1,720,969 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
L'insostenibile leggerezza dei Bund tedeschi nell'area Euro
No full textLa letteratura economica e quella dei principali organismi internazionali sui tassi di rendimento dei debiti sovrani dell’area euro non sembra aver preso in considerazione il tema in un ottica di intermediazione finanziaria, sul terreno suo proprio. In questo articolo si tenta di colmare questa lacuna con analisi quantitative e di scenario incentrata sui quattro maggiori paesi dell’area Euro (Germania, Italia, Francia e Spagna).
Le specificazioni adottate sono basate su una interazione tra effetti di breve periodo (modelli alla Capm di un ipotetico fondo di investimenti in titoli sovrani) e determinanti di medio lungo periodo legate ai fondamentali macro (debito e deficit su PIL).
Il fitting migliore rispetto ai modelli consensus deriva da Beta variabili di portafoglio che simulano i comportamenti divergenti tra tassi tedeschi e italiani, spagnoli e francesi (gli spread), non escludendo l’impatto dei fondamentali alla BCE in un’ottica di medio periodo. Dalle simulazioni, per la fine dell’anno in corso e in prospettiva per il 2014, sembrano emergere alcuni
interrogativi in merito alla efficacia delle misure di riequilibrio economico-finanziario suggerite dalla letteratura e/o dagli organismi internazionali nell’area euro: un rialzo dei tassi dei Bund tedeschi accoppiato ad un riallineamento delle politiche di bilancio simili quelle realizzate nei passati anni da Italia e Spagna ed in corso in Francia sembrerebbero, dalle proiezioni, implicare effetti di progressivo superamento dell’attuale stasi, da una parte, e di riduzione del rischio di break up dell’aera euro, dall'altra.Both the economic literature and the International Organizations Report’s on the sovereign debts yield’s in the Euro Area do not seem to have covered the issue on the asset allocation specific ground. This paper attempts to fill this gap with naïve quantitative analyses and scenario’s projections, focusing on the four major Eurozone countries (Germany, Italy, France and Spain). The approaches adopted are centered on the interactions between short-term behaviors (CAPM type models for a hypothetical fund investments in sovereign bonds) and medium- long behaviors linked to the macro fundamentals (debt and deficit on GDP). To some extent, the results fit better than obtained from current models (consensus models): portfolio time-varying Beta’s captures the divergent behaviors of the rates between Germany, Italy, France and Spain coupled with the impact of single countries macro-fundamentals. The various scenarios prospects covering the end of 2013 and the beginning of the 2014 exclude a realignment of South Countries spreads without a substantial increase of German rates (roughly speaking, the same rates of the Treasury Bills). Furthermore, 200 basis points spread between Italian Bond and benchmark Deutschland Bond is foreseeable only by (literature’s counterintuitive) measures to comprise Germany debt and deficit, similar to those adopted by Italy and Spain, and in progress in France. In all et all and finally, the results suggest a focus on the risks due to the effect of a dramatic reduction German bonds price, i.e. the risks that the asset purchases of German bonds (today a sort of safe haven and insurance for the other bonds of the euro area) may to shrink due to some shock/expectations changes of a big international investor and subsequent domino effects
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
“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
Why do banks react differently to short-selling bans? Evidence from the Asia-Pacific area and the United States
No full textThe use of short-selling bans in different countries has greatly caught the attention of policy modelling. Our study is among the first studies to try to explain the phenomenon of different bank price reactions in terms of country and stock market conditions, looking at both the stock price reaction and risk. Overall, our findings suggest that the impacts of these bans on overall market efficiency were heterogeneous and, in most cases, modest for the countries analysed. Indeed, either we do not observe any improvements or the improvements are only short-lived. For the first time, we document that banks react differently to ban restrictions mostly because of differences in terms of their fundamental factors (balance sheet indicators). Given that Us and Asia-Pacific banks react differently to a ban on short selling depending on the particular financial structure of each market, when taking these actions, policy makers should consider which firm characteristics are most important and should pay attention to whether these interventions are effective in the market. Moreover, short-selling restrictions did not contribute to containing the volatility of the financial stocks subjected to the bans; on the contrary, our results indicate that negative volatility increased in some countries
The Influence of External Contextual and Firm-Specific Stakeholder Voices on Banks' Greenwashing: Effective Monitoring or an Incentive to Deceive?
No full textThis article investigates the role of external stakeholder “voices” in shaping banks' greenwashing behaviors. We categorize these voices into two groups: “contextual” voices, including regulations, a country's climate change performance, and public attention, and “firm-specific” voices, represented by ESG (environmental, social, and governance) ratings and analyst coverage. The distinction between these categories lies in their scope: contextual voices affect industries and companies collectively, while firm-specific voices pertain to individual firms. We apply a panel data analysis to a sample of 65 banks from the G20 Forum between 2015 and 2022, using a novel greenwashing indicator based on discrepancies between disclosure and action, where “action” is made up of environmental project lending, asset management, and investment strategies. Our findings reveal that a country's environmental performance and ESG ratings can help reduce greenwashing, with ESG ratings showing a moderate and significant negative association with greenwashing intensity, whereas environmental and legal frameworks may even encourage deceptive practices, likely due to inconsistencies arising from a fast-evolving regulatory landscape and fragmented enforcement. Interestingly, while greater analyst coverage of a bank appears to increase the likelihood of greenwashing, public attention seems to have the opposite effect. This research contributes to understanding how external stakeholders can mitigate banks' greenwashing strategies and offers valuable insights for policymakers and regulators, suggesting that measures such as improving the quality of sustainability reporting standards and requiring third-party verification of environmental claims can significantly strengthen banks' commitment to green initiatives and curb greenwashing
On the dynamics of a SIR model for a financial risk contagion
No full textThis work starts from an analogy between fnancial systems and ecosystems so that the SIR
mathematical approach can be revisited in modeling a kind of risk contagion among fnancial players. We are interested on a specifc type of fnancial risk contagion which identifes
frms as the key participants responsible for propagating this contagion. In this respect, the
proposed mechanism facilitating this transmission is the Supply Chain framework. In this
direction, we focus on a new SIR dynamic with time delay which represents the “fnancial
immunity” after recovery. A complete and robust analysis about asymptotic stability is performed for both risk-free and not-free-risk steady states at the long run, by applying Lyapunov functional method. The model is applied to perform some simulations with application
in diferent Italian economic sectors
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