Research articles for the 2019-06-19

151 Estrategias de Trading (151 Trading Strategies)
Kakushadze, Zura,Serur, Juan A.
SSRN
The English version of this paper can be found at http://ssrn.com/abstract=3247865.Spanish Abstract: Este libro proporciona descripciones detalladas, que incluyen más de 550 fórmulas matemáticas, para más de 150 estrategias de trading para una gran cantidad de clases de activos y estilos de trading. Esto incluye acciones, opciones, bonos (renta fija), futuros, ETFs, índices, commodities, divisas, bonos convertibles, activos estructurados, volatilidad (como clase de activos), bienes inmuebles, activos en distress, efectivo, criptomonedas, misceláneos (como clima, energía, inflación), macro global, infraestructura y arbitraje impositivo. Algunas estrategias se basan en algoritmos de aprendizaje automático (como redes neuronales artificiales, Bayes, k vecinos más cercanos). El libro también incluye código para backtesting fuera de la muestra con notas explicativas; cerca de 2,000 referencias bibliográficas; más de 900 términos que comprenden el glosario, acrónimos y definiciones matemáticas. La presentación pretende ser descriptiva y pedagógica, y de particular interés para los profesionales de las finanzas, traders, investigadores, académicos y estudiantes de escuelas de negocios y programas de finanzas.English Abstract: This book, which is in Spanish, provides detailed descriptions, including over 550 mathematical formulas, for over 150 trading strategies across a host of asset classes (and trading styles). This includes stocks, options, fixed income, futures, ETFs, indexes, commodities, foreign exchange, convertibles, structured assets, volatility (as an asset class), real estate, distressed assets, cash, cryptocurrencies, miscellany (such as weather, energy, inflation), global macro, infrastructure, and tax arbitrage. Some strategies are based on machine learning algorithms (such as artificial neural networks, Bayes, k-nearest neighbors). We also give: source code for illustrating out-of-sample backtesting with explanatory notes; around 2,000 bibliographic references; and over 900 glossary, acronym and math definitions. The presentation is intended to be descriptive and pedagogical.

A Theory on Pre-ICO Venture Capital Involvement
Bocks, Karsten,Haas, Christian,Heyden, Thomas
SSRN
We study the trade-off venture capitalists (VCs) encounter in a staged financing framework. The VC has the option to raise funds either in an initial coin offering (ICO) or via equity but faces a hidden effort problem. Our results suggest that profits from projects that are subject to moral hazard can be higher under ICO financing. While projects with a low degree of moral hazard may only be financed by equity, tokens become superior as moral hazard increases. At very high levels of moral hazard tokens can even become the sole financing option. Thus ICOs can raise welfare by enabling the realization of projects that would not have been financed by equity.

Are Family Firms Friendly to Women?
Ting, Hsiu-I,Wang, Ming-Chun,Lin, Yi-Jun
SSRN
This study used listed firms in Shanghai and Shenzhen from 2004 to 2014 to investigate whether family firms help women break the glass ceiling. We find that family firms appoint female CEOs more often than non-family firms. Our finding does not result from the limitation of family firms to recruit talent from family members. Family firms even provide a female-friendly workplace for professional CEOs. Family firms that operate under conservative management are inclined to appoint women regarded as risk-averse. The tendency of family firms to hire female CEOs is partially attributed to homophily.

Are There Spillover Effects from Negative Say-on-Pay Votes?
Akhigbe, Aigbe,Frye, Melissa B.,Whyte, Ann Marie
SSRN
We provide evidence that when more than half of a firm’s voting shareholders disapprove of the executive pay plan, the spillover effects are significantly positive for peers of non-financial firms and significantly negative for peers of financial firms. The valuation effects are positively related to equity-based compensation and negatively related to cash-based and excess compensation. The no-votes are also associated with significant changes in compensation in the following year. In general, peer firms take action to align compensation with shareholder interests by lowering cash-based pay, increasing equity-based pay, and reducing excess compensation. Financial firms lower equity-based and excess compensation, suggesting they may alter pay in a manner that reduces regulatory pressure and the perception of CEO overpayment. Our results show far reaching effects from say-on-pay regulations, where even peer firms feel pressure to change their compensation following a no-vote.

