Research articles for the 2019-02-24

A Mathematical Analysis of Technical Analysis
Matthew Lorig,Zhou Zhou,Bin Zou

In this paper, we investigate trading strategies based on exponential moving averages (ExpMAs) of an underlying risky asset. We study both logarithmic utility maximization and long-term growth rate maximization problems and find closed-form solutions when the drift of the underlying is modeled by either an Ornstein-Uhlenbeck process or a two-state continuous-time Markov chain. For the case of an Ornstein-Uhlenbeck drift, we carry out several Monte Carlo experiments in order to investigate how the performance of optimal ExpMA strategies is affected by variations in model parameters and by transaction costs.

A threshold model for local volatility: evidence of leverage and mean reversion effects on historical data
Antoine Lejay,Paolo Pigato

In financial markets, low prices are generally associated with high volatilities and vice-versa, this well known stylized fact usually being referred to as leverage effect. We propose a local volatility model, given by a stochastic differential equation with piecewise constant coefficients, which accounts of leverage and mean-reversion effects in the dynamics of the prices. This model exhibits a regime switch in the dynamics accordingly to a certain threshold. It can be seen as a continuous-time version of the Self-Exciting Threshold Autoregressive (SETAR) model. We propose an estimation procedure for the volatility and drift coefficients as well as for the threshold level. Parameters estimated on the daily prices of 348 stocks of NYSE and S\&P 500, on different time windows, show consistent empirical evidence for leverageeffects. Mean-reversion effects are also detected, most markedly in crisis periods.

Cognitive Biases and Asset Prices: Evidence from Exchange Repo Market in China
Fang, Xuyun,Jiang, Zhiqian,Liu, Baixiao,McConnell, John J.,Zhou, Mingshan
We provide empirical evidence of a strong causal relation between investors’ cognitive biases and asset prices using an exogenous regulatory shock to the exchange repurchase agreements (repo) market in China. We find that average annualized daily repo returns are negatively correlated with the actual days of the outstanding funds. The negative correlation disappears immediately after the exchanges implementing new repo price quotation rules on May 22, 2017, which simplify but do not materially alter, the calculation of repo rates offered. We attribute this phenomenon to investors’ cognitive biases that hinder them from fully adjusting for the deviation of the actual days of the outstanding funds from the nominal days of the repo term when they offer repo rates in their trading.

Competition and Bank Systemic Risk: New Evidence From Japan's Regional Banking
Hirata, Wataru,Ojima, Mayumi
Bank competition and financial stability is a recurrent research issue, and researchers have begun to shed light on the competition effect on systemic-risk. Japan is an interesting case in this venue since its regional banking system has confronted intensified competition and there is growing evidence that the competition has led the portfolio of Japan's regional banks to be more overlapped, an indication of increased systemic risk. In this paper, we first examine the empirical relationship between competition and systemic-risk for Japan's regional banks. We find that the bank mark-up is negatively associated with the level of systemic risk, indicating that competition undermines the system-wide financial stability in Japan. However, this result is at odds with existing studies. To this end, we perform a theoretical analysis focusing on bank's portfolio diversification. We demonstrate that Japan's regional banks tend to diversify toward alternative lending when the profitability of the core business declines. This diversification results in the build-up of systemic risk through higher common exposure, a form of indirect interconnectedness.

Continuous Auditing and Risk-Based Audit Planning
Eulerich, Marc,Georgi, Christine,Schmidt, Alexander
Due to significant changes in the risk environment of organizations as a result of globalization and digitalization a continuous perspective in audit activities is required. Continuous Auditing (CA) is one possible way to meet these requirements. Specifically, the Internal Audit Function (IAF) could use CA as an audit technique in order to add this perspective to their risk assessment. This study examines factors associated with the use of CA information in risk-based audit planning (RBAP) of the IAF. We use survey data from 264 chief audit executives to address our research question. Consistent with our expectations, we find several factors having a significant positive influence on the use of CA information in RBAP. From the IAF’s point of view these factors include the collaboration with the external auditor, the use of rolling planning, the importance of the audit committee, the importance of data analysis in audit planning and the appropriation of results for fraud prevention. Finally, we discuss the implications of these findings for research and practice.

Controlling systemic risk - network structures that minimize it and node properties to calculate it
Sebastian M. Krause,Hrvoje Štefančić,Vinko Zlatić,Guido Caldarelli

Evaluation of systemic risk in networks of financial institutions in general requires information of inter-institution financial exposures. In the framework of Debt Rank algorithm, we introduce an approximate method of systemic risk evaluation which requires only node properties, such as total assets and liabilities, as inputs. We demonstrate that this approximation captures a large portion of systemic risk measured by Debt Rank. Furthermore, using Monte Carlo simulations, we investigate network structures that can amplify systemic risk. Indeed, while no topology in general sense is {\em a priori} more stable if the market is liquid [1], a larger complexity is detrimental for the overall stability [2]. Here we find that the measure of scalar assortativity correlates well with level of systemic risk. In particular, network structures with high systemic risk are scalar assortative, meaning that risky banks are mostly exposed to other risky banks. Network structures with low systemic risk are scalar disassortative, with interactions of risky banks with stable banks.

