# Research articles for the 2020-01-27

SSRN

This paper studies general equilibrium linear rational expectations models under incomplete information. We provide an accurate and efficient method of computing and analyzing the model solution based on frequency-domain techniques. At the heart of our approach is a policy function iteration method in which we define the frequency (z) as the sole 'state variable' and transform nonlinear functional equations of analytic functions into simple systems of linear algebraic equations. Conditional expectations, which are difficult to evaluate in the time domain, can be calculated efficiently in the frequency domain using the discrete Fourier transform. We apply our method to study four models in macroeconomics and finance that feature various setups of information frictions. We illustrate the power and scope of this method when endogenous information is present ('learning from the price' or rational inattention). Finally, we provide a user-friendly, Dynare-like MATLAB toolbox 'z-Tran' for implementing our method.

SSRN

Deviations of accounting fundamentals from their preceding means strongly predict future equity returns in the cross-section. Comprehensive measures based on such deviations yield annualized alphas that generally exceed 15% (6%) for equal- (value-) weighted portfolios. The return predictability goes beyond momentum, 52-week highs, profitability, and other prominent anomalies. The deviation-based investment profitability applies strongly to the long-leg and survives value weighting and excluding microcaps, unlike for other well-known return predictors. We provide evidence that the predictability arises because investors underreact to deviations from prevailing fundamental anchors.

arXiv

The Hartman-Watson distribution with density $f_r(t)$ is a probability distribution defined on $t \geq 0$ which appears in several problems of applied probability. The density of this distribution is expressed in terms of an integral $\theta(r,t)$ which is difficult to evaluate numerically for small $t\to 0$. Using saddle point methods, we obtain the first two terms of the $t\to 0$ expansion of $\theta(\rho/t,t)$ at fixed $\rho >0$. As an application we derive, under an uniformity assumption in $\rho$, the leading asymptotics of the density of the time average of the geometric Brownian motion as $t\to 0$. This has the form $\mathbb{P}(\frac{1}{t} \int_0^t e^{2(B_s+\mu s)} ds \in da) = (2\pi t)^{-1/2} g(a,\mu) e^{-\frac{1}{t} J(a)} (1 + O(t))$, with an exponent $J(a)$ which reproduces the known result obtained previously using Large Deviations theory.

arXiv

We propose a novel time discretization for the log-normal SABR model $dS_t = \sigma_t S_t dW_t, d\sigma_t = \omega \sigma_t dZ_t$, with $\mbox{corr}(W_t,Z_t)=\varrho$, which is a variant of the Euler-Maruyama scheme, and study its asymptotic properties in the limit of a large number of time steps $n\to \infty$ at fixed $\beta = \frac12\omega^2 n^2\tau,\rho = \sigma_0\sqrt{\tau}$. We derive an almost sure limit and a large deviations result for the log-asset price in the $n\to \infty$ limit. The rate function of the large deviations result does not depend on the discretization time step $\tau$. The implied volatility surface $\sigma_{\rm BS}(K,T)$ for arbitrary maturity and strike in the limit $\omega^2 T \to 0 , \sigma_0^2 T \to \infty$ at fixed $(\omega^2 T)(\sigma_0^2 T)$ is represented as an extremal problem. Using this representation we obtain analytical expansions of $\sigma_{\rm BS}(K,T)$ for small maturity and extreme strikes.

SSRN

The introduction of a central bank digital currency (CBDC) allows the central bank to engage in large-scale intermediation by competing with private financial intermediaries for deposits. Yet, since a central bank is not an investment expert, it cannot invest in long-term projects itself, but relies on investment banks to do so. We derive an equivalence result that shows that absent a banking panic, the set of allocations achieved with private financial intermediation will also be achieved with a CBDC. During a panic, however, we show that the rigidity of the central bankâ€™s contract with the investment banks has the capacity to deter runs. Thus, the central bank is more stable than the commercial banking sector. Depositors internalize this feature ex-ante, and the central bank arises as a deposit monopolist, attracting all deposits away from the commercial banking sector. This monopoly might endangered maturity transformation.

