# Research articles for the 2019-11-18

SSRN

This paper uses transaction-level data to decompose active mutual fund performance based on the past length of funds' holdings. We find that the majority of value added is related to holdings that have been in the portfolio for more than one year. Our decomposition is markedly different depending on fund size: on average trades of large (small) funds start to add value only after 7 months (10 days), arguably because of the large price impact that large funds have. Only small funds profit from short-term anomalies suggesting that funds actively adapt their strategies to the price impact of their trades and specialize in different investment horizons. Finally, the dispersion of fund turnover is large and persistent. Trades and holdings of high-turnover funds add a substantial amount of value within a year but destroy value beyond a year, whereas trades and holdings of low-turnover funds mainly add value beyond a year.

SSRN

Financial reporting aims to meet the needs of participants in capital markets. Yet, little is known about preferences of capital suppliers (e.g., households) regarding accounting standards. We build a model to investigate capital suppliersâ€™ preferences regarding accounting standards by endogenizing both their supply of capital and firmsâ€™ demand for capital. We find that capital suppliers prefer accounting standards that would lead to over-investment and under-consumption in the economy. Our approach also enables us to show that changes in accounting standards lead to different macroeconomic consequences â€" cost of capital, consumption, and aggregate investment â€" depending on householdsâ€™ wealth and their preferences for smooth consumption. Overall, our results provide welfare implications of accounting standards and their macroeconomic outcomes that encompass both sides of capital markets in the economy.

SSRN

Using monthly data from 01/1985 to 12/2012, we find that the accounting valuation-based predictor introduced in Lee, Myers, and Swaminathan (1999) has excellent in-sample and out-of-sample predictive performance. Our finding suggests that the accounting valuation-based predictor does not suffer the problem of instable in-sample and poor out-of-sample performance that Welch and Goyal (2008) document with a long list of predictors suggested by the academic literature. Moreover, we find that forecasts based on widely-used valuation ratios and business cycle variables do not encompass forecasts based on the accounting valuation-based predictor, suggesting that the accounting valuation-based predictor carries information not captured by these valuation ratios and business cycle variables. Furthermore, in line with Lee et al.â€™s (1999) reasoning that the predictive power of the accounting valuation-based predictor stems from its ability to capture market-wide mispricing, we find that contemporaneous investor sentiment and expectations account for a considerable proportion of the variance of the accounting valuation-based predictor. Consistent with Lee et al.â€™s (1999) observation, we provide further evidence that using time-varying industry-specific discount rates based on short-term T-bill rates and analyst forecasts to estimate the intrinsic value of equity is essential to the success of the accounting valuation-based predictor in predicting future market returns.

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.

SSRN

This note presents answers to the "Consultation on Final Parameters for the Spread and Term Adjustments in Derivatives Fallbacks for Key IBORs'' issued by ISDA. The consultation asks many questions related to technical details for the IBOR fallback term. Unfortunately it does not consider the main issue, which is that the proposed base solution of compounded setting in arrear is ill-conceived. It fundamentally changes the meaning of IBOR fixing and the questions and workarounds related to the term are the consequences of this ill-conception. Most of the questions consists of workaround for issues that have been described in detail over the last 18 months. The spread part also focuses on narrow technical questions on how to compute it. It does not answer the question on how to prevent this computation to harm end users through the implied massive value transfer.We review the different aspects related to the adjusted RFR and adjusted spread and detail for each of them the problem they introduce, the value transfer resulting and make some suggestion on improvements. We also indicate some potential consultation's manipulation.

arXiv

This paper analyses the Chinese Sovereign bond yield to find out the principal factors affecting the term structure of interest rate changes. We apply Principal Component Analysis (PCA) on our data consisting of the Chinese Sovereign bond from January 2002 till May 2018 with the different yield to maturity. Then we will discuss the multi-factor immunization model (method on hedging market risk) on a bond portfolio.