Emerging Technology-Related Issues in Finance and the IMFâ€"A Stocktaking
Demekas, Dimitri G.
This paper provides a stocktaking of the IMF’s work on three emerging technology-related issues in finance: (i) cyber risk and cyber security for financial systems; (ii) technology-driven innovation in the provision of financial services (“fintech”); and (iii) digital currencies or cryptocurrencies. Because these issues are relatively new, still evolving, and their economic impact is uncertain, it would be premature to try to assess the quality and impact of the Fund’s engagement and policy advice. Instead, this paper casts a wide net and takes stock of a broad range of relevant Fund activities. The stocktaking shows that the IMF has been paying increasing attention to technology-related issues in finance, both from an analytical perspective and as a topic for bilateral surveillance. This engagement is in its early stages and still evolving. It has so far been more visible on fintech and digital currencies than on cyber security issues. At the same time, the Fund has used its convening power to raise awareness of these issues â€" particularly cyber risk â€" and facilitate knowledge-sharing among developing and emerging market member jurisdictions. Most recently, the IMF has worked together with the World Bank to develop the Bali Fintech Agenda, a framework for the consideration of high-level issues in these areas by the international community and individual member countries. Looking forward, the challenge for the IMF is to continue working closely with member countries and relevant international bodies in order to best respond to the membership’s needs in this area.

Financial Markets and Climate Models: An Empirical Study on Corn Futures
Miftakhova, Alena,Pohl, Walter
Some economic sectors â€" particularly agriculture â€" are sensitive to weather conditions and hence to the accuracy of available forecasts. Unfortunately, even forecasts of the near future made by the most advanced climate models suffer from relatively low accuracy due to imperfect modeling of the climate system with its highly complex and uncertain nature. Participants in the markets for agricultural commodities would therefore benefit from improved prediction systems â€" such systems would facilitate better knowledge about the harvest in the coming season and suggest better strategies to be deployed on financial markets. If the markets are efficient, the prices of those commodities sensitive to weather are expected to reflect the best knowledge about the conditions for the following growing seasons.In this paper we look for evidence that the expectations of the financial markets for the coming growing seasons are superior to those formed purely from publicly available climate forecasts. We analyze the accuracy of the climate forecasts across corn growing areas of the largest producer of corn â€" the US â€" and find no evidence of corn futures markets having more information about future climate conditions than that contained in the publicly available forecasts of the multi-model ensemble studied here.

From SA-CCR to RSA-CCR: making SA-CCR self-consistent and appropriately risk-sensitive by cashflow decomposition in a 3-Factor Gaussian Market Model
Mourad Berrahoui,Othmane Islah,Chris Kenyon

SA-CCR has major issues including: lack of self-consistency for linear trades; lack of appropriate risk sensitivity (zero positions can have material add-ons; moneyness is ignored); dependence on economically-equivalent confirmations. We show that SA-CCR is, by parameter identification and re-construction, based on a 3-factor Gaussian Market Model. Hence we propose a Revised SA-CCR (RSA-CCR) based on cashflow decomposition and this 3-factor Gaussian Market Model. RSA-CCR is free of SA-CCR's issues, simple to use in practice, and can be extended easily given that it is model-based. We recommend updating SA-CCR to RSA-CCR in order to resolve SA-CCR's issues of lack of self-consistency for linear trades, lack of appropriate risk sensitivity (zero positions can have material add-ons; moneyness is ignored), dependence on economically-equivalent confirmations, and ambiguity of application for cases not explicitly described.

IFRS Adoption by UK Unlisted Firms: Subsidiary- Versus Group-Level Incentives
André, Paul,Kalogirou, Fani
We examine the subsidiary- and group-level determinants of IFRS adoption by unlisted UK firms. Many unlisted firms are part of large conglomerate groups. For these firms, decisions about reporting practices are expected to be made at the group-level. Consistent with this hypothesis, we find that subsidiaries adopt IFRS as part of their group’s strategy to improve within group monitoring and raise external debt capital. ROC curve analysis indicates that these incentives are more important than traditional subsidiary-level incentives studied before. Further, we find that adopting subsidiaries benefit from better accounting quality and higher investment efficiency.