arXiv

Recently dozens of school districts and college admissions systems around the world have reformed their admission rules. As a main motivation for these reforms the policymakers cited strategic flaws of the rules: students had strong incentives to game the system, which caused dramatic consequences for non-strategic students. However, almost none of the new rules were strategy-proof. We explain this puzzle. We show that after the reforms the rules became more immune to strategic admissions: each student received a smaller set of schools that he can get in using a strategy, weakening incentives to manipulate. Simultaneously, the admission to each school became strategy-proof to a larger set of students, making the schools more available for non-strategic students. We also show that the existing explanation of the puzzle due to Pathak and S\"onmez (2013) is incomplete.

SSRN

This paper investigates how reinforcement learning can be used to derive optimal hedging strategies for derivatives. We assume that the objective is to minimize a function equal to the mean hedging cost plus a constant times the standard deviation of the hedging cost. The paper illustrates the approach by showing the difference between using delta hedging and optimal hedging for a short position in a call option when there are transaction costs. Two situations are considered. In the first, the asset price follows a geometric Brownian motion. In the second, the asset price follows a stochastic volatility process, but the volatility exposure cannot be hedged. The paper shows how the standard deviation of the cost of hedging can be accurately incorporated into the objective function when reinforcement learning is used.

arXiv

This paper presents several numerical applications of deep learning-based algorithms that have been introduced in [HPBL18]. Numerical and comparative tests using TensorFlow illustrate the performance of our different algorithms, namely control learning by performance iteration (algorithms NNcontPI and ClassifPI), control learning by hybrid iteration (algorithms Hybrid-Now and Hybrid-LaterQ), on the 100-dimensional nonlinear PDEs examples from [EHJ17] and on quadratic backward stochastic differential equations as in [CR16]. We also performed tests on low-dimension control problems such as an option hedging problem in finance, as well as energy storage problems arising in the valuation of gas storage and in microgrid management. Numerical results and comparisons to quantization-type algorithms Qknn, as an efficient algorithm to numerically solve low-dimensional control problems, are also provided; and some corresponding codes are available on https://github.com/comeh/.

SSRN

We discuss the nature and importance of the concept of Sequence Risk, the risk that a bad return occurs at a particularly unfortunate time, such as around the point of maximum accumulation or the start of decumulation. This is especially relevant in the context of retirement savings, where the implications for withdrawal rates of a bad return can be particularly severe. We show how the popular â€˜glidepathâ€™ or target date savingsâ€™ products are very exposed to such risk. Three different measures of Sequence Risk are proposed, each of which is intended to inform investors of the probability that a chosen investment strategy may not deliver desired withdrawal rates and hence these measures are intended to aid investment choices; conventional performance measures such as Sharpe or Sortino ratios are only indirectly related to this ability to achieve a given withdrawal experience. Finally, we note that, using US data, very simple portfolios comprising equities and bonds can achieve very low probabilities of failure to achieve popular desired withdrawal rates such as 5% p.a. providing the equity component is â€˜smoothedâ€™ by switching in and out of cash using a simple trend following rule.

SSRN

It is puzzling that the single most important explanatory variable for municipal reserves is the state in which a municipality is located. In this paper we leverage a national panel of US municipalities to show that a pair of behavioral heuristics: anchoring and the bandwagon effect, are an excellent explanation for the state effect, and for municipal reserves. It appears that when it comes to deciding on how much to save, cities target the levels of savings they held in the past, adjusting for the savings levels of their neighbors.

SSRN

The paper examines the real effects of the financial crisis for private firms. Analyzing a novel dataset from the Netherlands and controlling for multiple key factors, we find that investments of small and medium-sized private enterprises reduced significantly during and after the financial crisis. We find that both internal and external financing sources had a significant positive relationship with investment during the pre-crisis and post-crisis periods. But, during the crisis period, internal finance became significantly less influential on investment compared to external finance. The findings of the study suggest that borrowing from banks played a more prominent role in determining the investments of SMEs during the financial crisis of 2008-2009.

