark and McCracken, 2009). The choice of window size is critical and can lead to different results regarding interconnectedness. The major novelty of the framework is that I recover a network of financial spillovers that is entirely dynamic. To do so, I make the modelling assumption that the connection between any two institutions evolves smoothly through time. I consider this assumption reasonable for three main reasons. First, since connections are the result of many financial contracts, it seems natural that they evolve smoothly rather than abruptly. Second, the assumption implies that the best forecast of a connection in the future is the state of that connection today. This is consistent with the notion of forward-looking prices. Third, the assumption allows for high flexibility and for the data to speak for itself. The empirical results show that financial interconnectedness peaked around two main events: the Long-Term Capital Management crisis of 1998 and the great financial crisis of 2008. During these two events, I found that large banks and broker/dealers were among the most interconnected sectors and that real estate companies were the most vulnerable to financial spillovers. At the individual financial institution level, I found that Bear Stearns was the most vulnerable financial institution, however, it was not a major propagator, and this might explain why its default did not trigger a systemic crisis. Finally, I ranked financial institutions according to their interconnectedness and I found that rankings based on the time-varying approach were more stable than rankings based on other market-based measures (e.g. marginal expected short fall by Acharya et al. (2012) and Brownlees and Engle (2016)). This aspect is significant for policy makers because highly unstable rankings are unlikely to be useful to motivate policy action (Danielsson et al. 2015; Dungey et al. 2013). In Chapter 3, rather than assuming interconnectedness as an exogenous process that has to be inferred, as is done in Chapter 2, I model interconnectedness as an endogenous function of market dynamics. Here, I take interconnectedness as the realized correlation of asset returns. I seek to understand how short selling can induce higher interconnectedness by increasing the negative price pressure on pairs of stocks. It is well known that realized correlation varies continually through time and becomes higher during market events, such as the liquidation of large funds. Most studies model correlation as an exogenous stochastic process, as is done, for example, in Chapter 2. However, recent studies have proposed to interpret correlation as an endogenous function of the supply and demand of assets (Brunnermeier and Pedersen, 2005; Brunnermeier and Oehmke, 2014; Cont and Wagalath, 2013; Yang and Satchell, 2007). Following these studies, I analyse the relationship between short selling and correlation between assets. First, thanks to new data on public short selling disclosures for the United Kingdom, I connect stocks based on the number of common short sellers actively shorting them. I then analyse the relationship between common short selling and excess correlation of those stocks. To this end, I measure excess correlation as the monthly realized correlation of four-factor Fama and French (1993) and Carhart (1997) daily returns. I show that common short selling can predict one-month ahead excess correlation, controlling for similarities in size, book-to-market, momentum, and several other common characteristics. I verify the confirm the predictive ability of common short selling out-of-sample, which could prove useful for risk and portfolio managers attempting to forecast the future correlation of assets. Moreover, I showed that this predictive ability can be used to establish a trading strategy that yields positive cumulative returns over 12 months. In the second part of the chapter I concentrate on possible mechanisms that could give rise to this effect. I focus on three, non-exclusive, mechanisms. First, short selling can induce higher correlation in asset prices through the price-impact mechanism (Brunnermeier and Oehmke, 2014; Cont and Wagalath, 2013). According to this mechanism, short sellers can contribute to price declines by creating sell-order imbalances i.e. by increasing excess supply of an asset. Thus, short selling across several stocks should increase the realized correlation of those stocks. Second, common short selling can be associated with higher correlation if short sellers are acting as voluntary liquidity providers. According to this mechanisms, short sellers might act as liquidity providers in times of high buy-order imbalances (Diether et al. 2009b). In this cases, the low returns observed after short sales might be compensations to short sellers for providing liquidity. In a multi-asset setting, this mechanism would result in short selling being associated with higher correlation mechanism. Both above-mentioned mechanisms deliver a testable hypothesis that I verify. In particular, both mechanisms posit that the association between short selling and correlation should be stronger for stocks which are low on liquidity. For the first mechanism, the price impact effect should be stronger for illiquid stocks and stocks with low market depth. For the liquidity provision mechanism, the compensation for providing liquidity should be higher for illiquid stocks. The empirical results cannot confirm that uncovered association between short selling and correlation is stronger for illiquid stocks, thus not supporting the price-impact and liquidity provision hypothesis. I thus examine a third possible mechanism that could explain the uncovered association between short selling and correlation i.e. the informative trading mechanism. Short sellers have been found to be sophisticated market agents which can predict future returns (Dechow et al. 2001). If this is indeed the case, then short selling should be associated with higher future correlation. I found that informed common short selling i.e. common short selling that is linked to informative trading, was strongly associated to future excess correlation. This evidence supports the informative trading mechanism as an explanation for the association between short selling and correlation. In order to further verify this mechanism, I checked if informed short selling takes place in the data, whilst controlling for several of the determinants of short selling, including short selling costs. The results show evidence of both informed and momentum-based non-informed short selling taking place. Overall, the results have several policy implications for regulators. The results suggest that the relationship between short selling and future excess correlation is driven by informative short selling, thus confirming the sophistication of short sellers and their proven importance for market efficiency and price informativeness (Boehmer and Wu, 2013). On the other hand, I could not dismiss that also non-informative momentum-based short selling is taking place in the sample. The good news is that I did not find evidence of a potentially detrimental price-impact effect of common short selling for illiquid stock, which is the sort of predatory effect that regulators often fear.
Doctorat en Sciences économiques et de gestion
info:eu-repo/semantics/nonPublished

