# Research articles for the 2019-04-17

A Dynamic Bayesian Model for Interpretable Decompositions of Market Behaviour
arXiv

We propose a heterogeneous simultaneous graphical dynamic linear model (H-SGDLM), which extends the standard SGDLM framework to incorporate a heterogeneous autoregressive realised volatility (HAR-RV) model. This novel approach creates a GPU-scalable multivariate volatility estimator, which decomposes multiple time series into economically-meaningful variables to explain the endogenous and exogenous factors driving the underlying variability. This unique decomposition goes beyond the classic one step ahead prediction; indeed, we investigate inferences up to one month into the future using stocks, FX futures and ETF futures, demonstrating its superior performance according to accuracy of large moves, longer-term prediction and consistency over time.

A Pyramid Scheme Model Based on "Consumer Rebate" Frauds
Yong Shi,Bo Li,Wen Long
arXiv

There are various types of pyramid schemes which have inflicted or are inflicting losses on many people in the world. We propose a pyramid scheme model which has the principal characters of many pyramid schemes appeared in recent years: promising high returns, rewarding the participants recruiting the next generation of participants, and the organizer will take all the money away when he finds the money from the new participants is not enough to pay the previous participants interest and rewards. We assume the pyramid scheme carries on in the tree network, ER random network, SW small-world network or BA scale-free network respectively, then give the analytical results of how many generations the pyramid scheme can last in these cases. We also use our model to analyse a pyramid scheme in the real world and we find the connections between participants in the pyramid scheme may constitute a SW small-world network.

A Review of Real Options Analysis Methods for R&D Valuation in Renewable Energy Research
Deeney, Peter,Cummins, Mark
SSRN
We propose a new real options analysis method for evaluating R&D investments using a novel Poisson process to simulate the discrete progress typical of R&D breakthroughs. We take explicit account of the technical risk of an R&D project, while the market risk and the effect of learning-by- doing in operational technologies are also explicitly modelled. We present a compound real option structure, where a European real option is used to model the fixed length term typical of early phase research, which is exercisable into an American real option to model later phase R&D. In this later phase, a successful outcome will be acted on immediately to operationalise the technology. We propose a Monte Carlo simulation approach, which models R&D progress in a stylised logistic function or 'S-bend' form, capturing the typically slow rate of R&D progress at the start of the early phase, through to more rapid improvement as the R&D advances, which then slows again as the limitations of the R&D are approached. We demonstrate that this method is applicable for evaluating the R&D investment potential in CO2 recycling technology, where an energy commodity (such as methane) is produced, using appropriate modelling for the price of the energy commodity. The method may be applied widely to R&D technology projects.

A memory-based method to select the number of relevant components in Principal Component Analysis
Anshul Verma,Pierpaolo Vivo,Tiziana Di Matteo
arXiv

We propose a new data-driven method to select the optimal number of relevant components in Principal Component Analysis (PCA). This new method applies to correlation matrices whose time autocorrelation function decays more slowly than an exponential, giving rise to long memory effects. In comparison with other available methods present in the literature, our procedure does not rely on subjective evaluations and is computationally inexpensive. The underlying basic idea is to use a suitable factor model to analyse the residual memory after sequentially removing more and more components, and stopping the process when the maximum amount of memory has been accounted for by the retained components. We validate our methodology on both synthetic and real financial data, and find in all cases a clear and computationally superior answer entirely compatible with available heuristic criteria, such as cumulative variance and cross-validation.

Artificial Intelligence and European Bank Profitability
Kaya, Orcun
SSRN
Using patent applications in artificial intelligence (AI) technologies as a proxy of AI implementation in individual countries, this paper empirically tests the link between AI implementation and European bank profitability. We show that there is a positive relation between AI patent applications and bank profitability. Our results indicate that AI contributes to bank profitability most likely via cost channel, the main contribution being a reduction in staff expenses. We further present that improvements in back-end processes via AI implementation probably are relevant in reducing staff expenses.

Averaging plus Learning in financial markets
Ionel Popescu,Tushar Vaidya
arXiv

This paper develops original models to study interacting agents in financial markets. The key feature of these models is how interactions are formulated and analysed. Agents learn from their observations and learning ability to interpret news or private information. Central limit theorems are developed but they arise rather unexpectedly. Under certain type of conditions governing the learning, agents beliefs converge in distribution that can be even fractal. The underlying randomness in the systems is not restricted to be of a certain class. Fresh insights are gained not only from developing new non-linear social learning models but also from using different techniques to study discrete time random linear dynamical systems.

