NONLINEAR FORECASTING OF THE GOLD MINER SPREAD: AN APPLICATION OF CORRELATION FILTERS

This paper models and forecasts the Gold Miner Spread from 23 May 2006 to 30 June 2011. The Gold Miner Spread acts as a suitable performance indicator for the relationship between physical gold and US gold equity. The contribution of this investigation is twofold. First, the accuracy of each model is evaluated from a statistical perspective. Second, various forecasting methodologies are then applied to trade the spread. Trading models include an ARMA (12,12) model, a cointegration model, a multilayer perceptron neural network (NN), a particle swarm optimization radial basis function NN and a genetic programming algorithm (GPA). Results obtained from an out‐of‐sample trading simulation validate the in‐sample back test as the GPA model produced the highest risk‐adjusted returns. Correlation filters are also applied to enhance performance and, as a consequence, volatility is reduced by 5%, on average, while returns are improved between 2.54% and 8.11% across five of the six models. Copyright © 2013 John Wiley & Sons, Ltd.     

贷款基础利率运行特征与影响

贷款基础利率（LPR）的推出是我国利率市场化的重要一步。LPR运行半年来,总体呈平稳单边上行走势,波动幅度较小,但与货币市场基准利率存在一定背离。文章分析指出,存贷款利率或额度管理、货币市场与信贷市场资金流通存在障碍是LPR与货币市场利率背离的主要原因;LPR缓慢上行主要受到银行存款贷款增速下滑的影响。文章从完善LPR报价模型、推广LPR在信贷产品中的运用等方面提出进一步发挥LPR基准作用的建议。     

2012年1季度银行问市场运行报告

2012年1季度，银行间市场整体平稳运行，主要特点是：货币市场成交量同比大幅增长，季节性因素主导货币市场利率走势呈M型特征；银行间债券市场以震荡调整为主，总体呈现先抑后扬态势；人民币汇率弹性趋于增强，升值趋势进一步减§弓；Shibor对利率互换的基准作用增强；外汇衍生品市场保持快速增长，市场结构持续优化。     

USING NEURAL NETS TO COMBINE INFORMATION SETS IN CORPORATE BANKRUPTCY PREDICTION

We demonstrate that the use of a neural network (NN) model to combine information from corporate financial statements and equity markets provides improved predictive estimates of the probability of corporate bankruptcy. Using performance measures, based on the receiver operating characteristic curve, the forecast combinations from the NN models are demonstrated to outperform the forecasts derived from a forecast combination generated using a logistic regression approach. This result provides support for the use of forecast combinations generated from NN models in the estimation of corporate bankruptcy probabilities as it outperforms the standard approach of forming a hybrid forecasting model which includes all the explanatory variables. Copyright © 2012 John Wiley & Sons, Ltd.     

GP algorithm versus hybrid and mixed neural networks

In the current paper, we present an integrated genetic programming (GP) environment called java GP modelling. The java GP modelling environment is an implementation of the steady-state GP algorithm. This algorithm evolves tree-based structures that represent models of inputs and outputs. The motivation of this paper is to compare the GP algorithm with neural network (NN) architectures when applied to the task of forecasting and trading the ASE 20 Greek Index (using autoregressive terms as inputs). This is done by benchmarking the forecasting performance of the GP algorithm and six different autoregressive moving average model (ARMA) NN combination designs representing a Hybrid, Mixed Higher Order Neural Network (HONN), a Hybrid, Mixed Recurrent Neural Network (RNN), a Hybrid, Mixed classic Multilayer Perceptron with some traditional techniques, either statistical such as a an ARMA or technical such as a moving average convergence/divergence model, and a naïve trading strategy. More specifically, the trading performance of all models is investigated in a forecast and trading simulation on ASE 20 time-series closing prices over the period 2001–2008, using the last one and a half years for out-of-sample testing. We use the ASE 20 daily series as many financial institutions are ready to trade at this level, and it is therefore possible to leave orders with a bank for business to be transacted on that basis. As it turns out, the GP model does remarkably well and outperforms all other models in a simple trading simulation exercise. This is also the case when more sophisticated trading strategies using confirmation filters and leverage are applied, as the GP model still produces better results and outperforms all other NN and traditional statistical models in terms of annualized return.     

