Existing studies document that institutional herding has a stabilizing effect on stock prices, as stock returns are positively correlated with herding over one- to three-quarter horizons. The literature also shows that short-term institutions are better informed than long-term institutions. Motivated by heterogeneity in the level of informed trading between short-term and long-term institutions, this study disentangles the herding effect of short-term and long-term institutions on stock prices. Our results show that herding by short-term institutions promotes price discovery. In contrast, herding by long-term institutions drives stock prices away from fundamentals. Taken together, our findings suggest that the stabilizing effect documented in the existing literature is mainly driven by short-term institutions, and herding by long-term institutions has a destabilizing effect on stock prices.

This paper investigates the price impact cost for MSCI constituents on the Taiwan Stock Exchange (TSE) from Jan. 2001 to Dec. 2004. While the behavior of price impact cost in U.S. security markets has been extensively analyzed, there are few studies about it in the pure limit-order markets. Unlike Breen, Hodrick, and Korajczyk (2002), a panel data model is applied to fit our cross-sectional and time series data. We find that the price impact cost is well predicted by predetermined firm characteristics and exhibits a Ushaped pattern over the trading day. Furthermore, the evidence suggests that the reformations of trading regulations and the improvements of information disclosures would have a significant effect on the price impact cost for overall stocks.

The grain and oil seed industry plays a major role in the South African economy; therefore, having access to market information is vital for this market to remain efficient and competitive. A shortage in market information causes many inefficiencies and uncertainties. Having market information allows the playing field to be level for all role players and reduces opportunities for manipulating prices. South Africa, just like most developing countries, needs to strengthen information flows, as well as institutions governing the grain and oil seed industry. In view of the major grain producing countries in the world and the amount of money and effort spent on releasing planting progress reports, the South Africa grain and oilseed sector should to take heed. This paper considers the importance of market information and how the South African grain and oil seed industry can benefit from that, grain planting progress reports are considered to be of importance as they fill a significant gap in the production season. Taking an institutional perspective into the economics of information, the study found that actors having little financial and social resources or political influence faced high costs in accessing information and that this prevents both market development and access to existing ones. The point of discussion is on weak information flows, as well as transaction costs that come with them, and the impact they have on prices and profitability. We therefore use New Institutional Economics to emphasise the importance of information in the market and the impact thereof in the absence of perfect information. The main underlying issue for imperfect information is that the lack of perfect and freely available information leads to risk and uncertainty in transactions. When trying to analyse the importance of information in the grain and oilseed industry, it was established that accuracy, value and market effect of information for public consumption were important. In particular, information communication technology was examined as a means of information dissemination in agriculture, especially in developing countries like South Africa. The study found that the major grain and oilseed producing countries that generate planting progress reports are the USA, Brazil, Argentina and Australia. The study looked at the methods used by these countries to compile such reports. Although they have varying methodologies, the key point is timely and frequent information which is readily available for public consumption. After analysing developments and methodologies globally, the focus shifted to South Africa where current information sources in the South African grain and oilseed industry, and the kind of information provided, were analysed. A pilot study was conducted in the summer grain production area of NWK Ltd to gain some insight and experience. The source of communication comprised mobile phones and farmers were able to respond on their progress, as well as receive feedback using the same communication media. Lastly in order to re-emphasis the benefits of a planting progress report, we review the impact of price volatility and how information in the market can help stabilise it. / Dissertation (MSc (Agric))--University of Pretoria, 2017. / Agricultural Economics, Extension and Rural Development / MSc (Agric) / Unrestricted

