101,884 research outputs found
The effect of monetary policy interventions on interbank markets, equity indices and G-SIFIs during financial crisis
No full textSince 2007, monetary authorities around the globe have reduced their key policy interest rates to unprece-dented low levels and intervened with non-standard policy measures (i.e., monetary easing and liquidityprovision) to support funding conditions for banks, enhance lending to the private sector and contain con-tagion in financial markets (e.g., European Central Bank, 2011). Using a detailed dataset of monetary policyinterventions between June 2007 and June 2012 in the most advanced monetary areas (the Euro area,Japan, the U.S., the UK and Switzerland), we analyze their effects at three different levels, including (1)the interbank credit market, considering the 3-month LIBOR-OIS spread as a measure of financial distress(e.g., Taylor and Williams, 2009); (2) the stock market, represented by wide equity indices; and (3) thebanking sector, focusing on global systematically important financial institutions (G-SIFIs). We demon-strate that different monetary policy interventions from single central banks have produced a diversemarket reaction. Standard measures have been more effective than non-conventional ones in restoringthe interbank market, which is fundamental for maintaining a fully operational traditional interest ratechannel and for guaranteeing the normal functioning of financial intermediation. Non-traditional mea-sures have registered a stronger stock market reaction with respect to standard interest rate decisions,both in terms of broad equity indices and single prices of large banks
Central Banks’ Commitment to Stakeholders: CSR in the Eurosystem: 2006–2016
No full textThis is a preprint version of the following final paper: Farina V, Galloppo G, Previati D (2019). Central Banks Commitment to stakeholders: CSR
in the Eurosystem: 2006-2016. In: Frontier topics in Banking. Palgrave
https://doi.org/10.1007/978-3-030-16295-5_
Fund manager performance in Emerging Market: factor specialization and financial crisis impact
No full textIn the branch of literature dealing with analysis of the consistency of management styles, this paper investigates the relation, between portfolio concentration and the performance of emerging market equity funds. Unlike previous studies, on global and us mutual fund, we focus on emerging markets equity, finding funds with higher levels of tracking error, display lower performance than funds with less diversified portfolios when we don’t take into account specific concentration in holding in different multifactor style. The explanatory power of local models that use local explanatory returns is recently investigated by De Groot et al. (2012), Cakici et. al (2013) and Fama and French (2012). Following same research line, most remarkable findings of this paper, is that the fund picking process only based on the level of track error from a broad benchmark, can contribute to disappointing results when it is not also accompanied, by information about the fund concentration in multiple market segment. According to previous work, overall we found, that local factor market model provide quite good representation of local average returns for portfolios formed on size, and style factors. Our novel in literature, consists in examining emerging market funds both from the perspective of active management and breath of underlined strategies. More as additional analysis with respect to most of previous paper, we also tested the effects of the crisis that we have found that main result has not affected from it
Alternative Neural Nets Approaches for Enhancing Stock Picking using Earnings Forecasts
No full textIn the last decade, neural networks have drawn noticeable attention from many computer and operations
researchers. Essentially, a Neural Network is a non-parametric estimation technique. It does not make any
distributional assumption regarding the underlying variable. In various studies Artificial Neural Network
(ANN) models, have been proved to be powerful predictive tools, where a variable is explained by a set of
explanatory variables without assuming any structural or linear relationship among the variables. Some
critical success factors to train ANN are the network architecture, network design algorithm, training
algorithm, and stop training conditions.
While some previous studies, have found encouraging results with using this artificial intelligence technique
to predict the movements of established financial markets, there is a lack of studies examining the stock
picking ability of different ANN paradigms, taking into account analyst earning forecasts as input data. Our
approach is based on the notion, that trading strategies guided by forecasts of stock earnings, may be
effective and lead to higher profits.
This paper attempts to enhance the stock selection process by employing ANN to select stocks in the Us
stock market. Neural networks are used to identify stocks for the portfolio which are likely to outperform the
market, given the forecast earnings information of stocks. Our purpose is to compare various ANN models,
to identify critical predictors to forecast stock prices and to see, which model show the best stock picking
ability, increasing in this way investment strategy profitability for the professionals in the market. The
competiting models, are examined in terms of various trading performance and economic criteria, like
annualized return, Sharpe ratio, maximum drawdown, annualized volatility, average gain/loss ratio etc via a
trading experiment.
