Research articles for the 2019-11-14

Adaptive Portfolio by Solving Multi-armed Bandit via Thompson Sampling
Mengying Zhu,Xiaolin Zheng,Yan Wang,Yuyuan Li,Qianqiao Liang

As the cornerstone of modern portfolio theory, Markowitz's mean-variance optimization is considered a major model adopted in portfolio management. However, due to the difficulty of estimating its parameters, it cannot be applied to all periods. In some cases, naive strategies such as Equally-weighted and Value-weighted portfolios can even get better performance. Under these circumstances, we can use multiple classic strategies as multiple strategic arms in multi-armed bandit to naturally establish a connection with the portfolio selection problem. This can also help to maximize the rewards in the bandit algorithm by the trade-off between exploration and exploitation. In this paper, we present a portfolio bandit strategy through Thompson sampling which aims to make online portfolio choices by effectively exploiting the performances among multiple arms. Also, by constructing multiple strategic arms, we can obtain the optimal investment portfolio to adapt different investment periods. Moreover, we devise a novel reward function based on users' different investment risk preferences, which can be adaptive to various investment styles. Our experimental results demonstrate that our proposed portfolio strategy has marked superiority across representative real-world market datasets in terms of extensive evaluation criteria.

Advances in Financial Machine Learning: Lecture 10/10 (Presentation Slides)
Lopez de Prado, Marcos
Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. In this course, we discuss scientifically sound ML tools that have been successfully applied to the management of large pools of funds.This material is part of Cornell University's ORIE 5256 graduate course at the School of Engineering.

An analysis of Uniswap markets
Guillermo Angeris,Hsien-Tang Kao,Rei Chiang,Charlie Noyes,Tarun Chitra

Uniswap---and other constant product markets---appear to work well in practice despite their simplicity. In this paper, we give a simple formal analysis of constant product markets and their generalizations, showing that, under some common conditions, these markets must closely track the reference market price. We also show that Uniswap satisfies many other desirable properties and numerically demonstrate, via a large-scale agent-based simulation, that Uniswap is stable under a wide range of market conditions.

Assessing Guaranteed Minimum Income Benefits and Rationality of Exercising Reset Options in Variable
Riley,Jones,Adriana Ocejo

A variable annuity is an equity-linked financial product typically offered by insurance companies. The policyholder makes an upfront payment to the insurance company and, in return, the insurer is required to make a series of payments starting at an agreed upon date. For a higher premium, many insurance companies offer additional guarantees or options which protect policyholders from various market risks. This research is centered around two of these options: the guaranteed minimum income benefit (GMIB) and the reset option. The sensitivity of various parameters on the value of the GMIB is explored, particularly the guaranteed payment rate set by the insurer. Additionally, a critical value for future interest rates is calculated to determine the rationality of exercising the reset option. This will be able to provide insight to both the policyholder and policy writer on how their future projections on the performance of the stock market and interest rates should guide their respective actions of exercising and pricing variable annuity options. This can help provide details into the value of adding options to a variable annuity for companies that are looking to make variable annuity policies more attractive in a competitive market.

Asset Growth and Stock Price Crash Risk
Choy, Siu Kai,Lobo, Gerald J.,Zheng, Ying
Given the managerial incentive for empire-building, we show that higher asset growth predicts higher future crash risk, with the predictive power lasting up to three years. Upon decomposing aggregate asset growth into its components, we find that growths in current assets, operating liabilities and retained earnings have the strongest predictive power, while growths in debt and stock financing are also significant in predicting future crash risk. Higher asset growth is related to lower future profitability. Moreover, agency problems tend to aggravate the asset growth-crash risk relationship whereas conditionally conservative accounting practices have mitigating effects. Overall, our results suggest that stockpiled bad news from asset growth contributes to future crash risk.

