This study is to examine the phenomenon of stock prices drop around the ex-dividend day in Taiwan. Investors purchasing the security before the ex-dividend date will receive the current dividend, whereas investors purchasing the security on or after this date will not receive the dividend. Consequently, the stock price should fall on the ex-dividend date. In a perfect market, the stock price is expected to fall by the amount of the dividend. I show that share prices do not fall by the full amount of dividend, on average. I focus on falling ratio of stock prices, along with stock return. I also study the factors which may influence stock price behavior and find that the drop of stock price is smaller than the amount of the dividend. That is, the stock price tends to rise on the ex-dividend day. The price drop ratio on the ex-dividend day is higher for firms with greater financial leverage, higher dividend pay out ratio and higher dividend yield. Finally, I also observe that stock return and trading volume increase around the ex-dividend day.

In this study, different from previous studies, the explanatory power of Student-t distribution is compared to normal distribution by employing both standard GARCH and EGARCH models to dollar/ lira (USD/TRY) exchange rate. Then the impact of Central Bank of Republic of the Turkey&rsquo / s (CBRT) decisions and actions on both the level of exchange rate and the volatility is investigated. Moreover the relationship between volatility and market liquidity is examined using spot foreign exchange (FX) market volume as a proxy. The results reveal that, in contrast to preceding findings, Student-t could not capture the leptokurtic property better than normal distribution does. Furthermore, an increase in Turkish government benchmark bond rates, CBRT FX purchase interventions and announcement of suspending/ decreasing-the-amount-of FX auctions lead Turkish lira to depreciate. Because of the significant positive leverage effect, the results of GARCH and EGARCH variance equations differ so much. Thereby the results should be evaluated cautiously. In addition it is observed that, only EGARCH model gives significant results when the spot market trading volume is included in the models

<p>Den här uppsatsen behandlar företag som är listade på Stockholmsbörsen som gjorde omvänd split mellan 1995 och 2004. Företagen är testade för abnormal avkastning kring tillkännagivandet av den omvända spliten, samt förändringar i köp-sälj ratio, handels volym och antalet handelsdagar där ingen handel skedde i aktien. Inga abnormala avkastningar eller signifikanta förändringar i köp-sälj ration eller handelsvolymen kunde hittas. Däremot så visar förändringen i antalet handelsdagar utan handel i aktien en försämring och antalet handelsdagar minskade i de aktier som genomgått en omvänd split. Detta medför att likviditeten minskade för de företag som genomförde en omvänd split.</p> / <p>This paper addresses reverse splits for firms trading on the Stockholm stock exchange between 1995 and 2004. The related sample are tested for abnormal returns surrounding the announcement day of the reverse split, as well as any changes in bid-ask spread, trading volume and the number of non-trading days. No findings of abnormal returns or significant changes in either bid-ask spread or trading volume could be found, while the number of non-trading days for the whole sample increased. This may suggest that the marketability decreased for the reverse splitting firms.</p>

The global foreign exchange market is the largest financial market with turnover in this market often outstripping the GDP of countries in which they are located. The dynamics in the foreign exchange market, especially price dynamics, have huge implications for financial asset values, financial returns and volatility in the international financial system. It is therefore an important area of study. Exchange rates have often departed significantly from the level implied by fundamentals and exhibit excessive volatility. This reality creates a role for central bank intervention in this market to keep the rate in line with economic fundamentals and the overall policy mix, to stabilize market expectations and to calm disorderly markets. Studies that attempt to measure the effectiveness of intervention in the foreign exchange market in terms of exchange rate trends and volatility have had mixed results. This, in many cases, reflects the unavailability of data and the weaknesses in the empirical frameworks used to measure effectiveness. This thesis utilises the most recent data available and some of the latest methodological advances to measure the effectiveness of central bank intervention in the foreign exchange markets of a variety of countries. It therefore makes a contribution in the area of applied empirical methodologies for the measurement of the dynamics of intervention in the foreign exchange market. It demonstrates that by using high frequency data and more robust and appropriate empirical methodologies central bank intervention in the foreign exchange market can be effective. Moreover, a framework that takes account of the interactions between different central bank policy instruments and price dynamics, the reaction function of the central bank, different states of the market, liquidity in the market and the profitability of the central bank can improve the effectiveness of measuring the impact of central bank policy in the foreign exchange market and provide useful information to policy makers.

This dissertation consists of 3 essays that examine the impact of electronic screen trading in futures markets. The research provides empirical evidence on increasingly significant issues given the rapid global advances in technology used in securities markets. Each essay addresses the scarcity of conclusive research in order to aid researchers, regulators, exchange policy makers and systems builders as they confront issues related to electronic trading systems.

