The Option to Stock Volume Ratio and Future Returns$
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Spread, Volatility, and Volume Relationship in Financial Markets
Spread, volatility, and volume relationship in financial markets and market maker’s profit optimization Jack Sarkissian Managing Member, Algostox Trading LLC email: [email protected] Abstract We study the relationship between price spread, volatility and trading volume. We find that spread forms as a result of interplay between order liquidity and order impact. When trading volume is small adding more liquidity helps improve price accuracy and reduce spread, but after some point additional liquidity begins to deteriorate price. The model allows to connect the bid-ask spread and high-low bars to measurable microstructural parameters and express their dependence on trading volume, volatility and time horizon. Using the established relations, we address the operating spread optimization problem to maximize the market-maker’s profit. 1. Introduction When discussing security prices it is customary to describe them with single numbers. For example, someone might say price of Citigroup Inc. (ticker “C”) on April 18, 2016 was $45.11. While good enough for many uses, it is not entirely accurate. Single numbers can describe price only as referring to a particular transaction, in which 푁 units of security are transferred from one party to another at a price $푋 each. Price could be different a moment before or after the transaction, or if the transaction had different size, or if it were executed on a different exchange. To be entirely accurate, one should specify these numerous details when talking about security price. We will take this observation a step further. Technically speaking, other than at the time of transaction we cannot say that price exists as a single number at all. -
CHAPTER 16 ESTIMATING EQUITY VALUE PER SHARE in Chapter 15, We Considered How Best to Estimate the Value of the Operating Assets of the Firm
1 CHAPTER 16 ESTIMATING EQUITY VALUE PER SHARE In Chapter 15, we considered how best to estimate the value of the operating assets of the firm. To get from that value to the firm value, you have to consider cash, marketable securities and other non-operating assets held by a firm. In particular, you have to value holdings in other firms and deal with a variety of accounting techniques used to record such holdings. To get from firm value to equity value, you have to determine what should be subtracted out from firm value – i.e, the value of the non-equity claims in the firm. Once you have valued the equity in a firm, it may appear to be a relatively simple exercise to estimate the value per share. All it seems you need to do is divide the value of the equity by the number of shares outstanding. But, in the case of some firms, even this simple exercise can become complicated by the presence of management and employee options. In this chapter, we will measure the magnitude of this option overhang on valuation and then consider ways of incorporating the effect into the value per share. The Value of Non-operating Assets Firms have a number of assets on their books that can be categorized as non- operating assets. The first and obvious one is cash and near-cash investments – investments in riskless or very low-risk investments that most companies with large cash balances make. The second is investments in equities and bonds of other firms, sometimes for investment reasons and sometimes for strategic ones. -
Forecasting Direction of Exchange Rate Fluctuations with Two Dimensional Patterns and Currency Strength
FORECASTING DIRECTION OF EXCHANGE RATE FLUCTUATIONS WITH TWO DIMENSIONAL PATTERNS AND CURRENCY STRENGTH A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES OF MIDDLE EAST TECHNICAL UNIVERSITY BY MUSTAFA ONUR ÖZORHAN IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PILOSOPHY IN COMPUTER ENGINEERING MAY 2017 Approval of the thesis: FORECASTING DIRECTION OF EXCHANGE RATE FLUCTUATIONS WITH TWO DIMENSIONAL PATTERNS AND CURRENCY STRENGTH submitted by MUSTAFA ONUR ÖZORHAN in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Engineering Department, Middle East Technical University by, Prof. Dr. Gülbin Dural Ünver _______________ Dean, Graduate School of Natural and Applied Sciences Prof. Dr. Adnan Yazıcı _______________ Head of Department, Computer Engineering Prof. Dr. İsmail Hakkı Toroslu _______________ Supervisor, Computer Engineering Department, METU Examining Committee Members: Prof. Dr. Tolga Can _______________ Computer Engineering Department, METU Prof. Dr. İsmail Hakkı Toroslu _______________ Computer Engineering Department, METU Assoc. Prof. Dr. Cem İyigün _______________ Industrial Engineering Department, METU Assoc. Prof. Dr. Tansel Özyer _______________ Computer Engineering Department, TOBB University of Economics and Technology Assist. Prof. Dr. Murat Özbayoğlu _______________ Computer Engineering Department, TOBB University of Economics and Technology Date: ___24.05.2017___ I hereby declare that all information in this document has been obtained and presented in accordance with academic rules and ethical conduct. I also declare that, as required by these rules and conduct, I have fully cited and referenced all material and results that are not original to this work. Name, Last name: MUSTAFA ONUR ÖZORHAN Signature: iv ABSTRACT FORECASTING DIRECTION OF EXCHANGE RATE FLUCTUATIONS WITH TWO DIMENSIONAL PATTERNS AND CURRENCY STRENGTH Özorhan, Mustafa Onur Ph.D., Department of Computer Engineering Supervisor: Prof. -