碩士
國立臺灣大學
財務金融學研究所
97
In the middle of 2007, since Bear Stearns hedge funds suspended redemption, the credit market turmoil prevailed. With Bears Stearns being taken over and Lehman Brothers ending in bankruptcy, credit default swap spreads spiked to historical high level. A lifer’s credit position had suffered huge loss if it held sub prime related investments or collateralized debt obligation (CDO). A manager can hedge its loss by using credit default swap to hedge the loss in default. However, regulatory or credit line limitations may prompt he/she to use other hedge tools. A negative relationship exists in a firm’s share price and credit spread. With the fall in equity buffer, the credit spread reflecting credit risk is likely to widen as well. Therefore, our study utilizes share short selling as a method to hedge against credit risk. By using a regression, a hedge ratio is determined by the return of share short-selling and long credit default swap position. Then, the hedge results are determined by dynamically rebalancing the hedge position. In order to achieve better hedge results, adjustments are made to the hedge ratio using the return’s correlation and standard deviation. We conclude by suggesting life insurance companies shall utilize the market opportunities to form a credit reserve for future potential credit loss.

碩士
銘傳大學
財務金融學系碩士班
105
Stock price crash risk means a large negative outlier in the distribution of returns. Recent academic studies argue that bad news hoarding leads to stock price crash risk and conjecture that short sellers are informed traders who are able to detect bad news hoarding activities by firms whose stock they short in anticipation of price crashes, then short interest should reflect the potential for bad news hoarding behavior in firms. This paper investigates whether short interest is positively related to future stock price crash risk by using individual and institutional investor’s short selling in Taiwan stock market. Second, we test whether arbitrage limits change the relation between short interest and stock price crash risk. To provide investors a reference indicator to avoid stock price crash risk. In this paper, the empirical results show that: (1) short interest is positively related to future stock price crash risk by using individual and institutional investor’s short selling, showing when the individual investor’s short selling ratio (institutional investor’s short selling ratio) more large, then stock price crash risk for the next year will more large, so the individual and institutional short selling activities have the ability to predict the future stock price crash risk. (2) In the case of the short selling limit of individual stocks, this paper finds that the higher Idiosyncratic risk and smaller firm size of individual stocks will reduce the positive relationship between the short selling and future stock price crash risk.