SSRN

We study the effect of geographically diverse information on sell-side research analysts' individual and consensus forecasts. Using data from satellite images of parking lots of US retailers, we first document that the car counts contain valuable information in aggregate. However, analysts tend to overweight their own forecast in the direction of local car counts relative to other analysts covering the same firm at the same time but in different locations. We find when firms have more geographically concentrated analyst coverage the consensus forecast error is higher, even after controlling for the number of analysts. Analyses using within-firm variation and exogenous shocks in geographic coverage due to brokerage closures suggest this relation is causal.

arXiv

For a long investment time horizon, it is preferable to rebalance the portfolio weights at intermediate times. This necessitates a multi-period market model in which portfolio optimization is usually done through dynamic programming. However, this assumes a known distribution for the parameters of the financial time series. We consider the situation where this distribution is unknown and needs to be estimated from the data that is arriving dynamically. We applied Bayesian filtering through dynamic linear models to sequentially update the parameters. We considered uncertain investment lifetime to make the model more adaptive to the market conditions. These updated parameters are put into the dynamic mean-variance problem to arrive at optimal efficient portfolios. Extensive simulations are conducted to study the effect of varying underlying parameters and investment horizon on the performance of the method. An implementation of this model to the S&P500 illustrates that the Bayesian updating is strongly favored by the data and that it is practically implementable.

arXiv

The paper examines the potential of deep learning to support decisions in financial risk management. We develop a deep learning model for predicting whether individual spread traders secure profits from future trades. This task embodies typical modeling challenges faced in risk and behavior forecasting. Conventional machine learning requires data that is representative of the feature-target relationship and relies on the often costly development, maintenance, and revision of handcrafted features. Consequently, modeling highly variable, heterogeneous patterns such as trader behavior is challenging. Deep learning promises a remedy. Learning hierarchical distributed representations of the data in an automatic manner (e.g. risk taking behavior), it uncovers generative features that determine the target (e.g., trader's profitability), avoids manual feature engineering, and is more robust toward change (e.g. dynamic market conditions). The results of employing a deep network for operational risk forecasting confirm the feature learning capability of deep learning, provide guidance on designing a suitable network architecture and demonstrate the superiority of deep learning over machine learning and rule-based benchmarks.

SSRN

In the simplest possible model that includes vols and correlations for interest rates in both currencies, the FX rate and the default intensity, a closed form solution is presented for the PV of a contingent credit default swap (CCDS) that pays in default the outstanding mark to market of a cross currency swap provided the latter is positive. Then some conditions are given that determine the direction of the sensitivities of this PV with respect to changes in the correlations. The three correlations involving only interest rates and FX determine the market risk of the underlying CCY swap. The other three correlations that involve the default intensity determine the right and wrong way risk of the CCDS. When some extra natural assumptions are made that prevent intensities, resp. PVs, from becoming negative then the sensitivities against the market risk correlations are uniform in the sense that their sign does not depend on the maturity, or the moneyness, of the underlying CCY swap, or whether the domestic rate is paid or received. This is quite clear intuitively because it is similar to the vega of a call and put being the same. In contrast, the paper derives necessary and sufficient conditions for the sensitivity directions against the credit related correlations. These conditions show a strong dependence on the characteristics of the CCY swap. In other words, right way risk can become wrong way risk when for example the maturity changes.

arXiv

We investigate the impact of available information on the estimation of the default probability within a generalized structural model for credit risk. The traditional structural model where default is triggered when the value of the firm's asset falls below a constant threshold is extended by relaxing the assumption of a constant default threshold. The default threshold at which the firm is liquidated is modeled as a random variable whose value is chosen by the management of the firm and dynamically adjusted to account for changes in the economy or the appointment of a new firm management. Investors on the market have no access to the value of the threshold and only anticipate the distribution of the threshold. We distinguish different information levels on the firm's assets and derive explicit formulas for the conditional default probability given these information levels. Numerical results indicate that the information level has a considerable impact on the estimation of the default probability and the associated credit yield spread.

SSRN

This paper quantifies the extent of heterogeneity in consumption responses to changes in real interest rates and house prices in the four largest economies in the euro area: France, Germany, Italy, and Spain. We first calibrate a life-cycle incomplete-markets model with a financial asset and housing to match the large heterogeneity of households asset portfolios, observed in the Household Finance and Consumption Survey (HFCS) for these countries. We then show that the heterogeneity in household finances implies that responses of consumption to changes in the real interest rate and in house prices differ substantially across countries, and within countries by household characteristics such as age, housing tenure, and asset positions. The different consumption responses quantified in this paper point towards important heterogeneity in monetary-policy transmission in the euro area.

arXiv

This paper investigates a financial market where returns depend on an unobservable Gaussian drift process. While the observation of returns yields information about the underlying drift, we also incorporate discrete-time expert opinions as an external source of information.

