Research articles for the 2020-08-31
arXiv
We consider a continuous-time game-theoretic model of an investment market with short-lived assets and endogenous asset prices. The first goal of the paper is to formulate a stochastic equation which determines wealth processes of investors and to provide conditions for the existence of its solution. The second goal is to show that there exists a strategy such that the logarithm of the relative wealth of an investor who uses it is a submartingale regardless of the strategies of the other investors, and the relative wealth of any other essentially different strategy vanishes asymptotically. This strategy can be considered as an optimal growth portfolio in the model.
SSRN
Prior studies show that creditorsâ simultaneous equity holding mitigates shareholder-creditor conflict. We show that a new type of conflict arises in syndicates with such dual holders, due to the heterogeneity across syndicate membersâ equity-to-loan positions. We find that loans with higher within-syndicate conflicts rely less on performance covenants, which serve as tripwires to facilitate ex-post control transfer from shareholders to creditors. Renegotiation is also less likely as conflict increases. Instead, high-conflict loans rely more on capital covenants, which align shareholder-creditor interests ex-ante and incentivize shareholders to monitor. Moreover, lead arrangers retain larger shares in high-conflict loans to commit to monitoring beyond contractual provisions. Finally, high-conflict loans tend to be smaller, shorter, and more costly.
SSRN
This paper addresses the question of optimal currency exposure for a risk-and-ambiguity-avers international investor. A robust mean-variance model with smooth ambiguity preferences is used to derive the optimal currency exposure. In the theoretical part, we show that the sample-efficient currency demand can be calculated as the solution to a generalized ridge regression. Through the lens of these results, we demonstrate that our ambiguity-based model offers a new explanation of the home currency bias. The investor's dislike for model uncertainty induces a disproportionately high currency hedging demand. The empirical analysis of currency overlay strategies employs the foreign exchange, equity, and bond returns over the period from 1999 to 2018. Our out-of-sample back-tests illustrate that accounting for ambiguity enhances the stability of estimated optimal currency exposures and significantly improves the portfolio performance net of transaction costs.
arXiv
We propose an adaptive and explicit fourth-order Runge-Kutta-Fehlberg method coupled with a fourth-order compact scheme to solve the American put options problem. First, the free boundary problem is converted into a system of partial differential equations with a fixed domain by using logarithm transformation and taking additional derivatives. With the addition of an intermediate function with a fixed free boundary, a quadratic formula is derived to compute the velocity of the optimal exercise boundary analytically. As such, it enables us to employ fourth-order spatial and temporal discretization with Dirichlet boundary conditions for obtaining the numerical solution of the asset option, option Greeks, and the optimal exercise boundary. The advantage of the Runge-Kutta-Fehlberg method is based on error control and the adjustment of the time step to maintain the error at a certain threshold. By comparing with some existing methods in numerical experiments, it shows that the present method has a better performance in terms of computational speed and provides a more accurate solution.
SSRN
Weekly, quarterly and yearly risk measures are crucial for risk reporting according to Basel III and Solvency II. For the respective data frequencies, the authors show in a simulation and back-test study that available data series are not sufficient in order to estimate Value at Risk and Expected Shortfall sufficiently, given confidence levels of 99.9% and 99.99%. Accordingly, this paper presents a semi-parametric estimation method, re-scaling data from high- to low-frequency which allows to obtain significantly more data points for the estimation of the respective risk measures. The presented methodology in the α-stable framework, which is able to mimic multi-fractal behavior in asset returns, provides tail events which never occurred in the original low-frequency data set.
arXiv
We propose and analyze semidefinite relaxation based locational marginal prices (RLMPs) for real and reactive power in electricity markets. Our analysis reveals that when the non-convex economic dispatch problem has zero duality gap, the RLMPs exhibit properties similar to locational marginal prices with linearized power flow equations. Otherwise, they behave similar to convex hull prices. Restricted to radial distribution networks, RLMPs reduce to second-order cone relaxation based distribution locational marginal prices. We illustrate our theoretical results on numerical examples.
