# Research articles for the 2021-07-26

RePEC

We build a semi-structural New Keynesian model with financial frictions to study the drivers of macroeconomic tail risk ('GDP-at-Risk'). We analyse the empirically observed fat left tail of the GDP distribution by modelling three key non-linearities emphasised in the literature: 1) an effective lower bound on nominal interest rates, 2) a credit crunch in bank credit supply when bank capital depletes, and 3) deleveraging by borrowers when debt service burdens become excessive. We obtain three key results. First, our model generates a significantly fat-tailed distribution of GDP – a finding that is absent in most linear New Keynesian and RBC models. Second, we show how these constraints interact with each other. We find that an economy prone to debt deleveraging will experience significantly more credit crunch and effective lower bound episodes than otherwise. Moreover, as the effective lower bound becomes more proximate, the frequency of credit crunch episodes increases significantly. As a rule of thumb, we find that each 50 basis point decline in monetary policy headroom requires additional capital buffers of 1% of assets or 2%–2.5% points lower debt service burdens to hold the risk level constant. Third, we use the model to generate a historical decomposition of GDP-at-Risk for the United Kingdom. The implied risk outlook deteriorates significantly in the run-up to the Global Financial Crisis, driven by depleted capital buffers and increasing debt burdens. Since then, GDP-at-Risk has remained elevated, with greater bank resilience and lower debt offset by the limited capacity of monetary policy to cushion adverse shocks.

arXiv

When individuals in a social network learn about an unknown state from private signals and neighbors' actions, the network structure often causes information loss. We consider rational agents and Gaussian signals in the canonical sequential social-learning problem and ask how the network changes the efficiency of signal aggregation. Rational actions in our model are a log-linear function of observations and admit a signal-counting interpretation of accuracy. This generates a fine-grained ranking of networks based on their aggregative efficiency index. Networks where agents observe multiple neighbors but not their common predecessors confound information, and we show confounding can make learning very inefficient. In a class of networks where agents move in generations and observe the previous generation, aggregative efficiency is a simple function of network parameters: increasing in observations and decreasing in confounding. Generations after the first contribute very little additional information due to confounding, even when generations are arbitrarily large.

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. Furthermore, we implement an extrapolation method to ensure that at least, a third-order accuracy in space is maintained at the boundary point when computing the optimal exercise boundary from its derivative. 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 the numerical experiment, it shows that the present method has a better performance in terms of computational speed and provides a more accurate solution.

arXiv

This paper is concerned with the process of risk allocation for a generic multivariate model when the risk measure is chosen as the Value-at-Risk (VaR). We recast the traditional Euler contributions from an expectation conditional on an event of zero probability to a ratio involving conditional expectations whose conditioning events have stricktly positive probability. We derive an analytical form of the proposed representation of VaR contributions for various parametric models. Our numerical experiments show that the estimator using this novel representation outperforms the standard Monte Carlo estimator in terms of bias and variance. Moreover, unlike the existing estimators, the proposed estimator is free from hyperparameters.

SSRN

This paper presents a bank capital structure model in which equity holders can increase asset risk once debt is in place. I study the effects of capital requirements and subsidized deposit insurance on the bank's privately optimal funding and operational risk level. The model predicts that there are synergetic effects of regulation and market discipline. When the regulator sets the capital charge and deposit insurance premium payments sufficiently high for a risky portfolio, the bank commits to the low-risk asset portfolio by setting a lower leverage ratio. This market discipline effect disappears when the regulatory costs become too high.

arXiv

We analyze an optimal trade execution problem in a financial market with stochastic liquidity. To this end we set up a limit order book model in continuous time. Both order book depth and resilience are allowed to evolve randomly in time. We allow for trading in both directions and for c\`adl\`ag semimartingales as execution strategies. We derive a quadratic BSDE that under appropriate assumptions characterizes minimal execution costs and identify conditions under which an optimal execution strategy exists. We also investigate qualitative aspects of optimal strategies such as, e.g., appearance of strategies with infinite variation or existence of block trades and discuss connections with the discrete-time formulation of the problem. Our findings are illustrated in several examples.