For estimating the hidden drift it is crucial to consider the conditional distribution of the drift given the available observations, the so-called filter. For an investor observing both the return process and the discrete-time expert opinions, we investigate in detail the asymptotic behavior of the filter as the frequency of the arrival of expert opinions tends to infinity. In our setting, a higher frequency of expert opinions comes at the cost of accuracy, meaning that as the frequency of expert opinions increases, the variance of expert opinions becomes larger. We consider a model where information dates are deterministic and equidistant and another model where the information dates arrive randomly as the jump times of a Poisson process. In both cases we derive limit theorems stating that the information obtained from observing the discrete-time expert opinions is asymptotically the same as that from observing a certain diffusion process which can be interpreted as a continuous-time expert.

We use our limit theorems to derive so-called diffusion approximations of the filter for high-frequency discrete-time expert opinions. These diffusion approximations are extremely helpful for deriving simplified approximate solutions of utility maximization problems.

SSRN

We propose a news-implied rare disaster risk indicator and study its predictive power on the returns of U.S. Treasury bonds. We find that the predictive power of this factor is both statistically significant and economically important and is not spanned by the current yield curve. The disaster risk factor delivers a counter cycle bond risk premium, and the predictability of disaster risk is more significant during periods of economic downturn. Our empirical findings show that disaster risk accounts for a sizable portion of variations in the time-varying bond risk premium.

SSRN

Expected idiosyncratic volatility and its relation to the expected return of Fu (2009) can be closely replicated, but only when we include all information up to t when estimating the idiosyncratic volatility of t. Since look-ahead bias may exist, we re- estimate the expected idiosyncratic volatility using information only up to t âˆ' 1. We find no significant relation between idiosyncratic volatility and return, and our results are robust to the sample extended to before and after that of Fu (2009). Our findings are consistent with the fact that idiosyncratic risk is not priced.

arXiv

While time series momentum is a well-studied phenomenon in finance, common strategies require the explicit definition of both a trend estimator and a position sizing rule. In this paper, we introduce Deep Momentum Networks -- a hybrid approach which injects deep learning based trading rules into the volatility scaling framework of time series momentum. The model also simultaneously learns both trend estimation and position sizing in a data-driven manner, with networks directly trained by optimising the Sharpe ratio of the signal. Backtesting on a portfolio of 88 continuous futures contracts, we demonstrate that the Sharpe-optimised LSTM improved traditional methods by more than two times in the absence of transactions costs, and continue outperforming when considering transaction costs up to 2-3 basis points. To account for more illiquid assets, we also propose a turnover regularisation term which trains the network to factor in costs at run-time.

SSRN

Volatility had been used as an indirect means for predicting risk accompanied with the asset. Volatility explains the variations in returns. Forecasting volatility had been a stimulating problem in the financial systems. The study examined the different volatility estimators and determined the efficient volatility estimator. The study described the accuracy of forecasting technique with respect to various volatility estimators. The methodology of volatility estimation includes Close, Garman-Klass, Parkinson, Roger-Satchell and Yang-Zhang methods and forecasting is done through ARIMA technique. The study evaluated the efficiency and bias of various volatility estimators. The comparative analyses based on various error measuring parameters like ME, RMSE, MAE, MPE, MAPE, MASE, ACF1 gave the accuracy of forecasting with the best volatility estimator. Out of five volatility estimators analysed over a period of 10 years and critically examined for forecasting volatility, the research obtained Parkinson estimator as the most efficient volatility estimator. Based on various error measuring parameters, Parkinson estimator had been examined as more accurate estimator than any other estimator based on RMSE, MPE and MASE in forecasting through ARIMA Technique. The study suggests that the forecasted values had been accurate based on the values of MAE and RMSE. This research was conducted in order to meet out the demand of knowing the efficient volatility estimator for forecasting volatility with high accuracy by the traders, option practitioners and various players of stock market.

SSRN

Firms trade derivatives with banks to mitigate the adverse impact of exchange-rate fluctuations. We study how the related migration of foreign exchange (FX) risk is managed by banks and affects both credit supply and real economic variables. For identification, we exploit the Brexit referendum in June 2016 as a quasi-natural Experiment in combination with detailed micro-level FX derivatives data and the credit register in Germany. We show that, prior to the referendum, the corporate sector substantially increased the usage of derivatives, and banks on the other side of the trade did not fully intermediate that FX risk, but retained a large proportion of it in their own books. As a result, the depreciation of the British pound in the aftermath of the Referendum poses a shock to the capital base of affected banks. We show that loss-facing banks in response cut back credit to firms, including to those without FX exposure to begin with. These results are stronger for less capitalized banks. Firms with ex-ante exposure to loss-facing banks experience a 32 percent larger reduction in credit than industry peers, and a stronger reduction in cash holdings and investment of about 8 and 2 percent, respectively. Our results show how a bank's uninsured derivatives book can take one corporation's FX risk and turn it into another corporation's financing risk.

