Research articles for the 2019-12-23

A Model Free Approach to the Pricing of Downside Risk in Argentinean Stocks
Dapena, José Pablo,Serur, Juan A.,Siri, Julián Ricardo
The return dynamics of Argentina's main stock index, the SP Mer.Val., show a high level of volatility, signaling a higher degree of downside risk. To hedge against that specific risk, investors could buy put options. However, the Argentinean capital market slacks variety of hedging contracts. The basic availability of putoptions depends on the possibility of short selling the underlying security, i.e. transfer risk to a third party, something not properly developed in the domestic market. Since data processing power has geometrically increased in the last decades and some mathematic formulas that were helpful for calculation had been surpassed by data gathering and processing that helps to find a better estimate when necessary, in this paper we show the point calculating protection against downside risk in the Argentinean stock market, using real data and programming an algorithm to perform calculations instead of resorting the standard Black-Scholes-Merton formulae, by means of a model free approach to acknowledge the issue.

Arbitrage in International Sovereign Debt Markets? Evidence from the Inflation Protected Securities of Six Countries
Kita, Arben,Tortorice, Daniel L.
We consider an arbitrage strategy which exactly replicates the cash flow of a sovereign nominal bond using inflation swaps and inflation-linked bonds. The strategy reveals a violation of the law of one price in the G7 countries which is largest for the eurozone. Testing the strategy’s exposure to deflation, volatility, liquidity, and macroeconomic risks shows the observed mispricing is a risk premium which is more pronounced in the eurozone. We find less support that financial limits to arbitrage explain the mispricing. We conclude that pure long-run arbitrage opportunities persist when these strategies are exposed to intermediate financial risks.

Building and Testing Yield Curve Generators for P&C Insurance
Gary Venter,Kailan Shang

Interest-rate risk is a key factor for property-casualty insurer capital. P&C companies tend to be highly leveraged, with bond holdings much greater than capital. For GAAP capital, bonds are marked to market but liabilities are not, so shifts in the yield curve can have a significant impact on capital. Yield-curve scenario generators are one approach to quantifying this risk. They produce many future simulated evolutions of the yield curve, which can be used to quantify the probabilities of bond-value changes that would result from various maturity-mix strategies. Some of these generators are provided as black-box models where the user gets only the projected scenarios. One focus of this paper is to provide methods for testing generated scenarios from such models by comparing to known distributional properties of yield curves.

P&C insurers hold bonds to maturity and manage cash-flow risk by matching asset and liability flows. Derivative pricing and stochastic volatility are of little concern over the relevant time frames. This requires different models and model testing than what is common in the broader financial markets.

To complicate things further, interest rates for the last decade have not been following the patterns established in the sixty years following WWII. We are now coming out of the period of very low rates, yet are still not returning to what had been thought of as normal before that. Modeling and model testing are in an evolving state while new patterns emerge.

Our analysis starts with a review of the literature on interest-rate model testing, with a P&C focus, and an update of the tests for current market behavior. We then discuss models, and use them to illustrate the fitting and testing methods. The testing discussion does not require the model-building section.

Central Clearing and Systemic Risk
Nowaczyk, Nikolai,O'Halloran, Sharyn
The G20's push towards central clearing changed the shape of the world's financial system: all standardized derivative contracts must now be cleared through central counterparties (CCPs). Despite considerable debate, the impact of central clearing nonetheless remains ambiguous and hard to measure as clearing regulations have been implemented alongside many other changes. In the present paper, we isolate the impact of CCPs by first representing all trade and risk relations of a financial system in a graph model. We then formalize clearing as an operator on those graphs and obtain sharp a priori bounds of its effect on total risk levels. Using numerical simulation, we then show how clearing alters the credit risk exposures of each bank depending on the netting structure of its trades. Further, we demonstrate how CCPs only reduce the total levels of risk in the system if their credit quality is substantially higher than that of the banks. We show, paradoxically, how the CCPs expose the system to substantial concentration risk and thereby undermine their initial purpose.