SSRN

This paper examines the effect of financialisation of futures markets has on the relationship between crude oil futures and equities by using the VAR-DCC-GARCH model. Specifically, by accounting for the systematic patterns of commodity price volatility, namely, seasonality and maturity effects for the pre-financialisation (1993-2003) and post-financialisation (2004-2019) period. While speculation that reflects non-commercial investorsâ€™ activity is found to have a negative impact on crude oil futuresâ€™ volatility before the financialisation period, open interest as a measure of liquidity has a negative effect after 2004. The finding indicates weakening seasonality in crude oil futures and diminishing Samuelson maturity effect i.e. volatility of the contract increases as it nears to expiration since financialisation. This confirms the importance of accounting for volatility dynamics while contributing to financialisation debate.

arXiv

We investigate the allegation that legacy U.S. airlines communicated via earnings calls to coordinate with other legacy airlines in offering fewer seats on competitive routes. To this end, we first use text analytics to build a novel dataset on communication among airlines about their capacity choices. Estimates from our preferred specification show that the number of offered seats is 2% lower when all legacy airlines in a market discuss the concept of "capacity discipline." We verify that this reduction materializes only when legacy airlines communicate concurrently, and that it cannot be explained by other possibilities, including that airlines are simply announcing to investors their unilateral plans to reduce capacity, and then following through on those announcements.

arXiv

We use deep neural networks to estimate an asset pricing model for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. The key innovations are to use the fundamental no-arbitrage condition as criterion function, to construct the most informative test assets with an adversarial approach and to extract the states of the economy from many macroeconomic time series. Our asset pricing model outperforms out-of-sample all benchmark approaches in terms of Sharpe ratio, explained variation and pricing errors and identifies the key factors that drive asset prices.

SSRN

Are investors willing to give up a higher return if the investment generates positive environmental impact? We investigate this question with a decision experiment among crowdfunders, where they choose between a higher return or environmental impact. Overall, 65% of investors choose environmental impact at the expense of a higher return for sufficiently large impact, 14% choose impact independent of the magnitude of impact, while 21% choose the higher return independent of impact. Combining the experimental data with historical investments, we find that investors allocate a larger share of funds to green projects if they value environmental impact more, and if they expect green projects to be more profitable. These findings suggest that investors have a preference for positive environmental impact, and satisfy it by investing in green projects. We further show that the preference for environmental impact is distinct from a preference for positive social impact. Finally, we introduce new survey measures of impact for future use, which are experimentally validated and predict field behavior.

arXiv

The COVID-19 pandemic has been influencing travel behaviour in many urban areas around the world since the beginning of 2020. As a consequence, bike-sharing schemes have been affected partly due to the change in travel demand and behaviour as well as a shift from public transit. This study estimates the varying effect of the COVID-19 pandemic on the London bike-sharing system (Santander Cycles) over the period March-December 2020. We employed a Bayesian second-order random walk time-series model to account for temporal correlation in the data. We compared the observed number of cycle hires and hire time with their respective counterfactuals (what would have been if the pandemic had not happened) to estimate the magnitude of the change caused by the pandemic. The results indicated that following a reduction in cycle hires in March and April 2020, the demand rebounded from May 2020, remaining in the expected range of what would have been if the pandemic had not occurred. This could indicate the resiliency of Santander Cycles. With respect to hire time, an important increase occurred in April, May, and June 2020, indicating that bikes were hired for longer trips, perhaps partly due to a shift from public transit.

SSRN

We propose a novel estimation procedure of bid-ask spreads from open, high, low, and close prices. Our estimator is asymptotically unbiased and optimally combines the full set of price data to minimize the estimation variance. When quote data are not available, our estimator generally delivers the most accurate estimates of effective bid-ask spreads numerically and empirically. The estimator is derived under permissive assumptions that allow for stylized facts typically observed in real market data, is easy to implement, and can be applied to liquid and illiquid market segments, both in low and high frequency.