SSRN

Is there a lending channel for monetary policy under negative interest rate policy (NIRP)? The purpose of this study is to shed light on the existence of a lending channel of monetary policy under NIRP. Concretely, we aim to provide an in-depth analysis of the relationship between negative interest rate policy and bank-lending behavior. To this end, we employ a large panel dataset of 5454 banks operating in 122 countries over the period 2009-2018 and a Difference-in-Differences methodology. We find that banks located in countries affected by negative interest rates have changed their bank-lending behavior, by increasing lending activity. The results suggest that in response to negative interest rates, banks have reduced their lending cost, and increased both lending supply and lending maturity. Finally, we also find that the transmission of monetary policy under negative interest rates to the real economy depends on banks' specific characteristics such as deposits, margins and size.

SSRN

I explore the term structure of interest rates, inflation expectations, and inflation risk premia in an endogenous inflation economy. I illustrate the implications of such an economy in a macro-finance model in which the Taylor rule shock and consumption growth have Markov-switching dynamics. A calibrated version of the model generates a nearly flat term structure of inflation expectations and an upward-sloping term structure of inflation risk premia. I then use the model to conduct monetary policy experiments and measure the effects of monetary policy changes on the dynamics of nominal quantities in the economy. I find that varying the monetary policy parameters in the Taylor rule has a large effect on both inflation expectations and inflation risk premia. A modified version of the model can capture the zero bound constraint on the short-term interest rate.

SSRN

We explore how audit committees (ACs) oversee risk management in UK Higher Education Institutions (HEIs), using semi-structured interviews, attendance at AC meetings and analysis of documentation. We find that the ACâ€™s oversight seems constrained by a fixation on the process of risk management, a reliance on risk registers, and varying levels of emphasis on operational risks. Theoretically, the ACâ€™s oversight reflects different shades of symbolic and substantive activities designed to maintain the HEIâ€™s legitimacy and that of its governing board, hence providing a symbolic representation. We raise concerns as to the ACâ€™s ability to monitor the risk management practices of HEIs effectively.

arXiv

In this note we review the basic mathematical ideas used in finance in the language of modern physics. In the framework of discrete time formalism we discuss the effect time rescaling, and derive path integral formulas for pricing. We also discuss various risk mitigation methods.

SSRN

This paper investigates the impact of corporate boardsâ€™ gender diversity on voluntary public disclosure of climate change risks in an emerging economy context in which environmental regulations are weak and markets are ineffective. The investigation relies on data from the CDP (formerly known as the Carbon Disclosure Project) as a corporate sustainability reporting initiative supported by institutional investors, based on a sample of Turkish firms that were invited to disclose their climate change risks and greenhouse gas emissions over the period of 2010â€"2019 through the CDP platform. We report that the presence of women on board committees, as a proxy for their active involvement in corporate governance, increases the likelihood of voluntary climate change disclosure. We, on the other hand, found no evidence of a positive impact on climate change reporting with womenâ€™s overall representation in boards. These findings lend support to board reforms that aim to increase effective representation of women on boards for the better management of sustainability risks and responsiveness to stakeholder demands in countries where legislators are reluctant to introduce climate change reforms.

SSRN

The previous bootstrap tests for funds with zero alpha have a distorted test size when the sample sizes of some funds are small and might fail to detect skilled and unskilled funds when both kinds coexist. We develop the theory for a valid bootstrap Hotelling's T-squared test to identify a pool of funds with zero alpha and propose two methods to confirm whether the top- (bottom-) ranking funds are skilled (unskilled) compared to the identified zero-alpha pool. An examination of those skilled funds and unskilled funds indicates that their characteristics are dramatically different from many perspectives.

