Testing the profitability of technical trading rules on stock markets
|
|
- Jade Marsh
- 8 years ago
- Views:
Transcription
1 Testing the profitability of technical trading rules on stock markets ANDREI ANGHEL Finance, Insurance, Banking and Stock Exchange* CRISTIANA TUDOR International Business and Economics* MARIA TUDOR Applied Mathematics* *The Bucharest University of Economic Studies 6, PiataRomana, Bucharest ROMANIA Abstract:This empirical study continues previous research [2] that investigated the profitability of simple technical trading rules. We have added Athena Stock Exchange Composite Index to our universe of three previously researched indices: FTSE in Great Britain, S&P in USA and DAX in Germany. We have applied a new test based on Hansen s SPA Test [6] with the primary goal of rejecting the hypothesis of data mining resulted from a possible survivorship bias of trading rules; we have also provided a detailed explanation of our bootstrap implementation of the test. We did manage to find lower p-values for the new test that compared favorably to White s Reality Check [9], but we were still not able to reject the null hypothesis that data mining could partially be responsible for the apparent superior performance of some of the trading rules that we have tested on the DAX and S&P indices. On the other hand, we are more confident that data mining cannot be ruled out as a cause for perceived superior performance for any of the 100 rules tested on the FTSE index. Finally, we have discovered not only that all the 100 tested strategies manage to beat the buy & hold benchmark on the Greek composite index, but also that they explain that market dynamics better than a random walk with drift, and that is highly unlikely that data mining could have distorted these findings, which are important both for academics and investment professionals. We suspect that the superior performance of these simple rules is influenced by:1. the inclusion of dividends in the researched index and 2. bythe degree of maturity of thatmarket. Key-Words: technical trading strategy, bootstrapping, data mining, reality check, superior predictive ability 1 Introduction and related literature Technical trading rules are one of the first tools used for analyzing stock markets and for taking investment decisions. As the current investment environment is filled with electronic trading platforms that promise to deliver high-speed order execution, technical rules are also the (apparently) easiest tool for the average investor to come up with some documented investment decisions in real time. Due to the aggressive marketing of electronic platforms vendors, technical analysis is been however promoted among inexperienced investors much more than its actual merits would justify. It is exactly these merits that our paper tries to analyze and check for consistency on recent data. Our endeavor is not unique and there are a number of papers debating the subject. One of the first attempts to bring light on the controversy of technical trading usability was the paper of Sidney Alexander, Price Movements in Speculative Markets: Trends or Random Walks (1961): the author seemed to have found compelling evidence that the simplest of trading rules can turn a profit. ISBN:
2 The study was later rebukedby Fama and Blume (1966) as it was based on a oversimplification of returns (the author had computed returns by using a clever mathematical modeling, that unfortunately did not account for fast markets). However, Brock, Lakonishok and LeBaron (1992) took another look at some of the simplest of the trading rules and using bootstrapping they found some strong evidence that simple moving averages were actually better at modeling market dynamics than some popular null models: random walk with a drift, AR(1) and Garch models. The simple but computer-intensive bootstrapping test did not account however for the danger of spurious results due to data mining, and the authors themselves acknowledged this fact and tried to mitigate the problem by presenting the results of all the tests that they have performed. However, data mining might be the result not only of some researcher s misguided effort, but also of multiple researchers and practitioners trials over the course of so many years since technical trading is been in use: it might very well be a fact that the parameters used by the authors were the results of a survivorship bias, that took place over the years, in exactly the same way that the average return of hedge funds is upwardly biased due to unintentional and hardly avoidable exclusion of poor performing or failed hedge funds. Facing this actual danger brought upon by the sheer quantity of information and computing power, White (2000) developed the Reality Check for data snooping, a test that compared the alleged performance of some trading rule with the supremum of performance for all the rules considered for each of the bootstrapped samples.anghel and Tudor (2013) applied the test to investigate the profitability of a collection of 100 trading rules on some market indices and found that none of trading rules managed to beat the buy and hold benchmark policy on the FTSE and only one rule beat the benchmark on the S&P index, between 1990 and Some of the rules howeverexplained market dynamics better than a simple random walk with a drift model, for S&P and DAX. For the DAX index 7 out of the 100 tested trading rules managed to beat the benchmark; the Reality Check p-value was however too large to reject the null that the apparent superiority of the best model was due to data mining. The large p-value was an indication that unintended data mining might be partially responsible with the apparently superior performance of some trading rules; it could also be the case that the Reality Check test for data snooping was in fact too weak in order to reject the hypothesis of data mining. Hansen (2005) improved the Reality Check by studentizing the statistic and by invoking a sample dependent null distribution, thus reducing the influence of erratic forecasts. It is this methodology that we use in this paper in a slightly modified form to check whether the assumption of superior performance for some trading rules is not seriously undermined in the presence of data mining. We provide a detailed description of a bootstrap implementation of the Superior Predictive Ability test and its results applied on recent data, on fourmarket indices: FTSE, S&P, DAX and Athex Composite. The remainder of the paper is organized as follows. In Section 2, the data and methodology are presented. The empirical results are reported in Section 3, while Section 4 concludes the study. 