Bank Capital (Requirements) and Credit Supply: Evidence from Pillar 2 Decisions
De Jonghe, Olivier,Dewachter, Hans,Ongena, Steven
SSRN
We analyze how time-varying bank-specific capital requirements affect bank lending to the non-financial corporate sector as well as banks' balance sheet adjustments. To do so, we relate Pillar 2 capital requirements to a comprehensive corporate credit register coupled with bank and firm balance sheet data. Our analysis consists of three components. First, we investigate how capital requirements affect the supply of bank credit to the corporate sector, both on the intensive and extensive margin, as well as for different types of credit. Subsequently, we document how bank and firm characteristics as well as the stance of monetary policy impact the relationship between bank capital requirements and the supply of credit. Finally, we examine how time-varying bank-specific capital requirements affect banks' balance sheet composition.

Bursting the Bitcoin Bubble: Assessing the Fundamental Value and Social Costs of Bitcoin
Podhorsky, Andrea
SSRN
This paper develops a microeconomic model of bitcoin production to analyze the economic effects of the Bitcoin protocol. I view the bitcoin as a tradable commodity that is produced by miners and whose supply is managed by the protocol. I show that bitcoin's volatile price path and inefficiency are related, and that both are a consequence of the protocol's system of supply management. I characterize the fundamental value of a bitcoin and demonstrate that the return on bitcoin appreciates proportionally to the rate of increase in the level of difficulty. In the model, where the price of a bitcoin is based on marginal production costs, successive positive demand shocks result in a rapidly increasing price path that may be mistaken for a bubble. I use the generalized supremum augmented Dickey-Fuller (GSADF) test to demonstrate that the model is able to account for the explosive behavior in the bitcoin price path, providing strong evidence that bitcoin is not a bubble. I also show that the difficulty adjustment mechanism results in social welfare losses from 17 March 2014 to 13 January 2019 of 323.8 million USD, which is about 9.3% of the miners' total electricity costs during this time period.

Can Mobility-on-Demand services do better after discerning reliability preferences of riders?
Prateek Bansal,Yang Liu,Ricardo Daziano,Samitha Samaranayake
arXiv

We formalize one aspect of reliability in the context of Mobility-on-Demand (MoD) systems by acknowledging the uncertainty in the pick-up time of these services. This study answers two key questions: i) how the difference between the stated and actual pick-up times affect the propensity of a passenger to choose an MoD service? ii) how an MoD service provider can leverage this information to increase its ridership? We conduct a discrete choice experiment in New York to answer the former question and adopt a micro-simulation-based optimization method to answer the latter question. In our experiments, the ridership of an MoD service could be increased by up to 10\% via displaying the predicted wait time strategically.

Do Shareholder Activists Care about Accounting and Audit Quality?
SSRN
We ask whether accounting and audit quality issues are determinants of shareholder activism. Activism refers to investor attempts to change firm policies and practices. Accounting and audit quality directly impact the information used by investors in investment decisions. If issues are present, we expect shareholders to act in an effort to improve information quality and overall governance. We proxy for accounting and audit quality and activism using abnormal audit fees and auditor size and governance proposals, respectively. Consistent with expectations, we find future activism intensity is negatively associated with accounting and audit quality. These results are concentrated in firms with low accounting and audit quality, are evident for different types of proposals, and are robust to sensitivity tests (e.g., the inclusion of firm fixed effects). Overall, the associations we document are consistent with accounting and audit quality being antecedents of activism.

Estimating the Cost of Capital for Renewable Energy Projects
Steffen, Bjarne
SSRN
Many models in energy economics assess the cost of alternative power generation technologies. As an input, the models require well calibrated assumptions for the cost of capital or discount rates to be used, especially for renewable energy for which the cost of capital differs widely across countries and technologies. In this article, we review the spectrum of estimation methods for the private cost of capital for renewable energy projects and discuss appropriate use of the methods to yield unbiased results. We then evaluate the empirical evidence from 46 countries for the period 2009â€"2017. We find a globally consistent rank order among technologies, with the cost of capital increasing from solar PV to onshore wind to offshore wind power. On average, the cost of capital in developing countries is significantly higher than in industrialized countries, with large heterogeneity also within the groups of industrialized or developing countries.