Growth, Profitability and Equity Value
Growth, Profitability and Equity Value Meng Li and Doron Nissim* Columbia Business School July 2014 Abstract When conducting valuation analysis, practitioners and researchers typically predict growth and profitability separately, implicitly assuming that these two value drivers are uncorrelated. However, due to economic and accounting effects, profitability shocks increase both growth and subsequent profitability, resulting in a strong positive correlation between growth and subsequent profitability. This correlation increases the expected value of future earnings and thus contributes to equity value. We show that the value effect of the growth-profitability covariance on average explains more than 10% of equity value, and its magnitude varies substantially with firm size (-), volatility (+), profitability (-), and expected growth (+). The covariance value effect is driven by both operating and financing activities, but large effects are due primarily to operating shocks. One implication of our findings is that conducting scenario analysis or using other methods that incorporate the growth-profitability correlation (e.g., Monte Carlo simulations, decision trees) is particularly important when valuing small, high volatility, low profitability, or high growth companies. In contrast, for mature, high profitability companies, covariance effects are typically small and their omission is not likely to significantly bias value estimates. * Corresponding author; 604 Uris Hall, 3022 Broadway, New York, NY 10027; phone: (212) 854-4249; [email protected]. -
There's More to Volatility Than Volume
There's more to volatility than volume L¶aszl¶o Gillemot,1, 2 J. Doyne Farmer,1 and Fabrizio Lillo1, 3 1Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501 2Budapest University of Technology and Economics, H-1111 Budapest, Budafoki ut¶ 8, Hungary 3INFM-CNR Unita di Palermo and Dipartimento di Fisica e Tecnologie Relative, Universita di Palermo, Viale delle Scienze, I-90128 Palermo, Italy. It is widely believed that uctuations in transaction volume, as reected in the number of transactions and to a lesser extent their size, are the main cause of clus- tered volatility. Under this view bursts of rapid or slow price di®usion reect bursts of frequent or less frequent trading, which cause both clustered volatility and heavy tails in price returns. We investigate this hypothesis using tick by tick data from the New York and London Stock Exchanges and show that only a small fraction of volatility uctuations are explained in this manner. Clustered volatility is still very strong even if price changes are recorded on intervals in which the total transaction volume or number of transactions is held constant. In addition the distribution of price returns conditioned on volume or transaction frequency being held constant is similar to that in real time, making it clear that neither of these are the principal cause of heavy tails in price returns. We analyze recent results of Ane and Geman (2000) and Gabaix et al. (2003), and discuss the reasons why their conclusions di®er from ours. Based on a cross-sectional analysis we show that the long-memory of volatility is dominated by factors other than transaction frequency or total trading volume. -
The Strength of the Euro
DIRECTORATE GENERAL FOR INTERNAL POLICIES POLICY DEPARTMENT A: ECONOMIC AND SCIENTIFIC POLICY The strength of the Euro Monetary Dialogue 14 July 2014 COMPILATION OF NOTES Abstract The notes in this compilation discuss the challenges for ECB monetary policy stemming from the recent appreciation of the Euro in the context of a nascent euro area recovery. The notes have been requested by the Committee on Economic and Monetary Affairs (ECON) as an input for the July 2014 session of the Monetary Dialogue between the Members of ECON and the President of the ECB. IP/A/ECON/NT/2014-02 July 2014 PE 518.782 EN This document was requested by the European Parliament's Committee on Economic and Monetary Affairs. AUTHORS Daniel GROS, Cinzia ALCIDI, Alessandro GIOVANNINI (Centre for European Policy Studies) Stefan COLLIGNON, Sebastian DIESSNER (Scuola Superiore Sant'Anna, London School of Economics) Ansgar BELKE (University of Duisburg-Essen) Sylvester C.W. EIJFFINGER, Louis RAES (Tilburg University) Guillermo DE LA DEHESA (Centre for Economic Policy Research) RESPONSIBLE ADMINISTRATOR Dario PATERNOSTER EDITORIAL ASSISTANT Iveta OZOLINA LINGUISTIC VERSIONS Original: EN ABOUT THE EDITOR Policy departments provide in-house and external expertise to support EP committees and other parliamentary bodies in shaping legislation and exercising democratic scrutiny over EU internal policies. To contact the Policy Department or to subscribe to its newsletter please write to: Policy Department A: Economic and Scientific Policy European Parliament B-1047 Brussels E-mail: [email protected] Manuscript completed in July 2014 © European Union, 2014 This document is available on the internet at: http://www.europarl.europa.eu/committees/en/econ/monetary-dialogue.html DISCLAIMER The opinions expressed in this document are the sole responsibility of the authors and do not necessarily represent the official position of the European Parliament. -
