# Research articles for the 2019-07-24

A Collective Investment Problem in a Stochastic Volatility Environment: The Impact of Sharing Rules
Chen, An,Nguyen, Thai,Rach, Manuel
SSRN
It is typical in collectively administered pension funds that employees delegate fund managers to invest their contributions. In addition, many pension funds still need to sustain guarantees (prescribed by law) in spite of the current low interest environment. In this paper, we consider an optimal collective investment problem for a pool of investors who (implicitly) demand minimum guarantees by deriving utility from the wealth exceeding their guarantees in two financial market settings, one with a stochastic and one with a constant volatility. We find that individual investors' well-being will not be worsened through the collective investment in both financial markets, as individual optimal solutions are attainable if a financially fair state-dependent sharing rule is applied. When more prevailing sharing rules like linear rules are applied, this holds no longer. Furthermore, the degree of sub-optimality imposed by linear sharing rules is more pronounced in the stochastic volatility market than in the constant volatility market.

A Novel Template for Understanding Priced Factors
Vyas, Krishna,van Baren, Michael
SSRN
We introduce a novel template which allows for robust inference when attempting to distinguish priced from unpriced risks in factor portfolios. This is of interest since factor portfolios are prone to unintended and potentially unpriced risk exposures. We demonstrate this template by decomposing industry risks for the style factors Value, Quality, Momentum, Low Volatility and Size on a global large- and mid-cap universe over 1994-2018. Our methodology reintroduces the notion of monotonicity into the discrete question at hand whether a factor is priced within and/or across industries, serving as a new perspective on this commonly researched topic. We present empirical evidence that Value is priced within industries over a wide variety of factor definitions, with no premium for industry-selection. We find no clear industry decomposition for Quality and show that different factor definitions vary in potential for earning within- and across-industry premiums. The Low Volatility and Momentum style factors do show consistent within- and across-industry premiums. The Low Volatility anomaly appears to be most pronounced within industries, whereas the Momentum anomaly is most consistent across industries. The evidence for Size points to a within-industry premium, whilst it is unpriced across industries.

Bank Regulation, Investment, and the Implementation of Capital Requirements
Rivera, Thomas J
SSRN
We study the optimal design of bank capital regulations in a model where banks face an adverse selection problem when raising capital. We show how the implementation of capital requirements is an important regulatory tool as it can help mitigate bank underinvestment by eliminating the information frictions that make raising capital costly. Specifically, the regulator can design incentive compatible requirements that induce the bankâ€™s to reveal their private information to the market through their choice of capital structure. The optimal implementation of capital requirements induces information revelation when the banking sector is weak and pools the banksâ€™ private information otherwise.

Bank efficiency in Vietnam: Do scale expansion strategies and non-performing loans matter?
Le, Chau,Matousek, Roman,Tzeremes, Panayiotis,Ngo, Trong
SSRN
In Vietnam the process of financial liberalization, the rapid banking expansion has resulted in structural frangibility and bad debt proliferation with negative implication for bank performance. This is the first comprehensive study that evaluate the performance of the Vietnamese banking system at the start of the Global Financial Crisis in 2008 up to 2016. We show that Vietnamese banking system experienced a downward trend in technical efficiency over the liberalization period. But there persists an efficiency gap among banks with different ownership structure, suggesting that privatization matters the performance improvement. An important contribution is the analysis of non-performing loans on bank performance. We argue that this category of loans and bank size have nonlinear effects on the estimated efficiency levels. Medium-sized banks are more efficient than big and small banks. This finding implies that the ongoing restructuring scheme pushing banksâ€™ scale expansion via capital build-up should be carefully taken into consideration.