SSRN

Lending to Small and Medium Enterprises (SME) is facilitated by the availability of advanced Machine Learning (ML) methods, embedded in financial technologies, which can accurately predict financial performance from the many data sources available. However, despite their high predictive accuracy, ML models may not provide sufficient explanations to investors and, therefore, may not be adequate for informed decision-making. We propose a financial machine learning model that is both accurate and explainable. To reach this aim, we propose to enhance random forest models with a model selection procedure that progressively removes the least explainable variable, according to the Shapley value method. We apply our proposal to 2,049 SMEs for which yearly financial performance indicators are available. Our results show that both the default and the expected return of SMEs can be well predicted and explained by a small set of indicators deduced from their balance sheets.

arXiv

In this research work, an explicit Runge-Kutta-Fehlberg (RKF) time integration with a fourth-order compact finite difference scheme in space and a high order analytical approximation of the optimal exercise boundary is employed for solving the regime-switching pricing model. In detail, we recast the free boundary problem into a system of nonlinear partial differential equations with a multi-fixed domain. We then introduce a transformation based on the square root function with a Lipschitz character from which a high order analytical approximation is obtained to compute the derivative of the optimal exercise boundary in each regime. We further compute the boundary values, asset option, and the option Greeks for each regime using fourth-order spatial discretization and adaptive time integration. In particular, the coupled assets options and option Greeks are estimated using Hermite interpolation with Newton basis. Finally, a numerical experiment is carried out with two- and four-regimes examples and results are compared with the existing methods. The results obtained from the numerical experiment show that the present method provides better performance in terms of computational speed and more accurate solutions with a large step size.

arXiv

We carry out a detailed large-scale data analysis of price response functions in the spot foreign exchange market for different years and different time scales. Such response functions provide quantitative information on the deviation from Markovian behavior. The price response functions show an increase to a maximum followed by a slow decrease as the time lag grows, in trade time scale and in physical time scale, for all analyzed years. Furthermore, we use a price increment point (pip) bid-ask spread definition to group different foreign exchange pairs and analyze the impact of the spread in the price response functions. We find that large pip spreads have a stronger impact on the response. This is similar to what has been found in stock markets.

arXiv

The quarterly financial statement, or Form 10-Q, is one of the most frequently required filings for US public companies to disclose financial and other important business information. Due to the massive volume of 10-Q filings and the enormous variations in the reporting format, it has been a long-standing challenge to retrieve item-specific information from 10-Q filings that lack machine-readable hierarchy. This paper presents a solution for itemizing 10-Q files by complementing a rule-based algorithm with a Convolutional Neural Network (CNN) image classifier. This solution demonstrates a pipeline that can be generalized to a rapid data retrieval solution among a large volume of textual data using only typographic items. The extracted textual data can be used as unlabeled content-specific data to train transformer models (e.g., BERT) or fit into various field-focus natural language processing (NLP) applications.

arXiv

Men and women systematically differ in their beliefs about their performance relative to others; in particular, men tend to be more overconfident. This paper provides support for one explanation for gender differences in overconfidence, performance-motivated reasoning, in which people distort how they process new information in ways that make them believe they outperformed others. Using a large online experiment, I find that male subjects distort information processing in ways that favor their performance, while female subjects do not systematically distort information processing in either direction. These statistically-significant gender differences in performance-motivated reasoning mimic gender differences in overconfidence; beliefs of male subjects are systematically overconfident, while beliefs of female subjects are well-calibrated on average. The experiment also includes political questions, and finds that politically-motivated reasoning is similar for both men and women. These results suggest that, while men and women are both susceptible to motivated reasoning in general, men find it particularly attractive to believe that they outperformed others.

arXiv

We investigate and extend the results of Golts and Jones (2009) that an $\alpha$-weight angle resulting from unconstrained quadratic portfolio optimisations has an upper bound dependent on the condition number of the covariance matrix. This implies that better conditioned covariance matrices produce weights from unconstrained mean-variance optimisations that are better aligned with each assets expected return. We provide further clarity on the mathematical insights that relate the inequality between the $\alpha$-weight angle and the condition number and extend the result to include portfolio optimisations with gearing constraints. We provide an extended family of robust optimisations that include the gearing constraints, and discuss their interpretation.

