# Research articles for the 2019-12-04

arXiv

We propose a mean field game model to study the question of how centralization of reward and computational power occur in the Bitcoin-like cryptocurrencies. Miners compete against each other for mining rewards by increasing their computational power. This leads to a novel mean field game of jump intensity control, which we solve explicitly for miners maximizing exponential utility, and handle numerically in the case of miners with power utilities. We show that the heterogeneity of their initial wealth distribution leads to greater imbalance of the reward distribution, or a "rich get richer" effect. This concentration phenomenon is aggravated by a higher bitcoin price, and reduced by competition. Additionally, an advanced miner with cost advantages such as access to cheaper electricity, contributes a significant amount of computational power in equilibrium. Hence, cost efficiency can also result in the type of centralization seen among miners of cryptocurrencies.

arXiv

Global fixed income returns span across multiple maturities and economies, that is, they naturally reside on multi-dimensional data structures referred to as tensors. In contrast to standard "flat-view" multivariate models that are agnostic to data structure and only describe linear pairwise relationships, we introduce a tensor-valued approach to model the global risks shared by multiple interest rate curves. In this way, the estimated risk factors can be analytically decomposed into maturity-domain and country-domain constituents, which allows the investor to devise rigorous and tractable global portfolio management and hedging strategies tailored to each risk domain. An empirical analysis confirms the existence of global risk factors shared by eight developed economies, and demonstrates their ability to compactly describe the global macroeconomic environment.

arXiv

We propose a mathematical model of momentum risk-taking, which is essentially real-time risk management, and discuss its implementation: an automated momentum equity trading system. Risk-taking is one of the key components of general decision-making, a challenge for artificial intelligence and machine learning; stock markets are quite a test for any risk-management theories. We begin with a relatively simple continuous model of news impact on share-prices. In spite of its simplicity, it describes well the power growth of share-prices, periodicity, logarithmic periodicity, and the market phenomena like using price targets and profit-taking; Bessel and hypergeometric functions are used. Its discretization is performed, adjusted to discontinuous functions and aimed at stock charts, the main examples for us. An automated trading system based on our approach proved to be successful in extensive historical and real-time experiments. Its preimage is a new contract card game presented at the end, a combination of bridge and poker.

SSRN

Using high frequency data and machine learning methods, I propose a new method to isolate a plausibly exogenous component of mutual fund flows and use it as an instrument to revisit classic empirical questions in Finance because previous methods are vulnerable to a reverse causality critique.The idea that mutual fund flows induce fire sales which drive asset prices away from fundamentals has been fruitful. Based on this idea the Asset Pricing literature finds markets are inefficient and fragile and the Corporate Finance literature shows market misvaluation distorts real outcomes. First, I argue these findings are partially driven by reverse causality. Assets on fire reduce mutual fund returns which trigger outflows. Empirically, this becomes apparent when increasing the frequency of the standard event study from quarterly to daily; returns precede flows. Second, I suggest a solution. In contrast to quarterly flows, an instrument constructed from daily surprise flows is exogenous to fundamentals. Also, this instrument can be strengthened by training machine learning models to predict how mutual funds trade in response to flows. Third, I use surprise flows to reevaluate important findings in the literature. Overall, while I confirm most findings qualitatively, the new estimates imply that equity markets are more efficient, less fragile and less distortive than suggested.

SSRN

We propose a novel, and simple, Bayesian estimation and model selection procedure for cross-sectional asset pricing. Our approach, that allows for both tradable and non-tradable factors, and is applicable to high dimensional cases, has several desirable properties. First, weak and spurious factors lead to diffuse, and centered at zero, posteriors for their market price of risk, making such factors easily detectable. Second, posterior inference is robust to the presence of such factors. Third, we show that flat priors for risk premia lead to improper marginal likelihoods, rendering model selection invalid. Therefore, we provide a novel prior, that is diffuse for strong factors but shrinks away useless ones, under which posterior probabilities are well behaved, and can be used for factor and (non necessarily nested) model selection, as well as model averaging, in large scale problems. We apply our method to a very large set of factors proposed in the literature, and analyse 2.25 quadrillion possible models, gaining novel insights on the empirical drivers of asset returns.