Changes in Analysts’ Stock Recommendations Following Regulatory Action Against Their Brokerage
Call, Andrew C.,Sharp, Nathan Y.,Wong, Paul A.
SSRN
Despite the importance of sell-side analysts in the capital markets, we know little about the effectiveness of routine monitoring of the sell-side industry. We examine the attributes of sell-side research issued by analysts before and after their brokerage is subject to regulatory sanctions. We find that after a regulatory action, analysts at sanctioned brokerages lower their stock recommendations, both in absolute terms and relative to the recommendations of other analysts following the same firms. Following a regulatory action, analysts at sanctioned brokerages are also more likely than analysts at other brokerages to downgrade a company’s stock after the receipt of unfavorable information about the firm. Importantly, we document that analysts at non-sanctioned brokerages also reduce the optimism in their stock recommendations when a peer analyst’s brokerage is sanctioned, consistent with regulatory spillovers as a result of routine regulatory monitoring. Our study provides empirical evidence that regulatory action against sell-side brokerages is associated with a reduction in sell-side analysts’ positive bias at both sanctioned and non-sanctioned brokerages.

Closed-form expansions with respect to the mixing solution for option pricing under stochastic volatility
Kaustav Das,Nicolas Langrené
arXiv

We consider closed-form expansions for European put option prices within several stochastic volatility frameworks with time-dependent parameters. Our methodology involves writing the put option price as an expectation of a Black-Scholes formula and performing a second-order Taylor expansion around the mean of its argument. The difficulties then faced are computing a number of expectations induced by the Taylor expansion in a closed-form manner. We establish a fast calibration scheme under the assumption that the parameters are piecewise-constant. Furthermore, we perform a sensitivity analysis to investigate the quality of our approximation and show that the errors are well within the acceptable range for application purposes. Lastly, we derive bounds on the remainder term due to the Taylor expansion.



Cross-Border Transmission of Geopolitical Uncertainty
Kim, Taehyun,Kim, Yongjun
SSRN
Using unexpected changes in geopolitical tension on the Korean peninsula as a quasi-natural experimental setting, we document the international transmission of geopolitical uncertainty. We find that U.S. firms that are more sensitive to Korean political uncertainty exhibit significant price responses in the short time window around a significant geopolitical event in Korea. The effects are more pronounced for U.S. multinationals, which are more sensitive to geopolitical risks stemming from outside the U.S.

Customers' Forward-Looking Disclosures and Suppliers' Asymmetric Cost Behavior
Hu, Nan,Liang, Peng
SSRN
We examine the impact of customers’ forward-looking disclosures (FLD) contained in the Management Discussion and Analysis section (MD&A) of its 10-K filings on the degree of suppliers’ cost asymmetric behavior (i.e., cost stickiness) using computer-intensive techniques. We find that the degree of suppliers’ cost asymmetry is positively associated with their major customers’ FLD. Moreover, such a positive association is more pronounced when customers are more profitable or when customers have a more positive tone in their MD&A sections. Last, using a decision made by the United States Supreme Court on March 1st, 2005 as a quasi-natural experiment setting, we show that this positive association becomes stronger when FLD becomes more informative. Our findings indicate that suppliers adjust their cost management practices based on the forward-looking disclosures of their major customers along the supply chain.

Does Speculation in Financial Markets Have Real Effects?
Li, Tao,Loewenstein, Mark
SSRN
This paper examines how speculation in financial markets can affect real investments and asset prices with asymmetric adjustment costs. Investors with recursive preferences have heterogeneous beliefs about real productivity and some extraneous risk and trade them in financial markets. Speculation in financial markets, even on extraneous risk uncorrelated with productivity, can significantly affect real investments and asset prices. Speculation can either decrease or increase real investment and asset prices depending on whether investors EIS is less or greater than 1. With the EIS above 1, speculation can generate various boom-and-bust patterns such as what happened in the recent US housing markets.