Black–Scholes versus artificial neural networks in pricing FTSE 100 options

This paper compares the performance of Black–Scholes with an artificial neural network (ANN) in pricing European‐style call options on the FTSE 100 index. It is the first extensive study of the performance of ANNs in pricing UK options, and the first to allow for dividends in the closed‐form model. For out‐of‐the‐money options, the ANN is clearly superior to Black–Scholes. For in‐the‐money options, if the sample space is restricted by excluding deep in‐the‐money and long maturity options (3.4% of total volume), then the performance of the ANN is comparable to that of Black–Scholes. The superiority of the ANN is a surprising result, given that European‐style equity options are the home ground of Black–Scholes, and suggests that ANNs may have an important role to play in pricing other options for which there is either no closed‐form model, or the closed‐form model is less successful than is Black–Scholes for equity options. Copyright © 2005 John Wiley & Sons, Ltd.     

How skilful are US fixed-income fund managers?

We develop a performance evaluation model that incorporates the factors proposed by Huij and Derwall (2008) and a fund-specific benchmark to analyse the performance of US fixed income funds. Using the full sample, and accounting for the possibility of false discoveries, we find that fund management companies extract most of any abnormal performance produced by their fund managers. Our sub-sample analysis indicates that after the Global Financial Crisis (GFC) there was a substantial increase in the number of bond funds with: both positive gross-of-fee alpha and positive net-of-fee alpha performance; and also a reduction in funds with negative-alpha performance. However, because the GFC was such a unique event, it would still be difficult to conclude that these managers offer value for money for investors compared to passive alternatives.     

The Use of Electronic Funds Transfers to Capture the Effects of Cash Management Practices on the Demand for Demand Deposits: A Note

The rapidly increasing use of more sophisticated cash management practices is a factor influencing the demand for money that is not considered in standard models of money demand. Within the framework of an inventory theoretic model of money demand, this paper provides theoretical grounds for using the number of electronic funds transfers as an indication of increasing cash management sophistication. Specifically, the demand for demand deposits is determined from the solution of a simultaneous equation system that also determines the optimal level of cash management. Therefore, the level of cash management services influences transactions costs, implying that transactions costs are endogenous. The number of electronic funds transfers is closely linked to the level of cash management services and is therefore related to transactions costs. Models of money demand that treat transactions costs as exogenous and fixed are therefore misspecified and will not perform well when transactions costs are changing. By explicitly incorporating the changing nature of transactions costs through the use of electronic funds transfers, the problems of instability and poor predictive power associated with the demand for money in the 1970's are overcome.     

Modelling and trading the EUR/USD exchange rate at the ECB fixing

The motivation for this paper is to investigate the use of alternative novel neural network (NN) architectures when applied to the task of forecasting and trading the euro/dollar (EUR/USD) exchange rate, using the European Central Bank (ECB) fixing series with only auto-regressive terms as inputs. This is done by benchmarking four different NN designs representing a higher-order neural network (HONN), a Psi Sigma Network and a recurrent neural network with the classic multilayer perception (MLP) and some traditional techniques, either statistical such as an auto-regressive moving average model, or technical such as a moving average convergence/divergence model, plus a naïve strategy. More specifically, the trading performance of all models is investigated in a forecast and trading simulation on the EUR/USD ECB fixing time series over the period 1999–2007 using the last one and half years for out-of-sample testing, an original feature of this paper. We use the EUR/USD daily fixing by the ECB as many financial institutions are ready to trade at this level and it is therefore possible to leave orders with a bank for business to be transacted on that basis. As it turns out, the MLP does remarkably well and outperforms all other models in a simple trading simulation exercise. However, when more sophisticated trading strategies using confirmation filters and leverage are applied, the HONN network produces better results and outperforms all other NN and traditional statistical models in terms of annualized return.     