SSRN

It has recently been shown that government ownership of banks has a significant role in addressing market failures, improving social welfare and economic development. This study explores and identifies the potential products and factors in agriculture that public banks should subsidize. In this paper, the author investigates statistical properties of the two-step generalized method of moments (GMM) estimator to analyze the direct and indirect consumption of inputs in agricultural production on national-level data for 32 crop products and 14 livestock products from Cambodia during the 1989â€"2018 period. Many specifications have statistical significance and negative competent production growth. These results suggest that the proposed subsidies should clearly define the types of specialty products by local producers and their potential markets, both local and international. This paper investigates some policy options for government ownership of banks to improve agriculture. However, it must also adapt to new climate change and emergency events for the long-run sustainable development of the sector. Future directions should consider studying micro-data for specific types of products and regions.

SSRN

The amount of credit in the economy is a heterogeneous aggregate that can be analyzed across different dimensions. Considering such dimensions provides insights into the effect of monetary policy interventions because the credit components are observed to respond differently. Several possible motivations are behind such a differential response and those relate to either demand and supply factors intrinsic to the transmission mechanism of monetary policy. Our objective is to unveil such a differential response across a couple of relevant dimensions and discuss the possible causes behind what observed. The analysis refers to the US and is based on a vector auto-regression estimated using Bayesian techniques and identified with a combination of sign and zero-restrictions.

arXiv

Inferring the uncertainties in economic conditions are of significant importance for both decision makers as well as market players. In this paper, we propose a novel method based on Hidden Markov Model (HMM) to construct the Economic Condition Uncertainty (ECU) index that can be used to infer the economic condition uncertainties. The ECU index is a dimensionless index ranges between zero and one, this makes it to be comparable among sectors, regions and periods. We use the daily electricity consumption data of nearly 20 thousand firms in Shanghai from 2018 to 2020 to construct the ECU indexes. Results show that all ECU indexes, no matter at sectoral level or regional level, successfully captured the negative impacts of COVID-19 on Shanghai's economic conditions. Besides, the ECU indexes also presented the heterogeneities in different districts as well as in different sectors. This reflects the facts that changes in uncertainties of economic conditions are mainly related to regional economic structures and targeted regulation policies faced by sectors. The ECU index can also be easily extended to measure uncertainties of economic conditions in different fields which has great potentials in the future.

arXiv

Most trading in cryptocurrency options is on inverse products, so called because the contract size is denominated in US dollars and they are margined and settled in crypto, typically bitcoin or ether. Their popularity stems from allowing professional traders in bitcoin or ether options to avoid transferring fiat currency to and from the exchanges. We derive new analytic pricing and hedging formulae for inverse options under the assumption that the underlying follows a geometric Brownian motion. The boundary conditions and hedge ratios exhibit relatively complex but very important new features which warrant further analysis and explanation. We also illustrate some inconsistencies, exhibited in time series of Deribit bitcoin option implied volatilities, which indicate that traders may be applying direct option hedging and valuation methods erroneously. This could be because they are unaware of the correct, inverse option characteristics which are derived in this paper.

arXiv

We study one-shot Nash competition between an arbitrary number of identical dealers that compete for the order flow of a client. The client trades either because of proprietary information, exposure to idiosyncratic risk, or a mix of both trading motives. When quoting their price schedules, the dealers do not know the client's type but only its distribution, and in turn choose their price quotes to mitigate between adverse selection and inventory costs. Under essentially minimal conditions, we show that a unique symmetric Nash equilibrium exists and can be characterized by the solution of a nonlinear ODE.

SSRN

Deep learning models have gained great popularity in statistical modeling because they lead to very competitive regression models, often outperforming classical statistical models such as generalized linear models. The disadvantage of deep learning models is that their solutions are difficult to interpret and explain, and variable selection is not easily possible because deep learning models solve feature engineering and variable selection internally in a nontransparent way. Inspired by the appealing structure of generalized linear models, we propose a new network architecture that shares similar features as generalized linear models, but provides superior predictive power benefiting from the art of representation learning. This new architecture allows for variable selection of tabular data and for interpretation of the calibrated deep learning model, in fact, our approach provides an additive decomposition in the spirit of Shapley values and integrated gradients.