碩士
國立暨南國際大學
資訊管理學系
101
Value-at-Risk (VaR) is Basel Accord standard in the financial market, but there are some drawbacks of it, Value-at-Risk cannot sufficient of convexity, sub-additive and tail risk. This study based on Benati and Rizzi’s (2007) and Lin’s (2009) nonlinear VaR model then proposed Linear VaR model to improve the Benati and Rizzi’s (2007) VaR model, our model only need to set . This study use the rolling window technique in multiple rebalancing periods with short selling and transaction cost among the VaR models. Four kinds of performance assessments which are global optimal solution, similarity index, sharpe ratio, and market value are used to compare the performances among these three models. This study simulate the historical data by using 28 kinds of investment targets of ETF, energy, minerals, and real estate investment trusts funds. The test of nonlinear VaR model will be local optimal solution in our research. The Linear VaR model can exhibit global optimal solution. The performance of sharpe ratio and VaR (return) in Linear VaR model are better than other models.

碩士
國立暨南國際大學
資訊管理學系
101
With different risk measurements, the rebalancing portfolio selection models including the Mean Variance model, the Mean Absolute Deviation model, the Downside Risk model, and the Conditional Value at Risk model with short selling and trading cost have been studied. Using the rolling window technique, multi-period trading simulation is performed while short selling and transaction cost are also taken into consideration to these four models. Four kinds of performance assessments, which are sharpe ratio, market value, portfolio weight and similarity index are used to compare performances among these four models. The simulating result from using the historical data which is consisted of different asset types including the Exchange Traded Funds, S&P 500 Volatility Index, Commodity, Energy, Precious Metal, Real Estate Investment Trust and Fixed Income. Then we try to find out which risk measure is suitable for which asset type. Therefore, according to this study, we can infer that Conditional Value at Risk is suitable for the asset types which are the combination of many different asset types. Downside risk measure is suitable for the portfolio which consists of assets with negative correlation to the market. The similarity proportion with Mean Absolute Deviation and Mean Variance are more similar than other two models. On the other hand, Buy and Hold strategy in each situation can not have a great benefit without fixed income. In different rebalancing period, the longer and shorter rebalancing period cannot have a good performance in our test with holding period 10 days, 20days, 40days and 60days. The best rebalancing period is 20days for portfolio selection according to our results.

Should marijuana be legalized? Since 2012 four US states have legalized commercial for-profit marijuana production and use, while Washington DC has legalized possession, growth and gifting of limited amounts of the plant. Other states, and even cities, have decriminalized possession, allowed for medical use, or reduced possession to a misdemeanor. While marijuana is forbidden by international treaties and by national and local laws across the globe, polls show that public support for legalization has continued to increase steadily over time. So why does the issue of marijuana legalization continue to be so controversial? One short answer is that it is an extremely complicated business, with approaches toward legalization just within the United States varying widely. What’s more, not all supporters of “legalization ” agree on what it is they want to legalize: Just using marijuana? Growing it? Selling it? Advertising it? If sales are to be legal, what regulations and taxes should apply? Different forms of legalization have demonstrated very different results. This second edition of Marijuana Legalization: What Everyone Needs to Know® provides readers with a non-partisan primer covering everything from the risks and benefits of using marijuana to what is happening with marijuana policy in the United States and abroad. The authors discuss the costs and benefits of legalization at the state and national levels and explore the “middle ground ” of policy options between prohibition and commercialized production. The book also considers the personal impact of marijuana legalization on parents, heavy users, medical users, employers, and even drug traffickers.

In global, short-life-cycle supply chains, the Make-to-Order (MTO) principle of purchasing is dominant in securing the match between customers' commitment and ordered goods. Risk can be addressed by linking knowledge on inbound logistics to the market by use of e-business solutions. This chapter describes a multi-method approach using both traditional B2B and B2C methods of sales initiatives as an e-business system connecting inbound and outbound supply chains. Initiatives can be reverse auctions, time limited discounts, co-selling, bundling, short campaigns supported with letters, e-mails, giveaways, discount schemes, and payment conditions. Interlinking is to secure full transparency at any given point in time. The discussed solution has the potential of diverting goods to retailers instead of warehouses with the probable effect of increasing revenue of both wholesale and retail with 5 – 10%. Concurrently, the attention created from selling on-ship creates side effects like higher store replenishment rate and attracts frequent shoppers interested in continuously new offerings.