# Research articles for the 2019-08-19

arXiv

The space of call price functions has a natural noncommutative semigroup structure with an involution. A basic example is the Black--Scholes call price surface, from which an interesting inequality for Black--Scholes implied volatility is derived. The binary operation is compatible with the convex order, and therefore a one-parameter sub-semigroup gives rise to an arbitrage-free market model. It is shown that each such one-parameter semigroup corresponds to a unique log-concave probability density, providing a family of tractable call price surface parametrisations in the spirit of the Gatheral--Jacquier SVI surface. An explicit example is given to illustrate the idea. The key observation is an isomorphism linking an initial call price curve to the lift zonoid of the terminal price of the underlying asset.

SSRN

We propose a dynamic portfolio choice model with the mean-variance criterion for log-returns. The model yields time-consistent portfolio policies and is analytically tractable even under some incomplete market settings. The portfolio policies conform with conventional investment wisdom (e.g. richer people should invest more absolute amount of money in risky assets; the longer investment time horizon, the more proportional amount of money should be invested in risky assets; and for long-term investment, people should not short sell major stock indices whose returns are higher than the risk-free rate), and the model provides a direct link with the CRRA utility maximization in a complete market.

SSRN

We set out in this study to review a vast amount of recent literature on machine learning (ML) approaches to predicting financial distress (FD), including supervised, unsupervised and hybrid supervised-unsupervised learning algorithms. Four supervised ML models including the traditional support vector machine (SVM), recently developed hybrid associative memory with translation (HACT), hybrid GA-fuzzy clustering and extreme gradient boosting (XGBoost) were compared in prediction performance to the unsupervised classifier deep belief network (DBN) and the hybrid DBN-SVM model, whereby a total of sixteen financial variables were selected from the financial statements of the publicly-listed Taiwanese firms as inputs to the six approaches. Our empirical findings, covering the 2010-2016 sample period, demonstrated that among the four supervised algorithms, the XGBoost provided the most accurate FD prediction. Moreover, the hybrid DBN-SVM model was able to generate more accurate forecasts than the use of either the SVM or the classifier DBN in isolation.

SSRN

We examine a specific form of what we term analyst contrarianism. We define contrarianism as cases where an analyst expresses a summary opinion contrary to the direction of a given earnings surprise or revision. Distinct from analyst optimism or boldness, we document that analysts interpret negative (positive) earnings news in a positive (negative) light in approximately 11-15 percent of reports. We conjecture that some analysts look for opportunities to make a contrarian stock call for their clients in order to gain visibility, recognition, and career advancement. Our empirical evidence, which is supported by analyst interviews and content analysis of analyst reports, shows that: i) analysts at non-top-tier brokerage houses are more likely to make a contrarian call, ii) analyst reports that contain contrarian opinions are associated with greater market reactions, and iii) contrarian analysts are more likely to exhibit career advancement.

arXiv

We consider a system of coupled free boundary problems for pricing American put options with regime switching. To solve this system, we first fix the optimal exercise boundary for each regime resulting in multi-variable fixed domains. We further eliminate the first order derivatives associated with the regime switching model by taking derivatives to obtain a system of coupled partial differential equations which we called the asset-delta-gamma-speed option equations. The fourth-order compact finite difference scheme and Gauss-Seidel iterative method are then employed in each regime for solving the system of the equations. In particular, the third order Hermite interpolation technique is used for estimating the coupled asset and delta options in the set of equations. The numerical method is finally tested with several examples. Our results show that the scheme provides an accurate solution with the convergent rate in space of 2.44 and the rate in time of 1.86, which is accurate and fast in computation as compared with other existing numerical methods.