Codetermination on the Audit Committee: An Analysis of Potential Effects on Audit Quality
Hillebrandt, Svenja,Ratzinger-Sakel, Nicole V.S.
This paper empirically investigates the association between codetermination on the audit committee (AC) and audit quality. Using a sample of 655 firm-year observations related to German CDAX companies, our results indicate that the presence of employee representatives on the AC is negatively associated with audit quality. This negative association can also be observed for the percentage of employee representatives serving on the AC. However, our additional analyses show that the mentioned findings turn insignificant when employee representatives have accounting experience, which highlights the importance of accounting expertise for AC members’ monitoring effectiveness. To the best of our knowledge, this study is the first that analyzes the impact of codetermination on the AC on audit quality.

Comparative Study of Two Extensions of Heston Stochastic Volatility Model
Gifty Malhotra,R. Srivastava,H.C. Taneja

In the option valuation literature, the shortcomings of one factor stochastic volatility models have traditionally been addressed by adding jumps to the stock price process. An alternate approach in the context of option pricing and calibration of implied volatility is the addition of a few other factors to the volatility process. This paper contemplates two extensions of the Heston stochastic volatility model. Out of which, one considers the addition of jumps to the stock price process (a stochastic volatility jump diffusion model) and another considers an additional stochastic volatility factor varying at a different time scale (a multiscale stochastic volatility model). An empirical analysis is carried out on the market data of options with different strike prices and maturities, to compare the pricing performance of these models and to capture their implied volatility fit. The unknown parameters of these models are calibrated using the non-linear least square optimization. It has been found that the multiscale stochastic volatility model performs better than the Heston stochastic volatility model and the stochastic volatility jump diffusion model for the data set under consideration.

Corporate Governance and Liquidity Risk of Mercedes-Benz
Azman, Nur Khalisa
This research purpose is to measure Mercedes-Benz’s corporate governance and its impact to firm liquidity risk and performance. The method of this research is by using regression analysis of Mercedes-Benz through SPSS Software. This research had analyse that MercedesBenz’s liquidity performance is worsen years by years. The regression analysis shows that internal factors and external factors influence liquidity risk in this company

Corporate Governance in Extreme Institutional Environment: Evidence From Emerging Economy
Arslan, Muhammad,Abidin, Sazali,Alqatan, Ahmad,Roudaki, Jamal
Corporate governance (CG) is often split among rule and principlebased methods to regulation in distinctive institutional contexts. Relying on an alternative theoretical framework (i.e. institutional theory), rather than the dominant agency theory, this study conceptualizes corporate governance practices and structures as institutionally resolute and directed and explores the key institutional determinants of good CG practices in an emerging economy. Drawing on qualitative and quantitative methods, this study conducted semistructured interviews from eight CG professionals, followed by a survey questionnaire (N=105) from PSX listed firms. The study explores the extent to which certain underlying formal and informal institutional determinants, such as the auditing, political, legal, board, shareholders awareness, voting, culture, and values play a determining role in corporate governance. Using exploratory factor analysis, this study identified five major barriers, i.e. firm-level barriers, external barriers, social barriers, education and training barriers and legal barriers which restrain good CG practices in Pakistan. In addition, this study identified four major drivers, i.e. internal drivers, regulatory drivers, motivational drivers and collaborative drivers which can promote good CG practices in Pakistan. The findings of multiple hierarchical regression analysis revealed that the CGI score has a significant positive relationship with both return on assets and return on equity. This study emphasizes the necessity to revisit the foundation of institutional and agency theories in the environment of developing countries.

Credit Union and Bank Subprime Lending in the Great Recession
van Rijn, Jordan,Li, Kangli
We develop a theoretical model that predicts that credit unions will offer relatively less risky loans (e.g., fewer “subprime” mortgages) compared to similar commercial banks due to credit unions’ focus on member utility as nonprofit financial cooperatives. The model also predicts that banks will increase subprime lending more than credit unions during economic expansions and decrease subprime lending more than credit unions during recessions. We use the financial crisis and Great Recession period of 2007 â€" 2009 to test our model and find that, as predicted, commercial banks engaged in approximately five times more subprime lending relative to credit unions during the period leading up to the financial crisis (2003 â€" 2006). Banks also had delinquency and charge-off rates that were two to three times higher during and immediately following the crisis. We also find that banks were about two-and-a-half times more likely to fail and were significantly more likely to receive TARP government assistance funds. The results are robust to controlling for important differences between credit unions and banks besides structure and incentives, including asset size, portfolio concentration, market share, earnings, liquidity, leverage, mortgages sold to the secondary market, core deposits, and state-level indicators of economic performance and housing prices. We argue that the findings explain why credit unions often appear more risk averse relative to commercial banks, and hold important implications for researchers, policymakers and regulators.

DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News
Xinyi Li,Yinchuan Li,Hongyang Yang,Liuqing Yang,Xiao-Yang Liu

Stock price prediction is important for value investments in the stock market. In particular, short-term prediction that exploits financial news articles is promising in recent years. In this paper, we propose a novel deep neural network DP-LSTM for stock price prediction, which incorporates the news articles as hidden information and integrates difference news sources through the differential privacy mechanism. First, based on the autoregressive moving average model (ARMA), a sentiment-ARMA is formulated by taking into consideration the information of financial news articles in the model. Then, an LSTM-based deep neural network is designed, which consists of three components: LSTM, VADER model and differential privacy (DP) mechanism. The proposed DP-LSTM scheme can reduce prediction errors and increase the robustness. Extensive experiments on S&P 500 stocks show that (i) the proposed DP-LSTM achieves 0.32% improvement in mean MPA of prediction result, and (ii) for the prediction of the market index S&P 500, we achieve up to 65.79% improvement in MSE.

Design of High-Frequency Trading Algorithm Based on Machine Learning
Boyue Fang,Yutong Feng

Based on iterative optimization and activation function in deep learning, we proposed a new analytical framework of high-frequency trading information, that reduced structural loss in the assembly of Volume-synchronized probability of Informed Trading ($VPIN$), Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Support Vector Machine (SVM) to make full use of the order book information. Amongst the return acquisition procedure in market-making transactions, uncovering the relationship between discrete dimensional data from the projection of high-dimensional time-series would significantly improve the model effect. $VPIN$ would prejudge market liquidity, and this effectiveness backtested with CSI300 futures return.

Dissecting Ethereum Blockchain Analytics: What We Learn from Topology and Geometry of Ethereum Graph
Yitao Li,Umar Islambekov,Cuneyt Akcora,Ekaterina Smirnova,Yulia R. Gel,Murat Kantarcioglu

Blockchain technology and, in particular, blockchain-based cryptocurrencies offer us information that has never been seen before in the financial world. In contrast to fiat currencies, all transactions of crypto-currencies and crypto-tokens are permanently recorded on distributed ledgers and are publicly available. As a result, this allows us to construct a transaction graph and to assess not only its organization but to glean relationships between transaction graph properties and crypto price dynamics. The ultimate goal of this paper is to facilitate our understanding on horizons and limitations of what can be learned on crypto-tokens from local topology and geometry of the Ethereum transaction network whose even global network properties remain scarcely explored. By introducing novel tools based on topological data analysis and functional data depth into Blockchain Data Analytics, we show that Ethereum network (one of the most popular blockchains for creating new crypto-tokens) can provide critical insights on price strikes of crypto-tokens that are otherwise largely inaccessible with conventional data sources and traditional analytic methods.

Economic Complexity: why we like "Complexity weighted diversification"
Luciano Pietronero,Andrea Gabrielli,Andrea Zaccaria

A recent paper by Hausmann and collaborators (1) reaches the important conclusion that Complexity-weighted diversification is the essential element to predict country growth. We like this result because Complexity-weighted diversification is precisely the first equation of the Fitness algorithm that we introduced in 2012 (2,3). However, contrary to what is claimed in (1), it is incorrect to say that diversification is contained also in the ECI algorithm (4). We discuss the origin of this misunderstanding and show that the ECI algorithm contains exactly zero diversification. This is actually one of the reasons for the poor performances of ECI which leads to completely unrealistic results, as for instance, the derivation that Qatar or Saudi Arabia are industrially more competitive than China (5,6). Another important element of our new approach is the representation of the economic dynamics of countries as trajectories in the GDPpc-Fitness space (7-10). In some way also this has been rediscovered by Hausmann and collaborators and renamed as "Stream plots", but, given their weaker metrics and methods, they propose it to use it only for a qualitative insight, while ours led to quantitative and successful forecasting. The Fitness approach has paved the way to a robust and testable framework for Economic Complexity resulting in a highly competitive scheme for growth forecasting (7-10). According to a recent report by Bloomberg (9): The new Fitness method, "systematically outperforms standard methods, despite requiring much less data".