Ergodicity-breaking reveals time optimal economic behavior in humans
David Meder,Finn Rabe,Tobias Morville,Kristoffer H. Madsen,Magnus T. Koudahl,Ray J. Dolan,Hartwig R. Siebner,Oliver J. Hulme
arXiv

Ergodicity describes an equivalence between the expectation value and the time average of observables. Applied to human behaviour, ergodic theory reveals how individuals should tolerate risk in different environments. To optimise wealth over time, agents should adapt their utility function according to the dynamical setting they face. Linear utility is optimal for additive dynamics, whereas logarithmic utility is optimal for multiplicative dynamics. Whether humans approximate time optimal behavior across different dynamics is unknown. Here we compare the effects of additive versus multiplicative gamble dynamics on risky choice. We show that utility functions are modulated by gamble dynamics in ways not explained by prevailing economic theory. Instead, as predicted by time optimality, risk aversion increases under multiplicative dynamics, distributing close to the values that maximise the time average growth of wealth. We suggest that our findings motivate a need for explicitly grounding theories of decision-making on ergodic considerations.



Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey
Lukas Ryll,Sebastian Seidens
arXiv

With increasing competition and pace in the financial markets, robust forecasting methods are becoming more and more valuable to investors. While machine learning algorithms offer a proven way of modeling non-linearities in time series, their advantages against common stochastic models in the domain of financial market prediction are largely based on limited empirical results. The same holds true for determining advantages of certain machine learning architectures against others. This study surveys more than 150 related articles on applying machine learning to financial market forecasting. Based on a comprehensive literature review, we build a table across seven main parameters describing the experiments conducted in these studies. Through listing and classifying different algorithms, we also introduce a simple, standardized syntax for textually representing machine learning algorithms. Based on performance metrics gathered from papers included in the survey, we further conduct rank analyses to assess the comparative performance of different algorithm classes. Our analysis shows that machine learning algorithms tend to outperform most traditional stochastic methods in financial market forecasting. We further find evidence that, on average, recurrent neural networks outperform feed forward neural networks as well as support vector machines which implies the existence of exploitable temporal dependencies in financial time series across multiple asset classes and geographies.



Hedge Funds and Financial Intermediaries
Dahlquist, Magnus,Sokolovski, Valeri,Sverdrup, Erik
SSRN
Hedge funds and financial intermediaries are connected through their prime brokerage relationship. We find that systematic financial intermediary risk, as measured by the covariation between the fund return and the return of a portfolio of key prime brokers, captures cross-sectional differences in hedge fund returns. Once we control for the systematic risk, we find little evidence that idiosyncratic financial intermediary risk matters. We evaluate if large adverse shocks to individual prime brokers propagate to their hedge fund clients and find a significant impact only in the case of the Lehman Brothers' bankruptcy. However, that impact was mitigated for funds with multiple prime brokers, suggesting that even extreme prime broker shocks are diversifiable.

In Search of Philippine Chaebols
Mendoza, Ronald U.,Arbo, Ma. Diyina Gem,Cruz, Jerome Patrick
SSRN
Historically, large-scale capital-intensive industries have played a key role in many developing countries' industrialization. Some countries created state-owned enterprises (SOEs) with heavy government intervention and support to complete production chains to meet demand and export markets. South Korea, for one, has been known for the success of the chaebol, a large family-controlled and diversified private company that received strong government industrial policy support. Similar to South Korean chaebols, the Philippines have conglomerates, large, family-owned corporations comprised of smaller subsidiaries with a considerable footprint on the national economy. This paper fills a gap in the policy literature through the review of historical developments in selected Philippine industries and where key players tend to dominate. Furthermore, this paper shows evidence that some Philippine conglomerates displayed expansion behavior similar to East Asian economies like South Korea. The study also uncovers behavioral trends of these firms' diversification into non-traded service industries that were less conducive to rapid industrial development. Finally, it concludes with a brief discussion on how Philippine conglomerates contribute to the country's inclusive development agenda.