SSRN

French Abstract: Nous mobilisons un modÃ¨le de micro-simulation sâ€™appuyant sur un jeu de donnÃ©es individuelles particuliÃ¨rement riche pour Ã©valuer lâ€™impact de la crise sanitaire sur la situation financiÃ¨re de plus de 645 000 entreprises franÃ§aises en 2020. Nous montrons que la quasi-stabilitÃ© de la dette nette au niveau macroÃ©conomique masque de fortes disparitÃ©s au niveau individuel. Lâ€™hÃ©tÃ©rogÃ©nÃ©itÃ© est marquÃ©e entre secteurs (avant et aprÃ¨s dispositifs de soutien public) mais Ã©galement au sein de chaque secteur. Lâ€™occurrence et lâ€™intensitÃ© des chocs nÃ©gatifs de trÃ©sorerie Ã fin 2020 sont par ailleurs corrÃ©lÃ©es Ã la qualitÃ© de crÃ©dit de lâ€™entreprise (cotation Banque de France) avant crise. Nos simulations montrent Ã©galement que les mesures de soutien rÃ©duisent la dispersion des chocs de trÃ©sorerie et ramÃ¨nent Ã une distribution Ã peu prÃ¨s identique Ã celle dâ€™une annÃ©e normale, sauf aux deux extrÃ©mitÃ©s de la distribution.English Abstract: Using rich granular data for over 645 000 French firms in 2020, this paper builds a micro-simulation model to assess the impact of the Covid-19 crisis on corporate liquidity. Going beyond the aggregate picture, we document that while net debt has been fairly stable at the macroeconomic level, individual heterogeneity is widespread. Significant dispersion in changes in net debt prevails both between and within industries, before as well as after public support. We show that the probability to experience a negative liquidity shock âˆ' as well as the intensity of this shock âˆ' are negatively correlated with the initial credit quality of the firm (based on Banque de France internal ratings). Our model also finds that public support dampens significantly the impact of Covid on the dispersion of liquidity shocks and brings back the distribution of liquidity shocks closer to its pre-crisis path but with fatter tails.

SSRN

We show strong over-extrapolation of earnings in the I/B/E/S managerial guidance. Firms whose earnings are less persistent, less volatile, or more salient exhibit more extrapolation. Managers who observe rapid growth in their local housing market also demonstrate more extrapolation, albeit weaker. Over-extrapolation is not particularly related to a managerâ€™s skills or tenure. Exploiting exogenous variations in firmsâ€™ profitability resulting from the 2017 Tax Cuts and Jobs Act, we provide clean evidence that managers over-extrapolate past earnings even when earnings experience a temporary shock. Consistent with Bordalo et al. (2020) and Afrouzi et al. (2021), our findings shed light on what factors affect the degree of over-extrapolation.

arXiv

We propose to derive deviation measures through the Minkowski gauge of a given set of acceptable positions. We show that, given a suitable acceptance set, any positive homogeneous deviation measure can be accommodated in our framework. In doing so, we provide a new interpretation for such measures, namely, that they quantify how much one must shrink or deleverage a position for it to become acceptable. In particular, the Minkowski Deviation of a set which is convex, stable under scalar addition, and radially bounded at non-constants, is a generalized deviation measure. Furthermore, we explore the relations existing between mathematical and financial properties attributable to an acceptance set, and the corresponding properties of the induced measure. Hence, we fill the gap that is the lack of an acceptance set for deviation measures. Dual characterizations in terms of polar sets and support functionals are provided.