Asset Management, Index Funds, and Theories of Corporate Control
Mallow, Matthew
Recently, several academic theories have expressed concern over the growth of index funds. Some have argued that the growth of index funds will afford the asset managers who provide them too much influence over the public companies they invest in, through increased voting power and engagement activities. This, they assert, may lead to the effective control of public companies by a few individuals. Conversely, others claim that index fund managers do not, and will not, sufficiently exercise their voting power and potential influence through engagement leading to increased deference to company managements and inadequate monitoring of companies. This paper seeks to ground the debate around asset managers, index funds and corporate control firmly in the practical context of the operation and regulation of asset managers. Acting on behalf of clients, asset managers are incentivized to monitor companies for long-term performance. As minority shareholders, they lack sufficient voting power to exercise control. Voting records exhibit variation in asset manager voting behavior, challenging the perception of coordinated voting blocs. Thousands of actors are involved in corporate decision making, many better positioned to influence public companies than asset managers. The investment stewardship activities of asset managers raise the bar on corporate governance and increase the focus on long term sustainability. Some policy measures suggested by academic commentators seeking to limit or silence the voice of asset managers would stifle this effort and harm ordinary savers and investors.

Bayesian state-space modeling for analyzing heterogeneous network effects of US monetary policy
Niko Hauzenberger,Michael Pfarrhofer

Understanding disaggregate channels in the transmission of monetary policy to the real and financial sectors is of crucial importance for effectively implementing policy measures. We extend the empirical econometric literature on the role of production networks in the propagation of shocks along two dimensions. First, we set forth a Bayesian spatial panel state-space model that assumes time variation in the spatial dependence parameter, and apply the framework to a study of measuring network effects of US monetary policy on the industry level. Second, we account for cross-sectional heterogeneity and cluster impacts of monetary policy shocks to production industries via a sparse finite Gaussian mixture model. The results suggest substantial heterogeneities in the responses of industries to surprise monetary policy shocks. Moreover, we find that the role of network effects varies strongly over time. In particular, US recessions tend to coincide with periods where between 40 to 60 percent of the overall effects can be attributed to network effects; expansionary economic episodes show muted network effects with magnitudes of roughly 20 to 30 percent.

Board Gender Diversity and Firm Performance: Evidence from Supply-Side Shocks in China
Bao, Yangming,Lu, Di
This paper identifies a positive causal effect of board gender diversity on firm performance by utilizing unique historical events in China. Specifically, the Famine resulted in an evident gender gap in the supply of qualified directors of certain cohorts. Since the shocks differ in both gender and cohorts, we construct a novel "Diff-in-Diff'" instrumental variable and a Bartik instrument for board gender representation. We find that a 10% increase in board female representation can lead to a 2.38% increase in return on assets (ROA). Moreover, our results support the critical mass theory and indicate that female directors are beneficial by lowering risk levels and improving solvency.

Change-point Analysis in Financial Networks
Sayantan Banerjee,Kousik Guhathakurta

A major impact of globalization has been the information flow across the financial markets rendering them vulnerable to financial contagion. Research has focused on network analysis techniques to understand the extent and nature of such information flow. It is now an established fact that a stock market crash in one country can have a serious impact on other markets across the globe. It follows that such crashes or critical regimes will affect the network dynamics of the global financial markets. In this paper, we use sequential change point detection in dynamic networks to detect changes in the network characteristics of thirteen stock markets across the globe. Our method helps us to detect changes in network behavior across all known stock market crashes during the period of study. In most of the cases, we can detect a change in the network characteristics prior to crash. Our work thus opens the possibility of using this technique to create a warning bell for critical regimes in financial markets.

Deep Hedging: Learning to Simulate Equity Option Markets
Wiese, Magnus,Bai, Lianjun,Wood, Ben,Buehler, Hans
We construct realistic equity option market simulators based on generative adversarial networks (GANs). We consider recurrent and temporal convolutional architectures, and assess the impact of state compression. Option market simulators are highly relevant because they allow us to extend the limited real-world data sets available for the training and evaluation of option trading strategies. We show that network-based generators outperform classical methods on a range of benchmark metrics, and adversarial training achieves the best performance. Our work demonstrates for the first time that GANs can be successfully applied to the task of generating multivariate financial time series.