SSRN
Using a comprehensive and proprietary dataset on international private equity activity, we study the determinants of buyout investments across 61 countries and 19 industries over the period of 1990-2017. We find evidence that macroeconomic conditions, development of stock and credit markets, and the regulatory environment in a country are important drivers of international buyout capital flows. We show that countries with low unemployment, more active stock and credit markets, and better rule of law receive more buyout capital. Using a difference-in-difference approach, we explore regulatory reforms some countries have adopted over the sample period and find that countries receive significantly more buyout capital following investor protection and contract enforcement reforms. The impact of regulatory reform is more pronounced in countries with better corporate governance standards and education. Buyout investment responds to these factors more so than foreign direct investment and gross domestic fixed investment.
arXiv
Every nation prioritizes the inclusive economic growth and development of all regions. However, we observe that economic activities are clustered in space, which results in a disparity in per-capita income among different regions. A complexity-based method was proposed by Hidalgo and Hausmann [PNAS 106, 10570-10575 (2009)] to explain the large gaps in per-capita income across countries. Although there have been extensive studies on countries' economic complexity using international export data, studies on economic complexity at the regional level are relatively less studied. Here, we study the industrial sector complexity of prefectures in Japan based on the basic information of more than one million firms. We aggregate the data as a bipartite network of prefectures and industrial sectors. We decompose the bipartite network as a prefecture-prefecture network and sector-sector network, which reveals the relationships among them. Similarities among the prefectures and among the sectors are measured using a metric. From these similarity matrices, we cluster the prefectures and sectors using the minimal spanning tree technique.The computed economic complexity index from the structure of the bipartite network shows a high correlation with macroeconomic indicators, such as per-capita gross prefectural product and prefectural income per person. We argue that this index reflects the present economic performance and hidden potential of the prefectures for future growth.
arXiv
Disagreement is an essential element of science and life in general. The language of probabilities and statistics is often used to describe disagreements quantitatively. In practice, however, we want much more than that. We want disagreements to be resolved. This leaves us with a substantial knowledge gap which is often perceived as a lack of practical intuition regarding probabilistic and statistical concepts.
Take for instance the R\'enyi divergence which is a well-known statistical quantity specifically designed as a measure of disagreement between probabilistic models. Despite its widespread use in science and engineering, the R\'enyi divergence remains a highly abstract axiomatically-motivated measure. Certainly, it offers no practical insight as to how disagreements can be resolved.
Here we propose to address disagreements using the methods of financial economics. In particular, we show how a large class of disagreements can be transformed into investment opportunities. The expected financial performance of such investments quantifies the amount of disagreement in a tangible way. This provides intuition for statistical concepts such as the R\'enyi divergence which becomes connected to the financial performance of optimized investments. Investment optimization takes into account individual opinions as well as attitudes towards risk. The result is a market-like social mechanism by which funds flow naturally to support a more accurate view. Such social mechanisms can help us with difficult disagreements (e.g., financial arguments concerning the future climate).
In terms of scientific validation, we used the findings of independent neurophysiological experiments as well as our own research on the equity premium.
SSRN
We use monthly Current Population Survey data to document employment changes during the COVID-19 pandemic at the occupation, industry, and metropolitan statistical area (MSA) levels. Over March-April 2020, jobs losses are larger for occupations with higher physical proximity or lower work-from-home feasibility, especially for lower-paying occupations. Nonessential industries also see greater declines in employment. Such occupational and industrial susceptibility to COVID-19 contributes to the variation in employment changes across MSAs: Employment shrinks more for MSAs with larger pre-crisis fractions of workers employed in occupations with higher infection risk. Over April-June 2020, occupations and industries that are hit harder recoup more jobs, but the recovery is only partial. Moreover, the gains are concentrated in lower-paying occupations and a few industries. Taken together, these abrupt changes in employment following the COVID-19 outbreak are unprecedented and potentially have long-term implications for occupational inequality and regional disparity.
arXiv
Recent studies concerning the point electricity price forecasting have shown evidence that the hourly German Intraday Continuous Market is weak-form efficient. Therefore, we take a novel, advanced approach to the problem. A probabilistic forecasting of the hourly intraday electricity prices is performed by simulating trajectories in every trading window to receive a realistic ensemble to allow for more efficient intraday trading and redispatch. A generalized additive model is fitted to the price differences with the assumption that they follow a zero-inflated distribution, precisely a mixture of the Dirac and the Student's t-distributions. Moreover, the mixing term is estimated using a high-dimensional logistic regression with lasso penalty. We model the expected value and volatility of the series using i.a. autoregressive and no-trade effects or load, wind and solar generation forecasts and accounting for the non-linearities in e.g. time to maturity. Both the in-sample characteristics and forecasting performance are analysed using a rolling window forecasting study. Multiple versions of the model are compared to several benchmark models and evaluated using probabilistic forecasting measures and significance tests. The study aims to forecast the price distribution in the German Intraday Continuous Market in the last 3 hours of trading, but the approach allows for application to other continuous markets, especially in Europe. The results prove superiority of the mixture model over the benchmarks gaining the most from the modelling of the volatility. They also indicate that the introduction of XBID reduced the market volatility.
