The objective of the study is to investigate the impact of the inflation on the stockexchangemarket return of Pakistan. For this we used the annual data for the inflation and KSE 100 Index and applied the Augmented Dickey Fuller (ADF) unit root test to find the stationary level of data and we found that both variables are stationary at first difference. Then we applied the Johansen Cointegration test to find the long run relationship between inflation and KSE 100 index returns. Our results are consistent with the literature above and show that there is significant, negative and long run relationship between both variables. The results from this test indicate that the null hypothesis is rejected because the value of both Trace statistics and Max-Eigen statistic is greater than the critical value at five percent level of significance. Inflation is the major problem of the country so any upward movement in inflation will adversely affect the prices and returns of the stockmarket.
In , a hybrid long-term and short-term evolutionary Trend Following (TF) algorithm is based on an eXtended Classifier System (XCS). TF investment strategies are combined so that long-term and short-term trends are integrated for evolution of the XCS. A proposed dynamic model shows returns from value stocks and growth stocks . The effectiveness of local modeling in the prediction of time series is investigated in  with three classical forecasting methods, Neural Networks (NN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Least-Squares Support Vector Machine (LS-SVM). Experiments show that local modeling enhances performance for time series prediction. A technique for crash prediction based on the analysis of durations between subsequent crashes of the Dow Jones Industrial Average was developed in  where a significant autocorrelation in the series of durations between significant crashes suggests autoregressive conditional duration models (ACD) to forecast crashes. Artificial neural networks (ANN) are applied to trade financial time series by training and testing ANNs within stockmarket trading systems . The forecasting performance of Bayesian Model Averaging (BMA) for a set of models of short-term rates was proposed by using weekly data for the one-month euro-dollar rate, BMA produces predictive likelihoods that are better than those associated with the majority of the short-rate models . The problem of predicting the direction of movement of the price index in indian stock markets was studied by comparing four prediction models, Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest and naïve-Bayes .
Our paper examines the volatility spillover between the stockmarket and the foreign exchangemarket in Pakistan. Pakistan has gone through financial sector reforms during last two decades. The two important outcomes of the financial sector reforms in Pakistan has been the opening up of the stock markets and adoption of flexible exchange rate system. Opening up of the stock markets resulted in a sharp increase in the inflows of portfolio investment. On one side such an investment helps in raising the investable funds and on other side it produced wild swings in the stockexchangemarket. For example, the Karachi StockExchange (KSE)-100 Index increased to 2600 in 1995 but declined sharply to just 878 in 1998. From this low level it crossed 10 thousand mark in early 2005 and by may 2005 it had declined to around 7 thousand mark. It is argued that the volatility is high in the ‘bullish’ market then the in the ‘bear’ market
To predict National StockExchangemarket precisely is very complex task till date. We propose to build simple user friendly stock analysis and prediction system based on technical data, fundamental data and external environment factor data derived from raw trading data generated by National StockExchange. Analysis based on this model will improve accuracy in identifying good stocks to invest as a part of individuals’ financial diversified portfolio. Proposed analysis and prediction system will be easy to operate, understand and provide clear categories of uptrend, down trend stocks, fundamentally strong stocks for mid to long term investment, buy, sell or wait signals for intraday and very short term investment. Depending on investors’ financial requirements and risk taking capacity, investor can choose investment strategies suitable to his style with the help of this system.
In another vein, the liquidity temporal dimension refers to the speed at which transactions could be concluded. Thus a market is said to be liquid when providing traders with immediate exchange opportunities. For limit order markets, the shorter waiting time on the order book is the more liquid the market becomes. In this case, transactions are closely spaced in time, (see Dufour and Engle, 2000). According to Handa and Schwartz (1996), limit order traders are exposed to two types of risks: the non-execution risk which corresponds to a long waiting time on the book and an adverse selection risk that refers to the risk of being picked off by more informed agents. Copeland and Galai (1985) pointed out that these orders offer free options to insiders with reference to their mentioned prices.
The theoretical literature on the role of the financial sector on economic growth and its implications in the different stages of development has been widely discussed in recent years. However, it is interesting to note that the tendency of this literature has not remained consistent over time. Thus, empirical investigation is becoming the main tool to assess for each country or a group of countries, the role of financial sector on the real economic activity. Recently, stock markets, as part of the whole financial system, are growing fast to compete the role of the banking intermediation to spread savings among investors. This benefit role could be offset by the instability characterizing the stockmarket and leading to negative repercussions on the real economy (Winkler, 1998).