SSRN

We show that supply side effects arising from the bond holdings of open-end mutual funds affect corporate credit risk through a refinancing channel. In our framework, bond funds exposed to flow-performance relationships become excessively reluctant to refinance bonds of companies with poor cash flow prospects. This lowers refinancing prices, enhancing incentives for strategic default, thus engendering a positive association between bond funds' presence and credit risk. Empirically, we find that firms with a large share of mutual fund holdings experience increases in CDS spreads, particularly for funds that are more sensitive to flows. We address potential endogeneity issues by using fund acquisitions as exogenous shocks to funds' flow concerns.

arXiv

As the decade turns, we reflect on nearly thirty years of successful manipulation of the world's public equity markets. This reflection highlights a few of the key enabling ingredients and lessons learned along the way. A quantitative understanding of market impact and its decay, which we cover briefly, lets you move long-term market prices to your advantage at acceptable cost. Hiding your footprints turns out to be less important than moving prices in the direction most people want them to move. Widespread (if misplaced) trust of market prices -- buttressed by overestimates of the cost of manipulation and underestimates of the benefits to certain market participants -- makes price manipulation a particularly valuable and profitable tool. Of the many recent stories heralding the dawn of the present golden age of misinformation, the manipulation leading to the remarkable increase in the market capitalization of the world's publicly traded companies over the past three decades is among the best.

SSRN

We use data from the Carbon Disclosure project (CDP) to measure firms' beliefs about climate regulation, their plans for future abatement, and their current actions on mitigating carbon emissions. These measures vary both across firms and time in a manner that is especially pronounced around the Paris climate change agreement announcement. A simple dynamic model of carbon abatement with a firm exposed to a certain future carbon levy, facing a trade-off between emissions reduction and capital growth, and convex emissions abatement adjustment costs cannot explain the data. A more complex two-firm dynamic model with both information asymmetry across firms and reputational concerns fits the data far better. Our findings imply that firms' abatement actions depend greatly on their beliefs about climate regulation, and that both informational frictions and reputational concerns can amplify responses to climate regulation, increasing its effectiveness.

arXiv

We propose a deep neural network-based algorithm to identify the Markovian Nash equilibrium of general large $N$-player stochastic differential games. Following the idea of fictitious play, we recast the $N$-player game into $N$ decoupled decision problems (one for each player) and solve them iteratively. The individual decision problem is characterized by a semilinear Hamilton-Jacobi-Bellman equation, to solve which we employ the recently developed deep BSDE method. The resulted algorithm can solve large $N$-player games for which conventional numerical methods would suffer from the curse of dimensionality. Multiple numerical examples involving identical or heterogeneous agents, with risk-neutral or risk-sensitive objectives, are tested to validate the accuracy of the proposed algorithm in large group games. Even for a fifty-player game with the presence of common noise, the proposed algorithm still finds the approximate Nash equilibrium accurately, which, to our best knowledge, is difficult to achieve by other numerical algorithms.

SSRN

We solve analytically a pure exchange general equilibrium model with a continuum of agents that agree to disagree on how they interpret information. Disagreement fluctuates with information quality and the disagreement model is estimated using data on professional forecasts. We fi nd that fluctuations in information quality generate about half the stock price volatility in the data, helps explain the equity premium, and explains empirical relations between the forecast dispersion and asset prices. Constant information quality cannot account for the variation in forecast dispersion and in this case, disagreement has almost no effect on the stock return volatility.

arXiv

In this paper, we present a Longstaff-Schwartz-type algorithm for optimal stopping time problems based on the Brownian motion filtration. The algorithm is based on Le\~ao, Ohashi and Russo and, in contrast to previous works, our methodology applies to optimal stopping problems for fully non-Markovian and non-semimartingale state processes such as functionals of path-dependent stochastic differential equations and fractional Brownian motions. Based on statistical learning theory techniques, we provide overall error estimates in terms of concrete approximation architecture spaces with finite Vapnik-Chervonenkis dimension. Analytical properties of continuation values for path-dependent SDEs and concrete linear architecture approximating spaces are also discussed.