SSRN
Weekly, quarterly and yearly risk measures are crucial for risk reporting according to Basel III and Solvency II. For the respective data frequencies, the authors show in a simulation and back-test study that available data series are not sufficient in order to estimate Value at Risk and Expected Shortfall sufficiently, given confidence levels of 99.9% and 99.99%. Accordingly, this paper presents a semi-parametric estimation method, re-scaling data from high- to low-frequency which allows to obtain significantly more data points for the estimation of the respective risk measures. The presented methodology in the α-stable framework, which is able to mimic multi-fractal behavior in asset returns, provides tail events which never occurred in the original low-frequency data set.
SSRN
We document a surprising finding that foreign capital inflows delegated through global mutual funds reduce the income of the top 1%. To rationalize this observation, we utilize a comprehensive database of worldwide ownership of both private and public firms for 2001-2013, which allows us to trace income inequality to its micro-foundations of sales revenue accrued to rich families. We find that large delegated foreign inflows induce local rich families to sell concentrated yet profitable assets, consistent with a diversification channel for financial globalization to influence income inequality. Alternative mechanisms fail to explain these findings. Our results have important normative implications.
SSRN
Using archival material and underutilized printed sources this article provides an intellectual history of Milton Friedmanâs critique of corporate social responsibility. This evidence demonstrates how his arguments developed within the context of âneoliberalâ discussion in Europe and the United States about the problematic nexus of business and political influence. Friedman and others interpreted Adam Smithâs warning about business and social responsibility to connect CSR with their suspicions that businesses constantly sought to use political resources to suppress competition. The article thereby demonstrates the critical stance of Friedmanâs critique and his attentiveness to the politicization of business. Yet his critique was also shaped by a specific historical understanding of corporate power and monopoly in the immediate post-war years in America. Finally, by using this analysis to rethink how we use historical knowledge to conceptualize the field of CSR, the article suggests how a political history of CSR can enrich contemporary debates.
SSRN
Using an agent-based model (ABM) with fundamentalists and chartists, prone to develop bubbles and crashes, we demonstrate the usefulness of direct market intervention by a policy maker, documenting strong performance in preventing bubbles and drawdowns and augmenting significantly the welfare of all investors. In our ABM, the policy maker diagnoses burgeoning bubbles by forming an expectation of the future return of the risky asset in the form of an exponential moving average of the excess return over the long-term return. The policy maker invests in the risky asset when he detects a small deviation of the return from the long-term growth rate in order to construct an inventory that he draws upon later to fight future market exuberance. Then, when this deviation between the current growth rate and the long-term growth rate exceeds the policy maker's tolerance level, he starts to sell the risky asset that he has accumulated earlier, in a countercyclical fight against future price increase. We find that the policy maker succeeds in preventing bubbles and crashes in our ABM. In simulations without bubbles, the policy maker behaves similarly to the fundamentalists and his impact is negligible, following the principle of "Primum non nocere". In simulations where bubbles form spontaneously as a result of the noise traders's strategies, the policy maker's intervention reduces the average drawdown by a factor of two when his market impact becomes significant. We find that the policy maker intervention improves all analysed metrics of market returns, including volatility, skewness, kurtosis and VaR, making the market less turbulent and more stable. The combination of fewer bubbles and crashes, lower market risks and the stability of the long-term growth rate make the policy maker intervention to improve the welfare of all investors as measured by their risk-adjusted return, increasing the Sharpe ratios from approximately 0.3 to 0.5 for noise traders, from 0.6 to 0.8 for fundamentalists as the market impact of the policy maker increases to the level of the fundamentalists. We also test the sensitivity of these results to variations of the key parameters of the strategy of the policy maker and find very robust outcomes. In particular, the conclusions are unchanged even under very large miscalibrated long-term expected returns of the risky asset.
arXiv
Failure to receive post-natal care within first week of delivery causes a 3% increase in the possibility of Acute Respiratory Infection in children under five. Mothers with unpaid maternity leave put their children at a risk of 3.9% increase in the possibility of ARI compared to those with paid maternity leave.