Executive Gender Pay Gap: The Role of Employer Learning and Regulatory Interventions
HomRoy, Swarnodeep,Mukherjee, Shibashish
SSRN
Using individual director-level data from sixteen countries, we examine both within-firm and cross-country determinants of the executive gender pay gap. We have two main results. First, female executives, on average, are paid about 34% less compared to equivalent males from the same cohort. This pay gap falls over the tenure of the executives within the firm but remains significant throughout the career. Second, both demand-side and supply-side policy interventions are partially effective: executive gender pay gap is lower in countries with board gender quotas, and in countries with significant parental leave provisions. The channels through which these policies work are different. Female directors in the youngest age group, for whom the gender pay gap is the highest, seems to benefit more from the supply side interventions, while female directors in the highest age bracket benefit from gender quotas. These results are stronger for banking and financial services industries. Our results highlight the role of employer learning and the effectiveness of supply-side gender policies.

Fragmentation and inefficiencies in US equity markets: Evidence from the Dow 30
Brian F. Tivnan,David Rushing Dewhurst,Colin M. Van Oort,John H. Ring IV,Tyler J. Gray,Brendan F. Tivnan,Matthew T. K. Koehler,Matthew T. McMahon,David Slater,Jason Veneman,Christopher M. Danforth
arXiv

