1,720,984 research outputs found
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
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
Volatility and liquidity in Eastern Europe financial markets under efficiency and transparency conditions
No full textFollowing the consequences of the global financial crises, transparency and efficiency conditions of a local economic system have become important remedies for restoring of financial markets. This study provides measure of transparency and efficiency with correlation to liquidity and volatility and is taking into account the stock price reaction of emerging financial stock markets of Eastern Europe area and Turkey. We find that observed countries don’t fully answer the expected sign of transparency, liquidity and risk measure, which meets the innovation from previous works (Berglöf, Pajuste, 2005). It raises doubts concerning functioning of legal basement in these countries and affects the decisions about investments. In line with previous research (Ivanov, Lomev and Bogdanova, 2012) our findings show that these countries don’t prove to have certain transparency expectations, which could result in a limited access to market information and in a decrease of market efficiency
'Much Ado About Nothing': Short Selling Ban Effectiveness on Bank Stock Prices
No full textMost regulators around the world reacted to the 2007-09 crisis by imposing bans on short selling. Using data from seven equity markets, this study empirically examines the impact of the 2008 short-selling bans on financial stocks.
Using panel and matching techniques, evidence indicates that bans on short-selling (i) on the whole widen volatility both in terms of High-Low spread and GARCH analysis, (ii) were not able to reduce systematic risk, (iii) overall failed to support prices. On the whole our results are in line with previous literature
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
“Much ado about nothing”: Short selling ban effectiveness on bank stock prices
No full textMost regulators around the world reacted to the 2007-09 crisis by imposing bans on short selling. Using data from seven equity markets, this study empirically examines the impact of the 2008 short-selling bans on financial stocks.
Using panel and matching techniques, evidence indicates that bans on short-selling (i) on the whole widen volatility both in terms of High-Low spread and GARCH analysis, (ii) were not able to reduce systematic risk, (iii) overall failed to support prices. On the whole our results are in line with previous literature
Crowdfunding en la union europea: factores impulsores y atractivo
No full textThis article analyzes crowdfunding activities in the EU area. We believe they might play a major role in the future, complementing the traditional activities of financial intermediation. In support of our beliefs, we notice the establishment of several crowdfunding platforms, particularly in the USA and in the EU, and the relevance assigned to crowdfunding procedures in the Obama’s JOBS Act of 2012 and in many support activities recently launched by the European Commission. By using the available data at European level we develop a Crowdfunding Attractiveness Index (CFA), with the aim to rank the crowfunding potential of different European countries
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