SSRN

Recent work shows that firms born in cohorts with weak job creation are persistently smaller, even when the aggregate economy recovers. As both demand-side and supply-side factors vary with the business cycle, it is challenging to establish what drives these patterns from aggregate data. We use comprehensive procurement auctions and register data from Norway to study the effect of cross-sectional variation in transient demand shocks on long-run outcomes for startups. Auction winners have more than 20% higher sales and employment than runners-up several years after the auction. They are also more profitable. Investment effects, broadly interpreted, appear important to understand the results.

arXiv

In recent years, it has been debated whether a reduction in working hours would be a viable solution to tackle the unemployment caused by technological change. The improvement of existing production technology is gradually being seen to reduce labor demand. Although this debate has been at the forefront for many decades, the high and persistent unemployment encountered in the European Union has renewed interest in implementing this policy in order to increase employment. According to advocates of reducing working hours, this policy will increase the number of workers needed during the production process, increasing employment. However, the contradiction expressed by advocates of working time reduction is that the increase in labor costs will lead to a reduction in business activity and ultimately to a reduction in demand for human resources. In this article, we will attempt to answer the question of whether reducing working hours is a way of countering the potential decline in employment due to technological change. In order to answer this question, the aforementioned conflicting views will be examined. As we will see during our statistical examination of the existing empirical studies, the reduction of working time does not lead to increased employment and cannot be seen as a solution to the long-lasting unemployment.

SSRN

We believe investors should be willing to pay a higher price for higher quality companies. We build a composite quality score, including one which incorporates publicly available ESG scores, using 'off-the-shelf' criteria and publicly available financial data and show that a quarterly-rebalanced, long-only portfolio of top-decile stocks selected using our score in Hong Kong significantly outperforms the Hang Seng Composite LargeCap Index (HSLI) - both in terms of absolute returns (by 2.87% pa) and risk adjusted returns - while having an moderate annual turnover (a mean turnover of 42.15%). We show that our quality score predicts the persistence of quality for up to 3 years and there is a weak relationship between the price multiple and the quality score. Furthermore, we demonstrate that quality needs to be reviewed regularly - and the moderate turnover ensures this. In the absence of cheap ETFs to get systematic exposure to quality, the systematic long-only strategy using 'off-the-shelf' criteria provides a practical, executable systematic investment methodology that exposes an investor to quality in the Hong Kong market. We show that socially conscious investors can generate risk adjusted returns in excess of the HSLI in the Hong Kong market using a systematic strategy.

SSRN

We believe investors should be willing to pay a higher price for higher quality companies. We build a composite quality score, including one which incorporates publicly available ESG scores, using 'off-the-shelf' criteria and publicly available financial data and show that a quarterly-rebalanced, long-only portfolio of top-decile stocks selected using our score in Singapore significantly outperforms the Straits Times Index - both in terms of absolute returns (by 7.93% pa) and risk adjusted returns - while having an moderate annual turnover (a mean turnover of 48.41% ). We show that our quality score predicts the persistence of quality for up to 3 years and there is a weak relationship between the price multiple and the quality score. Furthermore, we demonstrate that quality needs to be reviewed regularly - and the moderate turnover ensures this. In the absence of cheap ETFs to get systematic exposure to quality, the systematic long-only strategy using 'off-the-shelf' criteria provides a practical, executable systematic investment methodology that exposes an investor to quality in the Singapore market. We show that ESG data using the database we used is not yet comprehensive enough to build a robust systematic quality score.

arXiv

Within the well-known framework of financial portfolio optimization, we analyze the existing relationships between the condition of arbitrage and the utility maximization in presence of \emph{insider information}. We assume that, since the initial time, the information flow is altered by adding the knowledge of an additional random variable including future information. In this context we study the utility maximization problem under the logarithmic and the Constant Relative Risk Aversion (CRRA) utilities, with and without the restriction of no temporary-bankruptcy. In particular, we show that the value of the insider information may be bounded while the arbitrage condition holds and we prove that the insider information does not always imply arbitrage for the insider by providing an explicit example.