Using the most comprehensive source of commercially available data on the US National Market System, we analyze all quotes and trades associated with Dow 30 stocks in 2016 from the vantage point of a single and fixed frame of reference. Contrary to prevailing academic and popular opinion, we find that inefficiencies created in part by the fragmentation of the equity marketplace are widespread and potentially generate substantial profit for agents with superior market access. Information feeds reported different prices for the same equity---violating the commonly-supposed economic behavior of a unified price for an indistinguishable product---more than 120 million times, with "actionable" latency arbitrage opportunities totaling almost 64 million. During this period, roughly 22% of all trades occurred while the SIP and aggregated direct feeds were dislocated. The current market configuration resulted in a realized opportunity cost totaling over $160 million when compared with a single feed, single exchange alternative---a conservative estimate that does not take into account intra-day offsetting events. Market States and the Risk-Return Tradeoff Wang, Zijun,, Moosa SSRN We re-examine the risk-return trade off in U.S. equity market by allowing for time variation in the tradeoff and estimating the conditional variance by the new mixed data sampling method. The main finding is that the risk-return tradeoff is strongly time-varying with the state of the market and the average of the time-varying tradeoff is 1.43. The lagged market return is found to be the best indicator of market states. The empirical finding holds true for a battery of robustness checks during the post-Compustat sample period. The evidence from the international markets is similar to the U.S. one. Monetary Policy and Financial System Resilience Bruni, Franco,Lopez, Claude SSRN In a time of global crisis, international policy coordination is quite natural. Yet, in normal times such coordination becomes a challenge. This is an issue especially when it comes to monetary and macroprudential policy of globally influential countries. This is especially relevant now with the trend of monetary normalisation in many of these countries. In this brief, we propose four necessary steps to help addressing these challenges: (i) Monetary policy should take into account its spillovers on financial stability, (ii) Systemic central banks need to account for the global impact of their policy, (iii) Multilateral consultations may provide a useful platform to assess these impacts, (iv) The analysis that helps designing monetary and macroprudential policy should include global aggregates to capture the global economic and financial context. Off-Balance Sheet Activities, Inefficiency and Market Power of U.S. Banks Wheelock, David C.,Wilson, Paul W. SSRN The Lerner index is a well-established measure of firmsÃ¢â‚¬â„¢ market power, but estimation and interpretation present several challenges, especially for banks. We estimate Lerner indices for U.S. banks for 2001-2016 while (i) accounting for banksÃ¢â‚¬â„¢ off-balancesheet activities, (ii) estimating cost and profit functions nonparametrically to avoid mis-specification inherent in parametric estimation of translog functions on banking data, and (iii) allowing for cost and profit inefficiency that can otherwise bias index estimates. We find that banks have more market power than previous studies found, and that failure to account for off-balance-sheet activities or inefficiency can seriously bias estimates of market power. Optimal loss-carry-forward taxation for L\'{e}vy risk processes stopped at general draw-down time Wenyuan Wang,Zhimin Zhang arXiv Motivated by Kyprianou and Zhou (2009), Wang and Hu (2012), Avram et al. (2017), Li et al. (2017) and Wang and Zhou (2018), we consider in this paper the problem of maximizing the expected accumulated discounted tax payments of an insurance company, whose reserve process (before taxes are deducted) evolves as a spectrally negative L\'{e}vy process with the usual exclusion of negative subordinator or deterministic drift. Tax payments are collected according to the very general loss-carry-forward tax system introduced in Kyprianou and Zhou (2009). To achieve a balance between taxation optimization and solvency, we consider an interesting modified objective function by considering the expected accumulated discounted tax payments of the company until the general draw-down time, instead of until the classical ruin time. The optimal tax return function together with the optimal tax strategy is derived, and some numerical examples are also provided. Pathwise volatility: Cox-Ingersoll-Ross initial-value problems and their fast reversion exit-time limits Ryan McCrickerd arXiv Motivated by successes of fast reverting volatility models, and the implicit dependence of rough' processes on infinitesimal reversionary timescales, we establish a pathwise volatility framework which leads to a complete understanding of volatility trajectories' behaviour in the limit of infinitely-fast reversion. Towards this, we first establish processes that are weakly equivalent to Cox-Ingersoll-Ross (CIR) processes, but in contrast prove well-defined without reference to a probability measure. This provides an unusual example of Skorokhod's representation theorem. In particular, we become able to generalise Heston's model of volatility to an arbitrary degree, by sampling drivers$\omega$under any probability measure; a rough one if so desired. Our main analysis relates to separable initial-value problems of type $$x'= \omega(x) + t - x + 1,\quad x(0)=0,$$ with$\omega$only assumed continuous (not Lipschitz, nor H\"older), solutions of which$\varphi$correspond to time-averages of volatility trajectories$\varphi'$. Such solutions are shown to exist, be unique and bijective for any$\omega$, essentially placing no constraints on corresponding volatility trajectories, except for their non-negativity. After bounding these solutions in time, we prove a rare type of convergence result, towards c\adl\`ag exit-time limits, on Skorokhod's$M_1$topology. One immediate corollary of this limiting result is a weak connection between the time-averaged CIR process and the inverse-Gaussian L\'evy subordinator. Political Corruption and Accounting Choices Zhang, Huai,Zhang, Jin SSRN We examine how political corruption affects firmsâ€™ accounting choices. We hypothesize and find that firms headquartered in corrupt districts manipulate earnings downwards, relative to firms headquartered elsewhere. Our finding is robust to alternative corruption measures, restatement-based earnings management measures, and the instrumental variable approach. Consistent with the motive to depress earnings, we find that firms headquartered in corrupt districts prefer LIFO over FIFO, and the accelerated depreciation method over the straight-line method, they report high LIFO reserve and depreciation reserve, and they tend to select a low useful life estimate. Finally, we find that the effect of corruption on earnings management is more pronounced for geographically concentrated firms and for firms without political connections. In sum, our findings suggest that firms respond to corruption by lowering their accounting earnings. Power to the People: Voting Behavior at Shareholders' Meeting â€" An Experimental Study Dressler, Efrat SSRN Voting in shareholder meetings has become a popular mechanism of corporate governance throughout the world, and shareholders' main tool for communicating with the companyâ€™s management. A variety of factors have been shown in the behavioral economic literature to affect voters' behavior, including outcome preferences, other votersâ€™ position (peer effect), non-consequentialist elements such as social norms or inequality aversion, and self-interest. It is argued here that votersâ€™ behavior may also be affected by their probability to determine the outcome (henceforth, â€œvoting powerâ€). The effect of voting power on voting behavior is examined in an experimental study that controls for several motivations that have been demonstrated previously to impact voting. The participantsâ€™ power to affect the outcome was manipulated exogenously. Findings suggest that voting power nudged participants to oppose management and to choose the â€œrightâ€ alternative, that is, to vote against a proposal which does not serve the company's best interest. This effect emerged even when participantsâ€™ vote was in opposition to all their peers and to their own self-interest. The results might allow for some optimism regarding recent regulatory changes in Israel that reallocated voting power among shareholders. Scaling of inefficiencies in the U.S. equity markets: Evidence from three market indices and more than 2900 securities David Rushing Dewhurst,Colin M. Van Oort,John H. Ring IV,Tyler J. Gray,Christopher M. Danforth,Brian F. Tivnan arXiv Using the most comprehensive, commercially-available dataset of trading activity in U.S. equity markets, we catalog and analyze latency arbitrage opportunities and realized opportunity costs (ROC) incurred by market participants. We find that latency arbitrage opportunities are common, observing a total of over 3.1 billion latency arbitrage opportunities in the Russell 3000 during trading in 2016, or roughly 525 per second of trading. Up to 23% of observed trades may have contributed the the measured inefficiencies, leading to a ROC greater than$2 billion USD. A subset of the constituents of the S&P 500 index experience the greatest amount of ROC and may drive inefficiencies in other stocks. In addition, we identify fine structure and self-similarity in the intra-day distribution of latency arbitrage opportunity start times. These results point to universal underlying market mechanisms arising from the physical structure of the U.S. National Market System.