Corporate Diversification, Sales Growth, and Capital Market Development: Cross-Country Evidence
Kwon, Taek Ho,Bae, Sung C.,Park, Soonhong
SSRN
This paper highlights the interactions of sales growth with diversification strategies and their joint effects on firm value. Employing firm-level data for 39 countries, we report that firms diversify mainly to cope with their poor sales growth and/or slow growth in their core industries. Consistent with evidence in the literature, we confirm a diversification discount on average for our sample firms. After considering joint changes in sales growth and diversification, however, we uncover the existence of a diversification premium in two situations: (1) when firms expand diversification with their sales growing faster than industry peers; and (2) when firms reduce diversification with declining sales. We further find that the effects of the interactions between sales growth and diversification activity on firm value are more pronounced for developed market firms than for emerging market firms. Overall, our results strongly suggest that despite the negative diversification effect on average, a diversification premium is viable if a firmâ€™s diversification strategy is properly aligned with its business situations such as sales growth.

Curriculum Learning in Deep Neural Networks for Financial Forecasting
Allison Koenecke,Amita Gajewar
arXiv

For any financial organization, computing accurate quarterly forecasts for various products is one of the most critical operations. As the granularity at which forecasts are needed increases, traditional statistical time series models may not scale well. We apply deep neural networks in the forecasting domain by experimenting with techniques from Natural Language Processing (Encoder-Decoder LSTMs) and Computer Vision (Dilated CNNs), as well as incorporating transfer learning. A novel contribution of this paper is the application of curriculum learning to neural network models built for time series forecasting. We illustrate the performance of our models using Microsoft's revenue data corresponding to Enterprise, and Small, Medium & Corporate products, spanning approximately 60 regions across the globe for 8 different business segments, and totaling in the order of tens of billions of USD. We compare our models' performance to the ensemble model of traditional statistics and machine learning techniques currently used by Microsoft Finance. With this in-production model as a baseline, our experiments yield an approximately 30% improvement in overall accuracy on test data. We find that our curriculum learning LSTM-based model performs best, showing that it is reasonable to implement our proposed methods without overfitting on medium-sized data.

Deep Learning-Based Least Square Forward-Backward Stochastic Differential Equation Solver for High-Dimensional Derivative Pricing
Jian Liang,Zhe Xu,Peter Li
arXiv

We propose a new forward-backward stochastic differential equation solver for high-dimensional derivatives pricing problems by combining deep learning solver with least square regression technique widely used in the least square Monte Carlo method for the valuation of American options. Our numerical experiments demonstrate the efficiency and accuracy of our least square backward deep neural network solver and its capability to provide accurate prices for complex early exercise derivatives such as callable yield notes. Our method can serve as a generic numerical solver for pricing derivatives across various asset groups, in particular, as an efficient means for pricing high-dimensional derivatives with early exercises features.

Efficient priority rules and the Indian IBC
SSRN
India passed a comprehensive and new Insolvency and Bankruptcy Code (IBC) on May 28, 2016. Prior to this, institutional debt defaults were handled through a number of different laws and regulations, like SICA, 1985, Debt Recovery Act, 1993, SARFAESI, 2002 and Company Law, 2013. In addition, for companies above a certain size, the High Courts had to be involved, especially in winding up decisions. The IBC, being a uniform code, was meant to reallocate assets more efficiently and quickly. In this paper we develop a model that gives rise to credit rationing as a result of moral hazard and investigate the efficiency of three different possible priority rules: (a) when financial creditors have priority over operational creditors (b) when operational creditors have priority over financial creditors and (c) when both have equal priority. We argue that the extent of credit rationing is invariant under all three priority rules. We then show why a cost of business indicator that looks only at the price of inputs is flawed in determining efficiency.