SSRN
This work aims to investigate the (inter)relations of information arrival, news sentiment, volatilities and jump dynamics of intraday returns. Two parametric GARCH-type jump models which explicitly incorporate both news arrival and news sentiment variables are proposed, among which one assumes news affecting financial markets through the jump component while the other postulating the GARCH component channel. In order to give the most-likely format of the interactions between news arrival and stock market behaviors, these two models are compared with several other easier versions of GARCH-type models based on the calibration results on DJIA 30 stocks. The necessity to include news processes in intraday stock volatility modeling is justified in our specific calibration samples (2008 and 2013, respectively). While it is not as profitable to model jump process separately as using simpler GARCH process with error distribution capable to capture fat tail behaviors of financial time series. In conclusion, our calibration results suggest GARCH-news model with skew-t innovation distribution as the best candidate for intraday returns of large stocks in US market, which means one can probably avoid the complicatedness of modelling jump behavior by using a simplier skew-t error distribution assumption instead, but itâs necessary to incorporate news variables.
SSRN
This study examines short selling in stocks of firms that reveal partial earnings-related information prior to their eventual earnings announcements (EA). By decomposing short selling into two components where the first corresponds to the final partial earnings disclosure and the second captures the subsequent incremental short selling until just before the EA, we estimate that the relative informativeness of shorting activity based on public partial versus private information accounts for approximately 80% and 20%, respectively, of the short selling-related decrease in the EA return. Importantly, the negative return predictability of short selling significantly increases the longer a firm implicitly delays its EA. Further, our evidence indicates time-varying short-sale constraints, ineffective following the release of partial information but rising markedly just prior to the EA. The overall findings support the proposition that short sellers are skilled investors who profit from both public partial and private information. The informativeness of short selling, however, depends critically on the efficacy of short-sale constraints.
arXiv
We propose kernel-based collocation methods for numerical solutions to Heath-Jarrow-Morton models with Musiela parametrization. The methods can be seen as the Euler-Maruyama approximation of some finite dimensional stochastic differential equations, and allow us to compute the derivative prices by the usual Monte Carlo methods. We derive a bound on the rate of convergence under some decay condition on the inverse of the interpolation matrix and some regularity conditions on the volatility functionals.
SSRN
Risk transmission among financial markets and their participants is time- evolving, especially for the extreme risk scenarios. Possibly sudden time variation of such risk structures ask for quantitative technology that is able to cope with such situations. Here we present a novel localized multivariate CAViaR-type model to respond to the challenge of time-varying risk contagion. For this purpose a local adaptive approach determines homogeneous, low risk variation intervals at each time point. Critical values for this technique are calculated via multiplier bootstrap, and the statistical properties of this âlocalized multivariate CAViaRâ are derived. A comprehensive simulation study supports the effectiveness of our approach in detecting structural change in multivariate CAViaR. Finally, when applying for the US and German financial markets, we can trace out the dynamic tail risk spillovers and find that the US market appears to play dominate role in risk transmissions, especially in volatile market periods.
arXiv
We introduce a simulation method for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the dynamic value process of a portfolio from a finite sample of its cumulative cash flow. The learned value process is given in closed form thanks to a suitable choice of the kernel. We show asymptotic consistency and derive finite sample error bounds under conditions that are suitable for finance applications. Numerical experiments show good results in large dimensions for a moderate training sample size.
arXiv
This article studies the impact of carbon risk on stock pricing. To address this, we consider the seminal approach of G\"orgen \textsl{et al.} (2019), who proposed estimating the carbon financial risk of equities by their carbon beta. To achieve this, the primary task is to develop a brown-minus-green (or BMG) risk factor, similar to Fama and French (1992). Secondly, we must estimate the carbon beta using a multi-factor model. While G\"orgen \textsl{et al.} (2019) considered that the carbon beta is constant, we propose a time-varying estimation model to assess the dynamics of the carbon risk. Moreover, we test several specifications of the BMG factor to understand which climate change-related dimensions are priced in by the stock market. In the second part of the article, we focus on the carbon risk management of investment portfolios. First, we analyze how carbon risk impacts the construction of a minimum variance portfolio. As the goal of this portfolio is to reduce unrewarded financial risks of an investment, incorporating the carbon risk into this approach fulfils this objective. Second, we propose a new framework for building enhanced index portfolios with a lower exposure to carbon risk than capitalization-weighted stock indices. Finally, we explore how carbon sensitivities can improve the robustness of factor investing portfolios.
arXiv
Asian option, as one of the path-dependent exotic options, is widely traded in the energy market, either for speculation or hedging. However, it is hard to price, especially the one with the arithmetic average price. The traditional trading procedure is either too restrictive by assuming the distribution of the underlying asset or less rigorous by using the approximation. It is attractive to infer the Asian option price with few assumptions of the underlying asset distribution and adopt to the historical data with a nonparametric method. In this paper, we present a novel approach to price the Asian option from an imprecise statistical aspect. Nonparametric Predictive Inference (NPI) is applied to infer the average value of the future underlying asset price, which attempts to make the prediction reflecting more uncertainty because of the limited information. A rational pairwise trading criterion is also proposed in this paper for the Asian options comparison, as a risk measure. The NPI method for the Asian option is illustrated in several examples by using the simulation techniques or the empirical data from the energy market.