Inflation-Linked versus Nominal Bond Yields: On Liquidity and Inflation Risk Premiums Around the World
Bekaert, Geert,Ermolov, Andrey
SSRN
We provide a decomposition of nominal bond yields into its real and inflation components in an international context. We focus on 5 year yields for the UK, US and France, using inflation-linked and nominal Treasury yields since 2004. We find that expected inflation shows little variability and thus accounts for little of the variation in nominal yields. Inflation risk premiums are relatively more important, but have decreased over time. Liquidity premiums in inflation-linked debt remain relatively high, varying between 50 basis points and 1.10%, on average, but their variability has decreased over time. Real rate variation dominates the variation in inflation-linked and nominal yields. Real rates (mostly) correlate highly across countries, and are the main source of the observed high correlation between nominal yields. We show that a slow-moving risk aversion variable from a habit model explains a substantive part of the variation in real yields and explains (the change in) the correlation of real yields across countries (across time), thereby outperforming a measure of the monetary policy stance.

Information Disclosure and the Market for Acquiring Technology Companies
Chondrakis, George,Serrano, Carlos J.,Ziedonis, Rosemarie Ham
SSRN
The market for acquiring technology-intensive companies is rife with information frictions and valuation challenges. Although such frictions can stifle trading activity, they also provide room for strategic gain. We investigate this dual role of information frictions in takeover markets by exploiting an institutional reform that released technological information in U.S. patent applications to the public domain. Leveraging cross-sectoral variation in the magnitude of new information disclosure, we find that greater disclosure leads to an uptick in acquisitions. In line with predictions from strategic factor market theory, however, we find that acquirers profit less, especially when target firms are private. This evidence is consistent with the view that information disclosure facilitates trade in takeover markets yet has a leveling effect on the returns to acquirers.

Is 'Not Guilty' the Same as 'Innocent'? Evidence from SEC Financial Fraud Investigations
Solomon, David H.,Soltes, Eugene F.
SSRN
The Securities and Exchange Commission (SEC) routinely investigates firms for financial fraud, but investors only learn about regulators’ concerns if managers voluntarily disclose news of the investigation, or regulators sanction the firm. We investigate the effects of disclosing investigations using confidential records on all opened investigations, regardless of outcome. Markets exhibit some ability to identify which investigations will eventually lead to sanctions. Nonetheless, even when no charges are ultimately brought, firms that voluntarily disclose an investigation have significant negative returns, underperforming non-sanctioned firms that stayed silent by 12.7% for a year after the investigation begins. Consistent with limited investor attention, disclosing in a more prominent manner is associated with worse returns. CEOs who disclose an investigation are also 14% more likely to experience turnover. Our results are consistent with transparency about bad news being punished, rather than rewarded, by financial and labor markets.

Loss Aversion and the Demand for Index Insurance
Lampe, Immanuel,Würtenberger, Daniel
SSRN
This work analyzes if reference dependence and loss aversion can explain the puzzling low adoption rates of rainfall index insurance. We present a model that predicts the impact of loss aversion on index insurance demand to vary with different levels of insurance understanding. Index insurance demand of farmers who are unaware of the loss-hedging benefit that insurance provides decreases with loss aversion. In contrast, insurance demand of farmers who are aware of the loss-hedging benefit increases with loss aversion. The model further predicts that farmers who are unaware of the loss-hedging benefit will not demand an even highly subsidized index insurance. Using data from a randomized controlled trial involving a sample of Indian farmers we provide empirical support for our core conjecture that insurance understanding mitigates the negative impact of loss aversion on index insurance adoption.

Machine Learning for Pricing American Options in High-Dimensional Markovian and non-Markovian models
Ludovic Goudenège,Andrea Molent,Antonino Zanette
arXiv

In this paper we propose two efficient techniques which allow one to compute the price of American basket options. In particular, we consider a basket of assets that follow a multi-dimensional Black-Scholes dynamics. The proposed techniques, called GPR Tree (GRP-Tree) and GPR Exact Integration (GPR-EI), are both based on Machine Learning, exploited together with binomial trees or with a closed formula for integration. Moreover, these two methods solve the backward dynamic programming problem considering a Bermudan approximation of the American option. On the exercise dates, the value of the option is first computed as the maximum between the exercise value and the continuation value and then approximated by means of Gaussian Process Regression. The two methods mainly differ in the approach used to compute the continuation value: a single step of binomial tree or integration according to the probability density of the process. Numerical results show that these two methods are accurate and reliable in handling American options on very large baskets of assets. Moreover we also consider the rough Bergomi model, which provides stochastic volatility with memory. Despite this model is only bidimensional, the whole history of the process impacts on the price, and handling all this information is not obvious at all. To this aim, we present how to adapt the GPR-Tree and GPR-EI methods and we focus on pricing American options in this non-Markovian framework.



