Behavioralfinance realize a connection relating financial theory to practical investment analysis in order to provide a means of understanding the financial market complex situations. In fact, the main idea is finding an explanation for market inefficiencies such as : mispricings, irrational investment decision making and return anomalies. The influence of informational asymmetry, psychological, sociological and demographic structures can represent up to a certain level, a relevant answer to financial market anomalies. Investors are different some ofthe other in relation to numerous factors, such as : socio-economic background, financial context, level of education, religion, age, sex, traditions, ethnicity, marital status, and so on. They form expectations and beliefs that influence their investment decisions in a dramatic proportion. An optimum investment decision it cannot be achieved if the investor ignore all of these factors influence.
It is indeed relevant to test for financial contagion but, most importantly, the channels through which it occurs needs to be identified. Firstly, financial crisis have large costs, especially in terms ofthe stability of financial institutions, economic growth and employment. Therefore, understanding how these crises spread is important for policy makers so that they can take adequate measures to prevent or contain the spread of crises, especially by regulating financial markets and institutions, and managing expectations. Secondly, the existence of financial contagion has strong implications for international portfolio management. In fact, if stock return correlation across countries increases after a negative shock in a country, then the advantages of international diversification are reduced precisely when they were most needed (Longin and Solnik, 2001; Ang and Chen, 2002; Ang and Bekaert, 2002). Consequently, financial institutions will also be more exposed to risk in the presence of contagion. Thus, it is important to understand stockmarkets’ co- movements in crises so as to improve portfolio management and financial institutions supervision.
28 are causing significant losses for these specialized hedge funds. With a 16.42% monthly peak performance in August 1994 and a 23% maximum monthly drop in August 1998, the returns range is very wide. While most ofthe strategies registered a decline in their performance in 1994 during the bond crisis, Emerging Markets performance rose thanks to the expansion ofthe Asians "Tigers” and “Dragons". Strongly dependent onthe Asian countries performance, the index fell harshly in 1998 because ofthe Asian crisis. However, the Emerging Markets strategy recovered gradually and since 2001, had constantly positive performances even outperforming thestock market indexes since 2002. Emerging Markets volatility is very high (15.48%) but not higher than the DJW volatility and the DJW emerging markets (25.73%). The drawdown graph shows continuous periods of strong drops, with a particularly affected period (late 1997 until 2003). Thus, the maximum drawdown was registered in late 1998 declining -45.15%, almost as down as the Dedicated Short Bias strategy fell. This strategy index is significantly correlated with thestockmarkets, hence the drops experienced in the past.
In Methodology events (Table V) we found statistically significant abnormal returns in all regressions except one, the 6 months’ data in 2010 test. Yet they have different interpretations between them. The evidence shows that the Methodology release had a higher influence onthestockmarkets in comparison to the Announcement event. The 2010 test outputs a small positive abnormal return below 0.1% at 10% significance level. The market did not find the tests as being a real inspection onthe banks. In 2011 and 2014 these methodologies releases were differently assumed by the market, turning to small negative abnormal returns statistically significant at level of 5%. This negative impactonthe returns ofthe bank’s stocks occurred because the market’s fear the scrutiny
with wide variety of financial instruments provides opportunities for families in order to saving the various financial assets from efficiency firms. If there is different types of securities in the financial market , a large part ofthe surplus funds of individuals will be absorbed to the economic units; since in these conditions, people attracted on market with various degrees of risks and also , small saving will be absorbed , too ; also , efficient financial markets leads to the absorption the saving through increasing the liquidity ; since it provides that savers convert their financial assets whenever they need to own fortune. In addition, since the market cost information and monitoring on firms is extremely low in these markets , therefore , making sure from corporations and economical businesses will be obtain for households with the lowest cost, so , existing the efficient financial markets leads to the more equipment is of savings. Another function that is expressed to financial market is that such markets can be help to the process of price discovery because in such a market, prices usually reflect the information that The buyer and sellers need to it and the location and position of traders is established in such a way that trading between the buyers and sellers determine the exchanged asset prices. But it should be noted that real markets are much more complex than we can placed them into form of one or two theories and models and work basis on it in those markets . Complex causes ofbehavioral, psychological and economic have an much effect on prices of real and financial asset . By assumption the efficient financial market and data entry to the market , incorrect interpretation and analysis of information and even the level of optimism or pessimism to the mentioned information can be impact in determining the price . For example, optimist people behave optimistic with received information and they are ready to offer higher prices and also , they tolerate more risk on unreliable future. Also , weakness in interpretation of data and the lack of
Since companies are required to issue restatements to correct past reporting mistakes, restating activity may provide indications about the sources, the origins and the motivation of companies for providing poor quality financial reporting. Several authors have suggested that a high incidence of accounting accruals may be an indicator of low accounting quality. For example, Bradshaw, Richardson, and Sloan, (2001) and Sloan, (1996) suggest that high accrual level leads to an increase of information uncertainty, which causes an erroneous evaluation by investors since they are not able to use current earnings as an indicator of future earnings. In the same vein, Richardson, Tuna, and Wu (2003) suggests that companies are pressured by themarkets to report positive results in order to attract external finance and/or lower interest rates. This pressure may lead them to enhance earnings management through the use of accruals. Therefore, the discretionary use of accruals by management might also be an indicator ofthe low quality of financial reporting.