The diploma thesis deals with an influence of various impulses on increased trading activities of investors reflected in a number of stock titles contained in Dow Jones Industrial Average traded at the New York Stock Exchange and NASDAQ (Cisco, Intel and Microsoft) in 2010. The impulses had fundamentals characters, from macroeconomic data to company events. They were given out verbally in the various declarations of authorities or in a written form, but the contents and influence of which was spread immediately after publication by internet. The identification of impulses comprises determination of an important factor or specific news which might influence a number of stocks traded and its range in some trading day. The significance of an event will be expressed by a deviation upwards only from an average amount of stocks traded in each month and set at percentage.

Today, brokers within the stock market brokerage industry are having difficulties with accurately forecasting the trading volume that is conducted by their customers. This is especially a problem during periods of exceptionally high or low trading volumes. Solving this problem would lead to both monetary savings in terms of server costs and operational planning issues. This thesis uses three Machine Learning models (Random Forest Regressor, Linear Regression, and Support Vector Regression) to predict daily trading volume. In Machine Learning, features are variables that act as explanatory variables for the dependent variable, in this case, the daily trading volume. The primary focus of this study is to evaluate and analyze which types of feature categories are the most important. Therefore, this study uses a variety of features divided into five different categories (Temporal, Historical, Market, External, and Customer). The results from the models trained using each individual feature category are compared against each other. Secondly, this study also focuses on analyzing the performance of all feature categories together. A Naive model of a 20-day rolling average is used as a benchmark to evaluate the results. The findings of this study indicate that Machine Learning models perform better than the proposed Naive approach when predicting daily stock market trading volume. However, the difference is of a small nature. Further, the Historical feature category is the category that performs best and can therefore be argued to be the most important category when predicting daily trading volume. However, the results of this study are not of statistical significance. The findings of this study can be relevant to the research field and can be used in future studies to further investigate the feature importance in stock market trading volume prediction. / Idag har företag inom industrin för aktiemäklare svårigheter att på ett träffsäkert sätt prognostisera sina kunders handelsvolymer. Detta är särskilt ett problem under perioder med extremt höga eller låga volymer. Att lösa detta problem skulle leda till både monetära besparingar i form av serverkostnader, och även lösa operationella planeringsproblem. Denna studie använder tre olika maskininlärningsmodeller (Random Forest Regressor, Linear Regression, och Support Vector Regression) för att förutspå handelsvolym. Denna studie har som primärt fokus att utvärdera och analysera vilka typer av data som är av vikt i syfte att förutspå kommande daglig aktiehandelsvolym. Denna studie använder därmed en mängd olika variabler indelat i fem grupper (Tid, Historik, Marknad, Extern, Kund). Modellerna tränas individuellt med varje grupp och resultatet jämförs inbördes för att besvara studiens frågeställningar. Studien fokuserar även på att analysera resultatet av att träna modellerna på samtliga grupper tillsammans. För att utvärdera resultatet används en naiv modell med 20 dagars rullande medelvärde. Resultatet från denna studie indikerar att användning av maskininlärning presterar bättre än den använda naiva modellen, för att förutspå daglig handelsvolym på aktiemarknaden. Skillnaden i resultat är dock liten. Vidare visar studiens resultat att den grupp av variabler som presterar bäst är kategorin Historik. Därmed kan det sägas att denna grupp av variabler är den viktigaste gruppen för att förutspå daglig handelsvolym, av grupperna använda i denna studie. Det går dock inte att säga att resultaten i denna studie är signifikanta. Resultaten och slutsatserna från denna studie bidrar till forskningsområdet och resultaten kan i framtiden användas för att fortsätta undersöka vilka variabler som är av intresse när det kommer till att förutspå daglig handelsvolym på aktiemarknaden.

The intention of this thesis is to provide a primitive mathematical model for a financial market in which tradings affect the asset prices. Currently, the idea of a price-volume relationship is typically used in the form of empirical models for specific cases. Among the theoretical models that have been used in stock markets, few included the volume parameter. The thesis provides a general theoretical model with the volume parameter for the intention of a broader use. The core of the model is the correlation between trading volume and stock price, indicating that volume should be a function of the stock price and time. This function between price and time was made visible by the use of the trading volume process, also known as the Limit Order book. The development of this model may be of some use to investors, who could build their wealth process based on the dynamics of the process found through a Limit Order Book. This wealth process can help them build an optimal trading strategy design.

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Yes / This paper examines the existence of dynamic spillover effects across petroleum based commodities and among spot-futures volatilities, trading volume and open interest. Realized volatilities of spot-futures markets are used as inputs to estimate a VAR model following Diebold and Yilmaz (2014, 2015) and distinguish dynamic spillovers in total and net effects. Results reveal the existence of large and time-varying spillovers among the spot-futures volatilities and across petroleum-based commodities when examined pairwise. In addition, speculative pressures, as reflected by futures trading volume, and hedging pressures, as reflected by open interest, are shown to transmit large and persistent spillovers to the spot and futures volatilities of crude oil and heating oil-gasoline markets, respectively.