SSRN

When contemplating Chapter 11, firms often need to seek financing for their continuing operations in bankruptcy. Because such financing would otherwise be hard to find, the Bankruptcy Code authorizes debtors to offer sweeteners to debtor-in-possession (DIP) lenders. These inducements can be effective in attracting financing, but because they are thought to come at the expense of other stakeholders, the Code permits these inducements only if no less generous a package would have been sufficient to obtain the loan.Anecdotal evidence suggests that the use of certain controversial inducements â€" I focus on roll-ups and milestones â€" skyrocketed in recent years, leading critics to question whether DIP lenders were abusing their power. Lenders, however, respond that DIP loan terms simply reflect economic conditions: When credit is tight, as it was in recent years because of the Financial Crisis, more sweeteners are needed to induce lending.Using a hand-collected dataset reflecting contractual detail in DIP loan agreements, I examine the relationship between changes in credit availability and DIP loan terms before, during, and after the Crisis. As one might expect, I find that ordinary loan provisions like pricing and reporting covenants are sensitive to changes in credit availability. By contrast, I also find that the incidence of so-called â€œextraordinary provisionsâ€ has no statistically meaningful relationship with changes in credit availability. These findings have important implications for bankruptcy policymakers and judges struggling to evaluate whether extraordinary DIP lending inducements are necessary. Too-generous loan terms come at the expense of junior claimants and may distort the bankruptcy process in favor of senior claimants.

SSRN

Chava, Hsu, and Zeng (2019) find that investors don't fully incorporate business cycle variation in cash flow growth and thus conditional Sharpe ratio can be informative for future industry returns. It suggests that cash flow risk at the idiosyncratic level is not fully incorporated into the prices by investors. I develop a stochastic volatility framework to evaluate the unexpected cash flow news through the variance decomposition perspective and apply the method to U.S. industry data. I find that i) The common cash flow volatility estimated from unexpected industry-level cash flow news is highly correlated to Uncertainty index constructed by Jurado, Ludvigson, and Ng (2015); ii) the idiosyncratic cash flow risk is robustly priced and the explanation power cannot be consumed by current well-known risk factors and firm characteristics; iii) stocks with high conditional Sharpe ratios tend to have higher idiosyncratic cash flow volatility and higher compensated returns, which is consistent with Chava, Hsu, and Zeng (2019)'s finding. A strategy that goes long the decile portfolio with the largest idiosyncratic cash flow volatility and short the decile portfolio with the smallest idiosyncratic cash flow volatility yields a Fama-French-Five-Factor alpha of 37 bps per month (t-stat: 6.90) in long sample (1931-2018) and 64 bps per month (t-stat: 12.28) in the modern sample (1963-2018).

SSRN

This paper investigates the integration among sub-sectors within the environmentally friendly stock market and the integration between these sub-sectors and other financial asset classes. Using the recently developed cross-quantilogram framework, we contribute to the literature by quantifying the cross-quantile directional spillovers among regional green equity markets and other financial assets. First, we find that within the green equity market, the U.S. sector is the main transmitter of shocks while the Asian sector is the main receiver of shocks, however, the integration among regional green equity markets dissipates in the long run. Second, we find that the relationship between green equity markets and other financial assets such as energy commodity and conventional stock varies across regions and market conditions. Our results imply that understanding the heterogeneity in the internal and external integration of the green equity market is crucial for the design of successful investment strategies and effective policy incentives to promote environmentally friendly investments.

SSRN

This paper presents steps to lower the overall volatility in the stock market; as a large portion is unrewarded and unjustified and driven by overreaction accompanies with herd behavior. We first map the key factors that cause volatility, such as: earnings surprise, CEO turnovers, merge and acquisitions, etc. and then present our suggestions; where the key elements are: (i) fewer day/hours of trading, which in turn increases time to processing information; and (ii) semi-annual earnings reports instead of quarterly; smoothing the surprises. Following these suggestions, the volatility decreases by at least 15.5% per day. Collectively, we suggest to trade less and read more.

