Research articles for the 2019-03-31

(Presentation Slides) Dash for Cash: Monthly Market Impact of Institutional Liquidity Needs
Etula, Erkko,Rinne, Kalle,Suominen, Matti,Vaittinen, Lauri
Presentation slides for "Dash for Cash: Monthly Market Impact of Institutional Liquidity Needs", available at: In this paper, we present broad-based evidence that the monthly payment cycle induces systematic return patterns in liquid markets around the globe. First, we document temporary increases in the costs of debt and equity capital that coincide with key dates associated with month-end cash needs. Second, we present direct and indirect evidence on the role of institutions in the genesis of these patterns and derive estimates of the associated costs borne by market participants. Finally, we investigate the limits to arbitrage that prevent markets from functioning efficiently. Our results indicate that many investors and their agents, including mutual funds, suffer from liquidity-related trading.

Are the New-Generation Treasury Return-Predictive Factors Economically Significant? A Cross-Currency and Out-of-Sample Investigation
Rebonato, Riccardo
Recent studies claim that a new generation of return-predicting factors can predict excess returns in the US Treasury market far better than the slope-related factors. The new-generation factors are, however, often difficult to interpret and far less parsimonious, and therefore doubts have been raised about their robustness, and about whether their high predicting power may be an artifact of data mining and overfitting. By predicting excess returns in one currency using return-predicting factors estimated from a different currency, we present strong evidence suggesting that these new factors are statistically informative, economically meaningful and, at least in their `restricted' form, surprisingly robust. A new out-of-sample technique for same-currency returns that we introduce reinforces these conclusions. Our findings also point to a commonality of fundamental financial mechanism(s) at the origin of the predictability observed in all the Treasury markets examined.

Catching Insider Trading
Ryznar, Margaret,Sensenbrenner, Frank
This Columbia Law School Blue Sky Blog post on insider trading analyzes shares from NASDAQ, AMEX, the New York Stock Exchange, and over the counter (OTC) markets, which allows for an examination of insider behavior within different market structures. We find that trades are different from surrounding trades in both trade to trade price impact and trade lot volume, information that should aid the government in identifying and prosecuting insider trading.

Gender and Beauty in the Financial Analyst Profession: Evidence from Two Different Cultures
Li, Congcong,Lin, An-Ping,Lu, Hai,Veenstra, Kevin J.
We examine how gender and beauty affect the likelihood of being voted as an All-Star in the financial analyst profession in both the U.S. and China. We find that female analysts are more likely to be voted as All-Star analysts in the U.S., but good-looking female U.S. analysts are less likely to be voted as All-Stars. The conclusion is opposite for Chinese financial analysts. We find that female analysts in China are less likely to be voted as All-Stars, but the likelihood increases with their facial attractiveness. These findings implicate a beauty penalty for female analysts in the U.S. and gender discrimination against female analysts in China. This career path evidence from a competitive financial industry suggests that gender and beauty biases may be rooted deeply in culture and should not be treated homogenously.

Market Manipulation as a Security Problem
Vasilios Mavroudis

Order matching systems form the backbone of modern equity exchanges, used by millions of investors daily. Thus, their operation is strictly controlled through numerous regulatory directives to ensure that markets are fair and transparent. Despite these efforts, market manipulation remains an open problem.

In this work, we focus on a class of market manipulation techniques that exploit technical details and glitches in the operation of the exchanges (i.e., mechanical arbitrage). Such techniques are used by predatory traders with deep knowledge of the exchange's structure to gain an advantage over the other market participants. We argue that technical solutions to the problem of mechanical arbitrage have the potential to significantly thwart these practices. Our work provides the first overview of the threat landscape, models fair markets and their security assumptions, and discusses various mitigation measures.

Modeling, discretization, and hyperchaos detection of a conformable fractional-order financial system with market confidence and ethics risk
Baogui Xin,Wei Peng,Yekyung Kwon,Yanqin Liu

A new chaotic financial system is proposed by considering ethics involvement in a four-dimensional financial system with market confidence. A five-dimensional fractional-order financial system is presented by introducing conformable fractional calculus to the integer-order system. A discretization scheme is proposed to calculate numerical solutions of conformable fractional-order systems. The scheme is illustrated by testing hyperchaos for the system.

Nonparametric Bayesian volatility estimation
Shota Gugushvili,Frank van der Meulen,Moritz Schauer,Peter Spreij

Given discrete time observations over a fixed time interval, we study a nonparametric Bayesian approach to estimation of the volatility coefficient of a stochastic differential equation. We postulate a histogram-type prior on the volatility with piecewise constant realisations on bins forming a partition of the time interval. The values on the bins are assigned an inverse Gamma Markov chain (IGMC) prior. Posterior inference is straightforward to implement via Gibbs sampling, as the full conditional distributions are available explicitly and turn out to be inverse Gamma. We also discuss in detail the hyperparameter selection for our method. Our nonparametric Bayesian approach leads to good practical results in representative simulation examples. Finally, we apply it on a classical data set in change-point analysis: weekly closings of the Dow-Jones industrial averages.

