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Testing the Random Walk Hypothesis for Helsinki Stock Exchange
Testing the Random Walk Hypothesis for Helsinki Stock Exchange Niklas Miller School of Science Bachelor’s thesis Espoo, 16.4.2018 Supervisor and advisor Prof. Harri Ehtamo Copyright ⃝c 2018 Niklas Miller Aalto University, P.O. BOX 11000, 00076 AALTO www.aalto.fi Abstract of the bachelor’s thesis Author Niklas Miller Title Testing the Random Walk Hypothesis for Helsinki Stock Exchange Degree programme Engineering Physics and Mathematics Major Mathematics and Systems Analysis Code of major SCI3029 Supervisor and advisor Prof. Harri Ehtamo Date 16.4.2018 Number of pages 25+2 Language English Abstract This Bachelor’s thesis examines the random walk hypothesis for weekly returns of two indices, OMXHPI and OMXH25, and eight stocks in Helsinki Stock Exchange. The returns run from January 2000 to February 2018. In order to test the null hypothesis of a random walk, the study employs three variance ratio tests: the Lo– MacKinlay test with the assumption of heteroscedastic returns, the Chow–Denning test and the Whang–Kim test. The variance ratio estimates produced by the Lo–MacKinlay test are analyzed for various lag values. The results indicate that both indices and all stocks, except for UPM–Kymmene, follow a random walk at the 5 percent level of significance. Furthermore, the variance ratios are found tobe less than unity for shorter lags, which implies that stock returns may be negatively autocorrelated for short return horizons. Some stocks and both indices show a high variance ratio estimate for larger lag values, contradicting a mean reverting model of stock prices. The results demonstrate, in contrast to previous studies, that Helsinki Stock Exchange may be an efficient market and thus, that predicting future returns based on historical price information is difficult if not impossible. -
11.50 Margin Constraints and the Security Market Line
Margin Constraints and the Security Market Line Petri Jylh¨a∗ June 9, 2016 Abstract Between the years 1934 and 1974, the Federal Reserve frequently changed the initial margin requirement for the U.S. stock market. I use this variation in margin requirements to test whether leverage constraints affect the security market line, i.e. the relation between betas and expected returns. Consistent with the theoretical predictions of Frazzini and Ped- ersen (2014), but somewhat contrary to their empirical findings, I find that tighter leverage constraints result in a flatter security market line. My results provide strong empirical sup- port for the idea that funding constraints faced by investors may, at least partially, help explain the empirical failure of the capital asset pricing model. JEL Classification: G12, G14, N22. Keywords: Leverage constraints, Security market line, Margin regulations. ∗Imperial College Business School. Imperial College London, South Kensington Campus, SW7 2AZ, London, UK; [email protected]. I thank St´ephaneChr´etien,Darrell Duffie, Samuli Kn¨upfer,Ralph Koijen, Lubos Pastor, Lasse Pedersen, Joshua Pollet, Oleg Rytchkov, Mungo Wilson, and the participants at American Economic Association 2015, Financial Intermediation Research Society 2014, Aalto University, Copenhagen Business School, Imperial College London, Luxembourg School of Finance, and Manchester Business School for helpful comments. 1 Introduction Ever since the introduction of the capital asset pricing model (CAPM) by Sharpe (1964), Lintner (1965), and Mossin (1966), finance researchers have been studying why the return difference between high and low beta stocks is smaller than predicted by the CAPM (Black, Jensen, and Scholes, 1972). One of the first explanation for this empirical flatness of the security market line is given by Black (1972) who shows that investors' inability to borrow at the risk-free rate results in a lower cross-sectional price of risk than in the CAPM. -
The Performance of Value and Growth Stocks in Recessions and Booms and the Combination with Momentum
http://lib.ulg.ac.be http://matheo.ulg.ac.be The performance of value and growth stocks in recessions and booms and the combination with momentum Auteur : Reuter, Gregory Promoteur(s) : Lambert, Marie Faculté : HEC-Ecole de gestion de l'ULg Diplôme : Master en sciences de gestion, à finalité spécialisée en Banking and Asset Management Année académique : 2016-2017 URI/URL : http://hdl.handle.net/2268.2/3659 Avertissement à l'attention des usagers : Tous les documents placés en accès ouvert sur le site le site MatheO sont protégés par le droit d'auteur. Conformément aux principes énoncés par la "Budapest Open Access Initiative"(BOAI, 2002), l'utilisateur du site peut lire, télécharger, copier, transmettre, imprimer, chercher ou faire un lien vers le texte intégral de ces documents, les disséquer pour les indexer, s'en servir de données pour un logiciel, ou s'en servir à toute autre fin légale (ou prévue par la réglementation relative au droit d'auteur). Toute utilisation du document à des fins commerciales est strictement interdite. Par ailleurs, l'utilisateur s'engage à respecter les droits moraux de l'auteur, principalement le droit à l'intégrité de l'oeuvre et le droit de paternité et ce dans toute utilisation que l'utilisateur entreprend. Ainsi, à titre d'exemple, lorsqu'il reproduira un document par extrait ou dans son intégralité, l'utilisateur citera de manière complète les sources telles que mentionnées ci-dessus. Toute utilisation non explicitement autorisée ci-avant (telle que par exemple, la modification du document ou son résumé) nécessite l'autorisation préalable et expresse des auteurs ou de leurs ayants droit. -