Careers in Quantitative Finance by Steven E
Careers in Quantitative Finance by Steven E. Shreve1 August 2018 1 What is Quantitative Finance? Quantitative finance as a discipline emerged in the 1980s. It is also called financial engineering, financial mathematics, mathematical finance, or, as we call it at Carnegie Mellon, computational finance. It uses the tools of mathematics, statistics, and computer science to solve problems in finance. Computational methods have become an indispensable part of the finance in- dustry. Originally, mathematical modeling played the dominant role in com- putational finance. Although this continues to be important, in recent years data science and machine learning have become more prominent. Persons working in the finance industry using mathematics, statistics and computer science have come to be known as quants. Initially relegated to peripheral roles in finance firms, quants have now taken center stage. No longer do traders make decisions based solely on instinct. Top traders rely on sophisticated mathematical models, together with analysis of the current economic and financial landscape, to guide their actions. Instead of sitting in front of monitors \following the market" and making split-second decisions, traders write algorithms that make these split- second decisions for them. Banks are eager to hire \quantitative traders" who know or are prepared to learn this craft. While trading may be the highest profile activity within financial firms, it is not the only critical function of these firms, nor is it the only place where quants can find intellectually stimulating and rewarding careers. I present below an overview of the finance industry, emphasizing areas in which quantitative skills play a role. -
Global Trends in Mid-Market Private Equity Investing
Investment Perspectives MARCH 2021 GLOBAL TRENDS IN MID-MARKET PRIVATE EQUITY INVESTING GLOBAL TRENDS IN MID-MARKET PRIVATE EQUITY INVESTING | 1 We are delighted to share our aspects in the management of There are macro developments that perspective and insights on some of portfolio companies: workers’ give us reason to be optimistic. In the major industry trends influencing safety, cost control, and supply chain December 2020, within days of the our private equity business, and what maintenance. In particular, managers, transition period deadline, a new this means for 2021 and beyond. with the support of a banking Brexit deal was agreed between the system that showed more flexibility EU and the UK, removing significant As we reflect on 2020, it is clear that compared to the last recession along uncertainty and volatility from the this was a year of two halves. Private with a greater role of private credit market. As the global vaccine rollout equity deal-making fell sharply as capital as a buffer, particularly in the gathers pace and life returns to the pandemic took hold. Sponsors middle market, ensured portfolio some form of normality, increased quickly turned their attention to their companies implemented important consumer spending will have a portfolios rather than commit to new measures aimed at safeguarding direct benefit on consumer-facing investment opportunities. Indeed, liquidity. In addition, managers businesses, albeit gradually, which there were winners, such as tech and have taken full advantage of simulates the performance of healthcare companies that benefitted programs made available by national companies coming out of a recession. from healthy capital markets and the governments, including tax breaks, History shows that this is an excellent IPO window, and losers, such as travel social security safety-nets and other time to invest. -
Lecture 20: Technical Analysis Steven Skiena
Lecture 20: Technical Analysis Steven Skiena Department of Computer Science State University of New York Stony Brook, NY 11794–4400 http://www.cs.sunysb.edu/∼skiena The Efficient Market Hypothesis The Efficient Market Hypothesis states that the price of a financial asset reflects all available public information available, and responds only to unexpected news. If so, prices are optimal estimates of investment value at all times. If so, it is impossible for investors to predict whether the price will move up or down. There are a variety of slightly different formulations of the Efficient Market Hypothesis (EMH). For example, suppose that prices are predictable but the function is too hard to compute efficiently. Implications of the Efficient Market Hypothesis EMH implies it is pointless to try to identify the best stock, but instead focus our efforts in constructing the highest return portfolio for our desired level of risk. EMH implies that technical analysis is meaningless, because past price movements are all public information. EMH’s distinction between public and non-public informa- tion explains why insider trading should be both profitable and illegal. Like any simple model of a complex phenomena, the EMH does not completely explain the behavior of stock prices. However, that it remains debated (although not completely believed) means it is worth our respect. Technical Analysis The term “technical analysis” covers a class of investment strategies analyzing patterns of past behavior for future predictions. Technical analysis of stock prices is based on the following assumptions (Edwards and Magee): • Market value is determined purely by supply and demand • Stock prices tend to move in trends that persist for long periods of time. -