Electoral Crime Under Democracy: Information Effects from Judicial Decisions in Brazil
Andre Assumpcao

This paper examines voters' responses to the disclosure of electoral crime information in large democracies. I focus on Brazil, where the electoral court makes candidates' criminal records public before every election. Using a sample of local candidates running for office between 2004 and 2016, I find that a conviction for an electoral crime reduces candidates' probability of election and vote share by 10.3 and 12.9 percentage points (p.p.), respectively. These results are not explained by (potential) changes in judge, voter, or candidate behavior over the electoral process. I additionally perform machine classification of court documents to estimate heterogeneous punishment for severe and trivial crimes. I document a larger electoral penalty (6.5 p.p.) if candidates are convicted for severe crimes. These results supplement the information shortcut literature by examining how judicial information influences voters' decisions and showing that voters react more strongly to more credible sources of information.

Expected Losses, Unexpected Costs?
Ertan, Aytekin
This paper examines the real effects of banks switching to an expected credit loss (ECL) framework under IFRS 9. I identify the cross-bank variation in the ECL transition from banks’ mandatory reconciliation disclosures about the day-one impact of the accounting change. I find evidence that the ECL rules deteriorate the credit landscape for risky and opaque borrowers, i.e., small- and medium-sized enterprises (SMEs). Affected banks reduce lending to SMEs by a relative 23 percent and switch to corporate lending and non-loan assets. Consistent with a decline in credit supplyâ€"rather than in credit demandâ€"SMEs that work with affected banks receive less funding, conditional on applying for a loan. I also observe that in their contracts with SMEs, affected banks increase interest rates and collateral requirements, while reducing loan amounts and maturities. Despite these costs, my inferences do not imply that the ECL paradigm is socially undesirable.

Finance Without Brownian Motions: An Introduction To Simplified Stochastic Calculus
Černý, Aleš,Ruf, Johannes
The paper introduces a simple way of recording and manipulating stochastic processes without explicit reference to a probability measure. In the new calculus, operations traditionally presented in a measure-specific way are instead captured by tracing the behaviour of jumps (also when no jumps are physically present). The new calculus is thus intuitive and compact. The calculus is also fail-safe in that, under minimal assumptions, all formal calculations are guaranteed to yield mathematically well-defined stochastic processes. Several illustrative examples of the new concept are given, among them a novel result on the Margrabe option to exchange one defaultable asset for another.

Foreign Sentiment
Ben-Rephael, Azi,Dong, Xi,Massa, Massimo,Zhou, Changyun
We construct a direct measure of U.S. based foreign sentiment using flow shifts between U.S. and international mutual funds. Foreign sentiment predicts return reversals in international markets, while local sentiments predict reversals in local markets. Exploring this segmentation, we find that foreign sentiment predictability is driven by overreaction to non-U.S. local negative news, which increases with the foreignness of a country to U.S. investors. In contrast, non-U.S. local sentiment predictability is not driven by overreaction to the same news. A complementary analysis of the U.S. provides consistent results, suggesting that the U.S. is also not immune to foreign sentiment from international markets. Our findings shed light on a new behavioral explanation for how foreign sentiment is generated, in the spirit of Dumas et al. (2017) “foreign sentiment” concept.