arXiv
Financial markets provide a natural quantitative lab for understanding some of the most advanced human behaviours. Among them is the use of mathematical tools known as financial instruments. Besides money, the two most fundamental financial instruments are bonds and equities. More than 30 years ago Mehra and Prescott found the numerical performance of equities relative to government bonds could not be explained by consumption-based (mainstream) economic theories. This empirical observation, known as the Equity Premium Puzzle, has been defying mainstream economics ever since. The recent financial crisis revealed an even deeper need for understanding financial products. We show how understanding the rational nature of product design resolves the Equity Premium Puzzle. In doing so we obtain an experimentally tested theory of product design.
arXiv
Most of the existing literature on the current pandemic focuses on approaches to model the outbreak and spreading of COVID-19. This paper proposes a generalized Markov-Switching approach, the SUIHR model, designed to study border control policies and contact tracing against COVID-19 in a period where countries start to re-open. We offer the following contributions. First, the SUIHR model can include multiple entities, reflecting different government bodies with different containment measures. Second, constraints as, for example, new case targets and medical resource limits can be imposed in a linear programming framework. Third, in contrast to most SIR models, we focus on the spreading of infectious people without symptoms instead of the spreading of people who are already showing symptoms. We find that even if a country has closed its borders completely, domestic contact tracing is not enough to go back to normal life. Countries having successfully controlled the virus can keep it under check as long as imported risk is not growing, meaning they can lift travel restrictions with similar countries. However, opening borders towards countries with less controlled infection dynamics would require a mandatory quarantine or a strict test on arrival.
arXiv
Nested simulation arises frequently in financial or input uncertainty quantification problems, where the performance measure is defined as a function of the simulation output mean conditional on the outer scenario. The standard nested simulation samples $M$ outer scenarios and runs $N$ inner replications at each. We propose a new experiment design framework for a problem whose inner replication's inputs are generated from probability distribution functions parameterized by the outer scenario. This structure lets us pool replications from an outer scenario to estimate another scenario's conditional mean via the likelihood ratio method. We formulate a bi-level optimization problem to decide not only which of $M$ outer scenarios to simulate and how many times to replicate at each, but also how to pool these replications such that the total simulation effort is minimized while achieving the same estimation error as the standard nested simulation. The resulting optimal design requires far less simulation effort than $MN$. We provide asymptotic analyses on the convergence rates of the performance measure estimators computed from the experiment design. Empirical results show that our experiment design significantly reduces the simulation cost compared to the standard nested simulation as well as a state-of-the-art design that pools replications via regressions.
SSRN
The aim of this paper is to prove the phenotypic convergence of cryptocurrencies, in the sense that individual cryptocurrencies respond to similar selection pressures by developing similar characteristics. In order to retrieve the cryptocurrencies phenotype, we treat cryptocurrencies as financial instruments (genus proximum) and find their specific difference (differentia specifica) by using the daily time series of log-returns. In this sense, a daily time series of asset returns (either cryptocurrencies or classical assets) can be characterized by a multidimensional vector with statistical components like volatility, skewness, kurtosis, tail probability, quantiles, conditional tail expectation or fractal dimension. By using dimension reduction techniques (Factor Analysis) and classification models (Binary Logistic Regression, Discriminant Analysis, Support Vector Machines, K-means clustering, Variance Components Split methods) for a representative sample of cryptocurrencies, stocks, exchange rates and commodities, we are able to classify cryptocurrencies as a new asset class with unique features in the tails of the log-returns distribution. The main result of our paper is the complete separation of the cryptocurrencies from the other type of assets, by using the Maximum Variance Components Split method. More, we observe a divergent evolution of the cryptocurrencies species, compared to the classical assets, mainly due to the tails behavior of the log-returns distribution. The codes used here are available via www.quantlet.de.
arXiv
In behavioural economics, a decision maker's preferences are expressed by choice functions. Preference robust optimization(PRO) is concerned with problems where the decision maker's preferences are ambiguous, and the optimal decision is based on a robust choice function with respect to a preference ambiguity set. In this paper, we propose a PRO model to support choice functions that are: (i) monotonic (prefer more to less), (ii) quasi-concave (prefer diversification), and (iii) multi-attribute (have multiple objectives/criteria). As our main result, we show that the robust choice function can be constructed efficiently by solving a sequence of linear programming problems. Then, the robust choice function can be optimized efficiently by solving a sequence of convex optimization problems. Our numerical experiments for the portfolio optimization and capital allocation problems show that our method is practical and scalable.