Evaluating the Effectiveness of Common Technical Trading Models
Joseph Attia
arXiv

How effective are the most common trading models? The answer may help investors realize upsides to using each model, act as a segue for investors into more complex financial analysis and machine learning, and to increase financial literacy amongst students. Creating original versions of popular models, like linear regression, K-Nearest Neighbor, and moving average crossovers, we can test how each model performs on the most popular stocks and largest indexes. With the results for each, we can compare the models, and understand which model reliably increases performance. The trials showed that while all three models reduced losses on stocks with strong overall downward trends, the two machine learning models did not work as well to increase profits. Moving averages crossovers outperformed a continuous investment every time, although did result in a more volatile investment as well. Furthermore, once finished creating the program that implements moving average crossover, what are the optimal periods to use? A massive test consisting of 169,880 trials, showed the best periods to use to increase investment performance (5,10) and to decrease volatility (33,44). In addition, the data showed numerous trends such as a smaller short SMA period is accompanied by higher performance. Plotting volatility against performance shows that the high risk, high reward saying holds true and shows that for investments, as the volatility increases so does its performance.

Generalized Statistical Arbitrage Concepts and Related Gain Strategies
Rein, Christian,RÃ¼schendorf, Ludger,Schmidt, Thorsten
SSRN
Generalized statistical arbitrage concepts are introduced corresponding to trading strategies which yield positive gains on average in a class of scenarios rather than almost surely. The relevant scenarios or market states are specified via an information system given by a sigma-algebra and so this notion contains classical arbitrage as a special case. It also covers the notion of statistical arbitrage introduced in Bondarenko (2003). Relaxing these notions further we introduce generalized profitable strategies which include also static or semi-static strategies. Under standard no-arbitrage there may exist generalized gain strategies yielding positive gains on average under the specified scenarios. In the first part of the paper we characterize these generalized statistical no-arbitrage notions. In the second part of the paper we construct several profitable generalized strategies with respect to various choices of the information system. In particular, we consider several forms of embedded binomial strategies and follow-the-trend strategies as well as partition-type strategies. We study and compare their behaviour on simulated data. Additionally, we find good performance on market data of these simple strategies which makes them profitable candidates for real applications.

Going Mobile, Investor Behavior, and Financial Fragility
Cen, Xiao
SSRN
This study investigates how mobile trading technology affects retail investor behavior and mutual fund fragility using proprietary individual-level fund trading data. I exploit a natural experiment, the release of a popular mobile trading application by a leading investment adviser in China. My difference-in-difference analysis shows "going mobile" raises investor attention and trading volume through aggravating investors' over-confidence and self-control problem. The mobile app significantly boosts flow volatility, and makes investor flow more sensitive to short-term fund return and market sentiment. As a result, "going mobile" depresses fund performance by heightening indirect liquidity costs. The funds more exposed to the shock see a greater decline in abnormal return, explained by large fund flows through the trading app.Lastly, I combine Instrumental Variable method and a spatial discontinuity setting to strengthen causal inference. Overall, the paper shows "going mobile" intensifies financial fragility and dampens mutual fund performance by amplifying investors' cognitive biases.

Has the Split-Share Reform Influenced Corporate Behaviour? Chinese Firmsâ€™ Fixed Capital Investment 2002â€"2016
Shi, Min,Zhang, Dayong,Dickinson, David
SSRN
In 2005, China took an important step in its privatisation process by initiating the Split Share Reform, whereby state-owned shares became tradeable. As a consequence, there was a significant rise in private holdings of shares of listed companies which previously had high state ownership. This paper considers the impact of this change, by examining how the SSR impacted on firmsâ€™ capital investment. Using a value-maximising approach, we empirically model Chinese firmsâ€™ fixed capital investment recognising that this was a period of global instability caused by the Global Financial Crisis. Controlling for its effects, we are able to consider if and how the reform influenced firmsâ€™ behaviour.

High and Low Prices and the Range in the European Stock Markets: A Long-Memory Approach
Caporale, Guglielmo Maria,Gil-Alana, Luis A.,Poza, Carlos
SSRN
This paper uses fractional integration techniques to examine the stochastic behaviour of high and low stock prices in Europe and then to test for the possible existence of long-run linkages between them by looking at the range, i.e., the difference between the two logged series. Specifically, monthly, weekly and daily data on the following five European stock market indices are analysed: DAX30 (Germany), FTSE100 (UK), CAC40 (France), FTSE MIB40 (Italy) and IBEX35 (Spain). In all cases, the order of integration of the range is lower than that of the original series, which implies the existence of a long-run equilibrium relationship between high and low prices. Further, the estimated fractional differencing parameter is positive in all cases, which represents evidence of long memory.