This dissertation consists of two essays on the execution cost of financial markets. In the first essay, we study impacts of new block trading rules on two kinds of large trades, block trades (BTs) and splitting order trades (STs). We find some results with policy implications. First, targets traded in the block trading market are illiquid. The proportion of BTs (STs) is the decreasing (increasing) function of stock liquidity. Second, large orders are mainly executed by STs except for illiquid stocks, but investors prefer to trade in block trading market at times when trade size is large, probability of informed trading is lower and price volatility is mild. Third, under the new system, the conditional execution costs of BTs (STs) decline (do not decline) and the percentage of BTs (STs) increases (decreases).Fourth, BTs are uninformed with motivations of tax minimization, general trades and ownership transfer trades, while STs are information based. BTs for tax minimization (general trades) incur the lowest (highest) execution costs. Uninformed BTs incur higher execution costs than informed STs, reflecting ¡§premiums of trading with the specific counterparty¡¨ of BTs. Finally, simulation analyses confirm that the block trading market functions well especially for illiquid stocks. In the second essay, we attempt to provide evidence regarding the welfare effect of pre-trade transparency affected by investor and order types. In order to understand the effect of transparency on welfare, we need to explore investors¡¦ behavior adjustments (aggressiveness and order size adjustments) reacted to transparency. We find both individual and institutional investors are more willing to supply liquidity after transparency enhancement. Individual investors behave more aggressively and submit larger order as they supply and demand liquidity, while institutional investors are relatively conservative and submit smaller order in an open environment. We measure welfare of investors in terms of implementation shortfall, which is weighted average of price impacts and opportunity costs. Our main result is, on average, institutional and individual investors who demand immediacy benefit from pre-trade transparency, especially for institutional investors, while traders who supply liquidity are worse off except institutional investors. Intraday analysis further notices individual investors providing liquidity near the end of day lose most from transparency enhancement, while institutional and individual investors demanding liquidity win most in close interval.

This thesis contains three empirical studies on the US corporate bond market; each chapter is self-contained and can be read independently. Chapter 1 studies the impact of credit rating changes on corporate bond returns. This study uses a large dataset of corporate bond transactions from the TRACE database for the US corporate bond market, combined with credit rating changes from Fitch, Moody's and Standard and Poor's (S&P), to analyse over 22,000 bonds, coupled with approximately 28,400 rating events over nearly six years. The results show that the bond market responds to news on credit quality asymmetrically: credit rating downgrades, representing bad news for bond holders, produce the strongest response in returns, whilst upgrades do not generate a statistically significant increase in returns. Chapter 2 analyses how order flow (investor "buy" and "sell" trades), impacts corporate bond prices. Order flow plays an important informational role, acting as a conduit through which private information about fundamental value is aggregated into prices. Using intraday transaction data from the TRACE database, I analyse over 1,000 of the most liquid corporate bonds, a total of 9.5 million trades. Drawing on similar studies of other markets, the relationship between returns and order flow is modelled using a vector autoregression, and the information content of a trade is measured as the long-run price impact of a shock to order flow. Price impacts are particularly strong and significant for order flow from institutional investors and for bonds with higher default risk, higher volatility and lower liquidity. Chapter 3 provides novel evidence on the importance of high frequency measures of volatility and correlation for the corporate bond market. Realized measures of volatility have been shown to be important in modelling and forecasting equity, exchange rate, and Treasury bill return volatility. We merge the NYSE's TAQ database of high frequency equity prices with the TRACE database, and show that the information contained in high frequency data is valuable in modelling the dynamics of the firm-level covariance matrix of bond and stock returns, for over 100 individual U.S. firms.

In order to liquidate a large position in an asset, investors face a tradeoff between price volatility and market impact. The classical approach to this problem is to model volatility via a Brownian motion, and separate price impact into its permanent and temporary components. In this thesis, we consider two variations of the Chriss-Almgren model for temporary price impact. The first model investigates the infinite-horizon optimal liquidation problem in a market with float-dependent, nonlinear temporary price impact. The value function of the investor’s basket and the optimal strategy are characterized in terms of classical solutions of nonlinear parabolic partial differential equations. Depending on the price impact parameters, liquidation may require finite or infinite time. The second model considers time-varying market depth, in that intense trading increases temporary price-impact, which otherwise reverts to a long-term level. We find the optimal execution policy in a finite horizon for an investor with constant risk aversion, and derive the solution using calculus of variation techniques. Although the model potentially allows for price manipulation strategies, these policies are never optimal. We study the non time-constrained case as a limit to the finite-horizon case and explain the solution through a quasi-linear PDE.