arXiv
The paper is a collection of knowledge regarding the phenomenon of climate change, competitiveness, and literature linking the two phenomena to agricultural market competitiveness. The objective is to investigate the peer reviewed and grey literature on the subject to explore the link between climate change and agricultural market competitiveness and also explore an appropriate technique to validate the presumed relationship empirically. The paper concludes by identifying implications for developing an agricultural competitiveness index while incorporating the climate change impacts, to enhance the potential of agricultural markets for optimizing the agricultural sectors competitiveness.
arXiv
In this paper, we aim at solving the cardinality constrained high-order portfolio optimization, i.e., mean-variance-skewness-kurtosis model with cardinality constraint (MVSKC). Optimization for the MVSKC model is of great difficulty in two parts. One is that the objective function is non-convex, the other is the combinational nature of the cardinality constraint, leading to non-convexity as well dis-continuity. Based on the observation that cardinality constraint has the difference-of-convex (DC) property, we transform the cardinality constraint into a penalty term and then propose three algorithms including the proximal difference of convex algorithm (pDCA), pDCA with extrapolation (pDCAe) and the successive convex approximation (SCA) to handle the resulting penalized MVSK (PMVSK) formulation. Moreover, theoretical convergence results of these algorithms are established respectively. Numerical experiments on the real datasets demonstrate the superiority of our proposed methods in obtaining high utility and sparse solutions as well as efficiency in terms of time usage.
arXiv
We presented Bayesian portfolio selection strategy, via the $k$ factor asset pricing model. If the market is information efficient, the proposed strategy will mimic the market; otherwise, the strategy will outperform the market. The strategy depends on the selection of a portfolio via Bayesian multiple testing methodologies. We present the "discrete-mixture prior" model and the "hierarchical Bayes model with horseshoe prior." We define the Oracle set and prove that asymptotically the Bayes rule attains the risk of Bayes Oracle up to $O(1)$. Our proposed Bayes Oracle test guarantees statistical power by providing the upper bound of the type-II error. Simulation study indicates that the proposed Bayes oracle test is suitable for the efficient market with few stocks inefficiently priced. However, as the model becomes dense, i.e., the market is highly inefficient, one should not use the Bayes oracle test. The statistical power of the Bayes Oracle portfolio is uniformly better for the $k$-factor model ($k>1$) than the one factor CAPM. We present the empirical study, where we considered the 500 constituent stocks of S\&P 500 from the New York Stock Exchange (NYSE), and S\&P 500 index as the benchmark for thirteen years from the year 2006 to 2018. We showed the out-sample risk and return performance of the four different portfolio selection strategies and compared with the S\&P 500 index as the benchmark market index. Empirical results indicate that it is possible to propose a strategy which can outperform the market.
SSRN
We perform a series of 5 strategic analyses of Japanese Megabanks. In this paper we review how changes in relative prioritization of stakeholders may impact corporate strategy at Japanese Megabanks. We look to answer the following questions via a case study with reference to RUS Megabank.Which key stakeholders have had the highest priority for Japanese Megabanks up to now and which have had the lowest priority? How have these priorities been reflected in actual strategy? Is the relative prioritisation of stakeholders likely to change over the next five years and what are the strategy implications for Japanese Megabanks?
SSRN
We perform a series of 5 strategic analyses of Japanese Megabanks. In this paper we review how changes in relative prioritization of stakeholders may impact corporate strategy at Japanese Megabanks. We look to answer the following questions via a case study with reference to RUS Megabank.Which key stakeholders have had the highest priority for Japanese Megabanks up to now and which have had the lowest priority? How have these priorities been reflected in actual strategy? Is the relative prioritisation of stakeholders likely to change over the next five years and what are the strategy implications for Japanese Megabanks?
SSRN
We perform a series of 5 strategic analyses of Japanese Megabanks. This paper is the second analysis in the series, where we carry out a macro scenario analysis of financial services relevant to Japanese Megabanks. We look to study the implications such a scenario analysis has for corporate strategy, outline the time and scope of the analysis performed and highlight the limitations via a case study with reference to RUS Megabank.
SSRN
We perform a series of 5 strategic analyses of Japanese Megabanks. This paper is the second analysis in the series, where we carry out a macro scenario analysis of financial services relevant to Japanese Megabanks. We look to study the implications such a scenario analysis has for corporate strategy, outline the time and scope of the analysis performed and highlight the limitations via a case study with reference to RUS Megabank.