Machine learning with kernels for portfolio valuation and risk management
Lotfi Boudabsa,Damir Filipovic
arXiv

We introduce a computational framework for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the replicating martingale of a portfolio from a finite sample of its terminal cumulative cash flow. The learned replicating martingale is given in closed form thanks to a suitable choice of the kernel. We develop an asymptotic theory and prove convergence and a central limit theorem. We also derive finite sample error bounds and concentration inequalities. Numerical examples show good results for a relatively small training sample size.



Mobile Money and School Participation: Evidence from Low Income Countries
Rotondi, Valentina,Billari, Francesco C.
SSRN
This paper investigates the effect of using mobile money technology on children’s school participation in low-income societies. We argue that, by reducing transaction costs, and by making it easier and less expensive to receive remittances, mobile money technology reduces the need for coping strategies that are detrimental to child development, such as withdrawing children from school and sending them to work. We test this hypothesis using a set of comparative samples from seven low-income countries. We find that mobile money technology increases the chances of children attending school. This finding is robust to the use of estimation techniques that deal with possible endogeneity issues. We also show that the effect of mobile money is mainly driven by African countries and that, at least for girls, it is significantly higher when the household is living below the poverty line.

Multi-Likelihood Methods for Developing Stock Relationship Networks Using Financial Big Data
Xue Guo,Hu Zhang,Tianhai Tian
arXiv

Development of stock networks is an important approach to explore the relationship between different stocks in the era of big-data. Although a number of methods have been designed to construct the stock correlation networks, it is still a challenge to balance the selection of prominent correlations and connectivity of networks. To address this issue, we propose a new approach to select essential edges in stock networks and also maintain the connectivity of established networks. This approach uses different threshold values for choosing the edges connecting to a particular stock, rather than employing a single threshold value in the existing asset-value method. The innovation of our algorithm includes the multiple distributions in a maximum likelihood estimator for selecting the threshold value rather than the single distribution estimator in the existing methods. Using the Chinese Shanghai security market data of 151 stocks, we develop a stock relationship network and analyze the topological properties of the developed network. Our results suggest that the proposed method is able to develop networks that maintain appropriate connectivities in the type of assets threshold methods.



New Revolution in Fund Management: ETF/Index Design by Machines
Lee, Jaehoon
SSRN
Two ETFs were listed to track the trends in the secondary-battery industry on September 12th, 2018 in the Korea Stock Exchange market. They are virtually identical except that one is designed by humans while the other is made by machines. This paper compares the two ETFs and find little difference in their investment strategies except that machines are more likely to pick high book-to-market stocks than humans. It also finds that machines are more likely to pick past losers than humans, and these stocks are shown to perform better afterwards, contributing to the outperformance of machine-designed ETF over humans. The results suggest that machines can do equally good as humans as ETF/index designers.

Predicting Patent Citations to measure Economic Impact of Scholarly Research
Abdul Rahman Shaikh,Hamed Alhoori
arXiv

A crucial goal of funding research and development has always been to advance economic development. On this basis, a consider-able body of research undertaken with the purpose of determining what exactly constitutes economic impact and how to accurately measure that impact has been published. Numerous indicators have been used to measure economic impact, although no single indicator has been widely adapted. Based on patent data collected from Altmetric we predict patent citations through various social media features using several classification models. Patents citing a research paper implies the potential it has for direct application inits field. These predictions can be utilized by researchers in deter-mining the practical applications for their work when applying for patents.



Previsiones financieras (Financial Forecasts)
Casielles, Jorge
SSRN
Spanish Abstract: Este documento explica las formas de hacer previsiones financieras. Explica también qué es y cómo se calcula el nivel de crecimiento sostenible. Incluye múltiples ejercicios y preguntas de autoevaluación.English Abstract: This paper explains the ways to do financial forecasts. It also explains what the level of sustainable growth is and how to calculate it. The paper includes multiple exercises and practice questions.