Sparse Portfolio selection via Bayesian Multiple testing
Sourish Das,Rituparna Sen
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.

Spicing up a Portfolio with Commodity Futures: Still a Good Recipe?
Daigler, Robert T.,Dupoyet, Brice V.,You, Leyuan
SSRN
We investigate whether employing individual commodity futures provides a superior optimized risk-return strategy relative to an equity portfolio, in spite of recently increasing correlations between commodity and equity markets. We first construct Markowitz mean-variance optimized portfolios of commodity and financial futures contracts within sample. We then evaluate their subsequent out-of-sample performance using various time periods, targeted risk levels, and rebalancing frequencies. These portfolios generally outperform benchmark equity indices, both in terms of return and risk levels. These portfolios also exhibit lower tail risk (reduced potential extreme losses) relative to the equity indices. These findings support the use of commodity futures for both diversification and portfolio optimization purposes and illustrate appropriate application metrics. Moreover, the results are superior to using the commodity index approach emphasized by most previous studies.

The Impact of Crowding in Alternative Risk Premia Investing
Baltas, Nick
SSRN
Crowding is a major concern for investors in the alternative risk premia space. By focusing on the distinct mechanics of various systematic strategies, we contribute to the discussion with a framework that provides insights on the implications of crowding on subsequent strategy performance. Understanding such implications is key for strategy design, portfolio construction, and performance assessment. Our analysis shows that divergence premia, like momentum, are more likely to underperform following crowded periods. Conversely, convergence premia, like value, show signs of outperformance as they transition into phases of larger investor flows.

The Implied Convexity of VIX Futures
Daigler, Robert T.,Dupoyet, Brice V.,Patterson, Fernando
SSRN
An important component of theoretical CBOE Volatility Index (VIX) futures prices is a term correcting for the negative convexity of the square root function by subtracting from the forward-starting variance swap rate an estimate of the future volatility of VIX futures prices. In the same fashion that an index optionâ€™s traditional implied volatility can be viewed as an aggregate market consensus of future realized volatility, this convexity value can be viewed as an aggregate market consensus of future volatility of volatility. This article examines the predictive properties and features of this convexity adjustment needed to value VIX futures prices by extracting it from the relationship between observed VIX futures prices and the corresponding spot option market prices used to compute the forward-starting variance swap rate. The authors find that implied convexity levels can indeed be used to forecast the future volatility of VIX futures prices, even though implied convexity consistently underestimates future realized VIX futures variance. They also show that implied convexity can at times violate strict theoretical conditions by being negative, although we are able to rule out arbitrage opportunities. Finally, they examine the properties of this implied convexity adjustment, both as a time series and with respect to various market volatility factors with which they find positive and statistically significant relations.

The Marginal Effect of Government Mortgage Guarantees on Homeownership
Grundl, Serafin,Kim, You
SSRN
The U.S. government guarantees a majority of residential mortgages, which is often justified as a means to promote homeownership. In this paper we use property-level data to estimate the effect of government mortgage guarantees on homeownership, by exploiting variation of the conforming loan limits (CLLs) along county borders. We find substantial effects on government guarantees, but find no robust effect on homeownership. This finding suggests that government guarantees could be considerably reduced with modest effects on homeownership, which is relevant for housing finance reform plans that propose to reduce the governmentÃ¢â‚¬â„¢s involvement in the mortgage market by reducing the CLLs.

Typology of Stress Testing: Microprudential vs. Macroprudential Stress Testing of Risk Exposures