SSRN
We perform a series of five strategic analyses of Japanese Megabanks. This paper is the third analysis in the series, where we explore to what extent Japanese Megabanks and RUFG have a âcore competenciesâ that are valuable, rare, inimitable, non-substitutable and whose benefits are organisationally appropriable? How is this core competence different from that of key competitorâs? How do we see this core competence being developed to match strategic challenges over the next five years?
SSRN
We perform a series of five strategic analyses of Japanese Megabanks. This paper is the third analysis in the series, where we explore to what extent Japanese Megabanks and RUFG have a âcore competenciesâ that are valuable, rare, inimitable, non-substitutable and whose benefits are organisationally appropriable? How is this core competence different from that of key competitorâs? How do we see this core competence being developed to match strategic challenges over the next five years?
SSRN
We perform a series of five strategic analyses of Japanese Megabanks. This paper is the fourth analysis in the series, where we analyse the financial service industry dynamics to assess how Japanese Megabanks and RUFG should best position themselves to exploit opportunities, protect against threats and maximize corporate value.Michael E. Porter proposes a five forces industry structure is well-suited to assess the attractiveness of important markets and industries. We perform an industry analysis for financial services, suggest what RUS should do as a result of this analysis and comment on the value and limitations of the analysis for RUS.
SSRN
We have performed a series of five strategic analyses of Japanese Megabanks. This paper is the last analysis in the series, where we compare RUFG and key competitors using a âstrategy canvasâ to assess competitiveness. Furthermore we evaluate the prospect of finding âblue oceanâ opportunities to exploit, where profit margins are wider and markets are not congested.
SSRN
Using the Foreign Account Tax Compliance Act (FATCA) as an exogenous shock that reduces the tax advantages of offshore funds sold to U.S. investors, we document that affected funds significantly enhance their performance as a response. This effect is stronger for funds domiciled in tax havens and for skilled funds with low flow volatility. Moreover, in generating additional performance, FATCA-affected funds also increase the price efficiency of their invested stocks. Our analysis has important normative implications in showing that curbing offshore tax evasion could help improve efficiency in both the global asset management industry and the security market.
SSRN
This new version of the Patent Examination Research Dataset (PatEx) is based on data that the Office of the Chief Economist downloaded from Patent Examination Data System (PEDS) on April 26, 2020. We parsed the XML and organized the data into the familiar PatEx data files, following the organization of the Public PAIR portal. However, there are some minor differences between the new PatEx release and the previous ones. This is the main rationale behind the new technical documentation for this release and we advise users to read the relevant sections to better acclimate themselves to the new data.
SSRN
News move markets and contains incremental information about stock reactions. Future trading volumes, volatility and returns are a ected by sentiments of texts and opinions expressed in articles. Earlier work of sentiment distillation of stock news suggests that risk prole reactions might differ across sectors.Conventional asset pricing theory recognizes the role of a sector and its risk uniqueness that differs from market or rm specic risk.Our research assesses whether incorporating the sentiment distilled from sector specic news carries information about risk proles. Textual analytics applied to about 600K articles leads us with lexical projection and machine learning to classication of sentiment polarities.The texts are scraped from offcial NASDAQ web pages and with Natural Language Processing (NLP) techniques, such as tokenization, lemmatization, a sector specic sentiment is extracted using a lexical approach and a nancial phrase bank. Predicted sentence-level polarities are aggregated into a bullishness measure on a daily basis and fed into a panel regression analysis with sector indicators. Supervised learning with hinge or logistic loss and regularization yields good prediction results of polarity. Compared with standard lexical projections, the supervised learning approach yields superior predictions of sentiment, leading to highly sector specic sentiment reactions. The Consumer Staples, Health Care and Materials sectors show strong risk prole reactions to negative polarity.
SSRN
We investigate the economic effects of the COVID-19 pandemic and the role played by credit constraints in the transmission mechanism, using a novel survey of expectations and plans of Italian firms, taken just before and after the outbreak. Most firms revise downward their expectations for sales, orders, employment, and investment, while prices are expected to increase at a faster rate, with geographical and sectoral heterogeneity in the size of the effects. Credit constraints amplify the effects on factor demand and sales of the COVID-19 generated shocks. Credit-constrained firms also expect to charge higher prices, relative to unconstrained firms. The search for and availability of liquidity is a key determinant of firms' plans. Finally, both supply and demand shocks play a role in shaping firms' expectations and plans, with supply shocks being slightly more important in the aggregate.