Classical risk analysis models such as Markowitz’s model, Sharpe’s single- index model, and other similar models does not help select efficient stocks and portfolios, greatly because these theories include limiting and inappreciable assumptions such as the efficiency of the market portfolio. Evaluating factors affecting stock returns are a more serious problem in countries lacking an efficient stockmarket, because the market price of stocks is determined closely to the real value if the stockmarket is efficient. As a result, a multifactor model can result in the proper allocation of financial resources in the stockmarket by facilitating hypotheses and identifying certain factors other than the market index . Such a model can finally lead to the accurate analysis of risk and stock returns at different companies, something which is the ultimate goal of forming capital markets. In 1992-1993, Fama and French indicated that other factors should also be taken into account in addition to beta (Sharpe’s single-index model) in the capital asset pricing model. Fama and French studied the trend in earnings and returns at companies and analyzed the results. They concluded that there were other factors affecting the returns on stock at companies in addition to beta. According to Fama and Frech, either market did not act as they expected, or the capital asset pricing model was not accurate. Both cases may have also been possible. Therefore, Fama-French risk factors were used in this paper.
this local maxima and local minima will be done and we can use deep learning techniques with time serried data set. In this we compared all the models with the given data and finally do the risk analysis to predict the output comparisons of all the algorithms like LSTM, ANN and Sequential will be done. The capacity of ANN to take in and sum up from the non-linear information pattern is appropriate to issue space, for example, financial exchange expectation. Also, the LSTM can adjust to the information and connection between the information and yield, bringing about preferred expectation exactness over the conventional strategy.
market and exchange rate relationship and individually both variable get importance because they directly affect economy of any country. Economist and investors utilize both variable to predict future prices and etc (Kim, 2003). International competitiveness of firms directly get influenced by change in exchange rate on either way if they import inputs or exports output (Joseph, 2002). After the formation of new government in May 2013 and as now they unleashed their economic policies of which a major part is multinational organization investing in country. Multinational companies always take care of exchange rate as it directly affects their profitability. Recently Finance Minister Mr. Ishaq Dar emphasizes on controlling devaluing currency of Pakistan in order to get economy back on track. The exchange rate relationship with economy (KSE) is very important but in current scenario of country it becomes more concern for economist. In this paper we try to analyze the impact of exchange rate on economy and its magnitude and direction. We didn’t include year 2013 because due to formation of new government and huge expectations of nation stockmarket record a boom period. Literature Review
In the mid of 18th century, British East India Company established the StockExchange in India. In 1860, trade with 60 runners and it went very well, in 1874 with the participation of fast-commerce business development, riders gathered in a street (now known as "Dalal Street") to transact business. In 1946, India had only seven exchanges, in 1995 there was 22 exchanges.
Upward moving share prices are likely to attract new investors (both local and foreign) into the market. Where foreign investors are involved more Foreign Portfolio Equity Investment (FPEI) is likely to flow to Zimbabwe via managed macroeconomic policies such as those aimed at managing the exchange rate which this study finds to be an important variable for the HSE market capitalisation. Proper exchange rate management is likely to attract capital inflows, especially the FPEI, without the detrimental macroeconomic effects arising through the appreciation of the real exchange rates. The increase in foreign demand for local stocks will push up equity prices, which will lower the cost of capital and encourage new equity issues. FPEI is likely to bring benefits to the local economy through the mobilisation of additional finance, a reduction of capital costs for domestic firms and an improvement in the standards of local stock markets. The increased wealth of local investors is likely to induce an expansion of their consumption, encourage domestic production and investmen. Foreign sellers of stocks might also decide to use part of their wealth to finance local investments. The FPEI may also help in the further development of domestic stock markets - foreign investors would instil confidence among local investors to demand timely and quality information, and require adequate market and trading regulations. FPEI would also encourage the development of new institutions and services, transfer of technology and training of local personnel. Thus, the current market weaknesses related to small market size, high concentration of trading in a few major stocks, liquidity problems, limited number of active traders, high volatility and small number and size of listed companies could be reduced.