arXiv

Using Gretl, I apply ARMA, Vector ARMA, VAR, state-space model with a Kalman filter, transfer-function and intervention models, unit root tests, cointegration test, volatility models (ARCH, GARCH, ARCH-M, GARCH-M, Taylor-Schwert GARCH, GJR, TARCH, NARCH, APARCH, EGARCH) to analyze quarterly time series of GDP and Government Consumption Expenditures & Gross Investment (GCEGI) from 1980 to 2013. The article is organized as: (I) Definition; (II) Regression Models; (III) Discussion. Additionally, I discovered a unique interaction between GDP and GCEGI in both the short-run and the long-run and provided policy makers with some suggestions. For example in the short run, GDP responded positively and very significantly (0.00248) to GCEGI, while GCEGI reacted positively but not too significantly (0.08051) to GDP. In the long run, current GDP responded negatively and permanently (0.09229) to a shock in past GCEGI, while current GCEGI reacted negatively yet temporarily (0.29821) to a shock in past GDP. Therefore, policy makers should not adjust current GCEGI based merely on the condition of current and past GDP. Although increasing GCEGI does help GDP in the short-term, significantly abrupt increase in GCEGI might not be good to the long-term health of GDP. Instead, a balanced, sustainable, and economically viable solution is recommended, so that the short-term benefits to the current economy from increasing GCEGI often largely secured by the long-term loan outweigh or at least equal to the negative effect to the future economy from the long-term debt incurred by the loan. Finally, I found that non-normally distributed volatility models generally perform better than normally distributed ones. More specifically, TARCH-GED performs the best in the group of non-normally distributed, while GARCH-M does the best in the group of normally distributed.

SSRN

In this paper we show how and why to convert Fama/French Factors into currencies other than U.S. Dollars. Investors and researchers working with those factors from a non-U.S. perspective are prone to a substantial bias in both alphas and factor loadings estimated from the Fama/French models. We show the statistical and economical significance of this bias based on performance and style analysis of more than 1,700 European equity funds and common European market indices. We also provide straightforward solutions to avoid the bias and to use Fama/French Factors correctly from any non-U.S. perspective.

SSRN

Some investors assert there are weaknesses in the current accounting model for business combinations that limit the usefulness of information reported for acquired identifiable intangibles. Organically replenished intangible assets require future ongoing expenditures to replenish or maintain their value, creating uncertainty about the amount and timing of future cash flows. Wasting intangible assets do not require future investment and often have definite lives that are legally or contractually determined. The current accounting model for business combinations also requires recognition of identifiable intangibles that are not strategically important sources of economic benefits from the acquisition. Motivated by these claims, we develop testable hypotheses and examine differences in the associations between post-acquisition equity prices and different types of acquired intangibles. We predict and find that both wasting and organically replenished intangibles are positively associated with post-acquisition equity prices. However, we find that the association is less positive for organically replenished intangibles than wasting intangibles. In addition, we find that organically replenished intangibles exhibit a similar association with equity prices to goodwill. We also predict and find that strategically important intangibles are positively associated with post-acquisition equity prices, but find no association for other intangibles. Our findings highlight how differences in the underlying economic characteristics of acquired intangibles are reflected in the usefulness of financial reporting information for business combinations.

SSRN

This paper compares the performance of three momentum risk management techniques proposed in the literature â€" idiosyncratic momentum, constant volatility-scaling and dynamic scaling. Using data for individual stocks from the U.S. and across 48 international countries, we find that all three approaches decrease momentum crashes, lead to higher risk-adjusted returns and raise break even transaction costs. In a multiple model comparison test that also controls for other factors, idiosyncratic momentum emerges as the best momentum strategy. Finally, we find that the alpha stemming from volatility-scaling is distinctive from the idiosyncratic momentum alpha.

arXiv

In the article we describe an enhancement to the Demand for Labour (DL) survey conducted by Statistics Poland, which involves the inclusion of skills obtained from online job advertisements. The main goal is to provide estimates of the demand for skills (competences), which is missing in the DL survey. To achieve this, we apply a data integration approach combining traditional calibration with the LASSO-assisted approach to correct representation error in the online data. Faced with the lack of access to unit-level data from the DL survey, we use estimated population totals and propose a~bootstrap approach that accounts for the uncertainty of totals reported by Statistics Poland. We show that the calibration estimator assisted with LASSO outperforms traditional calibration in terms of standard errors and reduces representation bias in skills observed in online job ads. Our empirical results show that online data significantly overestimate interpersonal, managerial and self-organization skills while underestimating technical and physical skills. This is mainly due to the under-representation of occupations categorised as Craft and Related Trades Workers and Plant and Machine Operators and Assemblers.