Abstract: Problem statement: One ofthe main purposes of modeling variance is forecasting, which is crucial in many areas offinance. Despite the burgeoning interest in and evaluation of volatility forecasts, a clear consensus on witch volatility model/or distribution specification to use has not yet been reached. Therefore, the out of-sample forecasting ability should be a natural model selection criterion for volatility models. Approach: In this study, we used high-frequency to facilitate meaningful comparison of volatility forecast models. We compared the performance of symmetric GARCH, asymmetric EGARCH and non leaner asymmetric NAGARCH models with six error distributions (normal, skew normal, student-t, skew student-t, generalized error distribution and normal inverse Gaussian). Results: The results suggested that allowing for a heavy-tailed error distribution leads to significant improvements in variance forecasts compared to using normal distribution. It was also found that allowing for skewness in the higher moments ofthe distribution did not further improve forecasts. Conclusion: Successful volatility model forecast depended much more heavily onthe choice of error distribution than the choice of GARCH models.
Finally, the estimated models highlight the importance of modelling the cyclical behaviour ofstock market returns to identify the real significance ofthe influence of interest rates on returns. Nonlinear threshold adjustment between bond markets and stockmarkets has implications for an investment strategy based on only one of these markets. It appears that any potential benefits from international diversification are greater for bond investors than for stock investors. Aslanidis and Christiansen (2012) have explored the similar effects of large short rates onthe present value of future stock and bond returns, thereby implying a positive stock-bond correlation. In this context, investors flee stocks and rush into bonds [flight to safety]. This movement implies negative
Specialized studies realized over time indicate the differences between theoretical models and econometric models used to quantify the herding behaviour of investors. Theoretical frameworks make reference to abstract models in order to identify the features of herding, while the empirical research only verifies the appearance of a “collective behaviour” onthe market. In the literature, herding behaviour has become a debated topic for researchers, which try to provide eloquent answers regarding its impacton capital markets evolution. Regarding the developed stockmarkets from Asia, (Tan, Chiang, Mason, Nelling, 2008) have investigated the investors’ herding behaviour onthe Chinese stockmarkets, both A and B, by using CSAD and CSSD models. Their findings indicate that the herding behaviour occurs on short time intervals. Moreover, there is an asymmetry in herding, it is most probable to occur during bull markets and in conditions of high volumes traded onthe market. Another research for Asian stockmarkets was performed by (Demirer, Kutan, Chen, 2010) for the Taiwanese stock market. Compared with other studies, this research comes to enrich the financial literature by applying the state space model together with CSAD and CSSD and by conducting an analysis at sector level. The results provide evidence of herding behaviour for all sectors of activity.