Deep Prediction of Investor Interest: a Supervised Clustering Approach
Baptiste Barreau,Laurent Carlier,Damien Challet

We propose a novel deep learning architecture suitable for the prediction of investor interest for a given asset in a given time frame. This architecture performs both investor clustering and modelling at the same time. We first verify its superior performance on a synthetic scenario inspired by real data and then apply it to two real-world databases, a publicly available dataset about the position of investors in Spanish stock market and proprietary data from BNP Paribas Corporate and Institutional Banking.

Do Analysts Chase Prices When Prices Have No Information?
Starkweather, Austin
In a recent survey of analysts, 96% claim that returns are not very useful as earnings forecast model inputs. I find, though, that analysts actually do incorporate returns into their earnings forecasts, even if those returns have no underlying earnings information. This leads to forecast error, which is worse among inexperienced analysts and those with reduced attention. Finally, I show that the market is unable to anticipate this error, leading to mispricing that is not resolved until the earnings announcement date. The literature has so far been unable to explore these issues causally. However, I am able to do so by developing a novel identification strategy that isolates the non-earnings component of the Federal Open Market Committee (FOMC) announcement and separates out concurrent information using intraday and instrumented returns.

Dynamic Risk Management of Equity Market Factors
Clarke, Roger G,de Silva, Harindra,Thorley, Steven
Managing the inter-temporal risk of multi-factor portfolios adds to performance, in addition to the utility investors may derive from controlling how much risk they are exposed to over time. We derive a simple closed-form formula for security weights in optimal multi-factor portfolios with an active risk budget. We test the risk control of five factors; value, momentum, small size, low beta, and profitability, and the optimal multi-factor portfolio. Our empirical research on the large-cap U.S. equity market for the last 54 years (1966 to 2019) allows for transparent performance attribution and replication of the process in other markets and time periods. We conclude that for the U.S. equity market, more active factors are better than less if each factor sub-portfolio is pure and anchored to the passive benchmark, and that dynamic management of multi-factor portfolio exposures increases realized performance.

Econophysics deserves a revamping
Paolo Magrassi

The paper argues that attracting more economists and adopting a more-precise definition of dynamic complexity might help econophysics acquire more attention in the economics community and bring new lymph to economic research. It may be necessary to concentrate less on the applications than on the basics of economic complexity, beginning with expansion and deepening of the study of small systems with few interacting components, while until thus far complexity has been assumed to be a prerogative of complicated systems only. It is possible that without a thorough analysis at that level, the understanding of systems that are at the same time complex and complicated will continue to elude economics and econophysics research altogether. To that purpose, the paper initiates and frames a definition of dynamic complexity grounded on the concept of non-linear dynamical system.

Generosity and Wealth: Experimental Evidence from Bogota` Stratification
Blanco, Mariana,Dalton, Patricio S.
This paper combines laboratory experiments with a unique feature of the city of Bogota to uncover the relationship between generosity and wealth. Bogota is divided by law into six socio-economic strata which are close proxies of household wealth and income. We recruit subjects from different strata and run a series of double-blind dictator games where the recipient is the NGO Techo-Colombia, which builds transitional housing for homeless families. We identify the stratum of each subject anonymously and blindly, and match their donations with their stratum. In a first experiment we provide a fixed endowment to all participants and find that donations are significantly increasing with wealth. However, in a second experiment, we show that this is not because the rich are intrinsically more generous, but because the experimental endowment has lower real value for them. With endowments that are equivalent to their daily expenditures, the rich, the middle-class and the poor give a similar proportion of their stratum-equivalent endowment. Moreover, we nd that the motivation to donate is similar across strata, where the generosity act is explained mainly by warm-glow rather than pure altruism.