arXiv

We propose two new methods for identifying similarity and anomalies among collections of time series, and apply these methods to analyse cryptocurrencies. First, we analyse change points with respect to various distribution moments, considering these points as signals of erratic behaviour and potential risk. This technique uses the MJ$_1$ semi-metric, from the more general MJ$_p$ class of semi-metrics \citep{James2019}, to measure distance between these change point sets. Prior work on this topic fails to consider data between change points, and in particular, does not justify the utility of this change point analysis. Therefore, we introduce a second method to determine similarity between time series, in this instance with respect to their extreme values, or tail behaviour. Finally, we measure the consistency between our two methods, that is, structural break versus tail behaviour similarity. With cryptocurrency investment as an apt example of erratic, extreme behaviour, we notice an impressive consistency between these two methods.

arXiv

The design of integrated mobility-on-demand services requires jointly considering the interactions between traveler choice behavior and operators' operation policies to design a financially sustainable pricing scheme. However, most existing studies focus on the supply side perspective, disregarding the impact of customer choice behavior in the presence of co-existing transport networks. We propose a modeling framework for dynamic integrated mobility-on-demand service operation policy evaluation with two service options: door-to-door rideshare and rideshare with transit transfer. A new constrained dynamic pricing model is proposed to maximize operator profit, taking into account the correlated structure of different modes of transport. User willingness to pay is considered as a stochastic constraint, resulting in a more realistic ticket price setting while maximizing operator profit. Unlike most studies, which assume that travel demand is known, we propose a demand learning process to calibrate customer demand over time based on customers' historical purchase data. We evaluate the proposed methodology through simulations under different scenarios on a test network by considering the interactions of supply and demand in a multimodal market. Different scenarios in terms of customer arrival intensity, vehicle capacity, and the variance of user willingness to pay are tested. Results suggest that the proposed chance-constrained assortment price optimization model allows increasing operator profit while keeping the proposed ticket prices acceptable.

SSRN

We analyze a unique source of media data to assess the influence of different topics of corporate news on costs of financing for a sample of large European and US firms from 2006 to 2016. We distinguish between volume (number of news), tonality (positive, neutral, negative), source (financial and mass media) and focus on ESG and report the following results: (1) the volume of ESG related news is significantly associated with CDS spreads and therefore matters for companiesâ€™ refinancing costs; (2) news with positive (negative) tonality is associated with lower (higher) CDS spreads in the order of 5% (8%); (3) the tonality of ESG related news is even more pronounced leading to higher differences in CDS spreads. These results are robust in different subsamples, alternate specifications, and also in sensitivity tests to omitted variables. Overall, these results confirm the saying that it is not only what (ESG or Non-ESG) you (the media) say, but how (tonality) you say it.

SSRN

Contrary to expectations, the continuous decline in money market interest rates between 2009 and 2014, and the following negative era for European interbank markets, has positively affected profitability of Finnish cooperative banks. We obtain these results that contrast sharply with previous studiesâ€™ findings especially by using a risk-adjusted measure for bank profitability and analysing the role of increasing wholesale funding ratio, particularly during the period of negative interest rates. Our results serve as a primer for more detailed analysis of factors affecting bank profitability during the negative interest rate era that looks set to persist into the near future.

arXiv

In order to scale transaction rates for deployment across the global web, many cryptocurrencies have deployed so-called "Layer-2" networks of private payment channels. An idealized payment network behaves like a Credit Network, a model for transactions across a network of bilateral trust relationships. Credit Networks capture many aspects of traditional currencies as well as new virtual currencies and payment mechanisms. In the traditional credit network model, if an agent defaults, every other node that trusted it is vulnerable to loss. In a cryptocurrency context, trust is manufactured by capital deposits, and thus there arises a natural tradeoff between network liquidity (i.e. the fraction of transactions that succeed) and the cost of capital deposits.

In this paper, we introduce constraints that bound the total amount of loss that the rest of the network can suffer if an agent (or a set of agents) were to default - equivalently, how the network changes if agents can support limited solvency guarantees.