arXiv

Index tracking is a popular form of asset management. Typically, a quadratic function is used to define the tracking error of a portfolio and the look back approach is applied to solve the index tracking problem. We argue that a forward looking approach is more suitable, whereby the tracking error is expressed as expectation of a function of the difference between the returns of the index and of the portfolio. We also assume that there is an uncertainty in the distribution of the assets, hence a robust version of the optimization problem needs to be adopted. We use Bregman divergence in describing the deviation between the nominal and actual distribution of the components of the index. In this scenario, we derive the optimal robust index tracking strategy in a semi-analytical form as a solution of a system of nonlinear equations. Several numerical results are presented that allow us to compare the performance of this robust strategy with the optimal non-robust strategy. We show that, especially during market downturns, the robust strategy can be very advantageous.

arXiv

We study a model in which before a conflict between two parties escalates into a war (in the form of an all-pay auction), a party can offer a take-it-or-leave-it bribe to the other one for a peaceful settlement. We distinguish between various degrees of peace prospects--implementability, weak security and strong security. We first characterize the necessary and sufficient conditions for peace implementability and weak security. We then show that weak security implies strong security. We also consider a requesting-a-bribe game and characterize the necessary and sufficient conditions for existence of a robust peaceful equilibrium. We find that all such robust peaceful equilibria share the same request.

arXiv

This paper addresses reflected backward stochastic differential equations (RBSDE hereafter) that take the form of \begin{eqnarray*} \begin{cases} dY_t=f(t,Y_t, Z_t)d(t\wedge\tau)+Z_tdW_t^{\tau}+dM_t-dK_t,\quad Y_{\tau}=\xi,

Y\geq S\quad\mbox{on}\quad \Lbrack0,\tau\Lbrack,\quad \displaystyle\int_0^{\tau}(Y_{s-}-S_{s-})dK_s=0\quad P\mbox{-a.s..}\end{cases} \end{eqnarray*} Here $\tau$ is an arbitrary random time that might not be a stopping time for the filtration $\mathbb F$ generated by the Brownian motion $W$. We consider the filtration $\mathbb G$ resulting from the progressive enlargement of $\mathbb F$ with $\tau$ where this becomes a stopping time, and study the RBSDE under $\mathbb G$. Precisely, we focus on answering the following problems: a) What are the sufficient minimal conditions on the data $(f, \xi, S, \tau)$ that guarantee the existence of the solution of the $\mathbb G$-RBSDE in $L^p$ ($p>1$)? b) How can we estimate the solution in norm using the triplet-data $(f, \xi, S)$? c) Is there an RBSDE under $\mathbb F$ that is intimately related to the current one and how their solutions are related to each other? We prove that for any random time, having a positive Az\'ema supermartingale, there exists a positive discount factor ${\widetilde{\cal E}}$ that is vital in answering our questions without assuming any further assumption on $\tau$, and determining the space for the triplet-data $(f,\xi, S)$ and the space for the solution of the RBSDE as well.

SSRN

Using a sample of Chinese A-share listed firms from 2009 to 2019, we analyze the relationship and dynamics between derivatives usage and earnings smoothing. We show that financial derivatives usage increases the firmsâ€™ accruals earnings smoothing. Differences in property rights (SOEs or private firms), differences in overseas operations (firms with or without overseas affiliated companies), and levels of public scrutiny play the moderating roles between derivatives usage and earnings smoothing. Moreover, these three moderating factors have interactions inter se. Our results also imply that the futures might be the only type of derivatives that are used to smooth earnings. Overall, our paper reveals the relation of derivatives usage to earnings smoothing in China and the dynamics of the relation, and suggests that, during the process of IFRS convergence, extra attention on the derivatives, which could be used as tools to intentionally smooth earnings, would be required.