Style Investing$
Journal of Financial Economics 68 (2003) 161–199 Style investing$ Nicholas Barberisa,*, Andrei Shleiferb a Graduate School of Business, University of Chicago, Chicago, IL 60637, USA b Department of Economics, Harvard University, Cambridge, MA 02138, USA Received 28 November 2000; received in revised form 11 December 2001 Abstract We study asset prices in an economy where some investors categorize risky assets into different styles and move funds among these styles depending on their relative performance. In our economy, assets in the same style comove too much, assets in different styles comove too little, and reclassifying an asset into a new style raises its correlation withthatstyle. We also predict that style returns exhibit a rich pattern of own- and cross-autocorrelations and that while asset-level momentum and value strategies are profitable, their style-level counterparts are even more so. We use the model to shed light on several style-related empirical anomalies. r 2003 Elsevier Science B.V. All rights reserved. JEL classification: G11, G12, G14 Keywords: Style investing; Comovement; Positive feedback; Value; Momentum 1. Introduction One of the clearest mechanisms of human thought is classification, the grouping of objects into categories based on some similarity among them (Roschand Lloyd, 1978; Wilson and Keil, 1999). We group countries into democracies and dictator- ships based on features of political systems within each group. We classify $We have benefited from the comments of two anonymous referees and from discussions with John Campbell, Doug Diamond, Eugene Fama, Edward Glaeser, Will Goetzmann, Sanford Grossman, Rafael La Porta, David Laibson, Sendhil Mullainathan, Geert Rouwenhorst, Lawrence Summers, Jeffrey Wurgler, and seminar participants at the University of Chicago, Harvard University, London Business School, Princeton University, the University of Iowa, Wharton, the AFA, and the NBER. -
Factor Investing in European Offices Smart Beta Compared to Traditional Styles
AEW RESEARCH FACTOR INVESTING IN EUROPEAN OFFICES SMART BETA COMPARED TO TRADITIONAL STYLES Q2 2019 EUROPEAN QUARTERLY REPORT TABLE OF CONTENTS SUMMARY: GROWTH OFFERS BEST EXCESS RETURNS THROUGH THE CYCLE ................................. 3 SECTION 1: METHODOLOGY – OUR FACTOR INVESTING FRAMEWORK .......................................... 4 SECTION 2: RESULTS FOR FACTOR BASED FRAMEWORK ......................................................... 5 SECTION 3: COMPARISON OF FACTOR INVESTING WITH TRADITIONAL STYLES ............................... 7 HOW CAN INVESTORS USE FACTOR INVESTING IN EUROPEAN OFFICES? Factor investing is an investment approach successfully used in fixed income and equity investment management. It is based on academic work by Fama & French (1993) among many others. The approach identifies multiple factors that explain excess returns compared to the market portfolio. Initially these factors focused on small caps, value and growth stocks, but then expanded in scope. By identifying the most relevant underlying factors, investors can benefit from market inefficiencies in a rules-based and transparent way. If you consistently select stocks, bonds or sectors whose performance have been driven most by factors delivering excess return, you should beat the market benchmark in the long term. These so-called smart beta strategies use factors such as volatility, liquidity, quality, value, yield and growth. In this report, we apply this factor investing approach to close to 40 European office markets for the first time. We will also compare factor investing to the traditional core and value add investment styles. FACTORS USED IN FACTOR INVESTING FRAMEWORK Source: AEW FOCUSED ON THE FUTURE OF REAL ESTATE 2 EUROPEAN QUARTERLY REPORT SUMMARY: GROWTH OFFERS BEST EXCESS RETURNS THROUGH THE CYCLE . In this report, we apply our new factor investing approach to close to 40 European office markets for the first time. -
Frictional Finance “Fricofinance”
Frictional Finance “Fricofinance” Lasse H. Pedersen NYU, CEPR, and NBER Copyright 2010 © Lasse H. Pedersen Frictional Finance: Motivation In physics, frictions are not important to certain phenomena: ……but frictions are central to other phenomena: Economists used to think financial markets are like However, as in aerodynamics, the frictions are central dropping balls to the dynamics of the financial markets: Walrasian auctioneer Frictional Finance - Lasse H. Pedersen 2 Frictional Finance: Implications ¾ Financial frictions affect – Asset prices – Macroeconomy (business cycles and allocation across sectors) – Monetary policy ¾ Parsimonious model provides unified explanation of a wide variety of phenomena ¾ Empirical evidence is very strong – Stronger than almost any other influence on the markets, including systematic risk Frictional Finance - Lasse H. Pedersen 3 Frictional Finance: Definitions ¾ Market liquidity risk: – Market liquidity = ability to trade at low cost (conversely, market illiquidity = trading cost) • Measured as bid-ask spread or as market impact – Market liquidity risk = risk that trading costs will rise • We will see there are 3 relevant liquidity betas ¾ Funding liquidity risk: – Funding liquidity for a security = ability to borrow against that security • Measured as the security’s margin requirement or haircut – Funding liquidity for an investor = investor’s availability of capital relative to his need • “Measured” as Lagrange multiplier of margin constraint – Funding liquidity risk = risk of hitting margin constraint -