arXiv

An Entropic Dynamics of exchange rates is laid down to model the dynamics of foreign exchange rates, FX, and European Options on FX. The main objective is to represent an alternative framework to model dynamics. Entropic inference is an inductive inference framework equipped with proper tools to handle situations where incomplete information is available. Entropic Dynamics is an application of entropic inference, which is equipped with the entropic notion of time to model dynamics. The scale invariance is a symmetry of the dynamics of exchange rates, which is manifested in our formalism. To make the formalism manifestly invariant under this symmetry, we arrive at choosing the logarithm of the exchange rate as the proper variable to model. By taking into account the relevant information about the exchange rates, we derive the Geometric Brownian Motion, GBM, of the exchange rate, which is manifestly invariant under the scale transformation. Securities should be valued such that there is no arbitrage opportunity. To this end, we derive a risk-neutral measure to value European Options on FX. The resulting model is the celebrated Garman-Kohlhagen model.

arXiv

We develop an entropic framework to model the dynamics of stocks and European Options. Entropic inference is an inductive inference framework equipped with proper tools to handle situations where incomplete information is available. The objective of the paper is to lay down an alternative framework for modeling dynamics. An important information about the dynamics of a stock's price is scale invariance. By imposing the scale invariant symmetry, we arrive at choosing the logarithm of the stock's price as the proper variable to model. The dynamics of stock log price is derived using two pieces of information, the continuity of motion and the directionality constraint. The resulting model is the same as the Geometric Brownian Motion, GBM, of the stock price which is manifestly scale invariant. Furthermore, we come up with the dynamics of probability density function, which is a Fokker--Planck equation. Next, we extend the model to value the European Options on a stock. Derivative securities ought to be prices such that there is no arbitrage. To ensure the no-arbitrage pricing, we derive the risk-neutral measure by incorporating the risk-neutral information. Consequently, the Black--Scholes model and the Black--Scholes-Merton differential equation are derived.

arXiv

We consider a market of financial securities with restricted participation, in which traders may not have access to the trade of all securities. The market is assumed thin: traders may influence the market and strategically trade against their price impacts. We prove existence and uniqueness of the equilibrium even when traders are heterogeneous with respect to their beliefs and risk tolerance. An efficient algorithm is provided to numerically obtain the equilibrium prices and allocations given market's inputs.

SSRN

This paper makes specifics contributions in the methodology of event studies. First, it develops a financial econometrics framework for understanding, measuring and testing the impact of outlier returns on the estimated parameters of stock return models. Second, it presents a maximum likelihood robust estimation method that enables the decomposition of a firmâ€™s stock returns into regular and outlier returns for the purpose of computing robust or outlier resistant CAR statistics. Third, it presents analytical results on how outliers in the estimation sample affect OLS-CAR statistics. Results based on extensive Monte Carlo and actual data simulations, depict that outliers in the estimation sample affect adversely and significantly the performance of the OLS-CAR statistics in event studies. Outliers, however, do not impair the forecasting ability of the robust-CAR statistics introduced in this paper.

arXiv

The expected utility operators introduced in a previous paper, offer a framework for a general risk aversion theory, in which risk is modelled by a fuzzy number $A$. In this paper we formulate a coinsurance problem in the possibilistic setting defined by an expected utility operator $T$. Some properties of the optimal saving $T$-coinsurance rate are proved and an approximate calculation formula of this is established with respect to the Arrow-Pratt index of the utility function of the policyholder, as well as the expected value and the variance of a fuzzy number $A$. Various formulas of the optimal $T$-coinsurance rate are deduced for a few expected utility operators in case of a triangular fuzzy number and of some HARA and CRRA-type utility functions.

arXiv

This paper deals with the explicit design of the strategy formulations to make the best strategic choices from a conventional matrix form. The explicit strategy formulation is a new mathematical model which provides the analytical strategy framework to find the best moment for strategy shifting for preparing the rapid market changes. Analytically tractable results are obtained by using the fluctuation theory. These results enables to predict the moment for changing strategy in a matrix form and even predict the prior moment of changes. This explicit model could be adapted into practically every strategic decision making situations which are described as a matrix form with the quantitative measures of the decision parameters. This research helps strategy decision makers who want to find the optimal moments when the present strategy should be shifted.