On Behavior of the Hybrid Securities When Issuer Is in Distress: The Volkswagen AG Case
Jaworski, Piotr,Liberadzki, Kamil,Liberadzki, Marcin
Hybrid securities provide long term funding for financially sound issuers. The coupon deferral option and their perpetuity offer an issuer flexibility of equity without shareholder dilution. The authors present the case study of Volkswagen (VW) hybrid securities where authors compare the market performance of various financial instruments in the scenario of a corporate governance scandal. Analysis covers the interrelation between prices and yields of VW corporate hybrids and senior bonds in reaction to emissions scandal revealed in September 2015. To one’s surprise the prices of more risky equity-like VW hybrids fell less than prices of VW senior bonds. First, we explain this phenomenon through analysis of price, yield and bond duration. Then we estimate the term structure of the discount factors for senior bonds. We study six series of perpetuals issued by VW group. For each hybrid we consider a virtual bond (Proxy) with maturity at its first call day and the same nominals and coupons, paid at the same day of a year as corresponding hybrid. We observe that difference between spreads for different times to maturity is moderate and the spreads for longer times to maturity are bigger. In crisis periods the difference much increases several times and we observe a reverse effect. The spreads for shorter maturities are greater. As the first corporate hybrids were issued around 2003 and the dominant issuers are ‘blue chip’ companies, the VW case creates the first opportunity to observe real-world behavior of such securities class when an issuer is likely to defer scheduled coupon payments.

Optimal Reinsurance and Investment in a Diffusion Model
Matteo Brachetta,Hanspeter Schmidli

We consider a diffusion approximation to an insurance risk model where an external driver models a stochastic environment. The insurer can buy reinsurance. Moreover, investment in a financial market is possible. The financial market is also driven by the environmental process. Our goal is to maximise terminal expected utility. In particular, we consider the case of SAHARA utility functions. In the case of proportional and excess-of-loss reinsurance, we obtain explicit results.

Regional Convergence and Structural Change in US Housing Markets
Miles, William
If house prices are convergent at the national level, monetary policy is easier to implement and labor has an easier time achieving mobility across regions. There have accordingly been a number of studies on home price convergence. Some of these previous papers have methodological problems. In this paper we examine home price convergence across the different regions of the US using Pesaran’s pairwise approach. This method obviates some of the methodological issues which have plagued previous studies. We also test with a method that allows for structural breaks in the relationships between regional markets. We find, first, that overall the US housing market is not convergent across regions. We find some evidence that the high-priced regions of New England and the Pacific exhibit convergence. Analysis of structural change reveals that some of the increase in co-movement between these expensive markets, and the decrease in co-movement between these and other markets accelerated in the early-to-mid 1980s. Other papers on income convergence have shown the 1980s to be a time when convergence in output in the US began to slow. Moreover, the early 1980s were a period of major change in US housing finance, as securitization made credit available from new sources, rather than just depository institutions. This greater credit, including from global sources, appears to have played a role in creating divergent prices in regions which likely have differing elasticities of housing supply.

Spectral backtests of forecast distributions with application to risk management
Michael B. Gordy,Alexander J. McNeil

We study a class of backtests for forecast distributions in which the test statistic depends on a spectral transformation that weights exceedance events by a function of the modeled probability level. The weighting scheme is specified by a kernel measure which makes explicit the user's priorities for model performance. The class of spectral backtests includes tests of unconditional coverage and tests of conditional coverage. We show how the class embeds a wide variety of backtests in the existing literature, and further propose novel variants which are easily implemented, well-sized and have good power. In an empirical application, we backtest forecast distributions for the overnight P&L of ten bank trading portfolios. For some portfolios, test results depend materially on the choice of kernel.

Stock Returns and Long-Range Dependence
Nkrumah Ababio, Emmanuel, Ayertey Odonkor, Alexander,Adu, Stephen,Andoh, Richard
This study throws more light on the long memory behaviour of stock returns on the Ghana stock Exchange (GSE). The researchers examined the long memory of stock returns and volatility prioritizing the weak form efficiency of the Efficient Market Hypothesis. The estimates employed are based on the daily closing prices of seven stocks on the Ghana Stock Exchange. The results of the ARFIMA-FIGARCH model suggest that the stock returns are characterized by a predictable component; this demonstrates a complete departure from the Efficient Market Hypothesis suggesting that relevant market information was only partially reflected in the changes in stock prices. This pattern of time dependence in stock returns may allow for past information to be used to improve the predictability of future returns.

Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market
Rosdyana Mangir Irawan Kusuma,Trang-Thi Ho,Wei-Chun Kao,Yu-Yen Ou,Kai-Lung Hua

Stock market prediction is still a challenging problem because there are many factors effect to the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment and economic factors. This work explores the predictability in the stock market using Deep Convolutional Network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a Convolutional Neural Network model. This Convolutional Neural Network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of stock market. The effectiveness of our method is evaluated in stock market prediction with a promising results 92.2% and 92.1% accuracy for Taiwan and Indonesian stock market dataset respectively. The constructed model have been implemented as a web-based system freely available at this http URL for predicting stock market using candlestick chart and deep learning neural networks.