Deposit insurance and money market freezes

In the presence of deposit insurance, a rise in counterparty risk may cause a freeze in interbank money markets. We show this in a general equilibrium model with regionally segmented bank-based retail financial markets, in which money markets facilitate the reallocation of funds across banks from different regions. Counterparty risk creates an asymmetry between banks in savings-rich regions, which remain marginally financed by the abundant regional insured deposits, and in savings-poor regions, which have to pay large spreads in money markets. This asymmetry distorts the aggregate allocation of credit and, in the presence of demand externalities, can cause large output losses.     

2012年中国货币市场运行报告

文章介绍了2012年货币市场的总体运行情况，指出在国内外经济金融形势复杂多变、国际金融市场跌宕起伏的环境下，2012年我国货币市场平稳健康发展，主要运行特点有：市场规模大幅增长；市场利率有所下行，季节性特点明显；市场主体不断丰富，货币市场流动性管理等功能进一步发挥。     

2010年2月银行间市场运行报告

2010年2月份,银行间货币、外汇、债券、利率和汇率衍生品市场平稳运行,主要特点是：短期货币市场利率先扬后抑,短端Shibor各期限品种涨跌不一,中长端Shibor普遍上扬;银行间国债指数继续上升;人民币对美元汇率中间价与上月末相比升值0．001％,掉期价格显示人民币对美元升值预期有所减弱。     

The effects of foreign disturbances under flexible exchange rates

Foreign fiscal expansion has a contractionary, and monetary expansion an expansionary, effect on the small economy in the real wage model. The reverse conclusions hold in the money wage model only if the income elasticity of the demand for money is one or greater. The same results hold also for the stationay state effects. This theory provides an explanation—in the policy mix of the United States—for the recent lacklustre performance of Europe. Under rational expectations, foreign monetary expansion leads to an overshooting appreciation, fiscal expansion leading to several possible exchange rate responses. The new results are produced by consistently specified money demand and import functions, the supply side, and the wealth effect.     

Neural network copula portfolio optimization for exchange traded funds

This paper attempts to investigate if adopting accurate forecasts from Neural Network (NN) models can lead to statistical and economically significant benefits in portfolio management decisions. In order to achieve that, three NNs, namely the Multi-Layer Perceptron, Recurrent Neural Network and the Psi Sigma Network (PSN), are applied to the task of forecasting the daily returns of three Exchange Traded Funds (ETFs). The statistical and trading performance of the NNs is benchmarked with the traditional Autoregressive Moving Average models. Next, a novel dynamic asymmetric copula model (NNC) is introduced in order to capture the dependence structure across ETF returns. Based on the above, weekly re-balanced portfolios are obtained and compared using the traditional mean–variance and the mean–CVaR portfolio optimization approach. In terms of the results, PSN outperforms all models in statistical and trading terms. Additionally, the asymmetric skewed t copula statistically outperforms symmetric copulas when it comes to modelling ETF returns dependence. The proposed NNC model leads to significant improvements in the portfolio optimization process, while forecasting covariance accounting for asymmetric dependence between the ETFs also improves the performance of obtained portfolios.     

Informational role of analyst and investor days

This study empirically clarifies whether analyst and investor days (AI days) affect earnings expectations of participants. The analyses reveal that the tone of the question-and-answer (Q&A) session, rather than that of the management presentation, is positively associated with subsequent revisions of analysts' earnings forecasts. This suggests that such an interactive discussion during a Q&A session plays a key role in affecting analysts' expectations. Furthermore, abnormal returns around the AI days are irrelevant to the tone of the Q&A session. This suggests that AI days mainly provide a partially known (supplemental) information rather than offering new information to analysts and investors.     

Efficiently Inefficient Markets for Assets and Asset Management

We consider a model where investors can invest directly or search for an asset manager, information about assets is costly, and managers charge an endogenous fee. The efficiency of asset prices is linked to the efficiency of the asset management market: if investors can find managers more easily, more money is allocated to active management, fees are lower, and asset prices are more efficient. Informed managers outperform after fees, uninformed managers underperform, while the average manager's performance depends on the number of “noise allocators.” Small investors should remain uninformed, but large and sophisticated investors benefit from searching for informed active managers since their search cost is low relative to capital. Hence, managers with larger and more sophisticated investors are expected to outperform.     