Incentivizing the Owner: Why Family Firms offer Pay-for-Performance Contracts to their CEOs
Abrardi, Laura,Rondi, Laura
SSRN
We study the managersâ€™ compensation schemes adopted by publicly listed family firms by means of a theoretical model and an empirical analysis. Existing empirical literature finds puzzling evidence about the structure of family CEOsâ€™ pay, which apparently contradicts the fundamental tenets of principal-agent theory under moral hazard. In particular, family CEOs typically exhibit lower expected pay but higher pay-for-performance sensitivity than external managers, despite their large inside ownership. In a theoretical model, we show that the outcome-related compensation structure of family CEOs reduces the CEOâ€™s incentive to divert value from minority shareholders. We test the main hypotheses on a panel of Italian listed family firms (2000-2017), for which we have collected data on CEOsâ€™ parental ties, cash and equity-based components of CEOsâ€™ pay and internal corporate governance mechanisms. The evidence confirms our theoretical predictions.

Investor Sentiment, Behavioral Heterogeneity and Stock Market Dynamics
Li, Changtai,Tan, Sook Rei,Ho, Nick,Chia, Wai-Mun
SSRN
Recent empirical works corroborate importance of sentiment in asset pricing. We further propose that sentiment may not affect everyone in a homogeneous way. In this paper, we construct a sentiment indicator taking into consideration behavioral heterogeneity of interacting investors. From our model simulation, we find sentiment contributes to several financial anomalies such as fat tails and volatility clustering of returns. More importantly, investor sentiment could be a significant source of financial market volatility. Our model with sentiment is also able to replicate different types of crises. Our finding shows that severity of crisis intensifies with investorsâ€™ sentiment sensitivity.

Los Diferentes Tipos De Cash Flow (The Different Types of Cash Flow - Presentation Slides)
Casielles, Jorge
SSRN

Multivariate Modeling of Natural Gas Spot Trading Hubs Incorporating Futures Market Realized Volatility
Michael Weylandt,Yu Han,Katherine B. Ensor
arXiv

Financial markets for Liquified Natural Gas (LNG) are an important and rapidly-growing segment of commodities markets. Like other commodities markets, there is an inherent spatial structure to LNG markets, with different price dynamics for different points of delivery hubs. Certain hubs support highly liquid markets, allowing efficient and robust price discovery, while others are highly illiquid, limiting the effectiveness of standard risk management techniques. We propose a joint modeling strategy, which uses high-frequency information from thickly-traded hubs to improve volatility estimation and risk management at thinly traded hubs. The resulting model has superior in- and out-of-sample predictive performance, particularly for several commonly used risk management metrics, demonstrating that joint modeling is indeed possible and useful. To improve estimation, a Bayesian estimation strategy is employed and data-driven weakly informative priors are suggested. Our model is robust to sparse data and can be effectively used in any market with similar irregular patterns of data availability.

Optimal investment for participating insurance contracts under VaR-Regulation
arXiv

This paper studies a Value-at-Risk (VaR)-regulated optimal portfolio problem of the equity holders of a participating life insurance contract. In a setting with unhedgeable mortality risk and complete financial market, the optimal solution is given explicitly for contracts with mortality risk using a martingale approach for constrained non-concave optimization problems. We show that regulatory VaR constraints for participating insurance contracts lead to more prudent investment than in the case of no regulation. This result is contrary to the situation where the insurer maximizes the utility of the total wealth of the company (without distinguishing between contributions of equity holders and policyholders), in which case a VaR constraint may induce the insurer to take excessive risks leading to higher losses than in the case of no regulation. Compared to the unregulated problem, the VaR-constrained strategy leads to a higher expected utility for the policyholders, highlighting the potential usefulness of a VaR-regulation in the context of insurance. The prudent investment behavior is more significant if a VaR-type regulation is replaced by a portfolio insurance (PI)-type regulation. Furthermore, a stricter regulation (a smaller allowed default probability in the VaR problem or a higher minimum guarantee level in the PI problem) enhances the benefit of the policyholder but deteriorates that of the insurer. For both types of regulation, the gains in terms of expected utility are greater for higher participation rates, while being smaller for higher bonus rates. We also extend our analysis to frameworks where dividend and premature death benefit payments are made at an intermediate time date.

