Abstract: In the first decades of the 21st century, in the electronic trading environment, algorithmic capital investments became the primary tool to make a profit by speculations in financial markets. A significant number of traders, private or institutional investors are participating in the capital markets every day using automated algorithms. The autonomous trading software is today a considerable part in the business intelligence system of any modern financial activity. The trading decisions and orders are made automatically by computers using different mathematical models. This paper will present one of these models called Price Prediction Line. A mathematical algorithm will be revealed to build a reliable trend line, which is the base for limit conditions and automated investment signals, the core for a computerized investment system. The paper will guide how to apply these tools to generate entry and exit investment signals, limit conditions to build a mathematical filter for the investment opportunities, and the methodology to integrate all of these in automated investment software. The paper will also present trading results obtained for the leading German financial market index with the presented methods to analyze and to compare different automated investment algorithms. It was found that a specific mathematical algorithm can be optimized and integrated into an automated trading system with good and sustained results for the leading German Market. Investment results will be compared in order to qualify the presented model. In conclusion, a 1:6.12 risk was obtained to reward ratio applying the trigonometric method to the DAX Deutscher Aktienindex on 24 months investment. These results are superior to those obtained with other similar models as this paper reveal. The general idea sustained by this paper is that the Price Prediction Line model presented is a reliable capital investment methodology that can be successfully applied to build an automated investment system with excellent results.
Abstract: After the widespread release of electronic trading, automated trading systems have become a significant part of the business intelligence system of any modern financial investment company. An important part of the trades is made completely automatically today by computers using mathematical algorithms. The trading decisions are taken almost instantly by logical models and the orders are sent by low-latency automatic systems. This paper will present a real-time price prediction methodology designed especially for algorithmic trading. Based on the price cyclicality function, the methodology revealed will generate price cyclicality bands to predict the optimal levels for the entries and exits. In order to automate the trading decisions, the cyclicality bands will generate automated trading signals. We have found that the model can be used with good results to predict the changes in market behavior. Using these predictions, the model can automatically adapt the trading signals in real-time to maximize the trading results. The paper will reveal the methodology to optimize and implement this model in automated trading systems. After tests, it is proved that this methodology can be applied with good efficiency in different timeframes. Real trading results will be also displayed and analyzed in order to qualify the methodology and to compare it with other models. As a conclusion, it was found that the price prediction model using the price cyclicality function is a reliable trading methodology for algorithmic trading in the financial market.
Abstract: This paper presents an adaptive framework for
modelling financial markets using equity risk premiums, risk free
rates and volatilities. The recorded economic factors are initially
used to train four adaptive filters for a certain limited period of time
in the past. Once the systems are trained, the adjusted coefficients
are used for modelling and prediction of an important financial
market index. Two different approaches based on least mean squares
(LMS) and recursive least squares (RLS) algorithms are investigated.
Performance analysis of each method in terms of the mean squared
error (MSE) is presented and the results are discussed. Computer
simulations carried out using recorded data show MSEs of 4% and
3.4% for the next month prediction using LMS and RLS adaptive
algorithms, respectively. In terms of twelve months prediction, RLS
method shows a better tendency estimation compared to the LMS
algorithm.
Abstract: The paper analyses the usage of the Internet by university students in Visegrad Countries (4V Countries) who study economic fields in their formal and informal financial education and captures the areas of untapped potential of Internet in educational processes. Higher education and training, technological readiness, and the financial market development are in the group of pillars, that are key for efficiency driven economies. These three pillars have become an inspiration to the research on using the Internet in the financial education among economic university students as the group of the best educated people in finance. The financial education is a process that allows for improving the level of financial literacy. In turn, the financial literacy it is the set of financial knowledge, skills, awareness and patterns influencing the financial decisions. The level of financial literacy influences the level of financial well-being of individuals, determines the scale of saving of households and at the same time gives the greater chance for sustainable and more predictable development of the financial market with the positive impact on economy. The financial literacy is necessary for each group of society but its appropriate level is desirable especially in respect of economics students as future participants of financial markets as well as the experts and advisors in financial decision making. The low level of financial literacy is the great problem of many target groups in both developing and developed countries and the financial education is seen as the best way of improving this situation. Also the financial inclusion plays the special role in enhancing the level of financial literacy in the aspect of education by practice as well as due to interrelation between level of financial literacy and degree of financial inclusion. Despite many initiatives under financial education, the level of financial literacy is still very low. Scientists still search for new ways of solving this problem. One of the proposal is more effective usage of the new technology in financial education, especially the Internet, because of the growing popularity of e-learning and the increasing number of Internet users, especially among young people who are called the Generation Net. Due to special role of the university students studying the economics fields for the future financial markets, students of four universities from Visegrad Countries (Czech Republic, Hungary, Poland and Slovakia) were invited to participate in the survey. The aim of the article is to present the level and ways of using the Internet technology in financial education and indicating the so far unused or underused opportunities.
