A Simple Way to Estimate Bid-Ask Spreads from Daily High and Low Prices


Book Description

We develop a bid-ask spread estimator from daily high and low prices. Daily high (low) prices are almost always buy (sell) trades. Hence, the high-low ratio reflects both the stock's variance and its bid-ask spread. While the variance component of the high-low ratio is proportional to the return interval, the spread component is not. This allows us to derive a spread estimator as a function of high-low ratios over one-day and two-day intervals. The estimator is easy to calculate, can be applied in a variety of research areas, and generally outperforms other low-frequency estimators.




A Simple Estimation of Bid-Ask Spreads from Daily Close, High, and Low Prices


Book Description

We propose a new method to estimate the bid-ask spread when quote data are not available. Compared to other low-frequency estimates, this method utilizes a wider information set, namely, readily available close, high, and low prices. In the absence of end-of-day quote data, this method generally provides the highest cross-sectional and average time-series correlations with the TAQ effective spread benchmark. Moreover, it delivers the most accurate estimates for less liquid stocks. Our estimator has many potential applications, including an accurate measurement of transaction cost, systematic liquidity risk, and commonality in liquidity for U.S. stocks dating back almost one century. The appendix to "A Simple Estimation of Bid-Ask Spreads from Daily Close, High, and Low Prices" to is available at the following URL: 'http://ssrn.com/abstract=2809692' http://ssrn.com/abstract=2809692.







The Empirical Analysis of Liquidity


Book Description

We provide a synthesis of the empirical evidence on market liquidity. The liquidity measurement literature has established standard measures of liquidity that apply to broad categories of market microstructure data. Specialized measures of liquidity have been developed to deal with data limitations in specific markets, to provide proxies from daily data, and to assess institutional trading programs. The general liquidity literature has established local cross-sectional patterns, global cross-sectional patterns, and time-series patterns.




Portfolio Risk Analysis


Book Description

Portfolio risk forecasting has been and continues to be an active research field for both academics and practitioners. Almost all institutional investment management firms use quantitative models for their portfolio forecasting, and researchers have explored models' econometric foundations, relative performance, and implications for capital market behavior and asset pricing equilibrium. Portfolio Risk Analysis provides an insightful and thorough overview of financial risk modeling, with an emphasis on practical applications, empirical reality, and historical perspective. Beginning with mean-variance analysis and the capital asset pricing model, the authors give a comprehensive and detailed account of factor models, which are the key to successful risk analysis in every economic climate. Topics range from the relative merits of fundamental, statistical, and macroeconomic models, to GARCH and other time series models, to the properties of the VIX volatility index. The book covers both mainstream and alternative asset classes, and includes in-depth treatments of model integration and evaluation. Credit and liquidity risk and the uncertainty of extreme events are examined in an intuitive and rigorous way. An extensive literature review accompanies each topic. The authors complement basic modeling techniques with references to applications, empirical studies, and advanced mathematical texts. This book is essential for financial practitioners, researchers, scholars, and students who want to understand the nature of financial markets or work toward improving them.




Finding Alphas


Book Description

Discover the ins and outs of designing predictive trading models Drawing on the expertise of WorldQuant’s global network, this new edition of Finding Alphas: A Quantitative Approach to Building Trading Strategies contains significant changes and updates to the original material, with new and updated data and examples. Nine chapters have been added about alphas – models used to make predictions regarding the prices of financial instruments. The new chapters cover topics including alpha correlation, controlling biases, exchange-traded funds, event-driven investing, index alphas, intraday data in alpha research, intraday trading, machine learning, and the triple axis plan for identifying alphas. • Provides more references to the academic literature • Includes new, high-quality material • Organizes content in a practical and easy-to-follow manner • Adds new alpha examples with formulas and explanations If you’re looking for the latest information on building trading strategies from a quantitative approach, this book has you covered.




Advances in Financial Machine Learning


Book Description

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.




Stock Market Liquidity in Chile


Book Description

Chile has a large but relatively illiquid stock market. Global factors such as global risk appetite and monetary policy in advanced economies are key cyclical determinants of liquidity in Chilean equities. Evidence from a cross-section of emerging markets suggests strong protection of minority shareholders can help improve stock market liquitidity. Currently, illiquid in Chilean may have to pay 31⁄2 percent more as cost of equity. Corporate governance should be improved, namely through the adoption of a stewardship code.




Machine Learning for Financial Risk Management with Python


Book Description

Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, and risk analysts will explore Python-based machine learning and deep learning models for assessing financial risk. You'll learn how to compare results from ML models with results obtained by traditional financial risk models. Author Abdullah Karasan helps you explore the theory behind financial risk assessment before diving into the differences between traditional and ML models. Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Revisit and improve market risk models (VaR and expected shortfall) using machine learning techniques Develop a credit risk based on a clustering technique for risk bucketing, then apply Bayesian estimation, Markov chain, and other ML models Capture different aspects of liquidity with a Gaussian mixture model Use machine learning models for fraud detection Identify corporate risk using the stock price crash metric Explore a synthetic data generation process to employ in financial risk.




Contemporary Trends and Challenges in Finance


Book Description

This volume features a selection of contributions presented at the 2019 Wroclaw Conference in Finance, covering a wide range of topics in finance and financial economics, e.g. financial markets; monetary policy; corporate, personal and public finance; and risk management and insurance. Reflecting the diversity and richness of research in the field, the papers discuss both fundamental and applied finance, and offer a detailed analysis of current financial-market problems, including specifics of the Polish and Central European markets. They also examine the results of advanced financial modeling. Accordingly, the proceedings offer a valuable resource for researchers at universities and policy institutions, as well as graduate students and practitioners in economics and finance at both private and government organizations.