Monetary randomness and investment

This paper develops a neoclassical model in which the behavior of the money supply affects investment by affecting the real distribution of asset returns. Investment depends on wealthholders' demand for capital. A stochastic money growth rule influences portfolio choice by affecting the distribution of the inflation rate. The variance of inflation matters to wealthholders because of the existence of assets with returns that are not indexed to changes in the price level: money and bonds which are contracted in nominal terms. In a rational expectations environment, asset demands will thus be sensitive to the distribution of the money growth rate. Our principle conclusion is that an increase in the variance of the money growth rate lowers investment, which complements Tobin's (1965) result that an increase in the mean stimulates capital accumulation. The paper also represents a step toward incorporating an asset market into a macroeconomic model in a manner which takes account of Lucas' (1976) criticism of econometric policy evaluation. All variables in the model, including asset return distributions, are functions of technology, preferences and the money supply rule. Further, expectations are rational.     

Status and incentives

This article introduces status as reflecting an agent's claim to recognition in her work. This is a scarce resource: increasing an agent's status requires that another agent's status be decreased. Higher‐status agents are more willing to exert effort in exchange for money; better‐paid agents would exert higher effort in exchange for improved status. The results are consistent with actual management practices: (i) egalitarianism is desirable in a static context; (ii) in a long‐term work relationship, juniors' compensation is delayed; and (iii) past performance is rewarded by pay increases along with improved status within the organization's hierarchy.     

The Purpose of the Firm,Valuation, and the Management of Intangibles

This article provides a different way of thinking about, and responding to, four important issues that confront most public companies. First, in articulating the overarching corporate purpose, the author suggests a middle ground between shareholder value maximization and stakeholder theory that aims to achieve the end result of value maximization while taking a “holistic” view that meets most of the demands of stakeholder advocates. As described by the author, there are four critical steps for management and boards in creating such companies: (1) communicating a vision of the company and its purpose to employees as well as investors (and other key outsiders); (2) organizing to survive and prosper through efficiency and innovation; (3) working continuously to develop win‐win relationships with stakeholders and other companies; and (4) taking care of the environment and future generations. Second, in thinking about the corporate purpose and how to evaluate success in achieving it, managements and boards need a valuation model that provides a clear and insightful connection between long‐term corporate performance and market valuation, and how both might be expected to change as the firm matures. A strong case is presented for the life‐cycle valuation model, widely used by money management organizations, in which a company's projected cash flows reflect an expected “fade” in both economic returns on capital and reinvestment rates. The potential uses of this model are illustrated using lifecycle corporate performance data for 3M during the past 50 years. Third, in an effort to capture the value of innovation and investment in intangible assets, the author presents an alternative to the accounting approach of capitalizing and amortizing such assets that attempts to capture their expected future benefits by using more favorable forecasts of long‐term fade rates. Fourth, the author shows how incorporating Life‐cycle Reviews for each of a company's business units as part of its Integrated Reporting could improve management's resource allocation decisions, help build a shareholder base of long‐term investors, and provide management with the support and confidence to resist Wall Street's excessive emphasis on quarterly earnings.     

Financial innovation,new assets,and the behavior of money demand

Two fundamental changes in US banking regulations have affected the behavior of money demand (M1). The first authorized checkable deposit accounts paying explicit interest rates. The second allowed these rates to be market determined. The theoretical literature does not directly address the impact of these events, suggesting that they are primarily an empirical issue. However, the empirical literature has yet to agree on the impact of financial innovation on money demand; for example, several studies report an increase in the elasticity of money demand, several others report a decline. This paper uses a Lancaster-type choice model to analyze formally the expected impact of these two changes on the demand for money. The model derives specific conditions under which (i) the demand for money increases as new assets are introduced and (ii) the impact of either the introduction of new assets or the elimination of interest rate restrictions on the elasticity of money demand.