We show that these constraints preserve the analytical structure of a credit network. Furthermore, we show that aggregate borrowing constraints greatly simplify the network structure and in the payment network context achieve the optimal tradeoff between liquidity and amount of escrowed capital.

SSRN

This paper explores the application of machine learning methods to financial statement analysis. We investigate whether a range of models in the machine learning repertoire are capable of forecasting the sign and magnitude of abnormal stock returns around earnings announcements based on financial statement data alone. We find random forests and recurrent neural networks to outperform deep neural networks and linear models such as OLS and Lasso. Using the models' predictions in an investment strategy we find that random forests dominate all other models and that non-linear methods perform relatively better for predictions of extreme market reactions, while the linear methods are relatively better in predicting moderate market reactions. Analysing the underlying economic drivers of the performance of the random forests, we find that the models select as most important predictors accounting variables commonly used to forecast free cash flows and firm characteristics that are known cross-sectional predictors of stock returns.

SSRN

This paper assesses the aggregate and distributional effects of policies that seek to reduce mortgage default by limiting a borrower's debt payment-to-income ratio. I document empirically that highly creditworthy borrowers appear constrained by a current institutional debt payment-to-income limit. I propose a heterogeneous-agent life-cycle model with a competitive mortgage market, endogenous default, and mortgage contract choice consistent with the empirical findings. In the calibrated model, I show that, relative to the current uniformly applied debt payment-to-income cap, a policy that combines a more strict debt payment-to-income limit with a costly option to relax the limit lowers default and improves aggregate welfare, particularly for households in the middle of the wealth distribution who have low incomes.

SSRN

This study aims to connect the two strands of literature, i.e. behavioral corporate finance and agent-based macroeconomics to assess the impact of managerial overconfidence both at the micro and at the macro level. More specifically, we build a macroeconomic Agent-Based Model (ABM) calibrated for the specific case of Poland to explore whether overconfidence of top corporate managers in the context of their Initial Public Offering (IPO) decisions is detrimental or not for the firms being managed in that way, for the financial market dynamics and the selected macroeconomic indicators in Poland. We modelled heterogeneous firms with different IPO decision criteria depending on degree of managerial overconfidence. Our model also included a banking sector and a stock market that interact with the real economy. We found that there is a contradiction between the micro and the macro impact of overconfidence. Overconfident firms showed better performances in terms of output than other firms. But they were also more exposed to stock market volatility which makes them more likely to default. Higher default rate of overoptimistic firms negatively impacted the banking sector and increases financial instability. In turn, financial instability harmed the economy as a whole. We also observed that an increase in the proportion of overconfident firms is associated with a decrease in aggregate output although overconfident firms showed better individual performances. Finally, we run policy shocks and show that the increased financial instability can be offset by strengthening regulation of the banking sector.

SSRN

We use two market-based measures of inflation compensation to explore the transmission mechanism of monetary policy to inflation markets. New information about the Fed's monetary policy stance becomes available on the days of meetings of the Federal Open Market Committee (FOMC) and is reflected in asset prices. We measure the sensitivity of inflation compensation measures to changes in monetary policy and compare its magnitudes across different maturity horizons, and across conventional and unconventional monetary policy regimes. Our analysis reveals that risk premia embedded in inflation compensation are horizon and monetary policy regime dependent.