Index Tracking with Cardinality Constraints: A Stochastic Neural Networks Approach
Yu Zheng,Bowei Chen,Timothy M. Hospedales,Yongxin Yang

Partial (replication) index tracking is a popular passive investment strategy. It aims to replicate the performance of a given index by constructing a tracking portfolio which contains some constituents of the index. The tracking error optimisation is quadratic and NP-hard when taking the L0 constraint into account so it is usually solved by heuristic methods such as evolutionary algorithms. This paper introduces a simple, efficient and scalable connectionist model as an alternative. We propose a novel reparametrisation method and then solve the optimisation problem with stochastic neural networks. The proposed approach is examined with S&P 500 index data for more than 10 years and compared with widely used index tracking approaches such as forward and backward selection and the largest market capitalisation methods. The empirical results show our model achieves excellent performance. Compared with the benchmarked models, our model has the lowest tracking error, across a range of portfolio sizes. Meanwhile it offers comparable performance to the others on secondary criteria such as volatility, Sharpe ratio and maximum drawdown.

Learning Word Embeddings from 10-K Filings Using PyTorch
Sehrawat, Saurabh
With the rise of alternative data in finding trading signals, Natural Language Processing (NLP) on financial documents has gained significant importance in the recent years. Word Embeddings learned from text corpus are one of the most important inputs to various NLP models, especially Deep Learning based models. In this paper, we generate word embeddings learned from corpus of 10-K filings by corporates in U.S. to S.E.C from 1993 to 2018 using word2vec model implemented in PyTorch [5]. Word Embeddings learned from general corpus of articles from Google News, Wikipedia etc are readily available online for researchers to use in their models but embeddings learned from 10-K filings are not publicly available. Using word embeddings learned from general text for NLP tasks on financial documents may not yield accurate results as it has been proven that word embeddings learned from contextual text yields better and more accurate results compared to general word embeddings. We aim to publish the word embeddings learned from 10-K filings online so that they can be used by other researchers in their NLP tasks such as document classification, document similarity, sentiment analysis, readability index etc. on 10-K filings or other financial documents.

Levels versus Changes: Information Contents of Qualitative Verbal Information
Miwa, Kotaro
In addition to quantitative financial information, qualitative verbal information has also been continuously changing. In this study, we clarify the information contents of changes in qualitative verbal information. As an analyzable sample, we focus on textual opinion in analyst reports. The analyses reveal that stock prices react considerably to changes in the tone of the report, even after controlling for their levels. Besides, they show asymmetric reactions to the levels and changes of the report tone; while prices overreact to the levels, they underreact to the changes. Our evidence suggests that a change in the textual tone provides incremental information which investors overlook, supporting the informational value of changes in qualitative verbal information.

Mobile Phone for Financial Inclusion: Mobile Money Adoption in the West African Economic and Monetary Union
Ky, Serge
Mobile money, defined as the use of mobile phones to conduct financial transactions, can bring many benefits to individuals, particularly in developing countries. Yet, very little is known about the factors behind its adoption. This study uses nationally representative individual-level survey data on the West African Economic and Monetary Union (WAEMU) countries from the Global Financial Inclusion Database and employs logistic models to highlight drivers of mobile money adoption. Our results show that having a bank account, being in the workforce, receiving domestic remittances, owning a mobile phone, and having a national ID favor mobile money adoption when measured as a global state. Moreover, being female, better educated, and wealthier increase the likelihood of adopting mobile money. Taking mobile-money adoption as a four-step process, we find similar results: having a bank account; receiving domestic remittances; and being in the workforce, younger, female, better educated, and wealthier facilitate mobile money adoption. Furthermore, we take into account the particularity of each WAEMU-member country and find some disparities in the determinants of both measure of mobile money adoption. These findings are of particular interest to the design of policies that foster financial inclusion and target disadvantaged groups.

Nonlinear reserving and multiple contract modifications in life insurance
Marcus C. Christiansen,Boualem Djehiche

Insurance cash flows become reserve dependent whenever contract conditions are modified during the contract term while maintaining actuarial equivalence. As a result, insurance cash flows and prospective reserves depend on each other in a circular way, and it is a non-trivial problem to solve that circularity and make cash flows and reserves well-defined. The literature offers answers to that question in case of one or two contract modifications under Markovian assumptions. This paper studies multiple contract modifications in a general non-Markovian framework.