SSRN
This paper builds a model of delegated portfolio management to study the interplay between the career concerns of a manager and the prevailing market conditions. Investors allocate funds between a market index and a manager, who has private information about her ability to generate idiosyncratic returns. In each period, the investors observe the manager's decision to either follow a market neutral strategy, or an index tracking one. It is shown that the latter always results in a loss of reputation, reflected also on the fund's flows. This loss is smaller in bull markets, when investors expect more managers to use high beta strategies. As a result, a manager's performance in a bull market is less informative about her ability than in bear markets, because a high beta strategy does not rely on it. We empirically find that flows of funds that follow high beta strategies are less responsive to the fund's performance than those that follow market neutral strategies. Finally, we verify that the flow-performance sensitivity is higher in bear markets than in bull markets.
SSRN
The U.S. securities markets have recently undergone (or are undergoing) three fundamental transitions: (1) institutionalization (with the result that institutional investors now dominate both trading and stock ownership); (2) extraordinary ownership concentration (with the consequence that the three largest U.S. institutional investors now hold 20% and vote 25% of the shares in S&P 500 companies); and (3) the introduction of ESG disclosures (which process has been driven in the U.S. by pressure from large institutional investors). In light of these transitions, how should disclosure policy change? Do institutions and retail investors have the same or different disclosure needs? Why are large institutions pressing for increased ESG disclosures? This article will offer two reasons for the desire of institutions for greater ESG disclosures: (1) ESG disclosures overlap substantially with systematic risk, which is the primary concern of diversified investors; and (2) high common ownership enables institutions to take collective action to curb externalities caused by portfolio firms, so long as the gains to their portfolio from such action exceed the losses caused to the externality-creating firms. This transition to a portfolio-wide perspective (both in voting and investment decisions) has significant implications but also is likely to provoke political controversy. As institutions shift to portfolio-wide decision making, the disclosure needs of individual investors and institutional investors diverge and serious conflicts can arise.
arXiv
This study aims to investigate the behavior of stock prices throughout the episodes of foreign capital flows using data of daily stock prices and quarterly foreign capital flows from 14 EMEs. To this end, the episodes of capital flows are identified using the threshold and the k-means clustering approaches. Next, the stock index changepoints are detected using the Pruned Exact Linear Time (PELT) method. Finally, we combine the results by distributing the detected changepoints over the identified capital flows. The results reveal that the stock indices have been rarely pushed further during the entire surge episodes identified by both approaches, and thus surges of capital flows do not necessarily lead to further appreciation of stock prices. In the meantime, a significant appreciation of stock prices is observed during the normal state of capital flows. On the other hand, it is noticed that the stock prices have not often depreciated during the episodes of foreign capital outflows in all the selected EMEs, which means that stock prices have been less vulnerable to reversals of foreign capital flows
SSRN
This paper presents probability distributions for price and returns random processes for averaging time interval Î". These probabilities determine properties of price and returns volatility. We define statistical moments for price and returns random processes as functions of the costs and the volumes of market trades aggregated during interval Î". These sets of statistical moments determine characteristic functionals for price and returns probability distributions. Volatilities are described by first two statistical moments. Second statistical moments are described by functions of second degree of the cost and the volumes of market trades aggregated during interval Î". We present price and returns volatilities as functions of number of trades and second degree costs and volumes of market trades aggregated during interval Î". These expressions support numerous results on correlations between returns volatility, number of trades and the volume of market transactions. Forecasting the price and returns volatilities depend on modeling the second degree of the costs and the volumes of market trades aggregated during interval Î". Second degree market trades impact second degree of macro variables and expectations. Description of the second degree market trades, macro variables and expectations doubles the complexity of the current macroeconomic and financial theory.
arXiv
Groups of enterprises can serve as guarantees for one another and form complex networks when obtaining loans from commercial banks. During economic slowdowns, corporate default may spread like a virus and lead to large-scale defaults or even systemic financial crises. To help financial regulatory authorities and banks manage the risk associated with networked loans, we identified the default contagion risk, a pivotal issue in developing preventive measures, and established iConVis, an interactive visual analysis tool that facilitates the closed-loop analysis process. A novel financial metric, the contagion effect, was formulated to quantify the infectious consequences of guarantee chains in this type of network. Based on this metric, we designed and implement a series of novel and coordinated views that address the analysis of financial problems. Experts evaluated the system using real-world financial data. The proposed approach grants practitioners the ability to avoid previous ad hoc analysis methodologies and extend coverage of the conventional Capital Accord to the banking industry.