SSRN

I exploit the staggered implementation of the Trade Reporting and Compliance Engine (TRACE) system to study the effect of the secondary corporate bond market transparency on shareholder payout policy. I find that greater bond price transparency leads to a reduction in shareholder payout, consistent with a transparent price mechanism helping bondholders better discipline managerial actions that expropriate bondholdersâ€™ wealth. The treatment effect is more pronounced when (1) bondholdersâ€™ free-rider problem is more severe, (2) the restricted payout covenant is more likely to bind, (3) blockholder exit threat is more credible, and (4) the demand for bondholder monitoring is greater. Firms also increase cash holdings, preserve greater balance sheet liquidity, and reduce acquisition investment following enhanced bond market transparency. Finally, ex-ante commitment to a more conservative payout policy reduces the issuing firmsâ€™ agency costs of debt. The findings suggest that microstructure aspects of the secondary corporate bond market affect issuing firmsâ€™ real decisions beyond the direct effects on transparency.

SSRN

Andhra Pradesh State Financial Corporation came into being as a premier Financial Institutions on 1st November, 1956 with the avowed objectives of purveying financial assistance and managerial support to industrial entrepreneurs in Andhra Pradesh. It is clear that, the APSFC is concentrating on the industries so as to bring about a balanced industrial development in Andhra Pradesh. It has been providing financial support to various categories of industries in State. But, the overall progress of the industrial sector has not been up to the mark due to various constraints It needs to strengthen the financial base of the Corporation and its operational efficiency. In spite of the efforts made by APSFC to promote industrial development in the State, it has failed to meet its objective. It has not been distributing funds properly for the promotion of small-scale industrial sector in the state. Moreover funds supplied by the Corporation is not adequate for the small-scale industrial progress of the state. Some of the disquieting trends in the operations of the APSFC are failure to diversify the assistance port-folio, continuance of a wide gap between assistance sanctioned and disbursed, inadequate attention to the schemes benefiting the village craftsmen and rural artisans and inadequate assistance for the rehabilitation of the existing sick industrial units. It appears that the APSFC is not able to translate its policies and ideas into actions, which have merely remained on paper. Therefore, it has become necessary to make an in depth inquiry into the performance of the Corporation. With this intension the present study of the â€œIndustrial Financial Services by APSFC â€" A Studyâ€ has been undertaken in order to find out performance growth of the Corporation over a study period. In this paper the researcher made an attempt to examine the relationship between sanctions and disbursements, gross sanctions and sanctions to SSI sector, purpose wise, constitution wise, loan type wise, social class wise, region wise classification of assistance. At the end of the analysis some viable and useful suggestions are offered to tone up the overall performance of the Corporation for industrial development in Andhra Pradesh.

SSRN

Information on the climate risk exposure of firms is important for investorsâ€™ investment decisions, the efficient pricing of the risks and opportunities related to climate change, and financial stability. We survey institutional investors on firmsâ€™ climate risk disclosures. Many investors believe climate risk reporting to be as important as traditional financial reporting and that it should be mandatory and more standardized. However, they also view current quantitative and qualitative disclosure on climate risks as being insufficient and imprecise. The belief that current climate-related disclosure is deficient derives more from investors that believe climate risks are underpriced in equity markets.