Abstract: This paper analyzes, using descriptive statistics and econometrics data which span the period 1981 to 2014 to gauge the effects of trade policy incentives on economic growth in Nigeria. It argues that the provided incentives penalize economic growth during pre-trade liberalization eras, but stimulated a rapid increase in total factor productivity during the post-liberalization period of 2000 to 2014. The trend analysis shows that Nigeria maintained high tariff walls in economic regulation eras which became low in post liberalization era. The protections were in favor of infant industries, which were mainly appendages of multinationals but against imports of competing food and finished consumer products. The trade openness index confirms the undue exposure of Nigeria’s economy to the vagaries of international market shocks; while banking sector recapitalization and new listing of telecommunications companies deepened the financial markets in post-liberalization era. The structure of economic incentives was biased in favor of construction, trade and services, but against the real sector despite protectionist policies. Total Factor Productivity (TFP) estimates show that the Nigerian economy suffered stagnation in pre-liberalization eras, but experienced rapid growth rates in post-liberalization eras. The regression results relating trade policy incentives to TFP growth rate yielded a significant but negative intercept suggesting that a non-interventionist policy could be detrimental to economic progress, while protective tariff which limits imports of competing products could spur productivity gains in domestic import substitutes beyond factor growth with market liberalization. The main constraint to the effectiveness of trade policy incentives is the failure of benefiting industries to leverage on the domestic factor endowments of the nation. This paper concludes that there is the need to review the current economic transformation strategies urgently with a view to provide policymakers with a better understanding of the most viable options that could make for rapid success.
Abstract: Our goal is development of an algorithm capable of
predicting the directional trend of the Standard and Poor’s 500 index
(S&P 500). Extensive research has been published attempting to
predict different financial markets using historical data testing on an
in-sample and trend basis, with many authors employing excessively
complex mathematical techniques. In reviewing and evaluating these
in-sample methodologies, it became evident that this approach was
unable to achieve sufficiently reliable prediction performance for
commercial exploitation. For these reasons, we moved to an out-ofsample
strategy based on linear regression analysis of an extensive
set of financial data correlated with historical closing prices of the
S&P 500. We are pleased to report a directional trend accuracy of
greater than 55% for tomorrow (t+1) in predicting the S&P 500.
Abstract: The study is a review of the literature concerning the
consequences of non-standard monetary policy, which are used by
central banks during unconventional periods, threatening banking
sector instability. In particular, the attention was paid to the effects of
non-standard monetary policy tools for financial markets. However,
the empirical evidence about their effects and real consequences for
financial markets is still not final. The main aim of the study is to
survey consequences of standard and non-standard monetary policy
instruments, implemented during the global financial crisis in the
United States, United Kingdom and euro area, with particular
attention to the results for the stabilization of global financial
markets. The study consists mainly of the empirical review,
indicating the impact of the implementation of these tools for
financial markets. The following research methods were used in the
study: literature studies, including domestic and foreign literature,
cause and effect analysis and statistical analysis.
Abstract: In this paper it was examined the influence of margin
regulation on stock market volatility in EU 1993 – 2014. Regulating
margin requirements or haircuts for securities financing transactions
has for a long time been considered as a potential tool to limit the
build-up of leverage and dampen volatility in financial markets. The
margin requirement dictates how much investors can borrow against
these securities. Margin can be an important part of investment.
Using daily and monthly stock returns and there is no convincing
evidence that EU Regulation margin requirements have served to
dampen stock market volatility. In this paper was detected the
expected negative relation between margin requirements and the
amount of margin credit outstanding. Also, it confirmed that changes
in margin requirements by the EU regulation have tended to follow
than lead changes in market volatility. For the analysis have been
used the modified Levene statistics to test whether the standard
deviation of stock returns in the 25, 50 and 100 days preceding
margin changes is the same as that in the succeeding 25, 50 and 100
days. The analysis started in May 1993 when it was first empowered
to set the initial margin requirement and the last sample was in May
2014. To test whether margin requirements influence stock market
volatility over the long term, the sample of stock returns was divided
into 14 periods, according to the 14 changes in margin requirements.