SSRN

The main objective in this article is to shed new light on the term structure of subjective time preference rates using a conditional Consumption Capital Asset Pricing Model. Following Samuelsonâ€™s (1937)â€™s suggestion, we analyze the concept of â€œtime consistencyâ€. More precisely, we challenge the relevance of the exponential time discounting function assumption, which leads to a constant subjective time preference rate. First, we develop a parsimonious, consumption-based model of the term structure of interest rates. Second, we test its implications for US monthly data from 1970:4 to 2013:1. We use a bivariate two-factor model of inflation and real consumption (through a VAR-GARCH process) to condition the term premiums of bonds. Our results clearly cast doubt on the assumption of a flat term structure, as implied by the standard exponential discounting function. A decreasing term structure of time preference rates is reported. It is particularly clear for the 1991-2013 period. Our results give strong support for the hyperbolic time discounting function hypothesis and open the way for the hypothesis of time varying time preference rates.

arXiv

We discuss the objectives of automation equipped with non-trivial decision making, or creating artificial intelligence, in the financial markets and provide a possible alternative. Intelligence might be an unintended consequence of curiosity left to roam free, best exemplified by a frolicking infant. For this unintentional yet welcome aftereffect to set in a foundational list of guiding principles needs to be present. A consideration of these requirements allows us to propose a test of intelligence for trading programs, on the lines of the Turing Test, long the benchmark for intelligent machines. We discuss the application of this methodology to the dilemma in finance, which is whether, when and how much to Buy, Sell or Hold.

SSRN

As the proxy for expected return, the implied cost of capital (ICC) is subject to a mispricing-driven measurement error. For undervalued stocks, the mispricing-driven measurement error is positive and increases with the degree of undervaluation while for overvalued stocks, the mispricing-driven measurement error is negative and decreases with the degree of overvaluation. That is, ceteris paribus, lower ICC is equivalent to smaller undervaluation and thus higher fundamental valuation efficiency for undervalued stocks while lower ICC is equivalent to larger overvaluation and thus lower fundamental valuation efficiency for overvalued stocks. Fundamental valuation efficiency (FVE) refers to the extent to which the price of a stock deviates from the intrinsic value. We show that the estimated relation of an FVE-associated variable with ICC is a potentially biased estimate of its relation with expected return due to the mispricing-driven measurement error. Moreover, existing methods do not seem able to address the bias.

SSRN

We examine an optimal investment and consumption problem in the presence of inflation risk and capital gains tax. We show that the concern about inflation can significantly strengthen an investor's incentive to defer gains realizations, leading to a first order increase in the value of the tax-timing option. We also find that capital gains taxation can make low income investors unconditionally better-off if stock dividends are taken into account, and this effect is stronger when inflation risk is considered. Moreover, we demonstrate that capital gains indexation (i.e., the idea of indexing cost basis by inflation) has different implications for the optimal trading strategies of low or high income investors. It encourages low (high resp.) income investors to realize more (less resp.) capital gains. In contrast to popular views, we find that low income investors may benefit more from capital gains indexation.

RePEC

This article analyzes the complexity of female longevity improvements. As socio-economic status is found to influence health and mortality, we partition all individuals, at each age in every year, into five socio-economic groups based on an affluence measure that combine an individual's income and wealth. We identify the particular socio-economic groups that have been driving the standstill for Danish females. Within each socio-economic group, we further analyze the cause of death patterns. The decline in life expectancy for Danish females is present for four out of five subgroups, however with particular large decreases for the low-middle and middle affluence groups. Cancers, smoking related causes, and other diseases particularly contribute to the stagnation. Moreover, cardiovascular and cerebrovascular diseases are found to be important for capturing the following catch-up in longevity.

arXiv

As impressively shown by the financial crisis in 2007/08, contagion effects in financial networks harbor a great threat for the stability of the entire system. Without sufficient capital requirements for banks and other financial institutions, shocks that are locally confined at first can spread through the entire system and be significantly amplified by various contagion channels. The aim of this thesis is thus to investigate in detail two selected contagion channels of this so-called systemic risk, provide mathematical models and derive consequences for the systemic risk management of financial institutions. The first contagion channel we consider is default contagion. The underlying effect is here that insolvent institutions cannot service their debt or other financial obligations anymore - at least partially. Debtors and other directly impacted parties in the system are thus forced to write off their losses and can possibly be driven into insolvency themselves due to their incurred financial losses. This on the other hand starts a new round in the default contagion process. In our model we simplistically describe each institution by all the financial positions it is exposed to as well as its initial capital. In doing so, our starting point is the work of Detering et al. (2017) - a model for contagion in unweighted networks - which particularly considers the exact network configuration to be random and derives asymptotic results for large networks. We extend this model such that weighted networks can be considered and an application to financial networks becomes possible. More precisely, for any given initial shock we deduce an explicit asymptotic expression for the total damage caused in the system by contagion and provide a necessary and sufficient criterion for an unshocked financial system to be stable against small shocks. Moreover, ...