13 After the creation ofthe Euro as one common currency in 1999, it was assumed that big economies, like Germany and France, would never allow a member state to go “bankrupt” – “too big to fail” again – because that would have serious implications onthe complete Eurozone financial system. As a result, and since interest rates are related to the risk, countries like Greece and Portugal were able to borrow money at much lower rates (see figure 2.1) and ended up overspending and increasing sovereign debt (see appendix 4). When the subprime crisis hit first the US, especially when one ofthe largest investment banks, supposedly “too big to fail”, collapsed, investors and credit rating agencies became sceptical about the risk of some European countries. If Lehman Brothers went bankrupt, the possibility that some countries may also face credit default (even if they were in the EU) gained relevance.
Whereas traditional financial models of trade assume that agents have confidence reflecting the precision of their information, the empirics have proven that people only rarely show this characteristic: People tend to overestimate their ability to predict certain events when they have little or poor information. Surprisingly, people tend to be under confident in their ability to answer easy questions. Griffin and Tversky (1992) have coined this cognitive psychology phenomenon as the “hard- easy effect”. While their research was based on asking people hard and easy questions, financial research on financial markets come to the same conclusions: People are overconfident in their ability to predict hardly predictable markets, which can be shown proven by the excessive trade volume phenomenon (Odean, 1999). In regard to market reactions to information, Daniel, Hirschleifer and Subrahmanyam(1997) have proven that markets underreact to new information due to investors overconfidence in their previous beliefs. Further research by Daniel et al. (1998) has proven that traders overestimate their own ability of collecting precise information. Empirical examples from financial markets such as executives’ failure to exercise stock options before expiration, can be seen as overconfidence of people in their own abilities. As a result, investors overreact to private information while underreacting to public information. The previously outlined Post Earnings Announcement Drift paradigm can also be explained from a miscalibrated conference point of view as done by Bloomfild, Libby and Nelson (2003), who show that the PEAD comes from overconfident inferences of old earnings numbers with little information content once new numbers are available (Bloomfield 2006). As we will see in our study ofthe UAL undervaluation, Investors in other situations prove to be under-confident in their ability to exploit easily detectable arbitrage opportunities, while in turn being overconfident in their ability to predict extremely difficultly predictable scenarios.
Finally, we suggest as avenues for further research, the following. First, some preliminary results we obtain on day ofthe month effects, which we do not report, signal that this may be the type of calendar effect more relevant in European countries, justifying specific research. Second, the use of data on firms instead of indexes, allows the study of calendar effects by firm characteristics. Third, we need more studies using broader sets of countries, to determine if calendar effects are across-the-board or only country-specific. Fourth, a closer look at the low August / September returns in Europe, and the study ofthe reasons behind that effect, if it is confirmed. Fifth, there are several alternative variants ofthe GARCH model, like TGARCH and IGARCH; which one fits the data better? Sixth, we need to improve onthe microeconomics of calendar effects. We should strive to find the true economic (or behavioral) rationale behind calendar effects. We need to a better understanding on why calendar effects are expected to exist, and under what circumstances.
Several traditional studies tried to shed light onthe issue from an aggregate stock market return approach, looking for predictable cycles. Analyzing a specific period, these studies tried to match past high and low average market returns to political orientation ofthe incumbent president (or equivalent). In Niederhoffer et al. (1970), Allvine and O’Neill (1980) and Tufte (1980) the authors found evidence strongly suggesting the existence of a business cycle revolving the election periods in the US stock market, whereas Hensel and Ziemba (1995) showed that stocks from small-caps enjoyed higher returns during Democratic administrations. Analysis such as these cannot infer the causal nature ofthe cycles or market preferences (to left and right-wing governments) due to endogeneity problems and the because long periods analyzed usually span very different (and most likely not comparable) macroeconomic scenarios. Gärtner and Wellershoff (1995) expanded previous studies augmenting the estimation by using ARMA models and macroeconomic variables, arguably reducing the latter problem.
Since the genesis of financial markets, intermediaries such as brokers and dealers, hedge funds and other liquidity suppliers have played a crucial role for market completeness and the allocation of capital across financial assets. Provided the intermediaries’ wealth is limited, their willingness to provide liquidity will necessarily depend on their ability to obtain funding. Moreover, funding risk and shocks are contingent onthe status quo of credit conditions. Gromb and Vayanos (2010) document that market liquidity depends on intermediary capital, namely onthe collateral-based financial constraints that limit investment capacity. Likewise, Johnson (2009) argued about the importance ofthestockof liquid capital to accommodate trade demands and to adjust consumption as a determinant of market resiliency. Further, Valente (2010) underlines the existence of two extreme regimes: a binding regime in which funding and market liquidity are positively correlated, and a non-binding regime without any evidence of correlation, reaffirming the asymmetry property. Adrian and Shin (2010) describe how intermediaries adjust balance sheets and leverage through repurchase agreements, so as the direct impactof funding conditions to asset prices and liquidity.