SSRN

In 2015, as part of a program to reform Chinaâ€™s state-owned enterprises (SOEs), Guiding Opinions were issued by the Central Committee of the Chinese Communist Party (CCP) and the State Council requiring SOEs to amend their corporate charters to formalize and elevate the leadership role of the party in their corporate governance. We empirically examine the patterns of â€œparty-buildingâ€ (dangjian) charter amendments adopted in response to this policy by all listed nonfinancial Chinese firms in the four-year period from 2015-18 to better understand the contours of political conformity in Chinese corporate governance. Our findings support Milhaupt and Zhengâ€™s (2015) analysis of the blurred dichotomy between SOEs and privately owned enterprises (POEs) in the Chinese political economy. Not all SOEs abided by the dangjian policy, and although POEs were not subject to the Guiding Opinions, a significant number of POEs, particularly politically connected ones, also amended their charters to add party-building provisions. We find wide substantive variation in adoptions within and across firm ownership types. The template for charter amendments circulated pursuant to the Guiding Opinions can be grouped into symbolic, decision-oriented, and personnel-oriented provisions. SOEs did not uniformly adopt the entire panoply of recommended provisions. In particular, SOEs cross-listed on Hong Kong or foreign stock exchanges adopted less intrusive provisions than other SOEs, suggesting that the capital market constrained political interventions into corporate governance. POEs that amended their charters to include party-building provisions were far more likely to adopt symbolic provisions than decision-oriented and personnel-oriented provisions, suggesting that the amendments were undertaken to signal fealty to the CCP without changing substantive corporate governance practices. These findings raise a number of questions about the prospects for Chinese corporate governance, economic performance, and foreign investment activity.

arXiv

One of the spectacular examples of a complex system is the financial market, which displays rich correlation structures among price returns of different assets. The eigenvalue decomposition of a correlation matrix into partial correlations - market, group and random modes, enables identification of dominant stocks or "influential leaders" and sectors or "communities". The correlation-based network of leaders and communities changes with time, especially during market events like crashes, bubbles, etc. Using the eigen-entropy measure, computed from the eigen-centralities (ranks) of different stocks in the correlation network, we extract information about the "disorder" (or randomness) in the market correlation and its different modes. The relative-entropy measures computed for these modes enable us to construct a "phase space", where the different market events undergo "phase-separation" and display "order-disorder" transitions, as observed in critical phenomena in physics. We choose the US S&P-500 and Japanese Nikkei-225 financial markets, over a 32-year period, and study the evolution of the cross-correlation matrices and their corresponding eigen-entropies. One of the relative entropy measures displays "universal scaling" behavior with respect to the mean market correlation. Further, a functional of the relative entropy measure acts as a good gauge for the "market fragility" (minimum risk of the market portfolio) and the "market fear" (volatility index). This new methodology helps us to better understand market dynamics and characterize the events in different phases as anomalies, bubbles, crashes, etc. that display intriguing phase separation and universal scaling behavior.

arXiv

Player-Compatible Equilibrium (PCE) imposes cross-player restrictions on the magnitudes of the players' "trembles" onto different strategies. These restrictions capture the idea that trembles correspond to deliberate experiments by agents who are unsure of the prevailing distribution of play. PCE selects intuitive equilibria in a number of examples where trembling-hand perfect equilibrium (Selten, 1975) and proper equilibrium (Myerson, 1978) have no bite. We show that rational learning and some commonly used heuristics imply our compatibility restrictions in a steady-state setting.

arXiv

This paper proposes a hybrid credit risk model, in closed form, to price vulnerable options with stochastic volatility. The distinctive features of the model are threefold. First, both the underlying and the option issuer's assets follow the Heston-Nandi GARCH model with their conditional variance being readily estimated and implemented solely on the basis of the observable prices in the market. Second, the model incorporates both idiosyncratic and systematic risks into the asset dynamics of the underlying and the option issuer, as well as the intensity process. Finally, the explicit pricing formula of vulnerable options enables us to undertake the comparative statistics analysis.

arXiv

Technological developments worldwide are contributing to the improvement of transport infrastructures and they are helping to reduce the overall transport costs. At the same time, such developments along with the reduction in transport costs are affecting the spatial interdependence between the regions and countries, a fact inducing significant effects on their economies and, in general, on their growth-rates. A specific class of transport infrastructures contributing significantly to overcoming the spatial constraints is the airtransport infrastructures. Nowadays, the importance of air-transport infrastructures in the economic development is determinative, especially for the geographically isolated regions, such as for the island regions of Greece. Within this context, this paper studies the Greek airports and particularly the evolution of their overall transportation imprint, their geographical distribution, and the volume of the transport activity of each airport. Also, it discusses, in a broad context, the seasonality of the Greek airport activity, the importance of the airports for the local and regional development, and it formulates general conclusions.