Intermediated Implementation
Anqi Li,Yiqing Xing

We examine problems of "intermediated implementation," in which a single principal can only regulate limited aspects of the consumption bundles traded between intermediaries and agents with hidden characteristics. An example is sales, whereby retailers compete through offering consumption bundles to customers with hidden tastes, whereas a manufacturer with a potentially different goal than retailers' is limited to regulating the sold goods but not the charged prices by legal barriers. We study how the principal can implement through intermediaries any social choice rule that is incentive compatible and individually rational for agents. We demonstrate the effectiveness of per-unit fee schedule and distribution regulation, which hinges on whether intermediaries have private or interdependent values. We give further applications to healthcare regulation and income redistribution.

Liquidity Risk and the Beta Premium
Gong, Cynthia M,Luo, Di,Zhao, Huainan
As opposed to the “low beta low risk” convention, we show that low beta stocks are illiquid and exposed to high liquidity risk. After adjusting for liquidity risk, low beta stocks no longer outperform high beta stocks. Although investors who “bet against beta” earn a significant beta premium under the Fama-French three- or five-factor models, this strategy fails to generate any significant returns when liquidity risk is accounted for. Our work helps understand the beta premium from a new liquidity-risk perspective, and draws useful implications for both fund and corporate managers.

Mining the Automotive Industry: A Network Analysis of Corporate Positioning and Technological Trends
Niklas Stoehr,Fabian Braesemann,Michael Frommelt,Shi Zhou

The digital transformation is driving revolutionary innovations and new market entrants threaten established sectors of the economy such as the automotive industry. Following the need for monitoring shifting industries, we present a network-centred analysis of car manufacturer web pages. Solely exploiting publicly-available information, we construct large networks from web pages and hyperlinks. The network properties disclose the internal corporate positioning of the three largest automotive manufacturers, Toyota, Volkswagen and Hyundai with respect to innovative trends and their international outlook. We tag web pages concerned with topics like e-mobility and environment or autonomous driving, and investigate their relevance in the network. Sentiment analysis on individual web pages uncovers a relationship between page linking and use of positive language, particularly with respect to innovative trends. Web pages of the same country domain form clusters of different size in the network that reveal strong correlations with sales market orientation. Our approach maintains the web content's hierarchical structure imposed by the web page networks. It, thus, presents a method to reveal hierarchical structures of unstructured text content obtained from web scraping. It is highly transparent, reproducible and data driven, and could be used to gain complementary insights into innovative strategies of firms and competitive landscapes, which would not be detectable by the analysis of web content alone.

Model uncertainty in financial forecasting
Matthias J. Feiler,Thibaut Ajdler

Models necessarily capture only parts of a reality. Prediction models aim at capturing a future reality. In this paper we address the question of how the future is constructed (or: imagined) in an investment context where market participants form expectations on the returns of a risky investment. We observe that the participants' model choices are subject to unforeseeable change. The objective of the paper is to demonstrate that the resulting uncertainty may be reduced by incorporating relations among competing models in the estimation process.

On Information Coefficient and Directional Statistics
Yijian Chuan,Lan Wu

Cross-sectional "Information Coefficient"(IC) is a widely and deeply accepted measure in portfolio management. In this paper, we propose that IC is a linear operator on the components of a standardized random vector of next period cross-sectional returns. From the probability perspective, IC is a linear combination of the components of a directionally projected degenerated random vector. We deduct a solution to its optimization in expectation and obtain the maximum. Their closed-form expressions are given by directional statistics in a specific condition. Simulation analysis discloses the influence of market information, such as the number of stocks, on IC. The empirical analysis of the Chinese stock market uncovers a set of interesting facts about the standardized vectors of cross-sectional returns and helps to obtain the time series of the measure in the real market. Our research discovers a potential application of directional statistics in finance, reveals the nature of the IC measure, and deepens the understanding of active portfolio management.

Portfolio optimization based on forecasting models using vine copulas: An empirical assessment for the financial crisis
Maziar Sahamkhadam,Andreas Stephan

We employ and examine vine copulas in modeling symmetric and asymmetric dependency structures and forecasting financial returns. We analyze the asset allocations performed during the 2008-2009 financial crisis and test different portfolio strategies such as maximum Sharpe ratio, minimum variance, and minimum conditional Value-at-Risk. We then specify the regular, drawable, and canonical vine copulas, such as the Student-t, Clayton, Frank, Joe, Gumbel, and mixed copulas, and analyze both in-sample and out-of-sample portfolio performances. Out-of-sample portfolio back-testing shows that vine copulas reduce portfolio risk better than simple copulas. Our econometric analysis of the outcomes of the various models shows that in terms of reducing conditional Value-at-Risk, D-vines appear to be better than R- and C-vines. Overall, we find that the Student-t drawable vine copula models perform best with regard to risk reduction, both for the entire period 2005-2012 as well as during the financial crisis.