SSRN

Facebookâ€™s claim of Libra blockchain as a decentralized peer-to-peer electronic cash system is a blunt lie; if Facebook overcomes the massive regulatory hurdle and receives appropriate approvals, Libra will initially start off as a decentralized permissioned blockchain with a trusted-third party. Unlike Bitcoinâ€™s purely peer-to-peer decentralized permissionless blockchain without a trusted third party, all transactions on Libra blockchain will be governed by the Libra Association as a de facto central authority comprising 28 founding-members most of which are for-profit heavyweight firms from the United States such as Visa, MasterCard, PayPal, Facebook Calibra, and eBay. Facebookâ€™s Libra coin is a recent invention, but with apparent signs of a miscarriage. Facebookâ€™s already troubled past for violation of privacy and exploitation of usersâ€™ data (i.e. data disclosure scandals Cambridge Analytica) has intensified the opposition among authorities and skepticism among industry participants. In the midst of Facebookâ€™s planned launch date of first half of 2020, Libra is still running impetuous into increasing opposition from all sides; central banks, regulators, law makers, and tax agencies. As with any new paradigm-shifting technology (i.e. blockchain), Libra will cause a serious disruption in the short-term to the existing ecosystem of more than 2,400 digital coins that has taken a decade to form; however in the long-run, Libra as a stable global crypto-currency promises to revolutionize electronic payment systems and money transfers by enhancing financial inclusion and global stability as a public good. To launch Libra cryptocurrency, Facebook should not attempt to satisfy all of the concerns or issues brought by different branches of governments because this is both impractical and implausible to accomplish. A decade has passed since Bitcoinâ€™s debut in January 2009, and to this date still many countries throughout the world do not have any regulation dealing with cryptocurrencies. If Facebook Libra does not sputter out, it will spur central banks to launch their own cryptocurrency projects.

arXiv

Prediction problems in finance go beyond estimating the unknown parameters of a model (e.g. of expected returns). This is because such a model would have to include parameters governing the market participants' propensity to change their opinions on the validity of that model. This leads to a well--known circular situation characteristic of financial markets, where participants collectively create the future they wish to estimate. In this paper, we introduce a framework for organizing multiple expectation models and study the conditions under which they are adopted by a majority of market participants.

arXiv

We analyze an $N+1$-player game and the corresponding mean field game with state space $\{0,1\}$. The transition rate of $j$-th player is the sum of his control $\alpha^j$ plus a minimum jumping rate $\eta$. Instead of working under monotonicity conditions, here we consider an anti-monotone running cost. We show that the mean field game equation may have multiple solutions if $\eta < \frac{1}{2}$. We also prove that that although multiple solutions exist, only the one coming from the entropy solution is charged (when $\eta=0$), and therefore resolve a conjecture of ArXiv: 1903.05788.

SSRN

We investigate the fundamental linkages between â€œgeolocationâ€ (human movement) data and financial marketâ€™s equity price behavior. The geolocation positions were recorded intraday and cover the period from January 1, 2018 to July 17, 2018. Our initial data set spans over 54 billion observations and was provided by Fysical. We focus our study on a popular hedge fund trading strategy known as â€œpairs trading.â€ First, we collect Under Armour (UA) and Nikeâ€™s stock price and volume data. Second, we investigate the relative activity of people visiting a particular physical store of UA and Nike, as proxied from anonymous cell phone foot traffic. Third, we collect the relative sentiment for tweets to UA and Nike. After combining all the data, we glean the following fascinating results: (1) geolocation information is an important variable in pairs trading strategy between UA and Nike, as evidenced by the results from our feature selection popularity methodology; (2) surprisingly, pairs trading only using geolocation data yields positive returns, and employing machine learning methods and rolling analysis enhances the returns; and (3) pairs trading strategy incorporating geolocation information yields a cumulative return of 13.7% from January, 2018 to June, 2018, with an Annualized Sharpe Ratio of 3.9. Some caveats are in order â€" our results are very sensitive to transaction costs assumptions and our data set has serious limitations.

SSRN

This paper examines the cross-sectional properties of stock return forecasts based on Fama-MacBeth regressions using all firms contained in the STOXX Europe 600 index during the September 1999-December 2018 period. Our estimation approach is strictly out-of-sample, mimicking an investor who exploits both historical and real-time information on multiple firm characteristics to predict returns. The models capture a substantial amount of the cross-sectional variation in true expected returns and generate predictive slopes close to one, i.e., the forecast dispersion mostly reflects cross-sectional variation in true expected returns. The predictions translate into a high value added for investors. For an active trading strategy, we find strong market outperformance net of transaction costs based on a variety of performance measures.