The Efficient Market Hypothesis
THE EFFICIENT MARKET HYPOTHESIS A Thesis Presented to the Faculty of California State Polytechnic University, Pomona In Partial Fulfillment Of the Requirements for the Degree Master of Science In Economics By Nora Alhamdan 2014 SIGNATURE PAGE THESIS: The Efficient Market Hypothesis AUTHOR: Nora Alhamdan DATE SUBMITTED: Spring 2014 Economics Department Dr. Carsten Lange _________________________________________ Thesis Committee Chair Economics Dr. Bruce Brown _________________________________________ Economics Dr. Craig Kerr _________________________________________ Economics ii ABSTRACT The paper attempts testing the random walk hypothesis, which the strong form of the Efficient Market Hypothesis. The theory suggests that stocks prices at any time “fully reflect” all available information (Fama, 1970). So, the price of a stock is a random walk (Enders, 2012). iii TABLE OF CONTENTS Signature Page .............................................................................................................. ii Abstract ......................................................................................................................... iii List of Tables ................................................................................................................ vi List of Figures ............................................................................................................... vii Introduction .................................................................................................................. 1 History ......................................................................................................................... -
Technical Analysis and Random Walks
~ Technical Analysis and Random Walks There are two kinds of security analysis . Fundamental analysis seeks to forecast each stock 's return by studying the prospects for the company 's business . Technical analysis attempts to forecast the return by searching for patterns in past stock prices . Although a glance at any chart of past stock prices will often suggest such patterns , these could be no more than an optical illusion . Consider , for example , the following series of graphs . Figure 1.1 depicts the level of the Dow Jones Average during 1981. It appears to be characterized by typical short -term patterns . Yet when it is reconstructed in figure 1.2 as a chart of the weekly changes in the index , the symmetry disappears and is replaced by an apparently mean - ingless jumble . The next two diagrams reverse the process . Figure 1.3 is a hypothetical series of random price changes . There neither is nor appears to be any pattern in figure 1.3. Yet when it is reconstructed in figure 1.4 as a chart of the levels of the counterfeit prices , the resulting graph acquires many of the characteristics of actual charts of the market , even to the " head and shoulders " pattern that is beloved by technical analysts .] The moral of the story is this : Do not assume without questioning because there are regularities in price levels that The Reaction of Common Stock Prices to New Information 4 Figure 1.1 The weekly levels of the Dow JonesAverage in 1981 appear to be characterized by regular patterns. Figure 1.2 The changesin the Dow Jonesdo not appear to follow any pattern. -
Openair@RGU the Open Access Institutional Repository at Robert Gordon University
OpenAIR@RGU The Open Access Institutional Repository at Robert Gordon University http://openair.rgu.ac.uk Citation Details Citation for the version of the work held in ‘OpenAIR@RGU’: SANUSI, M. S., 2015. Market efficiency, volatility behaviour and asset pricing analysis of the oil & gas companies quoted on the London Stock Exchange. Available from OpenAIR@RGU. [online]. Available from: http://openair.rgu.ac.uk Copyright Items in ‘OpenAIR@RGU’, Robert Gordon University Open Access Institutional Repository, are protected by copyright and intellectual property law. If you believe that any material held in ‘OpenAIR@RGU’ infringes copyright, please contact [email protected] with details. The item will be removed from the repository while the claim is investigated. MARKET EFFICIENCY, VOLATILITY BEHAVIOUR AND ASSET PRICING ANALYSIS OF THE OIL & GAS COMPANIES QUOTED ON THE LONDON STOCK EXCHANGE Muhammad Surajo Sanusi A thesis submitted in partial fulfilment of the requirements of the Robert Gordon University for the degree of Doctor of Philosophy June 2015 i Abstract This research assessed market efficiency, volatility behaviour, asset pricing, and oil price risk exposure of the oil and gas companies quoted on the London Stock Exchange with the aim of providing fresh evidence on the pricing dynamics in this sector. In market efficiency analysis, efficient market hypothesis (EMH) and random walk hypothesis were tested using a mix of statistical tools such as Autocorrelation Function, Ljung-Box Q-Statistics, Runs Test, Variance Ratio Test, and BDS test for independence. To confirm the results from these parametric and non-parametric tools, technical trading and filter rules, and moving average based rules were also employed to assess the possibility of making abnormal profit from the stocks under study. -