Signatures of crypto-currency market decoupling from the Forex
Stanisław Drożdż,Ludovico Minati,Paweł Oświęcimka,Marek Stanuszek,Marcin Wątorek
arXiv

Based on the high-frequency recordings from Kraken, a cryptocurrency exchange and professional trading platform that aims to bring Bitcoin and other cryptocurrencies into the mainstream, the multiscale cross-correlations involving the Bitcoin (BTC), Ethereum (ETH), Euro (EUR) and US dollar (USD) are studied over the period between July 1, 2016 and December 31, 2018. It is shown that the multiscaling characteristics of the exchange rate fluctuations related to the cryptocurrency market approach those of the Forex. This, in particular, applies to the BTC/ETH exchange rate, whose Hurst exponent by the end of 2018 started approaching the value of 0.5, which is characteristic of the mature world markets. Furthermore, the BTC/ETH direct exchange rate has already developed multifractality, which manifests itself via broad singularity spectra. A particularly significant result is that the measures applied for detecting cross-correlations between the dynamics of the BTC/ETH and EUR/USD exchange rates do not show any noticeable relationships. This may be taken as an indication that the cryptocurrency market has begun decoupling itself from the Forex.



The Collaborative Innovation Bloc: A Reply to our Commentators
Elert, Niklas,Henrekson, Magnus
SSRN
We are grateful for the comments to our article, and for the opportunity to respond to them. In our original contribution, we argued that the application of the EOE perspective could help make Austrian economics more concrete, relevant and persuasive, especially regarding policy prescriptions. At the heart of this perspective is the idea that entrepreneurship, when construed as the act of building an innovative firm, is an inherently collaborative activity. The comments have strengthened our conviction that the EOE perspective is of value for Austrian economics and been of great help in furthering our thinking on the matter. The comments have also helped us see how the perspective fits in with the broader tradition of Austrian economics.

The Effects of Stock Market Development on Growth and Private Investment in Lower-Income Countries
Durham, J. Benson
SSRN
Recent literature argues that stock market liberalisation has positive long- and short-run effects on macroeconomic growth and private investment, respectively. However, given a sample of up to 64 countries from 1981 through 1998, positive results for long-run growth are largely dependent on the inclusion of higher-income countries in regression samples, which limits the relevance for lower-income nations. Indeed, some evidence in this study indicates that stock market development has a more positive impact on growth for greater levels of per capita GDP, lower levels of country credit risk, and higher levels of legal development. Similarly, using a sample of 26 countries from 1981 through 1998, lagged equity price appreciation seems to boost private investment growth in the short-run, but only in rich countries. All in all, these results imply subdued enthusiasm regarding emerging equity market development.

The Institutional Transformation of Microfinance Institutes: Sustainability at the Expense of the Poorest?
Wohlmann, Monika,Lessing, Jonathan
SSRN
The purpose of this paper is to introduce the three different forms of institutional transformation in microfinance - the upscaling-, the downscaling- and the greenfield-approach - and to assess in how far they contribute to a ‘mission drift’ in microfinance, away from providing affordable financial funds to the poorest towards a more profit-oriented business model. With the help of empirical findings in the existing literature on microfinance in Sub-Saharan Africa the main characteristics of these approaches are elaborated, and they are evaluated with respect to the initial aim of microfinance. The results of this paper show that the institutional transformation contributed to a broader implementation and sustainability of microfinance, though institutional transformation implies a certain mission drift. Between the poles of serving the poorest while maintaining self-sufficiency, the upscaling approach performs best, drifting less away from poor parts of the population while still stabilizing the MFI’s economic business model.

The Regulatory Effect: Did Regulatory Change Slow Credit Growth after the Great Depression and Great Recession?
Mahoney, Paul G.
SSRN
Both the Great Depression and the Great Recession followed systemic banking crises and preceded unusually weak and slow recoveries. The prior literature has identified monetary, household demand, and credit effects as contributors to the severe and prolonged downturns. This paper studies a regulatory effect. In 1933-35 and 2010, Congress enacted far-reaching regulatory reforms that imposed substantial compliance costs on commercial and investment banks and some of their borrowers. I ask whether increases in regulation-related costs reduced bank lending in the aftermath of both financial crises and discuss potential policy responses.