SSRN

This paper presents a calibrated closed-economy DSGE model with C and B grade borrowers. Banks maximize their profits by choosing the optimal allocation of credit between C and B borrowers. Bank lending is subject to capital costs for valuations based capital requirements and concentration risks. The capital requirements are build according to the BASEL IRB framework. The model shows that the procyclical movement of valuations-based capital requirements does not only strengthen the expansionary effect of monetary easing, but it also shows that the asymmetric decline of the capital requirements pushes the bank portfolio towards riskier lending. In addition we find that sticky target rate of return expectations of bank capital providers (shareholders) can create a search-for-yield impulse, which strengthens the risk-shift of bank lending.

SSRN

The aim of this study is to evaluate the usefulness and relevance of earnings disclosures, as the key determinant for share price changes. The main objective is to examine whether earnings response coefficient (ERC) behavior could explain more fully the share price changes, as to the reason why the stock price change is not equal to the amount of announced earnings. The study is conducted on Sweden as a developed economy for a recent period of fourteen years, 2001-2014. Two measures of abnormal returns are regressed against the size of the announced earnings. The first regression uses measures from individual events. The second regression uses a portfolio measure; that is, from portfolios that are made of all observations sorted by size of earnings into ten portfolios. The aim of the portfolio method was to control possible idiosyncratic-errors-in-variables problem using individual event measures. The findings using individual-event measures resulted in reasonable ERC sizes with high R2 explanatory power, a little higher than those reported in prior studies on other countries. Importantly, the portfolio-based ERC of the country is somewhat close to the magnitude of the earnings which supports the famous value relevance theory in accounting. This finding is new to this literature.

SSRN

We set out in this study to examine: (i) whether â€˜socially responsible investmentâ€™ (SRI) portfolios can outperform less-SRI portfolios in the emerging Asian stock markets; and (ii) whether investors within these emerging markets achieve awareness of SRI through publicly available news. Based on 2009-2013 data, we find that SRI portfolios tend to perform better in Japan. However, firms in the emerging Asian markets do not earn rewards for superior CSR practices. We also find that investors in the emerging Asian markets are indeed aware of SRI through public CSR news releases; in particular, investors in these markets reward high ESG-rated firms for their good CSR practices advertised through such news releases, relative to those with no news releases.

arXiv

Stochastic integrals are defined with respect to a collection $P = (P_i; \, i \in I)$ of continuous semimartingales, imposing no assumptions on the index set $I$ and the subspace of $\mathbb{R}^I$ where $P$ takes values. The integrals are constructed though finite-dimensional approximation, identifying the appropriate local geometry that allows extension to infinite dimensions. For local martingale integrators, the resulting space $\mathsf{S} (P)$ of stochastic integrals has an operational characterisation via a corresponding set of integrands $\mathsf{R} (C)$, constructed with only reference the covariation structure $C$ of $P$. This bijection between $\mathsf{R} (C)$ and the (closed in the semimartingale topology) set $\mathsf{S} (P)$ extends to families of continuous semimartingale integrators for which the drift process of $P$ belongs to $\mathsf{R} (C)$. In the context of infinite-asset models in Mathematical Finance, the latter structural condition is equivalent to a certain natural form of market viability. The enriched class of wealth processes via extended stochastic integrals leads to exact analogues of optional decomposition and hedging duality as the finite-asset case. A corresponding characterisation of market completeness in this setting is provided.

SSRN

This paper models the risk tradeoff between symmetry and asymmetry in the distribution of asset return as a core consideration for rational investor decision making under uncertainty and presents a closed-form solution for determining the optimal market portfolio using a classic utility maximization framework and stochastic dominance optimization. The optimization yields a portfolio separation for both risk-averse investors as well as those who also possess a preference for upside gains and an aversion to downside losses. In equilibrium, in addition to requiring compensation for bearing volatility risk â€" which is symmetrical in gains and losses â€" investors also require additional compensation for bearing the systematic risk associated with distributional asymmetries. The model serves as an expansion of CAPM and, as is demonstrated both theoretically and empirically, solves the long-lasting puzzle of the beta anomaly.