Empirical experimentation suggests that by using artificial neural networks for nonlinear predictions there is
potential economic value for subsequent portfolio choices. ANN-based investment strategies, once financial
analyst earning forecast are considered, obtain higher returns than other investment strategies examined in
this study. Consequently, we find that the returns obtained from the equally weighted portfolio formed by the
stocks selected by neural networks, outperform those generated by the buy and hold strategy, computed with
a benchmark index for a given period under investigation. The influences of the length of investment horizon
and the commission rate are also considered. The present study does not support efficient market hypotheses
Analysis of closed real estate funds in Italy
No full textIn this paper, we examine closed real estate funds, comparing the Italian case with the international closed real estate fund market. We study whether the “public hand” has acted in an efficient market way to achieve return results in line with private competitors. In the last 10 years, international closed real estate funds have had an annual average of 0.5%. This result represents a very poor performance when compared with the returns offered by international bonds (5.6%) or international l equity markets (6.9%). This positive trend, however, is not followed by the closed real estate investment fund sponsored by the Italian government. On average, during the recent financial crisis, the returns of the international closed real estate funds in the euro area increased by more than 14 percentage points, while those of the Swiss franc area were about 1.5%
Alternative Neural Nets Approaches for Enhancing Stock Picking using Earnings Forecasts
No full textIn the last decade, neural networks have drawn noticeable attention from many computer and operations
researchers. Essentially, a Neural Network is a non-parametric estimation technique. It does not make any
distributional assumption regarding the underlying variable. In various studies Artificial Neural Network
(ANN) models, have been proved to be powerful predictive tools, where a variable is explained by a set of
explanatory variables without assuming any structural or linear relationship among the variables. Some
critical success factors to train ANN are the network architecture, network design algorithm, training
algorithm, and stop training conditions.
While some previous studies, have found encouraging results with using this artificial intelligence technique
to predict the movements of established financial markets, there is a lack of studies examining the stock
picking ability of different ANN paradigms, taking into account analyst earning forecasts as input data. Our
approach is based on the notion, that trading strategies guided by forecasts of stock earnings, may be
effective and lead to higher profits.
This paper attempts to enhance the stock selection process by employing ANN to select stocks in the Us
stock market. Neural networks are used to identify stocks for the portfolio which are likely to outperform the
market, given the forecast earnings information of stocks. Our purpose is to compare various ANN models,
to identify critical predictors to forecast stock prices and to see, which model show the best stock picking
ability, increasing in this way investment strategy profitability for the professionals in the market. The
competiting models, are examined in terms of various trading performance and economic criteria, like
annualized return, Sharpe ratio, maximum drawdown, annualized volatility, average gain/loss ratio etc via a
trading experiment.
Empirical experimentation suggests that by using artificial neural networks for nonlinear predictions there is
potential economic value for subsequent portfolio choices. ANN-based investment strategies, once financial
analyst earning forecast are considered, obtain higher returns than other investment strategies examined in
this study. Consequently, we find that the returns obtained from the equally weighted portfolio formed by the
stocks selected by neural networks, outperform those generated by the buy and hold strategy, computed with
a benchmark index for a given period under investigation. The influences of the length of investment horizon
and the commission rate are also considered. The present study does not support efficient market hypotheses
Alternative neural network approaches for enhancing stock picking using earnings forecasts
No full textInterest in financial markets has increased in the last couple of decades, among fund managers, policy makers, investors, borrowers, corporate treasurers and specialized traders. Forecasting the future returns has always been a major concern for the players in stock markets and one of the most challenging applications studied by researchers and practitioners extensively. Predicting the financial market is a very complex task, because the financial time series are inherently noisy and non-stationary and more it is often argued that the financial market is very efficient. Fama (1970) defined efficient market hypothesis (EMH) where the idea is a market in which security prices at any time ‘fully reflect’ all available information both for firms’ production—investment decisions, and investors’ securities selection. Furthermore, in EMH context no investor is in a position to make unexploited profit opportunities by forecasting futures prices on the basis of past prices. On the other hand, a large number of researchers, investors, analysts, practitioners etc. use different techniques to forecast the stock index and prices. In the last decade, applications associated with artificial neural network (ANN) have drawn noticeable attention in both academic and corporate research
The mitigation role of corporate sustainability: Evidence from the CDS spread
No full textcorporations listed on the S&P500. We aim to shed new light on the risk mitigation effect by
disentangling the empirical evidence across economic sectors and the ESG determinants. Results
provide strong evidence that ESG scores are negatively associated with firm credit risk once
controlling for endogeneity. These conclusions are robust after controlling for restructuring
clauses, financial crisis period, and different maturities. Under the Basel framework, financial
intermediaries could operate with lower regulatory capital with an active risk management
strategy based on CDS trading policy
A Multi-Criteria Decision Analysis Framework for Prioritizing Investments Evaluation in Banking Sector: An Application on Asean Market
No full textThe purpose of the present study is the development of classification model taking into account a set of different criteria that could be used in the evaluation of strategic investment alternatives in the banking sector. The model takes into account the main criteria required to enrich the quality of a company's information system. The global financial crisis has exacerbated the problem of seeking new markets for financial intermediaries as well as competition between them. The same crisis has also highlighted the problem of limiting errors in the strategic decisions for the development of each banking institution new business. The planning and appraisal of a new business projects involve rather complex tasks. Multi-criteria methods provide a flexible tool that is able to handle and bring together a wide range of variables appraised in different ways and thus offer valid assistance in supporting financial economic decision processes. Unfortunately a great number of determinants can create problems with the evaluation. Thus it is necessary to select a limited number of important indicators. In this paper we build a synthetic index, starting from a selection of a set of indicators, and weighting them via linear and non linear multivariate analysis. We use a sample of 42 criteria, extracted from World Bank database, that includes indicators of the macroeconomic, institutional and regulatory environment, for all Asean countries, during 1995-2011, as well as basic characteristics of the banking and financial sector. The resulting ABA (Attractiveness of Banking activity) Index could be an useful tool in financial business opportunities evaluations
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