SSRN

We provide first-time evidence of the real-time characteristics and drivers of jumps in option prices. To this end, we employ high-frequency data from the 24-hour E-mini S&P 500 options market. We find that option prices do not jump simultaneously across strikes and maturities and are uncorrelated with jumps in the underlying futures price. We also find that 14% to 28% of detected option price jumps occur around scheduled news releases. However, it is illiquidity rather than the news content that drives these jumps. Evidence suggests that option traders increase bid-ask spreads to account for trading against investors who are skilled processors of public news releases. Interestingly, illiquidity does not drive jumps in the S&P 500 index options market, where we also find sizable and idiosyncratic price jumps.

SSRN

We show that U.S. banks price deposits almost uniformly across their branches and that this pricing practice is crucial to explain the deposit rate dynamics following bank mergers. We find a strong and sharp post-merger convergence between the deposit rates of the acquired branches and the median deposit rate of the acquirer. This pattern is almost fully explained by adjustments in the deposit rates of the acquired branches, irrespective of whether their rates were above or below those practiced by the acquirer. Acquired branches lose deposits and local market share, especially when they decrease their rates due to uniform pricing. Local competitors respond to changes in deposit rates at the acquired branches by adjusting their own deposit rates in the same direction. We find that pre-merger differences in deposit rates between merged entities explain more of the post-merger evolution of deposit rates than the predicted changes in local market concentration induced by the merger. This result indicates that competition authorities would be well-advised to review the potential impact of pre-merger pricing differences in evaluating a merger within an industry with strong uniform pricing practices.

SSRN

We study the role of ambiguityâ€"Knightian uncertaintyâ€"in mergers and acquisitions (M&A). Ambiguity that bidders face increases the probability, count, dollar value, and speed of M&A activity. However, ambiguity that targets face has opposite effects. Concerning the method of payment, all-stock deals are less likely when bidders face more ambiguity, and all-cash deals are less likely when targets face more ambiguity. Further, same-industry acquisitions are less likely for bidders that face more ambiguity, and deal premiums are higher for targets that face less ambiguity.

arXiv

In the continuous time mean-variance model, we want to minimize the variance (risk) of the investment portfolio with a given mean at terminal time. However, the investor can stop the investment plan at any time before the terminal time. To solve this kind of problem, we consider to minimize the variances of the investment portfolio at multi-time state. The advantage of this multi-time state mean-variance model is that we can minimize the risk of the investment portfolio along the investment period. To obtain the optimal strategy of the multi-time state mean-variance model, we introduce a sequence of Riccati equations which are connected by a jump boundary condition. Based on this sequence Riccati equations, we establish the relationship between the means and variances of this multi-time state mean-variance model. Furthermore, we use an example to verify that minimizing the variances of the multi-time state can affect the average of Maximum-Drawdown of the investment portfolio.

arXiv

We develop and study stability properties of a hybrid approximation of functionals of the Bates jump model with stochastic interest rate that uses a tree method in the direction of the volatility and the interest rate and a finite-difference approach in order to handle the underlying asset price process. We also propose hybrid simulations for the model, following a binomial tree in the direction of both the volatility and the interest rate, and a space-continuous approximation for the underlying asset price process coming from a Euler-Maruyama type scheme. We show that our methods allow to obtain efficient and accurate European and American option prices. Numerical experiments are provided, and show the reliability and the efficiency of the algorithms.

SSRN

We consider optimal taxation in a model with wealth-poor and wealth-rich households, where wealth derives from business capital and homeownership, and investigate the consequences on these tax rates of a rising wealth inequality at steady state. The optimal tax structure includes some taxation of labor, zero taxation of financial and business capital, a housing wealth tax on the wealth-rich households and a housing subsidy on the wealth-poor households. When wealth inequality increases, the optimal balance between labor and housing wealth taxes depends on the source of the increasing wealth.