Pricing of the Geometric Asian Options Under a Multifactor Stochastic Volatility Model
Gifty Malhotra,R. Srivastava,H.C. Taneja

This paper focuses on the pricing of continuous geometric Asian options (GAOs) under a multifactor stochastic volatility model. The model considers fast and slow mean reverting factors of volatility, where slow volatility factor is approximated by a quadratic arc. The asymptotic expansion of the price function is assumed, and the first order price approximation is derived using the perturbation techniques for both floating and fixed strike GAOs. Much simplified pricing formulae for the GAOs are obtained in this multifactor stochastic volatility framework. The zeroth order term in the price approximation is the modified Black-Scholes price for the GAOs. This modified price is expressed in terms of the Black-Scholes price for the GAOs. The accuracy of the approximate option pricing formulae is established, and the model parameter is also estimated by capturing the volatility smiles.

Quant GANs: Deep Generation of Financial Time Series
Magnus Wiese,Robert Knobloch,Ralf Korn,Peter Kretschmer

Modeling financial time series by stochastic processes is a challenging task and a central area of research in financial mathematics. As an alternative, we introduce Quant GANs, a data-driven model which is inspired by the recent success of generative adversarial networks (GANs). Quant GANs consist of a generator and discriminator function, which utilize temporal convolutional networks (TCNs) and thereby achieve to capture long-range dependencies such as the presence of volatility clusters. The generator function is explicitly constructed such that the induced stochastic process allows a transition to its risk-neutral distribution. Our numerical results highlight that distributional properties for small and large lags are in an excellent agreement and dependence properties such as volatility clusters, leverage effects, and serial autocorrelations can be generated by the generator function of Quant GANs, demonstrably in high fidelity.

Quantile Diffusions
Holly Brannelly,Andrea Macrina,Gareth W. Peters

This paper focuses on the development of a new class of diffusion processes that allows for direct and dynamic modelling of quantile diffusions. We constructed quantile diffusion processes by transforming each marginal of a given univariate diffusion process under a composite map consisting of a distribution function and quantile function, which in turn produces the marginals of the resulting quantile process. The transformation allows for the moments of the underlying process to be directly interpreted with regard to parameters of the transformation. For instance, skewness or kurtosis may be introduced to enable more realistic modelling of data such as financial asset returns, as well as the recycling of samples of the underlying process to make simulation of the transformed quantile process easier. We derive the stochastic differential equation satisfied by the quantile diffusion, and characterise the conditions under which strong and weak solutions exist, both in the general case and for the more specific Tukey $g$-$h$, $g$-transform and $h$-transform families of quantile diffusions.

The Black-Scholes-Merton dual equation
Shuxin Guo,Qiang Liu

We derive the Black-Scholes-Merton dual equation, which has exactly the same form as the Black-Scholes-Merton equation. The new equation is general and works for European, American, Bermudan, Asian, barrier, lookback, etc. options and leads to new insights into pricing and hedging. Trivially, a put-call equality emerges - all the above-mentioned put (call) options can be priced as their corresponding calls (puts) by simply swapping stock price (dividend yield) for strike price (risk-free rate) simultaneously. More importantly, deltas (gammas) of such puts and calls are linked via analytic formulas. As one application in hedging, the dual equation is utilized to improve the accuracy of the recently proposed approach of hedging options statically with short-maturity contracts.