This paper explores theimpactofthe large stock prices increases positive shocks and decreases negative shocks from the New York Stock Exchange onthe returns and volatility of some European developed capital markets. We found that more than a half of shocks from these European stockmarkets were related to the shocks from US capital market. The results of GARC( models suggest that only the negative shocks from New York Stock Exchange increased the volatility ofthe European developed capital markets.
First, for a better interpretation ofthe EMH concept, the author gives a simple example of three conditions that would be sufficient for capital market efficiency: “consider a market in which (i) there are no transaction costs in trading securities, (ii) all available information is costlessly available to all market participants, and (iii) all agree onthe implications of current information for the current price and distributions of future prices of each security” (Fama, 1970: 387). However, these are clearly not conditions we can observe in markets nowadays, but fortunately they are not mandatory requirements for market efficiency. Fama (1970) states that if at least the right amount of investors own the available information and there are no investors who constantly have better judgements of these information we can also be in the presence of an efficient market, it will always depend on these market specifications. Moreover, quantifying the effect that these several conditions have onthe financial markets is the author´s main focus.
Carbon steel C120U grade is largely used onthe tools for cutting, for dies and knives, for stamping and drawing tools, hobs, thread rolling tools and in many other applications due to her typical properties - high hardness, good toughness and compressive strength. The surface ofthe steel can be modified by using surface engineering's techniques. Remelting ofthe surface layer by the source of concentrated energy is promising technique to improve properties ofthe materials [1-6]. Laser or electron beam use to melting ofthe surface of tool steels aims to obtain a modified layer with increased microhardness and abrasion resistance [7,8]. The surface remelted layer has usually a finer and more homogenous structure than its original base material. The remelting with the arc plasma (TIG- tungsten inert gas or GTAW - gas tungsten arc welding) used as an economical and easily
the results achieved. Finally, the purely local volatility effects are larger for Austria, Ireland and Portugal (means of 81.75%, 81.91% and 88%, respectively) than for the other countries (the means range between 27% and 78%). To all purposes of our study all the Non-EMU countries and the Non-EU country behave like EMU-member countries, which give an idea of how integrated the euro countries are. Figures 4 to 7 show the time series evolution ofthe variance ratios for Germany, UK (representing the non-EMU countries), Portugal and Spain. We chose these countries because Germany and the UK are a wide representation ofthe European stockmarkets, and the most liquid ones; and Portugal and Spain because ofthe interest that those two indexes bring to our study once we are in their financial space. The European variance ratio generally increases over the sample period for all countries except (again) Sweden and the UK, for which it appears to be stable.
The results of calculations ofthe areas of non-planar grain surfaces and the grain areas onthe projection plane for correct and incorrect macrostructures are presented as distributions with a logarithmic width of classes in Figs. 6 and 7, respectively. 7. Parameters ofthe grain size distributions in 3D and 2D spaces are presented in Table 1. A supplementary evaluation ofthe grain size consists of a calculation ofthe shape and elongation coefficients. The results ofthe calculations are presented as distributions these values in Figures 8, 9, 10 and 11, respectively, while statistical parameters are shown in Tables 2 and 3.
The paper presents a statistical assessment ofthe effect of chemical composition on mechanical properties of hypereutectic AlSi17 silumin, which is expected to act as a counterpart of alloys used by automotive industry and aviation for casting of high-duty engine parts in West European countries and USA. The studies onthe choice of chemical composition of silumins were preceded by analysis ofthe reference literature to state what effect some selected alloying elements and manufacturing technology may have onthe mechanical properties (HB, R m and A 5 ) of these alloys. As alloying additives, Cu, Ni and Mg in proper combinations were used. The alloy after