arXiv

The ongoing Rohingya refugee crisis is considered as one of the largest human-made humanitarian disasters of the 21st century. So far, Bangladesh is the largest recipient of these refugees. According to the United Nations Office for the Coordination of Humanitarian Affairs (UN OCHA), approximately 650,000 new entrants have been recorded since the new violence erupted on 25 August 2017 in the Rakhine state of Myanmar.1 However, such crisis is nothing new in Bangladesh, nor are the security-related challenges new that such an exodus brings with it. Ever since the military came to power in Myanmar (in 1962), Rohingya exodus to neighboring countries became a recurring incident. The latest mass exodus of Rohingyas from Rakhine state of Myanmar to Bangladesh is the largest of such influxes. Unlike, the previous refugee crisis, the ongoing crisis has wide-ranging security implications on Bangladesh. They are also varied and multifaceted. Thus, responsibilities for ensuring effective protection have become operationally multilateral. The problem of security regarding the Rohingya refugee issue is complicated by the Islamist insurgency, illicit methamphetamine/yaba drug trafficking, and HIV/AIDS/STI prevalence factors. The chapter examines the different dimensions of security challenges that the recent spell of Rohingya exodus brings to Bangladesh and the refugees themselves. In order to understand the challenges, firstly the chapter attempts to conceptualize the prominent security frameworks. Secondly, it examines the context and political economy behind the persecution of Rohingyas in the Rakhine state. Thirdly, it explores the political and military aspects of security. Fourthly, it explores the social and economic dimensions. Finally, it examines the environmental impacts of Rohingya crisis in Bangladesh.

SSRN

This paper analyzes changes in firms' cash flows and discount rates around share repurchase announcements. Both cash flow and discount rate volatility decrease significantly after repurchase announcements. The decrease in volatility is smallest for firms that are likely to be underpriced and that experience the highest initial market reactions and long-term returns after the announcement. Firms with the largest decrease in volatility do not experience significantly positive long-term returns. Moreover, the level of the discount rate decreases from one quarter before until up to three years after the repurchase announcement for firms that are likely to be underpriced. The findings suggest that financial market participants learn about firms' systematic risk when firms announce share repurchases.

RePEC

With the Global Financial Crisis, the COVID-19 pandemic, and the looming Climate Change, investors and policymakers around the world are bracing for a new global environment with heightened tail risk. Asymmetric exposure to this risk across countries raises the private and social value of arrangements improving insurance. We offer an analytical decomposition of the welfare effects of efficient capital market integration into a "smoothing" and a "level effect". Enhancing risk sharing affects the volatility of consumption, but also brings about equilibrium adjustment in asset and goods prices. This in turn drives relative wealth and consumption, as well as labor and capital allocation, across borders. Using model simulation, we explore quantitatively the empirical relevance of the different channels through which riskier and safer countries benefit from sharing macroeconomic risk. We offer an algorithm for the correct solution of the equilibrium using DSGE models under complete markets, at higher order of approximation.

SSRN

The present research study examined the impact of different dividend rate announcements on stocks prices in the Indian stock market. The results observe in twenty-four times based on market capitalization wise and dividend rate wise for final dividend announcement. The results of the study are not the same for different dividend rate classifications and different market capitalizations. The study found positive abnormal returns on event day in most of the classifications and it is similar to Litzenberger and Ramaswamy (1982), Asquith and Mullins Jr (1983), Grinblatt, Masulis and Titman (1984), Chen, Nieh, Da Chen, and Tang (2009) and many previous research results studied in major developed stock markets and emerging stock markets. Full sample and small-cap final dividend rate 100 percent to 199 percent average abnormal returns are positively significant and other final dividend rate classification abnormal returns are positive in most of the observations, but returns are not significant. Large-cap average abnormal returns are more sensitive to different dividend rates and small-cap reacts positively in all classifications. So, different market capitalization final dividend actions impact on stocks in India varies in different dividend rate classifications

SSRN

In this paper we propose an empirical measure of information sensitivity based on historical prices. If long term Treasury bonds with riskless payments are not held to maturity its information sensitivity is 0.4% higher than the S&P500 index in the period 2010 to 2020. We derive an information sensitivity channel of government asset purchases and show that large scale stock purchases by the Chinese National Team during the stock market crash in June 2015 reduce the information sensitivity of intervened stocks by 16% compared to other stocks. When stocks become less information sensitive, there is less issuance of equity reports.

SSRN

Since the mid-1980s, the share of household net worth intermediated by US financial institutions has shifted from defined benefit plans to life insurers and defined contribution plans. Life insurers have primarily grown through variable annuities, which are mutual funds with longevity insurance, a potential tax advantage, and minimum return guarantees. The minimum return guarantees change the primary function of life insurers from traditional insurance to financial engineering. Variable annuity insurers are exposed to interest and equity risk mismatch and suffered especially low stock returns during the COVID-19 crisis. We consider regulatory changes, such as more detailed financial disclosure and standardized stress tests, to monitor potential risk mismatch and to ensure stability of the insurance sector.