SSRN

We develop a modelling framework for multiple yield curves driven by continuous-state branching processes with immigration (CBI processes). Exploiting the self-exciting behavior of CBI jump processes, this approach can reproduce the relevant empirical features of spreads between different interbank rates.We provide a complete analytical framework, including a detailed study of discounted exponential moments of CBI processes. The proposed framework yields explicit valuation formulae for all linear interest rate derivatives as well as semi-closed formulae for non-linear derivatives via Fourier techniques and quantization. We show that a simple specification of the model can be successfully calibrated to market data.

arXiv

Recent technology advances have enabled firms to flexibly process and analyze sophisticated employee performance data at a reduced and yet significant cost. We develop a theory of optimal incentive contracting where the monitoring technology that governs the above procedure is part of the designer's strategic planning. In otherwise standard principal-agent models with moral hazard, we allow the principal to partition agents' performance data into any finite categories and to pay for the amount of information the output signal carries. Through analysis of the trade-off between giving incentives to agents and saving the monitoring cost, we obtain characterizations of optimal monitoring technologies such as information aggregation, strict MLRP, likelihood ratio-convex performance classification, group evaluation in response to rising monitoring costs, and assessing multiple task performances according to agents' endogenous tendencies to shirk. We examine the implications of these results for workforce management and firms' internal organizations.

arXiv

This paper introduces a search problem where a consumer has to first become aware of an alternative, before being able to search it. Initially, the consumer is aware of only a few alternatives. During search, the consumer sequentially decides between searching alternatives he is already aware of and expanding awareness to discover more products. I show that the optimal policy for this search problem is fully characterized by simple reservation values. Moreover, I prove that the purchase outcome of a consumer optimally solving the search problem is equivalent to the consumer simply choosing the product offering the largest value on a predetermined index.

SSRN

Which components of the order flow convey information and signal toxicity? We empirically show that the net flow of non-marketable orders conveys more information than the widely used trade-initiator-based order imbalance. The net order flow by HFTs rapidly loses information content with time aggregation, consistent with these traders trading on short-lived valuable signals. Updates of standing limit orders, mostly due to HFT, carry information beyond order submissions, suggesting that HFTsâ€™ flickering quotes primarily reflect active risk management rather than manipulative practices. Finally, we find that the HFTsâ€™ order flow, both marketable and non-marketable, signals toxicity, while the non-HFTsâ€™ order flow does not. We conclude that market authorities should track the HFTsâ€™ order flow at or near the best quotes to develop effective leading indicators of order flow toxicity and circuit breaking mechanisms.

SSRN

Using data from the representative IAB Establishment Panel in Germany and estimating a panel probit model with fixed effects, this paper finds a negative relationship between the existence of owner-management in an establishment and the probabilities of having a works council or a collective bargaining agreement. We show that family firms which are solely, partially or not managed by the owners significantly differ in the presence of works councils and collective bargaining agreements. The probabilities of having works councils and collective agreements increase substantially if just some of the managers do not belong to the owner family. We argue that these differences cannot simply be attributed to an aversion of the owners against co-determination and unions but require taking account of the notion of socio-emotional wealth prevalent in family firms. In addition, our results support the idea that external managers mainly act as agents rather than stewards in family firms.

SSRN

We study how the trade-off between liquidity provision and value preservation shapes redemption rules for financial intermediaries. In the literature, a bank prioritizes liquidity provision in a run by selling all assets. In reality, default followed by a mandatory stay on payments is triggered once a bank runs out of liquidity, removing withdrawal queue priority and reducing run incentives. Orderly resolution avoids fire sales, but reduces liquidity provision so strict sequential service dominates when assets are highly liquid (e.g., Treasury MMFs). We show that run frequency under sequential service may be nonmonotonic in asset liquidity. For high enough asset liquidity and sufficiently small liquidity benefits, a payment stay with immediate asset sale and payout is best, rationalizing new rules on gates on Prime MMFs. Our results explain the asset allocation and investor sorting across commercial, narrow, and shadow banks.