Similarly, Keim (2008) defines financial market anomalies as the cross-sectional and time series patterns in security returns that are not predicted by a central paradigm or theory. Identifying a number of anomalies, he ascertained that time series patterns in returns include anomalies such as the weekend effect and the January effect. The week- end effect, because of its existence in many different markets, cannot be explained by differences in settlement periods for transactions occurring on different weekdays, mea- surement error in recorded prices, market maker trading activity, or systematic patterns in investor buying and selling behaviour (Keim, 2008). Also called the Monday effect, it is built on the observation that stock prices do not take into account the money-value of the two-day weekend and start off on a Monday morning where they left off on Friday at closing time. This anomaly suggests that Fridays have the tendency to exhibit relatively larger returns than Mondays (Naffa, 2009). Similarly to Jones and Ligon (2009) and Sharma and Narayan (2012) who showed the existence of the Monday effect on the USA financial market, in South Africa, Jooste (2006) suggest the existence of the Monday effect on seven major JSE indices namely the All Share, Industrial 25, Mid Cap, Small Cap and Top 40 indices over the period 20 December 1995 - 11 November 2006 and the Resource 20 and Financial 15 indices over the period 2 March 1998 - 11 November 2006. It is interesting to note that the pattern in the South African market is the inverse of the common pattern witnessed in various international markets where negative Monday returns were recorded (Jooste, 2006; Lean, Smyth and Wong, 2005).
Figure 3 demonstrates the evolution of the DCC estimates between the Dollar exchange rate and the Tunindex from 2004 to mid- 2017. These dynamics reveal a number of interesting facts. First, the behavior of the DCC series is relatively dissimilar, proposing that the Tunisian stockmarket has a different co-movement with the Exchange rate for the Dollar. Then, the dynamic conditional correlations between the Stock-Exchange markets vary extensively over the study period and typically fluctuate between −0.25 and 0.2. This important volatility is due to several important economic, financial and geopolitical events. In fact, they are low when international economies pass through financial recessions like the famous recent subprime crisis that started in the USA in 2007 and lasted till 2008. During this crisis, the DCC dropped sharply to reach a value of −0.24. After that, the relationships between the Stock- Exchange markets usually increased following signs of economic recovering. Later on, the European debt crisis, and lately, the Arab spring events influenced the correlation between the two markets. During the Arab spring events, particularly during the Tunisian Revolution that took place in late 2010 and early 2011, we can notice that the DCC between both exchange markets and the Tunindex Dropped sharply reaching −0.09. After that, the DCC was stagnant until 2013, and it was varying between −0.10 and 0.14. However, it decreased dramatically in mid-2013 to reach −0.25. In fact, this plummet was due to disturbing events that the country has witnessed.
Following Forbes and Rigobon (2002, p. 2224), in this paper contagion is de ﬁ ned as “a signi ﬁ cant increase in cross-market linkages after a shock to one country (or group of countries). According to this de ﬁ nition, if two markets show a high degree of co-movement during periods of stability, even if the markets continue to be highly correlated after a shock to one market, this may not constitute contagion. According to this paper’s de ﬁ nition, it is only contagion if cross-market co-movement increases signiﬁcantly after the shock”. This de ﬁ nition presents two advantages. First, it provides a straightforward test to measure contagion, by measuring the cross-market correlations before and after a shock. Second, tests based on this deﬁnition can provide evidence in favour of or against each of the two groups of theories discussed above.
Order is our most fundamental need, and uncertainty is the enemy of order. The decision to disrupt the medium of exchange for so many people, and then implementing it in such a haphazard way, has generated enormous uncertainty. This uncertainty that has been unleashedcould have unpredictable consequences, which ex-ante cost-benefit analysis cannot consider. For example, conspiracy theories and fraud are thriving due to this large scale disruption. Policy decisions should always try to minimise uncertainty, especially in decisions implemented at large scale. This decision fails this test. Governments are expected to put in place system to reduce uncertainty, and to solve the problems that create uncertainty. For example, systemic crisis in the economy creates uncertainty. When Lehman Brothers failed and the global financial crisis began, the chief task of the central government and RBI was to reduce uncertainty and maintain stability in the financial system. One of the main ways of reducing uncertainty is to uphold the rule of law. Rule of law is a complex notion, but at its heart lie certain core principles. First, laws should be consistent with natural rights and principles of natural justice. Second, laws should be clear, predictable and widely known beforehand. Third, laws should be applied uniformly across similar situations. Fourth, due process should be followed, which means every application of law should provide the private party with information about the application of the law, the reasoning behind the application, and a mechanism for appeal.
A stockmarket usually trades hundreds of listed company shares on a daily basis. For investors to evaluate the performance of a stockmarket, they observe the level of the various composite market indices before investing their surplus funds. A market index is an aggregate value that is produced by combining several stocks together and expressing their total values against a base value, usually from a specific date. Market indices provide historical stockmarket performance as well as a benchmark for comparison against performance of individual investor portfolios. Analysis of market indices can also provide investors with forecasts of future market trends (Zhang, 2009).