Religiosity, Neglected Risk and Asset Returns: Theory and Evidence from Islamic Finance Industry
SSRN
This paper studies the sociological influence of religion on the risk and return in the financial markets with particular context of Islamic finance. The paper builds a theoretical model to show how intermediaries serve their customersâ€™ religious needs by creating innovative Islamic financial instruments. The customerâ€™s emphasis on religiosity exposes the industry to a theological risk, which can increase the financial fragility of the system. In our model, the theological risk emerges as a neglected component, which can be realized in the event of a bad news challenging the religious legitimacy of (Islamic) finance structures. Using stock prices data for 104 Islamic bond (Sukuk) issuers, our analysis shows that Islamic bond issuers experienced a significant decline in their stock prices, following multiple formal and informal announcements in 2008, which challenged the religious legitimacy of Islamic bond structures. We complement our analysis using 1361 new Sukuk issues in Malaysia from 2005 to 2016 to investigate the impact of a regulatory change (introduced by Bank Negara in 2015) that limited the supply of sovereign Sukuk to serve only the Islamic banking industry.nter Abstract Body]

Risk Pooling, Leverage, and the Business Cycle
Dindo, Pietro,Modena, Andrea,Pelizzon, Loriana
SSRN
This paper investigates the interdependence between the risk-pooling activity of the financial sector and: output, consumption, risk-free rate, and Sharpe ratio in a dynamic general equilibrium model of a productive economy. Due to their exposure to idiosyncratic shocks and market segmentation, heterogeneous households/entrepreneurs (h/entrepreneurs) are willing to mitigate their risk through a financial sector. The financial sector pools risky claims issued by different firms within its assets, faces an associated intermediation cost and, via leverage, provides a risk-free asset to h/entrepreneurs. Exogenous systematic shocks change the relative size of the financial sector, and thus the equilibrium amount of pooled risk, making financial leverage state-dependent and counter-cyclical. We study how this mechanism endogenously channels amplification of consumption and mitigation of output fluctuations.In equilibrium, financial sector leverage also determines counter-cyclical Sharpe ratios and pro-cyclical risk-free interest rates. Last, we investigate the relationship between the size of the financial sector, leverage, and welfare. We show that limiting financial sector leverage determines a sub-optimal pooling of idiosyncratic risk but fosters the growth rate of the h/entrepreneurs' consumption. On the other side, when the financial sector is too large, it destroys too many resources after intermediation costs. Therefore, the h/entrepreneurs benefit the most when the financial sector is neither too small nor too big.

arXiv

Tensor Processing Units for Financial Monte Carlo
Francois Belletti,Davis King,Kun Yang,Roland Nelet,Yusef Shafi,Yi-Fan Chen,John Anderson
arXiv

Monte Carlo methods are core to many routines in quantitative finance such as derivatives pricing, hedging and risk metrics. Unfortunately, Monte Carlo methods are very computationally expensive when it comes to running simulations in high-dimensional state spaces where they are still a method of choice in the financial industry. Recently, Tensor Processing Units (TPUs) have provided considerable speedups and decreased the cost of running Stochastic Gradient Descent (SGD) in Deep Learning. After having highlighted computational similarities between training neural networks with SGD and stochastic process simulation, we ask in the present paper whether TPUs are accurate, fast and simple enough to use for financial Monte Carlo. Through a theoretical reminder of the key properties of such methods and thorough empirical experiments we examine the fitness of TPUs for option pricing, hedging and risk metrics computation. We show in the following that Tensor Processing Units (TPUs) in the cloud help accelerate Monte Carlo routines compared to Graphics Processing Units (GPUs) which in turn decreases the cost associated with running such simulations while leveraging the flexibility of the cloud. In particular we demonstrate that, in spite of the use of mixed precision, TPUs still provide accurate estimators which are fast to compute. We also show that the Tensorflow programming model for TPUs is elegant, expressive and simplifies automated differentiation.