Effects of Estimating Systematic Risk in Equity Stocks in the Nairobi Securities Exchange (NSE) (An Empirical Review of Systematic Risks Estimation)
International Journal of Academic Research in Accounting, Finance and Management Sciences Vol. 4, No.4, October 2014, pp. 228–248 E-ISSN: 2225-8329, P-ISSN: 2308-0337 © 2014 HRMARS www.hrmars.com Effects of Estimating Systematic Risk in Equity Stocks in the Nairobi Securities Exchange (NSE) (An Empirical Review of Systematic Risks Estimation) Paul Munene MUIRURI Department of Commerce and Economic Studies Jomo Kenyatta University of Agriculture and Technology Nairobi, Kenya, E-mail: [email protected] Abstract Capital Markets have become an integral part of the Kenyan economy. The manner in which securities are priced in the capital market has attracted the attention of many researchers for long. This study sought to investigate the effects of estimating of Systematic risk in equity stocks of the various sectors of the Nairobi Securities Exchange (NSE. The study will be of benefit to both policy makers and investors to identify the specific factors affecting stock prices. To the investors it will provide useful and adequate information an understanding on the relationship between risk and return as a key piece in building ones investment philosophy. To the market regulators to establish the NSE performance against investors’ perception of risks and returns and hence develop ways of building investors’ confidence, the policy makers to review and strengthening of the legal and regulatory framework. The paper examines the merged 12 sector equity securities of the companies listed at the market into 4sector namely Agricultural; Commercial and Services; Finance and Investment and Manufacturing and Allied sectors. This study Capital Asset Pricing Model (CAPM) of Sharpe (1964) to vis-à-vis the market returns. -
The Capital Asset Pricing Model (Capm)
THE CAPITAL ASSET PRICING MODEL (CAPM) Investment and Valuation of Firms Juan Jose Garcia Machado WS 2012/2013 November 12, 2012 Fanck Leonard Basiliki Loli Blaž Kralj Vasileios Vlachos Contents 1. CAPM............................................................................................................................................... 3 2. Risk and return trade off ............................................................................................................... 4 Risk ................................................................................................................................................... 4 Correlation....................................................................................................................................... 5 Assumptions Underlying the CAPM ............................................................................................. 5 3. Market portfolio .............................................................................................................................. 5 Portfolio Choice in the CAPM World ........................................................................................... 7 4. CAPITAL MARKET LINE ........................................................................................................... 7 Sharpe ratio & Alpha ................................................................................................................... 10 5. SECURITY MARKET LINE ................................................................................................... -
Style Investing for Real Estate : an Introduction
STYLE INVESTING FOR REAL ESTATE : AN INTRODUCTION BNP Paribas REIM April 2019 STYLE INVESTING FOR REAL ESTATE : AN INTRODUCTION MAURIZIO GRILLI - HEAD OF INVESTMENT MANAGEMENT ANALYSIS AND STRATEGY FOREWORD One of the motives that led us to research the application of style (or factor)1 investing to the real estate sector descends from our desire to describe real estate investment to investment generalist or specialists of other asset classes. It is well known that direct real estate investments is more illiquid than other major asset classes such as bonds and equity. Moreover, real estate jargon is very technical. The combination of these dynamics has resulted in some investors believing that real estate is fundamentally different from other major asset classes. However, all asset classes can actually be explained in terms of risk and performance. This is exactly what we like about style investing, as we can use it as a tool to create a link between the ‘classical’ asset management and the real estate worlds. Style investing in the stock market has been studied since the 1970s. Nowadays, this approach has become very popular and is increasingly gaining media exposure. Why? Because style investing methodology is intuitive, albeit rigorous and data-driven, and can be applied to the reality of day-to-day investment. In short, the beauty of this methodology is that it creates the link between behavioural finance and quantitative analysis. Through the adoption of style investing, investors can spell out their preferences in terms of risk and reward while the manager, on the other side, can create tailor-made solutions that cater to their precise needs.