On the Statistical Differences between Binary Forecasts and Real World Payoffs
Nassim Nicholas Taleb

What do binary (or probabilistic) forecasting abilities have to do with overall performance? We map the difference between (univariate) binary predictions, bets and "beliefs" (expressed as a specific "event" will happen/will not happen) and real-world continuous payoffs (numerical benefits or harm from an event) and show the effect of their conflation and mischaracterization in the decision-science literature. We also examine the differences under thin and fat tails. The effects are:

A- Spuriousness of many psychological results particularly those documenting that humans overestimate tail probabilities and rare events, or that they overreact to fears of market crashes, ecological calamities, etc. Many perceived "biases" are just mischaracterizations by psychologists. There is also a misuse of Hayekian arguments in promoting prediction markets.

We quantify such conflations with a metric for "pseudo-overestimation".

B- Being a "good forecaster" in binary space doesn't lead to having a good actual performance}, and vice versa, especially under nonlinearities. A binary forecasting record is likely to be a reverse indicator under some classes of distributions. Deeper uncertainty or more complicated and realistic probability distribution worsen the conflation .

C- Machine Learning: Some nonlinear payoff functions, while not lending themselves to verbalistic expressions and "forecasts", are well captured by ML or expressed in option contracts.

D- Fattailedness: The difference is exacerbated in the power law classes of probability distributions.

Peer Review Mandates and CPA Entrepreneurship
Vetter, Felix
I study how mandatory peer review affects CPA entrepreneurship â€" that is, CPAs’ decisions to start, continue, or cease operating their own CPA firms. In an effort to promote quality, CPA firms have to monitor each other to meet licensing requirements. While peer review is the main oversight mechanism for CPA firms without public clients, little is known about its consequences. I exploit the staggered introduction of peer review mandates and, using a novel dataset based on CPA licenses, find that CPA entrepreneurship declines with the introduction of peer review mandates, as evidenced by declines in CPA firm formation rates and increases in CPA entrepreneur exits. Exits are pronounced for young female CPA entrepreneurs but not for low-quality or non-white CPA entrepreneurs. Collectively, the findings suggest that differences in the costs of meeting licensing requirements rather than screening on quality or group favoritism explain the observed heterogeneity in exits effects.

Pivots and Prestige: Venture Capital Contracts with Experimentation
Li, Xuelin,Szydlowski, Martin
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.

Predicting Indian stock market using the psycho-linguistic features of financial news
B. Shravan Kumar,Vadlamani Ravi,Rishabh Miglani

Financial forecasting using news articles is an emerging field. In this paper, we proposed hybrid intelligent models for stock market prediction using the psycholinguistic variables (LIWC and TAALES) extracted from news articles as predictor variables. For prediction purpose, we employed various intelligent techniques such as Multilayer Perceptron (MLP), Group Method of Data Handling (GMDH), General Regression Neural Network (GRNN), Random Forest (RF), Quantile Regression Random Forest (QRRF), Classification and regression tree (CART) and Support Vector Regression (SVR). We experimented on the data of 12 companies stocks, which are listed in the Bombay Stock Exchange (BSE). We employed chi-squared and maximum relevance and minimum redundancy (MRMR) feature selection techniques on the psycho-linguistic features obtained from the new articles etc. After extensive experimentation, using the Diebold-Mariano test, we conclude that GMDH and GRNN are statistically the best techniques in that order with respect to the MAPE and NRMSE values.

Public Versus Private Equity
Stulz, René M.
The last twenty years or so have seen a sharp decline in public equity. I present a framework that explains the forces that cause the listing propensity of firms to change over time. This framework highlights the benefits and costs of a public listing compared to the benefits and costs of financing with private equity. With this framework, the decline in public equity is explained by the increased supply of funds for private equity and changes in the nature of firms. The increase in the importance of intangible assets makes it costlier for young firms to be public when the alternative is funding through private equity from investors who have specialized knowledge that enables them to better understand the business model of young firms and contribute to the development of that business model in contrast to passive public equity investors.