SSRN

In this paper we develop a general framework to analyze state space models with time-varying system matrices, where time variation is driven by the score of the conditional likelihood. We derive a new filter that allows for the simultaneous estimation of the state vector and of the time-varying matrices. We use this method to study the time-varying relationship between the price dividend ratio, expected stock returns and expected dividend growth in the US since 1880. We find a significant increase in the long-run equilibrium value of the price dividend ratio over time, associated with a fall in the long-run expected rate of return on stocks. The latter can be attributed mainly to a decrease in the natural rate of interest, as the long-run risk premium has only slightly fallen.

arXiv

Since the introduction of risk-based solvency regulation, pro-cyclicality has been a subject of concerns from all market participants. Here, we lay down a methodology to evaluate the amount of pro-cyclicality in the way finnancial institutions measure risk, and identify factors explaining this pro-cyclical behavior. We introduce a new indicator based on the Sample Quantile Process (SQP, a dynamic generalization of Value-at-Risk), conditioned on realized volatility to quantify the pro-cyclicality, and evaluate its amount in the markets, considering 11 stock indices as realizations of the SQP. Then we determine two main factors explaining the pro-cyclicality: the clustering and return-to-the-mean of volatility, as it could have been anticipated but not quantified before, and, more surprisingly, the very way risk is measured, independently of this return-to-the-mean effect.

SSRN

In the two decades leading up to the 2008 financial crisis, numerous significant changes in federal law greatly reduced transactions costs in financial markets and made possible new types of trading in new types of financial instruments. The driving policy assumption behind these and similar regulatory changes was that making financial markets more complete would increase social welfare by moving financial markets closer toward Arrow-Debreu.In this paper, we present a model that can explain why, contrary to this policy assumption, regulatory changes that made financial markets more complete in the years leading up to 2008 seemed to produce the opposite effect. Our model shows how when agents have heterogeneous expectations, making financial markets more complete can reduce social welfare by increasing aggregate risk; reducing aggregate returns; and skewing perceptions of wealth in ways that inefficiently distort aggregate consumption decisions, causing agents to over-consume in some periods so that they are forced to cut back consumption sub-optimally in subsequent periods. The intuition underlying our model is that when agents have heterogeneous expectations, making markets for financial instruments more complete amplifies the magnitude of the â€œwinnerâ€™s curseâ€ observed in many common-value auctions with the detrimental welfare effects we describe.This paper remains in draft form because of the untimely death of the first author. The second author is posting it to honor her memory and to share what is some of her last work. She will be missed.

SSRN

We model listing decisions in the housing market, and structurally estimate household preference and constraint parameters using comprehensive Danish data. Sellers optimize expected utility from property sales, subject to down-payment constraints, and internalize the effect of their choices on final sale prices and time-on-the-market. The data exhibit variation in the listing price-gains relationship with "demand concavity;" bunching in the sales distribution; and a rising listing propensity with gains. A new fact is that gains and down-payment constraints have interactive effects on listing prices. We find reference-dependence around the nominal purchase price and modest loss-aversion, but our canonical model cannot fully explain the new facts.

SSRN

This paper shows that monetary policy and prudential policies interact. U.S. banks issue more commercial and industrial loans to emerging market borrowers when U.S. monetary policy eases. The effect is less pronounced for banks that are more constrained

SSRN

Research finds strong links between credit booms and macroeconomic outcomes like financial crises and output growth. Are impacts also seen in financial asset prices? We document this robust and significant connection for the first time using a large sample of historical data for many countries. Credit boom periods tend to be followed by unusually low returns to equities, in absolute terms and relative to bonds. Return predictability due to this leverage factor is distinct from that of established factors like momentum and value and generates trading strategies with meaningful excess profits out-of-sample. These findings pose a challenge to conventional macro-finance theories.

SSRN

The maturity of a firmâ€™s liabilities affects the information financiers produce about the firmâ€™s assets. In my model, long-term financing creates an excessive tendency for financiers to acquire information and screen out lower quality borrowers. In contrast, short-term financing deters information production at origination but induces it when firms are forced to liquidate, depressing the market value of assets due to adverse selection. Through the feedback effect between firmsâ€™ maturity structures and asset prices, increases in uncertainty can impair the aggregate volume of short-term financing and investment. The analysis can jointly rationalize: i) the widespread use of short-term debt by financial firms, ii) fire sales in financial assets, iii) periodic disruptions in short term funding markets and iv) regulatory concerns about excessive short-term debt.