Security Transaction Taxes and Market Quality

We examine nine changes in the New York State Security Transaction Taxes (STT) between 1932 and 1981. We find that imposing or increasing an STT results in wider bidask spreads, lower volume, and increased price impact of trades. In contrast to theories of STT imposition as a means to reduce volatility, we find no consistent relationship between the level of an STT and volatility. We examine the propensity of traders to switch trading locations to avoid the tax and find no consistent evidence that they will change locations. We do find evidence to suggest that taxes imposed on the par value of stock will result in corporations managing the par value in the direction of minimizing the impact of the tax on investors.Econometric and statistical methods; Financial markets; Market structure and pricing

Two exogenous issues related to market quality

I examine two exogenous issues and their impact on market quality. The first is the New York State imposed Securities Transaction Tax (STT). Over the last two decades there has been an ongoing debate among academics, politicians and regulators, across various jurisdictions, on the impact a STT has on market quality. I contribute to this debate by examining eight changes in the level of a STT, include all firms trading on the New York Stock Exchange (NYSE) and American Stock Exchange (AMEX) to test the impact on volatility and liquidity, and employ a dataset that offers the opportunity to study the impact of a STT on market share. Overall, there is no impact of a STT on volatility, contrary to proponents of the tax suggesting that a STT reduces volatility. I also find that spreads are directly related to a STT as spreads widen (tighten) when there is an increase (decrease) in the level of the tax. In addition, I find support for the hypothesis that a STT in New York State drives volume to regional exchanges. These findings confirm what opponents of the STT suggest: a STT does not decrease volatility, increases spreads and is accompanied by a decline in market share on the local exchange. The second issue I study is the impact of forced consolidation. There is a general lack of agreement among academics as to the optimal level of consolidation in markets. This paper contributes to the ongoing debate by examining the impact of the SEC mandated consolidation of US markets on various measures of market quality. The overall findings suggest that consolidation improves market quality. In particular I find that volatility, effective spreads, and Amihud's illiquidity measure all decline following the imposition of three consolidating systems on US markets in the 1970s: the Consolidated Tape System, the Inter-market Trading System, and the Consolidated Quote System. My findings are robust to changes in firm-specific variables, market wide trends, and the endogeneity of control variables. Given recent announcements concerning consolidation efforts on both sides of the Atlantic, this paper has immediate policy implications.Ph.D.Includes bibliographical referencesIncludes vitaby Anna Pomeranet

Big Data: Deep Learning for financial sentiment analysis

Abstract Deep Learning and Big Data analytics are two focal points of data science. Deep Learning models have achieved remarkable results in speech recognition and computer vision in recent years. Big Data is important for organizations that need to collect a huge amount of data like a social network and one of the greatest assets to use Deep Learning is analyzing a massive amount of data (Big Data). This advantage makes Deep Learning as a valuable tool for Big Data. Deep Learning can be used to extract incredible information that buried in a Big Data. The modern stock market is an example of these social networks. They are a popular place to increase wealth and generate income, but the fundamental problem of when to buy or sell shares, or which stocks to buy has not been solved. It is very common among investors to have professional financial advisors, but what is the best resource to support the decisions these people make? Investment banks such as Goldman Sachs, Lehman Brothers, and Salomon Brothers dominated the world of financial advice for more than a decade. However, via the popularity of the Internet and financial social networks such as StockTwits and SeekingAlpha, investors around the world have new opportunity to gather and share their experiences. Individual experts can predict the movement of the stock market in financial social networks with the reasonable accuracy, but what is the sentiment of a mass group of these expert authors towards various stocks? In this paper, we seek to determine if Deep Learning models can be adapted to improve the performance of sentiment analysis for StockTwits. We applied several neural network models such as long short-term memory, doc2vec, and convolutional neural networks, to stock market opinions posted in StockTwits. Our results show that Deep Learning model can be used effectively for financial sentiment analysis and a convolutional neural network is the best model to predict sentiment of authors in StockTwits dataset