Reinforcement Learning for Market Making in a Multi-agent Dealer Market
Sumitra Ganesh,Nelson Vadori,Mengda Xu,Hua Zheng,Prashant Reddy,Manuela Veloso

Market makers play an important role in providing liquidity to markets by continuously quoting prices at which they are willing to buy and sell, and managing inventory risk. In this paper, we build a multi-agent simulation of a dealer market and demonstrate that it can be used to understand the behavior of a reinforcement learning (RL) based market maker agent. We use the simulator to train an RL-based market maker agent with different competitive scenarios, reward formulations and market price trends (drifts). We show that the reinforcement learning agent is able to learn about its competitor's pricing policy; it also learns to manage inventory by smartly selecting asymmetric prices on the buy and sell sides (skewing), and maintaining a positive (or negative) inventory depending on whether the market price drift is positive (or negative). Finally, we propose and test reward formulations for creating risk averse RL-based market maker agents.

State Pricing, Effectively Complete Markets, and Corporate Finance
Grinblatt, Mark,Wan, Kam-Ming
Event study, panel regression, and difference-in-difference techniques are among the most prominent research methodologies in corporate finance. However, these techniques are inappropriate if corporate events are anticipated to some degree, as most events are. This paper proposes options as an additional model-free source of information to identify the likelihood and impact of corporate events. We show how to quantify event impact in a simple example and assert that few restrictions on the state space are required for the approach to work in more complex settings.

The CDS-Bond Basis: Negativity Persistence and Limits to Arbitrage
Guesmi, Sahar,Ben-Abdallah, Ramzi,Breton, Michèle,Dionne, Georges
We reinvestigate the CDS-bond basis negativity puzzle after the financial crisis. This puzzle is defined as the unexpected persistence of the dislocation between bond and derivative credit markets. We show that the first two moments of the basis are described by three distinct Markov regimes identified with periods related to the 2008 financial crisis. We observe that the post-crisis regime differs significantly from the crisis and the pre-crisis regimes. We then explore the cross-sectional variation of the CDS-bond basis in each regime. Using a model with several limit-to-arbitrage factors, we validate that the negative basis can be explained by liquidity risk in both the bond and CDS markets, together with counterparty risk, collateral quality, and funding constraints. Finally, we propose a model to empirically affirm that the basis negativity persistence during the post-crisis period is mainly related to a significant decrease in basis arbitrage activity, which is partly explained by the post-crisis regulatory reforms.

The Inflationary Effects in a Crisis and the Risk Management Strategy with International Reserves
Silva Jr., Antonio F. A.
This work presents a model of a two-period economy where the household has a CRRA utility function and monetary policy impacts his budgetary constraint. There are two possible states of nature in the second period: a normal state and a crisis state. The policy maker buys an Arrow-Debreu security to insure against crisis. The costs of the Arrow-Debreu security are equal to the costs of holding international reserves. The model considers that the central bank wants to avoid crisis and to smooth inflation and then the main contribution of this paper is to connect monetary policy with the precautionary motivation of holding international reserves. As a result, the framework provides an equation to calculate the level of international reserves and the model calibration shows that the equation can help to evaluate international reserves holdings. The calibration also shows that not considering monetary policy in the risk management strategy can lead to an underestimated amount of international reserves.

The Partisanship of Financial Regulators
Engelberg, Joseph,Henriksson, Matthew,Manela, Asaf,Williams, Jared
We analyze the partisanship of Securities and Exchange Commissioners (SEC) and members of the Federal Reserve Board of Governors (Fed) using the language-based approach of Gentzkow, Shapiro, and Taddy (Econometrica, 2019). The level of partisanship among these regulators is greater than that of Congress, but this is driven by a handful of speakers who repeat polarizing phrases across several speeches. When we quantify how much Republican (Democrat) regulators sound like Republicans (Democrats) in Congress we find no discernible pattern among Fed governors but a recent spike among SEC commissioners.