arXiv

This paper proposes a new method for financial portfolio optimisation based on reducing simultaneous asset shocks across a portfolio of assets. We adopt the new semi-metrics of \citep{James2019} to determine the distance between two time series' structural breaks. We build on the optimal portfolio theory of \citep{Markowitz1952}, but utilize distance between asset structural breaks, rather than portfolio variance, as our penalty function. Our experiments are promising: on synthetic data, they indicate that our proposed method does indeed diversify among time series with highly similar structural breaks. On real data, experiments illustrate that our proposed optimisation method produces higher risk-adjusted returns than mean variance portfolio optimisation. The predictive distribution is superior in every measure, producing a higher mean, lower standard deviation and less kurtosis. The main implication for this method in portfolio management is reducing simultaneous asset shocks and potentially sharp associated drawdowns, during periods of highly similar structural breaks, such as a market crisis.

arXiv

In multi-period stochastic optimization problems, the future optimal decision is a random variable whose distribution depends on the parameters of the optimization problem. We analyze how the expected value of this random variable changes as a function of the dynamic optimization parameters in the context of Markov decision processes. We call this analysis \emph{stochastic comparative statics}. We derive both \emph{comparative statics} results and \emph{stochastic comparative statics} results showing how the current and future optimal decisions change in response to changes in the single-period payoff function, the discount factor, the initial state of the system, and the transition probability function. We apply our results to various models from the economics and operations research literature, including investment theory, dynamic pricing models, controlled random walks, and comparisons of stationary distributions.

arXiv

Prediction of future movement of stock prices has been a subject matter of many research work. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We select the NIFTY 50 index values of the National Stock Exchange of India, over a period of four years, from January 2015 till December 2019. Based on the NIFTY data during the said period, we build various predictive models using machine learning approaches, and then use those models to predict the Close value of NIFTY 50 for the year 2019, with a forecast horizon of one week. For predicting the NIFTY index movement patterns, we use a number of classification methods, while for forecasting the actual Close values of NIFTY index, various regression models are built. We, then, augment our predictive power of the models by building a deep learning-based regression model using Convolutional Neural Network with a walk-forward validation. The CNN model is fine-tuned for its parameters so that the validation loss stabilizes with increasing number of iterations, and the training and validation accuracies converge. We exploit the power of CNN in forecasting the future NIFTY index values using three approaches which differ in number of variables used in forecasting, number of sub-models used in the overall models and, size of the input data for training the models. Extensive results are presented on various metrics for all classification and regression models. The results clearly indicate that CNN-based multivariate forecasting model is the most effective and accurate in predicting the movement of NIFTY index values with a weekly forecast horizon.

SSRN

Rational frictionless asset pricing models imply that inflation swap rates and break-even inflation rates with same maturity must be equal. The data, however, suggest a persistent positive difference between these two quantities, which the literature attributes to mispricing of Treasury Inflation-Protected Securities (TIPS). In theory, factors driving TIPS mispricing are not directly observable to the econometrician. To reveal these factors, we analyze the daily term structure of TIPS mispricing and uncover its information content. To assess its economic value, we derive novel high-frequency stylized facts about its dynamics. We document strong relationships with stock market returns, option-implied volatility and variance risk premium, and an important channel for predicting inflation, bond and equity excess returns, jointly.

SSRN

This study provides a detailed analysis of the impact of financial sanctions on publicly traded companies. We consider the effect of imposing and lifting sanctions on the target country's publicly traded firms and examine the differences in the reaction of politically connected firms and those without such connections. The paper focuses on Iran due to (1) its sizable financial markets, (2) imposition of sanctions of varying severity and duration on private and state-owned companies, (3) the significant presence of politically connected firms in the stock market, and (4) the unique event of the 2015 nuclear deal, resulting in fairly rapid lifting of a sizable portion of imposed sanctions. We find that sanctions affect politically connected firms more than ordinary firms, have lasting negative effects on profitability ratios, and that politically connected firmsâ€™ stock prices bounce back more slowly after removal of sanctions. Firms targeted by financial sanctions decrease their leverage and increase their cash holding to manage their perceived increase in risk profile.