This thesis consists of three essays examining the impact and consequences of the trading behaviour of a finely disaggregated category of investors in an electronic limit order book equity market, the Malta Stock Exchange (MSE). The three essays in market microstructure are closely related and examine how investor heterogeneity impacts the informational content of the limit order book, the informational content of individual trades, the price impact of investor trades, the aggressiveness of order submission strategies and the price discovery process within such a market. The first essay investigates the role of the financial intermediary in the price discovery process in a limit order market. We address this issue by analysing the trades of brokers within the Malta Stock Exchange by comparing the profitability of their individual trades and the impact of these trades on the price discovery process. The results of a Weighted Price Contribution methodology indicate that more active brokers that dominate the market in terms of volume and amount traded account for a significant portion of the price discovery process. We also find that the level of profitability of these brokers is directly proportional to the amount of volume traded and their relative share in the price discovery process. This appears to rule out the possibility of manipulative trades by these brokers in order to influence profitabilityThe second essay examines the price impact of the order flow emanating from finely disaggregated classes of investor with the aim of determining whether detectable differences exist in the extent to which orders emanating from particular groups of investors impact on the evolution of stock prices. On the aggregate stock level, results indicate price impact is inversely related to liquidity and as such the price impact of trades is of a higher magnitude and significance in stocks that are less liquid. Significantly, we find that stocks with higher liquidity and trading volume adjust quickly to price changes and the cumulative impact is realised earlier for these stocks. Similarly, for investor classes, our results show that the magnitude and significance of individual price impact increases as liquidity of the stocks declines, showing that as liquidity increases in the order book, the impact of information asymmetry begins to diminish. Institutional investors consistently have the highest significant impact on the evolution of prices across all the stocks. The final essay examines how investors structure their order submission choice in response to changes in the limit order book and market conditions (such as order depth, volatility, returns, and height of the limit order book). We identify 7 distinct investor classes who differ in their trading requirement and the information set available to them, and as such we expect that these investors will adopt different strategies to maximise their trades. The results show variability in the submission strategies adopted by investors as trade sides changes from buy to sell trades. It also indicate that investors have to balance between execution risk, the timely use of private information and the risk of being picked-off by other informed investors. In analysing the varied responses of these investors, we find that the order submission strategies adopted is most responsive to the risk of non-execution.

This dissertation addresses the fundamental question of what factors drive equity prices and investigates the mechanisms through which the drivers influence the price dynamics. The studies are based on the two different frequency levels of financial data. The first part aims to identify what systematic risk factors affect the expected return of stocks based on historical data with frequency being daily or monthly. The second part aims to explain how the hidden supply-demand of a stock affects the stock price dynamics based on market data observed at frequency levels generally between a millisecond and a second. With more and more financial market data becoming available, it greatly facilitates quantitative approaches for analyzing asset price dynamics and market microstructure problems. In the first part, we propose an econometric measure, terms as modularity, for characterizing the cluster structure in a universe of stocks. A high level of modularity implies that the cluster structure of the universe of stocks is highly evident, and low modularity implies a blurred cluster structure. The modularity measure is shown to be related to the cycle of the economy. In addition, individual stock's sensitivity to the modularity measure is shown to be related to its expected return. From 1992 to 2011, the average annual return of stocks with the lowest sensitivity exceeds that of the stocks with highest sensitivities by approximately 7.6%. Considerations of modularity as an asset pricing factor expand the investment opportunity set to passive investors. In the second part, we analyze the effect of hidden demands/supplies in equity trading market on the stock price dynamics. We propose a statistical estimation model for average hidden liquidity based on the limit orderbook data. Not only the estimated hidden liquidity explains the probabilistic property in market microstructure better, it also refines the existing price impact model and achieves higher explanation powers. Our enhanced price impact model offers a base for devising optimal order execution strategies. After we develop an optimal execution strategy based on the price impact function, the advantage of this strategy over benchmark strategies is tested on a simulated stock trading model calibrated by historical data. Simulation tests indicate that our strategy yields significant savings in transaction cost over the benchmark strategies.