The Global Impact of Brexit Uncertainty
Hassan, Tarek Alexander,Hollander, Stephan,van Lent, Laurence,Tahoun, Ahmed
Using tools from computational linguistics, we construct new measures of the impact of Brexit on listed firms in the United States and around the world; these measures are based on the proportion of discussions in quarterly earnings conference calls on the costs, benefits, and risks associated with the UK's intention to leave the EU. We identify which firms expect to gain or lose from Brexit and which are most affected by Brexit uncertainty. We then estimate effects of the different types of Brexit exposure on firm-level outcomes. We find that the impact of Brexit-related uncertainty extends far beyond British or even European firms; US and international firms most exposed to Brexit uncertainty lost a substantial fraction of their market value and have also reduced hiring and investment. In addition to Brexit uncertainty (the second moment), we find that international firms overwhelmingly expect negative direct effects from Brexit (the first moment) should it come to pass. Most prominently, firms expect difficulties from regulatory divergence, reduced labor mobility, limited trade access, and the costs of post-Brexit operational adjustments. Consistent with the predictions of canonical theory, this negative sentiment is recognized and priced in stock markets but has not yet significantly affected firm actions.

The Impact of the Financial Crisis on Tax Avoidance around the Global Financial Crisis in the Companies Accepted in Tehran Stock Exchange
Heidari Gandoman, Shaho
The purpose of this paper is to examine the impact of the global financial crisis on corporate’ tax avoidance has been investigated. The statistical sample consists of 107 active firms in Tehran Stock Exchange between the years 2005 and 2014 which were selected through systematic removal. To test the research hypotheses linear regression test was used. Eviews software was used for data analysis and research hypotheses testing. The results of this study showed that there is a significant relationship between financial crisis and tax avoidance. There is a significant relationship between the global financial crisis and corporate tax avoidance and. There is no significant relationship between financial crisis and corporate’ tax avoidance in the global financial crisis.

Unraveling the Value Premium: A Reward for Risk or Mispricing?
Serur, Claudio E.,Siri, Julián Ricardo,Serur, Juan A.,Dapena, José Pablo
A value investing strategy consists of purchasing stocks relatively undervalued to their fundamental values and selling those relatively overvalued. Finding this kind of companies has been one of the most challenging goals for investors throughout the history. The main objective of this paper is to test the value factor, but not limited to the traditional Price-To-Book ratio, but exploring diverse alternatives constructed on different metrics in order to determine if it possible to obtain excess returns relative to the traditional one. In addition, these factors were blended different quality factors. First, we tested the so-called high mispricing portfolios, with long positions in value/high quality stocks and short positions in growth/low quality stocks. When blend-ing these portfolios with quality factors, we observe quite an improvement in terms of Sharpe Ratio and maximum draw downs relative to pure value portfolios. In this case, we see that ex-cluding riskier low-quality stocks reduces the overall risk of the portfolio. Regarding the low mispricing portfolio, the results show that growth/high quality stocks outperform value/low quality stocks. This is consistent with the hypothesis of behavioral-based theories as we see that only undervalued and high-quality stocks generate excess returns. Finally, we test the results against the three-factor Fama-French models, achieving statistically significant alphas in some cases.

Volatility Dependent Structured Products
Dyachenko, Artem,Farkas, Walter,Rieger, Marc Oliver
We construct a derivative that depends on the SPY and VIX and, in this way, incorporates both the market risk premium and the variance risk premium. We show that our product has a Sharpe ratio that is at least as high as the Sharpe ratio of the SPY. If one could invest $10,000 either in the product or the SPY at the end of 2008, the payoff of the product would be around $80,000 at the end of 2018 whereas the payoff of the SPY - around $30,000.

When Are Extreme Daily Returns Not Lottery? At Earnings Announcements!
Nguyen, Harvey,Truong, Cameron
Using a sample of U.S. stocks over the period 1973â€"2015, we find that quarterly earnings announcements account for more than 18% of the total maximum daily returns in the top MAX portfolio. Maximum daily returns as triggered by earnings announcements do not entail lower future returns. Both portfolio and regression analyses show that the MAX phenomenon completely disappears when conditioning MAX returns on earnings announcements. We further show that earnings announcement MAX returns do not indicate a probability of future large short-term upward returns. Excluding earnings announcement MAX returns in constructing the lottery demand factor results in not only a larger lottery demand premium but also superior factor model performance.