Abstract: The New Basel Capital Accord (Basel II) influences how financial institutions around the world, and especially European Union institutions, determine the amount of capital to reserve. However, as the recent global crisis has shown, the revision of Basel II is needed to reflect current trends, such as increased volatility and correlation, in the world financial markets. The overall objective of Basel II is to increase the safety and soundness of the international financial system. Basel II builds on three main pillars: Pillar I deals with the minimum capital requirements for credit, market and operational risk, Pillar II focuses on the supervisory review process and finally Pillar III promotes market discipline through enhanced disclosure requirements for banks. The aim of this paper is to provide the historical background, key features and impact of Basel II on financial markets. Moreover, we discuss new proposals for international bank regulation (sometimes referred to as Basel III) which include requirements for higher quality, constituency and transparency of banks' capital and risk management, regulation of OTC markets and introduction of new liquidity standards for internationally active banks.
Abstract: In this paper, first we introduce the stable distribution, stable process and theirs characteristics. The a -stable distribution family has received great interest in the last decade due to its success in modeling data, which are too impulsive to be accommodated by the Gaussian distribution. In the second part, we propose major applications of alpha stable distribution in telecommunication, computer science such as network delays and signal processing and financial markets. At the end, we focus on using stable distribution to estimate measure of risk in stock markets and show simulated data with statistical softwares.
Abstract: Computer-mediated communication technologies which provide for virtual communities have typically evolved in a cross-dichotomous manner, such that technical constructs of the technology have evolved independently from the social environment of the community. The present paper analyses some limitations of current implementations of computer-mediated communication technology that are implied by such a dichotomy, and discusses their inhibiting effects on possible developments of virtual communities. A Socio-Technical Indicator Model is introduced that utilizes integrated feedback to describe, simulate and operationalise increasing representativeness within a variety of structurally and parametrically diverse systems. In illustration, applications of the model are briefly described for financial markets and for eco-systems. A detailed application is then provided to resolve the aforementioned technical limitations of moderation on the evolution of virtual communities. The application parameterises virtual communities to function as self-transforming social-technical systems which are sensitive to emergent and shifting community values as products of on-going communications within the collective.
Abstract: This paper fist examines three set of bivariate cointegrations between any two of current accounts, stock markets, and currency exchange markets in ten Asian countries. Furthermore, we examined the effect of country characters on this bivariate cointegration. Our findings suggest that for three sets of cointegration test, each sample country at least exists one cointegration. India consistently exhibited a bi-directional causal relationship between any two of three indicators. Unlike Pan et al. (2007) and Phylaktis and Ravazzolo (2005), we found that such cointegration is influenced by three characteristics: capital control; flexibility in foreign exchange rates; and the ratio of trade to GDP. These characteristics are the result of liberalization in each Asian country. This implies that liberalization policies are effective on improving the cointegration between any two of financial markets and current account for ten Asian countries.
Abstract: Recognizing behavioral patterns of financial markets
is essential for traders. Japanese candlestick chart is a common tool to
visualize and analyze such patterns in an economic time series. Since
the world was introduced to Japanese candlestick charting, traders
saw how combining this tool with intelligent technical approaches
creates a powerful formula for the savvy investors.
This paper propose a generalization to box counting method of
Grassberger-Procaccia, which is based on computing the correlation
dimension of Japanese candlesticks instead commonly used 'close'
points. The results of this method applied on several foreign
exchange rates vs. IRR (Iranian Rial). Satisfactorily show lower
chaotic dimension of Japanese candlesticks series than regular
Grassberger-Procaccia method applied merely on close points of
these same candles. This means there is some valuable information
inside candlesticks.
Abstract: The concurrent era is characterised by strengthened interactions among financial markets and increased capital mobility globally. In this frames we examine the effects the international financial integration process has on the European bond markets. We perform a comparative study of the interactions of the European and international bond markets and exploit Cointegration analysis results on the elimination of stochastic trends and the decomposition of the underlying long run equilibria and short run causal relations. Our investigation provides evidence on the relation between the European integration process and that of globalisation, viewed through the bond markets- sector. Additionally the structural formulation applied, offers significant implications of the findings. All in all our analysis offers a number of answers on crucial queries towards the European bond markets integration process.
Abstract: If price and quantity are the fundamental building
blocks of any theory of market interactions, the importance of trading
volume in understanding the behavior of financial markets is clear.
However, while many economic models of financial markets have
been developed to explain the behavior of prices -predictability,
variability, and information content- far less attention has been
devoted to explaining the behavior of trading volume. In this article,
we hope to expand our understanding of trading volume by
developing a new measure of herding behavior based on a cross
sectional dispersion of volumes betas. We apply our measure to the
Toronto stock exchange using monthly data from January 2000 to
December 2002. Our findings show that the herd phenomenon
consists of three essential components: stationary herding, intentional
herding and the feedback herding.