SSRN

We study venture capital finance with experimentation. An entrepreneur contracts with an investor and has private information about a project, which requires costly experimentation by both parties to succeed. In equilibrium, investors learn about the project from the arrival of exogenous information and from the entrepreneur's contract offers. The optimal contract features vesting and dilution, consistent with empirical evidence. Early payouts, pivots, and prestige projects emerge as signaling devices. Surprisingly, technological progress, which lowers the cost of experimentation or which increases the rate of learning, delays separation of types and worsens adverse selection. Liquidation rights for investors also delay separation.

SSRN

Public firms may choose to have private subsidiaries to hide bad news and/or proprietary information. We hypothesize that the private subsidiariesâ€™ information disclosure (PSID) level reflects the degree of information hiding activities of their parental firm, and may be able to forecast the future performance of parental firm. Empirically, we find that the higher the PSID level, the larger the future returns of their public parent firms. A long-short value-weighted portfolio of public parent firms sorted on their PSID yields a significant Fama and French (2018) six-factor alpha of more than 50 bps per month. This PSID based abnormal return cannot be explained by a variety of risk factors, firm characteristics, and risk-based arguments.

SSRN

We find that quasi-indexer ownership is negatively associated with the ratio of non-audit service fees to total fees paid to the audit firm performing the audit service and with the likelihood of paying the audit firm more non-audit service fees than audit service fees. Using the annual Russell 1000/2000 index reconstitution as plausibly exogenous variation in quasi-indexer ownership, we show that the dampening effect of quasi-indexer ownership on companiesâ€™ purchase of non-audit services from auditors seems to be causal. We interpret our finding as evidence consistent with quasi-indexersâ€™ preference for auditor independence and belief about the impairment effect of nonaudit services on auditor independence and with companies catering to their preference. Moreover, we show that the dampening effect of quasi-indexer ownership varies across subsamples in ways consistent with our interpretation.

arXiv

We introduce a reinforcement learning framework for retail robo-advising. The robo-advisor does not know the investor's risk preference, but learns it over time by observing her portfolio choices in different market environments. We develop an exploration-exploitation algorithm which trades off costly solicitations of portfolio choices by the investor with autonomous trading decisions based on stale estimates of investor's risk aversion. We show that the algorithm's value function converges to the optimal value function of an omniscient robo-advisor over a number of periods that is polynomial in the state and action space. By correcting for the investor's mistakes, the robo-advisor may outperform a stand-alone investor, regardless of the investor's opportunity cost for making portfolio decisions.

arXiv

The possibility of re-switching of techniques in Piero Sraffa's intersectoral model, namely the returning capital-intensive techniques with monotonic changes in the profit rate, is traditionally considered as a paradox putting at stake the viability of the neoclassical theory of production. It is argued here that this phenomenon can be rationalized within the neoclassical paradigm. Sectoral interdependencies can give rise to non-monotonic effects of progressive variations in income distribution on relative prices. The re-switching of techniques is, therefore, the result of cost-minimizing technical choices facing returning ranks of relative input prices in full consistency with the neoclassical perspective.

SSRN

The value premium has disappeared over the last decade and this paper provides a risk-based explanation for its disappearance. I document a positive linear relationship among the value premium and the expected inflation at both high frequency and lower business cycle frequency. A heterogeneous cash flow model featuring inflation non-neutrality is proposed to justify the observed pattern. The estimated results suggest that value firms are more exposed to high-frequency fluctuations in aggregate consumption growth but less exposed to the low-frequency consumption risk. This finding is consistent with the documented inflation-return relationship but it contrasts with the previous findings suggesting that value firms are more sensitive to long-run consumption risk. Simulation-based results show that the positive linear relationship among the value premium and the expected inflation can be recovered when inflation is non-neutral and the relationship turns into uncorrelated when inflation is neutral. Therefore we argue that inflation non-neutrality can justify the positive relationship among inflation and value premium. Meanwhile, value firms tend to under-perform growth firms when the inflation is in low range, and it leads to the disappearance of the value premium.

SSRN

In this paper, we demonstrate that there is an absolute physical limit on how small the smallest money unit can be, no matter how much we are able to improve our technology. The smallest money unit seems to be directly linked to the smallest possible energy unit needed to store one bit. If the smallest money unit is smaller than the cost of energy of storing one bit then there seems to be an arbitrage, which will also constrain money producers such as central banks from issuing money with a smaller denomination than this minimum money unit.