Predicting stock price movements is a challenging task for academicians and practitioners. In particular, forecasting price movements in emerging markets seems to be more elusive because they are usually more volatile often accompa- nied by thin trading-volumes and they are susceptible to more manipulation compared to mature markets. Technical analysis of stocks and commodities has become a science on its own; quantitative methods and techniques have been applied by many practitioners to forecast price movements. Lagging and sometimes leading technical indicators pro- vide rich quantitative tools for traders and investors in their attempt to gain advantage when making investment or trading decisions. Artificial Neural Networks (ANN) have been used widely in predicting stock prices because of their capability in capturing the non-linearity that often exists in price movements. Recently, Polynomial Classifiers (PC) have been applied to various recognition and classification application and showed favorable results in terms of recog- nition rates and computational complexity as compared to ANN. In this paper, we present two prediction models for predicting securities’ prices. The first model was developed using back propagation feed forward neural networks. The second model was developed using polynomial classifiers (PC), as a first time application for PC to be used in stock prices prediction. The inputs to both models were identical, and both models were trained and tested on the same data. The study was conducted on Dubai Financial Market as an emerging market and applied to two of the market’s leading stocks. In general, both models achieved very good results in terms of mean absolute error percentage. Both models show an average error around 1.5% predicting the next day price, an average error of 2.5% when predicting second day price, and an average error of 4% when predicted the third day price.
Investors try to keep themselves abreast with latest information which they gather from corporate announcements. On the basis of this information, investment strategies are given the shape and decisions are made. Investors always make attempt to maximize their return on investment and minimize risk. The information available in the market helps them to maximize return and minimize risk, i.e. they are perceived to be signals of company future. Although there are different types of announcements which companies float in market from time to time but the most important among them that influences is dividend related announcements. In this paper we have studied the impact of dividend announcements on stock price volatility. Further, the study also investigates the impact of dividend announcements on return generated by investors of 25 selected companies in five selected sectors of NSE. The results of study highlight that dividend announcements have significant impact on share prices of the company and investors can have abnormal returns from the market when companies declare dividend.
thrown into this contradiction by Bachelier’ s hypothesis of random walk (Walter, 2003). The crash of the stockmarket precipitated the intellectual movement to review the fundamental hypotheses at work in market efficiency. In financial theory, the efficient market was a good idea but later it crashed. The theory initiated a revolution, but failed to explain why investors panicked during the late 1980s. In practice, several bankruptcies resulting from risk management and hedging techniques based on a Gaussian conception of stockmarket fluctuations brought a new surge in research into finance, in order to better understand the nature of randomness at work in market fluctuations. According to Walter (2003) this movement was reinforced by a growing concern on the part of supervising authorities and international groups, which wished, after these accidents, to establish prudential rules of operation, by imposing minimum levels of solvency ratios on the financial institutions active in these markets. He further argued that as these ratios calculate the capital equivalent corresponding to confidence intervals on the probability densities of market returns, it became important to better quantify these risks. All these led to the questioning of the validity of the efficiency concept. It was from now on acknowledged and well recognized that the financial markets did not possess the basic statistical behaviour assumed by Gaussian density (Walter, 2003 c.f. Green and Figlewski, 1999).
BET is the first index developed by the BSE (Release date: 09.19.1997, Number of Companies: 10, Index Value: 1000 points) and represents the benchmark index for the local capital market reflecting the evolution of ten most liquid companies listed on BSE regulated market, except for financial investment companies (SIFs). It is possible that their number might increase in the future due to the listing on BSE of new representing companies for sectors of national economy and the recording of relevant events with impact on the listed companies. BET Index is a free float capitalization-weighted price index, the maximum weight of a symbol is 20%. BET- XT Index reflects the price evolution of the most traded/liquid 25 Romanian companies listed on BSE regulated market, including the financial investment companies (SIFs). It was launched on July 1 st , 2008 with a starting value of 1000 points, calculated retroactively from January 2 nd , 2007. Being the third index developed by BSE, BET-FI is the first sector index and reflects the evolution of financial investment companies (SIFs) and other similar institutions. It was launched on October 31 st , 2000, with a starting value of 1000 points. BET-NG is a sector index reflecting the evolution of the whole sector and all companies listed on the regulated market of BSE which main activity is energy and related utilities. The maximum weight of a symbol in the index is 30% and the number of composing companies is variable. BET-NG Index was launched on July 1 st , 2008, with a starting value of 1000 points, calculated retroactively from January 2 nd , 2007.