Trading Volume and Serial Correlation in Stock Returns
Trading Volume and Serial Correlation in Stock Returns The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Campbell, John Y., Sanford J. Grossman, and Jiang Wang. 1993. Trading volume and serial correlation in stock returns. Quarterly Journal of Economics 108, no. 4: 905-939. Published Version http://dx.doi.org/10.2307/2118454 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:3128710 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA TRADING VOLUME AND SERIAL CORRELATION IN STOCK RETURNS* JOHN Y. CAMPBELL SANFORD J. GROSSMAN JIANG WANG This paper investigates the relationship between aggregate stock market trading volume and the serial correlation of daily stock returns. For both stock indexes and individual large stocks, the first-order daily return autocorrelation tends to decline with volume. The paper explains this phenomenon using a model in which risk-averse "market makers" accommodate buying or selling pressure from "liquidity" or "noninformational" traders. Changing expected stock returns re- ward market makers for playing this role. The model implies that a stock price decline on a high-volume day is more likely than a stock price decline on a low-volume day to be associated with an increase in the expected stock return. I. INTRODUCTION There is now considerable evidence that the expected return on the aggregate stock market varies through time. -
The Effect of Portfolio Size on the Financial Performance of Portfolios of Investment Firms in Kenya
THE EFFECT OF PORTFOLIO SIZE ON THE FINANCIAL PERFORMANCE OF PORTFOLIOS OF INVESTMENT FIRMS IN KENYA PRESENTED BY MBOGO PETER KIMANI: D61/61748/2010 A Research Project Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Business Administration (MBA), School of Business, University of Nairobi. AUGUST 2012 DECLARATION This research project is my original work and has not been submitted for the award of a degree in any other university. Signed: …………..……………………………….. Date: ………………………… Mbogo Peter Kimani Reg. No.: D61 /61748/2010 This research project has been submitted for examination with my approval as university supervisor. Signed: …………………………………………… Date: ………………………… Dr. Josiah Aduda Lecturer, Department of Finance and Accounting ii DEDICATION I dedicate this work to my wife and my children for their support during its preparation. Your patience and encouragement as I stayed away for long, either in class throughout the weekends, or in the field was really touching. iii ACKNOWLEDGEMENT A major research project like this is never the work of anyone alone. The contributions of many different people, in their different ways, have made this possible. First, I would like to thank God for the wisdom and perseverance that HE has bestowed upon me during this research project, and indeed, throughout my life. Second, I offer my sincerest gratitude to my supervisors; Dr. Josiah Aduda and Mr. Mirie Mwangi who have supported me throughout this research project with their patience and knowledge whilst allowing me the room to work in my own way. I appreciate the odd hours we spent discussing the reports. I wish to thank the respondents who participated in this study. -
MODELING of STOCK RETURNS and TRADING VOLUME Abstract 1
MODELING OF STOCK RETURNS AND TRADING VOLUME TAISEI KAIZOJI1 Graduate School of Arts and Sciences, International Christian University, 3-10-2 Osawa, Mitaka, Tokyo 181-8585, Japan Abstract In this study, we investigate the statistical properties of the returns and the trading volume. We show a typical example of power-law distributions of the return and of the trading volume. Next, we propose an interacting agent model of stock markets inspired from statistical mechanics [24] to explore the empirical findings. We show that as the interaction among the interacting traders strengthens both the returns and the trading volume present power-law behavior. 1. Introduction Over half a century, a considerable number of researches have been made on trading volume and its relationship with asset returns [1-12]. Although the existence of the relationships between trading volume and future prices is inconsistent with the weak form of market efficiency [10], the analysis of the relationship has received increasing attention from researchers and investors. One of the causes of great attention to the relationship is that many have considered that price movements may be predicted by trading volume. The researchers in a new field of science called ‘econophysics’ have worked on this problem from a slightly different angle. In the literature of econophysics [13-20], 1 The corresponding author: Taisei Kaizoji, International Christian University, 3-10-2 Osawa, Mitaka, Tokyo 181-8585, Japan. E-mail: [email protected] 1 most studies on price fluctuations and trading volume have focused primarily on finding some universal characteristics which are often observed in complex systems with a large number of interacting units, such as power laws, and have modeled the statistical properties observed.