SSRN

Over the past decade a long-term process of digitization of finance has increasingly combined with datafication and new technologies including cloud computing, blockchain, big data and artificial intelligence in a new era of FinTech (â€œfinancial technologyâ€). This process of digitization and datafication combined with new technologies is taking place in developed global markets and at times even faster in emerging and developing markets. The result: cybersecurity and technological risks are now evolving into major threats to financial stability and national security. In addition, the entry of major technology firms into finance â€" TechFins â€" brings two new issues. The first arises in the context of new forms of potentially systemically important infrastructure (such as data and cloud services providers). The second arises because data â€" like finance â€" benefits from economies of scope and scale and from network effects and â€" even more than finance â€" tends towards monopolistic or oligopolistic outcomes, resulting in the potential for systemic risk from new forms of â€œToo Big to Failâ€ and â€œToo Connected to Failâ€ phenomena. To conclude, we suggest some basic principles about how such risks can be monitored and addressed, focusing in particular on the role of regulatory technology (â€œRegTechâ€).

SSRN

Using confidential supervisory data on dealer-identified corporate bond trading, we examine how the Volcker rule affected the provision of liquidity. By exploiting the ruleâ€™s underwriting exemption to identify the Volcker ruleâ€™s effects separate from other post-crisis changes in regulation and broader trends in market liquidity, we find significant adverse liquidity effects on covered firmsâ€™ corporate bond trading, borne by both customers and other dealers trading with the covered firm. We find no reduction in the volatility of covered firmsâ€™ trading costs due to the rule, while their market share has decreased. These effects do not appear to be transitional.

arXiv

In this paper we study the impact of errors in wind and solar power forecasts on intraday electricity prices. We develop a novel econometric model which is based on day-ahead wholesale auction curves data and errors in wind and solar power forecasts. The model shifts day-ahead supply curves to calculate intraday prices. We apply our model to the German EPEX SPOT SE data. Our model outperforms both linear and non-linear benchmarks. Our study allows us to conclude that errors in renewable energy forecasts exert a non-linear impact on intraday prices. We demonstrate that additional wind and solar power capacities induce non-linear changes in the intraday price volatility. Finally, we comment on economical and policy implications of our findings.

SSRN

In this study we shed a new light on Amihudâ€™s illiquidity measure, used here as a relevant measure of consensus belief among investors about new information (Amihud, 2002). This paper demonstrates the relevance of this new approach/ dimension in the context of M&A transactions. Using a large sample of M&A in the U.S., Canada and Europe over the 2000-2013 period, we report that Amihudâ€™s (2002) measure is a significant determinant of cumulative abnormal returns observed after M&A. This metric is also consistent with financial analyst forecast activity and stands as a relevant proxy measure of price informativeness.

arXiv

We establish an explicit expression for the conditional Laplace transform of the integrated Volterra Wishart process in terms of a certain resolvent of the covariance function. The core ingredient is the derivation of the conditional Laplace transform of general Gaussian processes in terms of Fredholm's determinant and resolvent. Furthermore , we link the characteristic exponents to a system of non-standard infinite dimensional matrix Riccati equations. This leads to a second representation of the Laplace transform for a special case of convolution kernel. In practice, we show that both representations can be approximated by either closed form solutions of conventional Wishart distributions or finite dimensional matrix Riccati equations stemming from conventional linear-quadratic models. This allows fast pricing in a variety of highly flexible models, ranging from bond pricing in quadratic short rate models with rich autocorrelation structures, long range dependence and possible default risk, to pricing basket options with covariance risk in multivariate rough volatility models.

arXiv

We review a numerical technique, referred to as the Transport-based Mesh-free Method (TMM), and we discuss its applications to mathematical finance. We recently introduced this method from a numerical standpoint and investigated the accuracy of integration formulas based on the Monte-Carlo methodology: quantitative error bounds were discussed and, in this short note, we outline the main ideas of our approach. The techniques of transportation and reproducing kernels lead us to a very efficient methodology for numerical simulations in many practical applications, and provide some light on the methods used by the artificial intelligence community. For applications in the finance industry, our method allows us to compute many types of risk measures with an accurate and fast algorithm. We propose theoretical arguments as well as extensive numerical tests in order to justify sharp convergence rates, leading to rather optimal computational times. Cases of direct interest in finance support our claims and the importance of the problem of the curse of dimensionality in finance applications is briefly discussed.