Investor Sentiment and the Pricing of Macro Risks for Hedge Funds
Chen, Zhuo,Lu, Andrea,Zhu, Xiaoquan
Hedge funds with larger macroeconomic-risk betas do not earn higher returns, contrast to the theoretically predicted risk-return tradeoff. Meanwhile, high macro-beta funds deliver higher returns than low macro-beta funds following low-sentiment months, whereas the risk-return relation is flat following high-sentiment months. Our findings are consistent with the conjecture that standard asset pricing theory is still at work when market participants are rational. On the other hand, sophisticatedly managed portfolios including hedge funds are possibly affected by sentiment-induced mispricing, especially for those with high macro-risk loadings.

Optimal dividends with partial information and stopping of a degenerate reflecting diffusion
Tiziano De Angelis

We study the optimal dividend problem for a firm's manager who has partial information on the profitability of the firm. The problem is formulated as one of singular stochastic control with partial information on the drift of the underlying process and with absorption. In the Markovian formulation, we have a 2-dimensional degenerate diffusion, whose first component is singularly controlled and it is absorbed as it hits zero. The free boundary problem (FBP) associated to the value function of the control problem is challenging from the analytical point of view due to the interplay of degeneracy and absorption. We find a probabilistic way to show that the value function of the dividend problem is a smooth solution of the FBP and to construct an optimal dividend strategy. Our approach establishes a new link between multidimensional singular stochastic control problems with absorption and problems of optimal stopping with `creation'. One key feature of the stopping problem is that creation occurs at a state-dependent rate of the `local-time' of an auxiliary 2-dimensional reflecting diffusion.

Stacking with Neural network for Cryptocurrency investment
Avinash Barnwal,Hari Pad Bharti,Aasim Ali,Vishal Singh

Predicting the direction of assets have been an active area of study and a difficult task. Machine learning models have been used to build robust models to model the above task. Ensemble methods is one of them showing results better than a single supervised method. In this paper, we have used generative and discriminative classifiers to create the stack, particularly 3 generative and 6 discriminative classifiers and optimized over one-layer Neural Network to model the direction of price cryptocurrencies. Features used are technical indicators used are not limited to trend, momentum, volume, volatility indicators, and sentiment analysis has also been used to gain useful insight combined with the above features. For Cross-validation, Purged Walk forward cross-validation has been used. In terms of accuracy, we have done a comparative analysis of the performance of Ensemble method with Stacking and Ensemble method with blending. We have also developed a methodology for combined features importance for the stacked model. Important indicators are also identified based on feature importance.

The Endowment Model and Modern Portfolio Theory
Dimmock, Stephen G.,Wang, Neng,Yang, Jinqiang
We develop a dynamic portfolio-choice model with illiquid alternative assets to analyze conditions under which the “Endowment Model,” used by some large institutional investors such as university endowments, does or does not work. The alternative asset has a lock-up, but can be voluntarily liquidated at any time at a cost. Quantitatively, our model’s results match the average level and cross-sectional variation of university endowment funds’ spending and asset allocation decisions. We show that asset allocations and spending crucially depend on the alternative asset’s expected excess return, risk unspanned by public equity, and investors’ preferences for inter-temporal spending smoothing.

The Importance of Sovereign Reference Rates for Corporate Debt Issuance: Mind the Gap
van Bekkum, Sjoerd,Grundy , Bruce D.,Verwijmeren, Patrick
We show that sovereign bond reference rates are theoretically and empirically important determinants of corporate bond issuance and maturity. By providing reference rates, government issues complement corporate issues. Government and corporate bond issues are also substitutes and corporations issue more long-term bonds when sovereign alternatives are in short supply. The substitution weakens when sovereign bonds fail to provide high-quality reference rates and we conclude that reference rates are a prerequisite for gap-filling behavior in an international sample. Introductions and suspensions of sovereign bond issues that change the maximum reference rate precede similar changes in the maximum maturity of corporate issues.

When Buffett Meets Bollinger: An Integrated Approach to Fundamental and Technical Analysis
Zhu, Zhaobo,Sun, Licheng
We study the portfolio performance of investment strategies that jointly apply both fundamental analysis and technical analysis. Compared with strategies that rely on one-dimensional fundamental or technical information, the integrated approach to fundamental and technical investing significantly improves portfolio performance. In addition, the joint strategies appear to perform better for stocks with higher idiosyncratic risks. We also find that when combined with fundamental signals, the Bollinger Bands, a reversal-type technical indicator, outperforms the moving average, which captures recent momentum in prices. Contrary to earlier findings from the literature, we show that moving average based strategies tend to incur higher volatility, lower Sharpe ratio, and larger tail risk. Our findings are consistent with the notion that, especially among small firms, the trading activities from investors with limited information capacity can lead to inefficient prices.