SSRN

This paper offers an option value-based rationale for the consideration of a non-compliance record in sentencing decisions. We study compliance decisions of a population of individuals who live for two periods. We show that when non-compliance benefits are random and independent across periods, increasing or decreasing sanctions produce different option values to current-period compliance and non-compliance. The difference between these option values provides incentives for present-period compliance and facilitates a more efficient allocation of sanctions across periods. The optimal sanction scheme depends on the magnitude of the overall sanction relative to the distribution of benefits from non-compliance.

SSRN

The last decade or so has witnessed a proliferation in the introduction of corporate organisational constructs to facilitate social enterprise across many European jurisdictions. The purpose of this paper is to investigate this phenomenon, and provide an (initial) analytical framework through which the social enterprise company can be understood, both on its own terms and with respect to the traditional business organisation. The paper begins by laying out policymakersâ€™ collective intentions for designing the social enterprise company. From this departure point, the discussion then turns to theorising the social enterprise companyâ€™s organisational architecture. The social enterprise company is a hybrid organisational construct, which combines specific legal mechanisms and institutional logics of public, private and social economy organisations together. The social enterprise company is designed to create social value. For this reason it operates according to the principle of publicness. The intention was also for the social enterprise company to be resource flexible and attract altruistic investors and managers. The paper then further extends the theoretical discussion by examining the social enterprise companyâ€™s isomorphic prevention mechanisms, which encourage impact fidelity in the context of a conversion or a winding up. The paper concludes with some criticisms and suggestions for improvement.

arXiv

Network Science is an emerging discipline using the network paradigm to model communication systems as pair-sets of interconnected nodes and their linkages (edges). This paper applies this paradigm to study an interacting system in regional economy consisting of daily road transportation flows for labor purposes, the so-called commuting phenomenon. In particular, the commuting system in Greece including 39 non-insular prefectures is modeled into a complex network and it is studied using measures and methods of complex network analysis and empirical techniques. The study aims to detect the structural characteristics of the Greek interregional commuting network (GCN) and to interpret how this network is related to the regional development. The analysis highlights the effect of the spatial constraints in the structure of the GCN, it provides insights about the major road transport projects constructed the last decade, and it outlines a populationcontrolled (gravity) pattern of commuting, illustrating that high-populated regions attract larger volumes of the commuting activity, which consequently affects their productivity. Overall, this paper highlights the effectiveness of complex network analysis in the modeling of systems of regional economy, such as the systems of spatial interaction and the transportation networks, and it promotes the use of the network paradigm to the regional research.

SSRN

I discuss an intellectual revolution, social economics and finance: the study of the social processes that shape economic thinking and behavior. This emerging field recognizes that people observe and talk to each other. A key, under-exploited building block of social economics and finance is social transmission bias: a systematic directional shift in signals or ideas in social transactions. I use five â€œfablesâ€ (models) to illustrate the novelty and scope of the transmission bias approach, and offer several emergent themes. For example, social transmission bias compounds recursively, which can help explain booms, bubbles, return anomalies, and swings in economic sentiment.

SSRN

Hedging market downturns without sacrificing upside has long been sought by investors. If VIX was directly investable, adding it as a hedge to the S&P 500 would result in significantly improved performance over the equity only portfolio. However, tradable VIX products do not provide the hedge or returns investors seek over long-term horizons. Alternatively, deconstructing VIX to find the key S&P 500 options which drive VIX movements leads to a synthetic VIX portfolio that provides a more effective hedge. Using these options captures correlations and returns similar to VIX, and combined with the S&P 500, outperforms the buy-and-hold index portfolio.