Int. J. Financial Stud.2015, 3(4), 431-450; doi:10.3390/ijfs3040431 - published 28 September 2015 Show/Hide Abstract
Abstract: Undiversifiable (or systematic risk) has long been an enemy of investors. Many countercyclical strategies have been developed to counter this. However, like all insurance types, these strategies are generally costly to implement, and over time can significantly reduce portfolio returns in long and extended bull markets. In this paper, we discuss an alternative technique, founded on the premise of physiological bias and risk-aversion. We take a behavioral discussion in order to contextualize the insurance like characteristics of option pricing and discuss how this can lead to a mispricing of the asymmetric relationship between the VIX and the S&P 500. To test this, we perform studies in which we find statistical inefficiencies, thereby making it possible to implement a method of hedging index option premium in a way that has displayed no monthly drawdowns in bullish periods, while still providing large returns in major sell-offs. The three versions of the strategy discussed have negative betas to the S&P 500, while exhibiting similar risk-adjusted excess returns over both bull and bear markets. Further, the performance generated over the entire period, for all three strategies, is highly statistically significant. The results challenge the weak form of the Efficient Market Hypothesis and provide evidence that the methods of hedging could be a valuable addition to an equity rich portfolio for the purpose of counteracting systematic risk.
Int. J. Financial Stud.2015, 3(3), 423-430; doi:10.3390/ijfs3030423 - published 9 September 2015 Show/Hide Abstract
Abstract: This Guest Editor’s note reflects on the contributions of each article in the Special Issue on family firms’ behavior and performance. Building on this, several under-researched areas concerning family involvement in businesses are identified and the resulting impact on firm behavior and performance is explained. Finally, future research directions and insights for practitioners are outlined.
Int. J. Financial Stud.2015, 3(3), 411-422; doi:10.3390/ijfs3030411 - published 8 September 2015 Show/Hide Abstract
Abstract: Risk management is one of the most important branches of business and finance. Classification models are the most popular and widely used analytical group of data mining approaches that can greatly help financial decision makers and managers to tackle credit risk problems. However, the literature clearly indicates that, despite proposing numerous classification models, credit scoring is often a difficult task. On the other hand, there is no universal credit-scoring model in the literature that can be accurately and explanatorily used in all circumstances. Therefore, the research for improving the efficiency of credit-scoring models has never stopped. In this paper, a hybrid soft intelligent classification model is proposed for credit-scoring problems. In the proposed model, the unique advantages of the soft computing techniques are used in order to modify the performance of the traditional artificial neural networks in credit scoring. Empirical results of Australian credit card data classifications indicate that the proposed hybrid model outperforms its components, and also other classification models presented for credit scoring. Therefore, the proposed model can be considered as an appropriate alternative tool for binary decision making in business and finance, especially in high uncertainty conditions.
Int. J. Financial Stud.2015, 3(3), 393-410; doi:10.3390/ijfs3030393 - published 27 August 2015 Show/Hide Abstract
Abstract: The study aims at examining how fiscal deficits affect the performance of the stock market in India by using annual data from 1988–2012. The study makes use of Ng-Perron unit root tests to check the non-stationarity property of the series; the Auto Regressive Distributed Lag (ARDL) bounds test and a Vector Error Correction Model (VECM) for testing both short and long run dynamic relationships. The variance decomposition (VDC) is used to predict the exogenous shocks of the variables. The findings of the bounds test reveal that the estimated equation and the series are co-integrated. The ARDL results suggest a long run negative relationship exists between budget deficit and stock prices and do not show any significant relationship in the short run. The VECM result shows that fiscal deficits influence the stock price only in the short run. The results of the Variance Decomposition show that stock price movement in the long run is mostly explained by shocks of fiscal deficits. The study implies that the government must adopt appropriate macroeconomic policies to reduce budget deficit, which will result in stock market growth and in turn will lead to the financial development of the country.
Int. J. Financial Stud.2015, 3(3), 381-392; doi:10.3390/ijfs3030381 - published 13 August 2015 Show/Hide Abstract
Abstract: The paper provides probability estimates of the state of the GDP growth. A regime-switching model defines the probability of the Greek GDP being in boom or recession. Then probit models extract the predictive information of a set of explanatory (economic and financial) variables regarding the state of the GDP growth. A contemporaneous, as well as a lagged, relationship between the explanatory variables and the state of the GDP growth is conducted. The mean absolute distance (MAD) between the probability of not being in recession and the probability estimated by the probit model is the function that evaluates the performance of the models. The probit model with the industrial production index and the realized volatility as the explanatory variables has the lowest MAD value of 6.43% (7.94%) in the contemporaneous (lagged) relationship.
Int. J. Financial Stud.2015, 3(3), 351-380; doi:10.3390/ijfs3030351 - published 12 August 2015 Show/Hide Abstract
Abstract: Financial disasters to hedge funds, bank trading departments and individual speculative traders and investors seem to always occur because of non-diversification in all possible scenarios, being overbet and being hit by a bad scenario. Black swans are the worst type of bad scenario: unexpected and extreme. The Swiss National Bank decision on 15 January 2015 to abandon the 1.20 peg against the Euro was a tremendous blow for many Swiss exporters, but also Swiss and international investors, hedge funds, global macro funds, banks, as well as the Swiss central bank. In this paper, we discuss the causes for this action, the money losers and the few winners, what it means for Switzerland, Europe and the rest of the world, what kinds of trades were lost and how they have been prevented.