This thesis covers three topics in Market Microstructure. Chapter 1 demonstrates that market access frictions may play a significant role in the competition between trading platforms. Analyzing a recent dataset of the trading activity in French and German stocks, we provide evidence that the incumbent markets dominate because the sole market entrant exposes liquidity providers to an excessive adverse selection risk due to a lack of noise traders. Chapter 2 presents a theoretical model of price formation in a dynamic limit order market with slow human traders and fast algorithmic traders. We show that in most cases, algorithmic trading has a detrimental effect on human traders’ welfare. Finally, Chapter 3 empirically analyzes the role of pre-trade transparency in call auctions. Comparing the trading mechanisms in place on the French and German stock exchanges, we find that transparency is associated with higher trading volume, greater liquidity, and better price discovery. / Esta tesis estudia tres temas diferentes de la microestructura de los mercados financieros. El capítulo 1 demuestra que fricciones en el acceso al mercado pueden desempeñar un papel significativo en la competencia entre plataformas de negociación de activos. El análisis de un conjunto de datos recientes de la actividad en acciones francesas y alemanas demuestra que los mercados primarios dominan debido a que el único mercado satélite expone los proveedores de liquidez a un riesgo excesivo de selección adversa, causado por una falta de noise traders. El capítulo 2 presenta un modelo teórico de formación de precios en un mercado dinámico con limit order book poblado por agentes humanos lentos y agentes algorítmicos rápidos. Se demuestra que, en la mayoría de los casos, la negociación algorítmica tiene un efecto negativo sobre el bienestar de agentes humanos. Por último, el capítulo 3 analiza empíricamente el papel de la transparencia pre-negociación en las subastas de apertura y de cierre. Comparando los mecanismos en las bolsas francesas y alemanas, encontramos que la transparencia está asociada con un volumen mayor, una liquidez mayor y un mejor price discovery.

La théorie classique de la valorisation des produits dérivés se repose sur l'absence de coûts de transaction et une liquidité infinie. Ces hypothèses sont toutefois ne plus véridiques dans le marché réel, en particulier quand la transaction est grande et les actifs non-liquides. Dans ce marché imparfait, on parle du prix de sur-réplication puisque la couverture parfaite est devenue parfois infaisable.La première partie de cette thèse se concentre sur la proposition d’un modèle qui intègre à la fois le coût de transaction et l’impact sur le prix du sous-jacent. Nous commençons par déduire la dynamique de l’actif en temps continu en tant que la limite de la dynamique en temps discret. Sous la contrainte d’une position nulle sur l’actif au début et à la maturité, nous obtenons une équation quasi-linéaire pour le prix du dérivé, au sens de viscosité. Nous offrons la stratégie de couverture parfaite lorsque l’équation admet une solution régulière. Quant à la couverture d’une option européenne “covered” sous la contrainte gamma, le principe de programme dynamique utilisé précédemment n'est plus valide. En suivant les techniques du cible stochastique et de l’équation différentielle partielle, nous démontrons que le prix de la sur-réplication est devenue une solution de viscosité d’une équation non linéaire de type parabolique. Nous construisons également la stratégie ε-optimale, et proposons un schéma numérique.La deuxième partie de cette thèse est consacrée aux études sur un nouveau schéma numérique d'EDSR, basé sur le processus de branchement. Nous rapprochons tout d’abord le générateur Lipschitzien par une suite de polynômes locaux, puis appliquons l’itération de Picard. Chaque itération de Picard peut être représentée en termes de processus de branchement. Nous démontrons la convergence de notre schéma sur l’horizon temporel infini. Un exemple concret est discuté à la fin dans l’objectif d’illustrer la performance de notre algorithme. / Classical derivatives pricing theory assumes frictionless market and infinite liquidity. These assumptions are however easily violated in real market, especially for large trades and illiquid assets. In this imperfect market, one has to consider the super-replication price as perfect hedging becomes infeasible sometimes.The first part of this dissertation focuses on proposing a model incorporating both liquidity cost and price impact. We start by deriving continuous time trading dynamics as the limit of discrete rebalancing policies. Under the constraint of holding zero underlying stock at the inception and the maturity, we obtain a quasi-linear pricing equation in the viscosity sense. A perfect hedging strategy is provided as soons as the equation admits a smooth solution. When it comes to hedging a covered European option under gamma constraint, the dynamic programming principle employed previously is no longer valid. Using stochastic target and partial differential equation smoothing techniques, we prove the super-replication price now becomes the viscosity solution of a fully non-linear parabolic equation. We also show how ε-optimal strategies can be constructed, and propose a numerical resolution scheme.The second part is dedicated to the numerical resolution of the Backward Stochastic Differential Equation (BSDE). We propose a purely forward numerical scheme, which first approximates an arbitrary Lipschitz driver by local polynomials and then applies the Picard iteration to converge to the original solution. Each Picard iteration can be represented in terms of branching diffusion systems, thus avoiding the usual estimation of conditional expectation. We also prove the convergence on an unlimited time horizon. Numerical simulation is also provided to illustrate the performance of the algorithm.