SSRN

This paper uses daily panel data to study the effects that entrepreneursâ€™ social networks have on the success of their projects seeking capital from a potentially large group of individual investors (i.e. crowdfunding). Much of the literature to date demonstrates both theoretically and empirically that the benefit of large social networks accrues at the beginning of the crowdfunding campaign and are commonly the initial contributions that the project receives. We find this is consistent with unsuccessful campaigns, however, among successful campaigns many of the benefits of large online social networks occur only after the project has met its funding goal. In particular, we find that entrepreneurs with relatively large online social networks receive a statistically significantly larger number of backers only after the project is successfully funded. It is hypothesized this result is due to the composition of strong and weak ties in the entrepreneurs social network. Importantly, when a project reaches its funding goal a positive signal of its quality is sent to those in the entrepreneurâ€™s social network and motivates the relatively large group of weak ties in it to contribute. As a result, it puts into question the value that strong ties can have in aiding entrepreneurs in reaching their funding goal.

SSRN

Largely constant average acquirer returns over the past four decades mask fundamental changes in the takeover market. Controlling for bidder composition, the common component of acquirer returns has increased by as much as five percentage points relative to the 1980s. Offsetting this increase, the average bidder-specific component has declined. The increase in the common component is pervasive and cannot be explained by learning, maturity, industry concentration, or improved corporate governance. However, better advisors may have contributed to this upward trend. Conceptually, the evidence is consistent with a general increase in merger synergies that have become less bidder-specific over time.

arXiv

While pump-and-dump schemes have attracted the attention of cryptocurrency observers and regulators alike, this paper represents the first detailed empirical query of pump-and-dump activities in cryptocurrency markets. We present a case study of a recent pump-and-dump event, investigate 412 pump-and-dump activities organized in Telegram channels from June 17, 2018 to February 26, 2019, and discover patterns in crypto-markets associated with pump-and-dump schemes. We then build a model that predicts the pump likelihood of all coins listed in a crypto-exchange prior to a pump. The model exhibits high precision as well as robustness, and can be used to create a simple, yet very effective trading strategy, which we empirically demonstrate can generate a return as high as 60% on small retail investments within a span of two and half months. The study provides a proof of concept for strategic crypto-trading and sheds light on the application of machine learning for crime detection.

SSRN

Using data from 1980 through 2005, and implementations of the Intertemporal Capital Asset Pricing Model (ICAPM), this study consistently generates positive intertemporal risk-return relations within venture capital markets. During the first five years of business, venture capitalists (VCs) are shown to be risk averse, and are characterized by a Coefficient of Relative Risk Aversion (CRRA) estimated at about 2.75. From the sixth year of business onwards, VCs are characterized by CRRAs that are no higher than 1.91. Combination of rigorous theoretical and empirical evidence establishes that CRRAs less than or equal to 2.00 are evidence for risk seeking preferences. The CRRA for the representative risk bearing agent who aggregates preferences of risk averse and risk seeking agents is shown to aggregate to 3.59. This outcome, to wit, a higher CRRA (3.59) for an agent who aggregates risk averse and risk seeking preferences, in relation to the CRRA for the embedded representative risk averse agent (2.75) is formally and theoretically shown to be outcome that subsists in equilibrium. Totality of the formal theoretical and empirical evidence demonstrates interpretations of CRRAs that are higher than 2.75 as evidence for stock markets that consist in entirety of risk averse agents is not robust.

SSRN

We analyse the equity stocks and single stock futures of the National Stock Exchange of India (NSE) to analyse how trading protocols affect information efficiency, measured by price discovery. Our results indicate that price discovery occurs in the spot market compared to the derivative market. Hasbrouckâ€™s (1995) Information Share measurement for price discovery shows the spot market represents 62% of the information while the futures market contributes 38%. We also analyse the price discovery with Gonzalo and Grangerâ€™s (1995) Component Share, with stronger results than Information Share for the spot market, 68%, than the futures market, 32% indicating that the spot market leads in the informational efficiency regardless of the price discovery metric.We show that the some of the strongest explanatory variables that inhibit the futures market are a market wide position limit, lot size, margin requirements per lot and wider spreads than the spot market. We also show that lagged indicators of price discovery in a market also contribute to the spot marketâ€™s informational efficiency.