SSRN

Prior research finds that signals of remediation of internal control weaknesses do not guarantee that all weaknesses are fully resolved. However, why certain remediation strategies fail is unclear. This study examines how remediation timing and actions affect the likelihood of a failed remediation. I find that the likelihood of a failed remediation is decreasing in both the time a company takes to remediate and in the extent of remediation actions employed. Importantly, this study documents that disclosures of material changes in internal control provide information useful in assessing the likelihood of a failed remediation, as well as evidence that prompt remediation does not necessarily result in a successful remediation. Moreover, I find that there are consequences to remediation failures in the form of a higher likelihood of management and board turnover. Finally, I find evidence that economic benefits of remediation found in prior research may be understated. This study can provide stakeholders with insights into how the nature, extent, and timing of a remediation strategy can reduce the likelihood of a failed remediation.

SSRN

One prominent aspect of the MiFID II regulation that became effective in Europe in 2018 is the unbundling of research and execution costs. We exploit the early adoption of this rule in Sweden already in 2016 to provide early evidence on the implications for fund investors. Using a diff-in-diff framework and mostly hand-collected data on bundled and unbundled commissions, we find basically no impact of the regulation on fund investors: neither total expense ratios nor fund performance changed in response to the unbundling. We also fail to document any information gains for investorsâ€™ fund selection process from the increased transparency of observing execution and research costs separately. Overall, we are skeptical that the unbundling of commissions has had any positive impact on fund investors.

SSRN

Through the unexpected event of the US-China trade war, this article explores whether the valuation of overseas listed companies is affected in the context of the trade war between the two countries and the changes in the connection between business valuation and the two stock markets. By taking the Trade Policy Uncertainty (TPU) Index as the proxy variable of the trade war, this paper uses the daily return of China concept stocks (CCSs) and other related data to verify the trade war's negative impact on the valuation of overseas listed companies. After controlling other factors, as the US TPU Index rises, the daily return of CCSs decreases remarkably, and its connection with the daily return of bilateral markets is strengthened. Furthermore, this paper finds that with the four stages of the trade war â€œoutbreak-truce-recurrence-mitigationâ€, the daily return of CCSs shows the notable characteristics of â€œfall-stabilization-fall-stabilizationâ€. Besides, this paper verifies the empirical results through a robustness test of data about listed companies in both two markets and a placebo test of the impact of the COVID-19 pandemic.

arXiv

Price movement forecasting aims at predicting the future trends of financial assets based on the current market conditions and other relevant information. Recently, machine learning(ML) methods have become increasingly popular and achieved promising results for price movement forecasting in both academia and industry. Most existing ML solutions formulate the forecasting problem as a classification(to predict the direction) or a regression(to predict the return) problem in the entire set of training data. However, due to the extremely low signal-to-noise ratio and stochastic nature of financial data, good trading opportunities are extremely scarce. As a result, without careful selection of potentially profitable samples, such ML methods are prone to capture the patterns of noises instead of real signals. To address the above issues, we propose a novel framework-LARA(Locality-Aware Attention and Adaptive Refined Labeling), which contains the following three components: 1)Locality-aware attention automatically extracts the potentially profitable samples by attending to their label information in order to construct a more accurate classifier on these selected samples. 2)Adaptive refined labeling further iteratively refines the labels, alleviating the noise of samples. 3)Equipped with metric learning techniques, Locality-aware attention enjoys task-specific distance metrics and distributes attention on potentially profitable samples in a more effective way. To validate our method, we conduct comprehensive experiments on three real-world financial markets: ETFs, the China's A-share stock market, and the cryptocurrency market. LARA achieves superior performance compared with the time-series analysis methods and a set of machine learning based competitors on the Qlib platform. Extensive ablation studies and experiments demonstrate that LARA indeed captures more reliable trading opportunities.