An application of a random level shifts model to the volatility of Peruvian stock and exchange rate returns

The literature has shown that the volatility of stock and forex rate market returns shows the characteristic of long memory. Another fact that is shown in the literature is that this feature may be spurious and volatility actually consists of a short memory process contaminated with random level shifts (RLS). In this paper, we follow recent econometric approaches estimating an RLS model to the logarithm of the absolute value of stock and forex returns. The model consists of the sum of a short-term memory component and a component of level shifts. The second component is specified as the cumulative sum of a process that is zero with probability ‘1-alpha’ and is a random variable with probability ‘alpha’. The results show that there are level shifts that are rare, but once they are taken into account, the characteristic or property of long memory disappears. Also, the presence of General Autoregressive Conditional Heteroscedasticity (GARCH) effects is eliminated when included or deducted level shifts. An exercise of out-of-sample forecasting shows that the RLS model has better performance than traditional models for modelling long memory such as the models ARFIMA (p,d,q).     

A predictive system integrating intrinsic mode functions,artificial neural networks,and genetic algorithms for forecasting S&P500 intra‐day data

There is an abundant literature on the design of intelligent systems to forecast stock market indices. In general, the existing stock market price forecasting approaches can achieve good results. The goal of our study is to develop an effective intelligent predictive system to improve the forecasting accuracy. Therefore, our proposed predictive system integrates adaptive filtering, artificial neural networks (ANNs), and evolutionary optimization. Specifically, it is based on the empirical mode decomposition (EMD), which is a useful adaptive signal‐processing technique, and ANNs, which are powerful adaptive intelligent systems suitable for noisy data learning and prediction, such as stock market intra‐day data. Our system hybridizes intrinsic mode functions (IMFs) obtained from EMD and ANNs optimized by genetic algorithms (GAs) for the analysis and forecasting of S&P500 intra‐day price data. For comparison purposes, the performance of the EMD‐GA‐ANN presented is compared with that of a GA‐ANN trained with a wavelet transform's (WT's) resulting approximation and details coefficients, and a GA‐general regression neural network (GRNN) trained with price historical data. The mean absolute deviation, mean absolute error, and root‐mean‐squared errors show evidence of the superiority of EMD‐GA‐ANN over WT‐GA‐ANN and GA‐GRNN. In addition, it outperformed existing predictive systems tested on the same data set. Furthermore, our hybrid predictive system is relatively easy to implement and not highly time‐consuming to run. Furthermore, it was found that the Daubechies wavelet showed quite a higher prediction accuracy than the Haar wavelet. Moreover, prediction errors decrease with the level of decomposition.     

The role of attribute selection in Deep ANNs learning framework for high‐frequency financial trading

In financial trading, technical and quantitative analysis tools are used for the development of decision support systems. Although these traditional tools are useful, new techniques in the field of machine learning have been developed for time‐series forecasting. This paper analyses the role of attribute selection on the development of a simple deep‐learning ANN (D‐ANN) multi‐agent framework to accomplish a profitable trading strategy in the course of a series of trading simulations in the foreign exchange market. The paper evaluates the performance of the D‐ANN multi‐agent framework over different time spans of high‐frequency (HF) intraday asset time‐series data and determines how a set of the framework attributes produces effective forecasting for profitable trading. The paper shows the existence of predictable short‐term price trends in the market time series, and an understanding of the probability of price movements may be useful to HF traders. The results of this paper can be used to further develop financial decision‐support systems and autonomous trading strategies for the financial market.     

Earnings Quality under Rules‐ versus Principles‐based Accounting Standards: A Test of the Skinner Hypothesis / LA QUALITÉ DES RÉSULTATS SELON QUE LES NORMES COMPTABLES SONT AXÉES SUR LES RÈGLES OU SUR LES PRINCIPES: VÉRIFICATION DE L'HYPOTHÈSE DE SKINNER*

We provide preliminary evidence, consistent with Skinner (1995), that Canada's relatively principles‐based GAAP yield higher accrual quality than the United States' relatively rules‐based GAAP. These results stem from a comparison of the Dechow‐Dichev (2002) measure of accrual quality for cross‐listed Canadian firms reporting under both Canadian and U.S. GAAP. However, we document lower accrual quality for Canadian firms reporting under U.S. GAAP than for U.S. firms, which are subject to stronger U.S. oversight, reporting under U.S. GAAP. The latter results suggest that stronger U.S. oversight compensates for inferior accrual quality associated with rules‐based GAAP. Consistent with the positive effect of Canada's principles‐based GAAP and the offsetting negative effect of Canada's weaker oversight, we find no overall difference in accrual quality between Canadian firms reporting under Canadian GAAP and U.S. firms reporting under U.S. GAAP. Our results imply that (1) policymakers who wish to compare the effectiveness of oversight across jurisdictions must control for the GAAP effect; and (2) accounting standard‐setters who wish to compare the effectiveness of principles‐ versus rules‐based GAAP must control for oversight strength.     

Use of the Proceeds and Long‐term Performance of French SEO Firms

This paper examines the long‐term stock performance of French SEO with rights by looking at the intended use of the proceeds. Firms that raise equity for pure capital structure motives are separated from the ones that use the SEO proceeds to finance specific investment projects. Issuers in the first category are concerned about preserving their financial flexibility and they are expected to evolve in a capital structure irrelevancy framework. On the other hand, issuers in the second category are more inclined to be sensitive to adverse selection problems or agency conflicts and thus, they should be more exposed to under‐reaction on the long‐run. According to a matching firm methodology, ‘Financing New Investment’ issuers underperform their benchmark at a rate of 4% to 8% per year over a 36‐month horizon while ‘Capital Structure’ issuers do not show any abnormal performance. These results are robust according to alternative Beta pricing models. In addition, managers of both issuer's types time the SEO after a period of positive abnormal performance in order to sell overpriced securities. However, only the ‘Financing New Investment’ sample experiences a performance reversal; the abnormal returns decreasing gradually from the issue on, to become significantly negative 24 months after the event.     

Long‐Term Care Across Europe and the United States: The Role of Informal and Formal Care

Large cross‐country variation in long‐term‐care (LTC) policy in conjunction with household‐level data on caregiving provides a valuable laboratory for policy analysis. However, there is a lack of comprehensive cross‐country data on how care is provided. In order to close this gap, we draw on data from the Survey of Health, Ageing, and Retirement in Europe (SHARE) and the Health and Retirement Study (HRS) in the United States. Because care hours are missing for some care forms (especially for nursing‐home residents), we propose a selection model to impute these. The model allows selection into care forms to differ by country. Our estimates imply that nursing‐home residents have higher care needs, even when conditioning on observed characteristics. In contrast to the bulk of the literature, we also take into account care provision from persons in the same household, and we find that this contributes one‐third of all care hours. Informal‐care provision in Europe follows a steep North–South gradient, with the United States falling in between Central European and Southern European countries. The results are robust to alternative imputation schemes.