Transparency and Financial Inclusion: Experimental Evidence from Mobile Money
Dalton, Patricio S.,Pamuk, Haki,Ramrattan, Ravindra,van Soest, Daan,Uras, Burak
Electronic payment instruments have the potential to spur the transparency of business transactions and thereby reduce information frictions. We design a field experiment to understand whether e-payments facilitate the financial inclusion of SMEs in developing world and to study adoption barriers. We encourage a random sample of Kenyan merchants to adopt a new mobile-money payment instrument and find that the decision to adopt is hampered by the combination of information, know-how and seemingly small transaction costs barriers. In addition, we find that business owners who are more averse to transparency are more reluctant to adopt. Sixteen months after the intervention, we observe that treated firms have better access to finance in the form of mobile loans. The impact on financial access is more pronounced for smaller establishments, which also experience a considerable reduction in sales volatility. We conclude that e-payments can help un-collateralized firms become transparent and get financially integrated.

Unconventional Monetary Policy and Corporate Bond Issuance
De Santis, Roberto A.,Zaghini, Andrea
We assess the effect and the timing of the corporate arm of the ECB quantitative easing (CSPP) on corporate bond issuance. Because of several contemporaneous measures, to isolate the programme effects we rely on one key eligibility feature: the euro denomination of newly issued bonds. We find that the significant increase in bonds issuance by eligible firms is due to the CSPP and that this effect took at least six months to unfold. This result holds even when comparing firms with similar ratings, thus providing evidence that unconventional monetary policy can foster a financing diversification regardless of firms risk profile.

Unveil stock correlation via a new tensor-based decomposition method
Giuseppe Brandi,Ruggero Gramatica,Tiziana Di Matteo

Portfolio allocation and risk management make use of correlation matrices and heavily rely on the choice of a proper correlation matrix to be used. In this regard, one important question is related to the choice of the proper sample period to be used to estimate a stable correlation matrix. This paper addresses this question and proposes a new methodology to estimate the correlation matrix which doesn't depend on the chosen sample period. This new methodology is based on tensor factorization techniques. In particular, combining and normalizing factor components, we build a correlation matrix which shows emerging structural dependency properties not affected by the sample period. To retrieve the factor components, we propose a new tensor decomposition (which we name Slice-Diagonal Tensor (SDT) factorization) and compare it to the two most used tensor decompositions, the Tucker and the PARAFAC. We have that the new factorization is more parsimonious than the Tucker decomposition and more flexible than the PARAFAC. Moreover, this methodology applied to both simulated and empirical data shows results which are robust to two non-parametric tests, namely Kruskal-Wallis and Kolmogorov-Smirnov tests. Since the resulting correlation matrix features stability and emerging structural dependency properties, it can be used as alternative to other correlation matrices type of measures, including the Person correlation.

When It Pays to Follow the Crowd: Strategy Conformity and CTA Performance
Bollen, Nicolas P. B.,Hutchinson, Mark C.,O'Brien, John
Prior research in hedge fund and mutual fund management finds a positive relation between portfolio distinctiveness and subsequent performance, suggesting that strategy differentiation is associated with superior skill. We find that CTAs with returns that correlate more strongly with those of peers feature higher performance and are more highly exposed to a time series momentum factor. In contrast to hedge funds and mutual funds, CTA strategy conformity appears to be a signal of managerial skill. These results indicate that a common trend following strategy drives CTA returns and that CTAs offer investors an opportunity to invest in momentum.

Why Do U.S. CEOs Pledge Their Own Company's Stock?
Fabisik, Kornelia
Between 2007 and 2016, 7.6% of publicly listed U.S. firms disclosed that their CEOs had pledged company stock as collateral for a loan. On average, CEOs pledge 38% of their shares. The mean loan value is an economically sizeable $65 million. CEOs use the funds to either double down (6.0%), hedge their ownership (3.5%), or to obtain liquidity while maintaining ownership (90.5%). My event study results reveal that stock market participants view pledging as value-enhancing, but perceive significant pledging as value-destroying. Similarly, I find no evidence of its negative shareholder value consequences, except for CEOs who engage in significant pledging.