Testing Sectoral Herding in the Jordanian Stock Market
SSRN
The main purposes of this quantitative study were to examine the existence of herding behavior among investors in Amman stock exchange (ASE) at market and sector level in addition to testing the behavior during the market rising and falling and examining whether the behavior existence is different before and after the global financial crisis of 2008. The theoretical base of the study was the behavioral finance which assumes that investors are not completely rational and they may follow others when taking investment decisions. The main enquires of the study were about the existence of herding in the Jordanian market, whether it's affected by conditions of market rising and falling, and whether its affected by the financial crisis. A quantitative design was employed to achieve the purposes of this study which covers the period 2000 - 2018. Data were obtained from ASE website and analyzed using ordinary least squares method. The results indicated that herding is absent in the Jordanian market if tested at market level while it exists in services and industrial sectors if tested at sectors level. The financial crisis did not affect the presence of herding at market level while it did affect the behavior in services and industrial sectors. Moreover, the results revealed that market condition of rising and falling affected herding at market level but not at sectors level. It is also concluded that the global financial crisis changed the presence of herding behavior during conditions of rising and falling in market and in each sector.

Testing new property of elliptical model for stock returns distribution
Petr Koldanov
arXiv

Wide class of elliptically contoured distributions is a popular model of stock returns distribution. However the important question of adequacy of the model is open. There are some results which reject and approve such model. Such results are obtained by testing some properties of elliptical model for each pair of stocks from some markets. New property of equality of $\tau$ Kendall correlation coefficient and probability of sign coincidence for any pair of random variables with elliptically contoured distribution is proved in the paper. Distribution free statistical tests for testing this property for any pair of stocks are constructed. Holm multiple hypotheses testing procedure based on the individual tests is constructed and applied for stock markets data for the concrete year. New procedure of testing the elliptical model for stock returns distribution for all years of observation for some period is proposed. The procedure is applied for the stock markets data of China, USA, Great Britain and Germany for the period from 2003 to 2014. It is shown that for USA, Great Britain and Germany stock markets the hypothesis of elliptical model of stock returns distribution could be accepted but for Chinese stock market is rejected for some cases.

The Earnings Announcement Premium as Uncertainty Aversion: Theory and Evidence
Dicks, David L.,Kim, Hwanki Brian
SSRN
We argue the earnings announcement premium is a measure of firm-specific uncertainty aversion. We show that earnings announcements, as pure news events, are priced only if investors are uncertainty averse, and we further show that the earnings announcement return is negatively correlated to future investment only if there is time-varying uncertainty or uncertainty aversion. Consistent with predictions of the model, we empirically show that when the earning announcement premium is higher, investment falls, cash levels and savings increase. We also show the earnings announcement return is higher for more uncertain stocks, proxied by listing on NASDAQ or AMEX, small firms, or complexity (many segments).

The Size Effect Revisited
Brandon Flores,Taran Grove,Yi Liu,Andrey Sarantsev
arXiv

We compare performance of US stocks based on their size (market capitalization). We regress alpha and beta over size and other factors for individual stocks in Standard & Poor 500, for randomly generated portfolios. The novelty of our research is that we compare exchange-traded funds (ETFs) consisting of large-, mid- and small-cap stocks, including international ETFs. Conclusions: Size and market exposure (beta) are inversely related (strong evidence for ETFs, weaker for individual stocks). No conclusive evidence about dependence of excess return (alpha) on size, or international markets.