2 Data and method Daily index closing values are obtained for the four markets indices mentioned above: FTSE100 in United Kingdom, S&P500 in United States, DAX in Germany and Athex Compos in Greece. The data starts with November 26 th, 1990 and ends with May 17 th, The length of the samples is different for each of the markets, depending on the actual number of trading days. We follow White s notation on testing whethera financial market trading strategy yields returns superior to a benchmark by taking, where and are signal functions with two permissible values (0 and 1)that convert indicators or and parameters or into market position.we then compute the average excess returns for 100 trading rules of our choice and we use Simple Moving Averages, defined bythe following two parameters and : ISBN:
3 , that represents the length of the short and, respectively, the long moving average corresponding to the SMA(, ) strategy.more details on exactly the same implementation of the Simple Moving Average (SMA) strategies can be found on Anghel and Tudor (2013). We only note the fact that the best performing strategies for FTSE was SMA (10,500), for S&P was SMA (10,500) and for DAX was SMA (1,300). A short summary with the best trading strategies and the results of simple bootstrapping and Reality check tests for them is presented in Table 1: Table 1 Best SMA strategies and their corresponding p- values Best rule bootstrap p value Reality Check FTSE SMA(10,500) S&P SMA(10,500) DAX SMA(1,300) As of this point, none of the 100 trading rules that we have tested seems to work on empirical data after we take into account data snooping, be it erroneously introduces by researchers or by a survivorship bias. However, since we have obviously tried to chose our coefficients randomly, and since for the DAX index a rather large number of strategies historically outperformed the benchmark (7 out of 100), we are inclined to believe that the Reality Check might incorrectly throw away some otherwise good investment strategies, which leads us to compute Hansen s Test for Superior Predictive Ability, as follows. We start by re-sampling the original return series and chain-linking them back to a pseudo-time series, by keeping the same fixed initial price. The re-sampling scheme that we use is a simple n-1 sampling with replacement, where n is the length of the original price series (there is no return corresponding to the first price, hence there are only n-1 returns to be re-sampled). Our re-sampling scheme is different than that used in Hansen (2005) which employed the stationary bootstrap of Politis and Romano (2005), or the recommended implementation of block bootstrap of Künsch (1989). Instead we rely on Sullivan, Timmerman and White (1999) that found little sensitivity to the choice of q (the smoothing parameter for the two schemes)and we continue by using a standard re-sampling with replace.the result of the selection step, the boostrapp p-value and the Reality Check p- value are provided for comparison purposes intable 1 and are detailed in Anghel and Tudor (2013). The test statistic that we want to compute requires the estimate of, with k =1,, m and m is thenumber of strategies that we test (100).We implement the earlier version of the estimator described by Hansen (2005) and make sure we use a large number of iterations for the bootstrap process (1000), thus obtaining an estimator that is consistent for the true variance as in Goncalves and de Jong (2003): The studentized test statistic becomes: We seek the distribution of the test statistic under the null hypothesis so we impose the null as described in Hansen (2005) by recentering around where, and denotes the indicator function and we also provide lower. We also provide lower and upper bounds for the corresponding p-values so we define where We will approximate the distribution of the test statistic under the null by the empirical ISBN:
4 distribution obtained from the bootstrapped : we calculate and thus the 3 bootstrap p-values are given by: where the null hypothesis ( Evidence about superior performance might be the resultsof data mining ) is rejected for small p-values. 3 Empirical results The inclusion of Athex Compos Index resulted in the discovery of some very interesting empirical properties of the returns associated with SMA strategies. The most notable finding is that ALL of the 100 strategies that we have tested manage to beat the buy & hold strategy for the Greek market. In Table2we present the annualized daily returns in excess of benchmark for all the tested strategies, for Athex Compos. Table2 Annualized returns in excess of buy & hold for Athex Compos (1 st column short MA, 1 st row long MA) tested. This strategy would have been able to provide 36% annually above the market since November This strong result, coupled with the fact that all the tested strategies proved to be profitable, would entitle us to expect that data mining bias to be strongly ruled out. Indeed, both the Reality Check and the newly applied SPA test manage to reject the null hypothesis, as can be seen from Table3which summarizes the empirical results of our study for the four markets. Table3 Excess returns and test results for the four indices Index Best rule Excess return (%) p value Reality Check FTSE SMA(10,500) S&P SMA(10,500) DAX SMA(1,300) ATC SMA(1,25) SPA In these four cases, we can see that the SPA test is able to provide stronger evidence than the Reality Check, with a larger value for the obviously random results (.99 vs..62 for the FTSE index) and a slightly smaller value for the weaker results of the Reality Check (.20 vs..21 for S&P, and.21 vs..22 for DAX). Both tests manage to reject the null for the Greek Market strongly suggesting that the superior performance of the SMA rules has nothing to do with data mining. We are not able however to reject the null ( Superior performance might be related to data mining ) for the historically best performing rules for the S&P and DAX indices even after we employ the stronger SPA test, although we manage to obtain slightly improved (lower) p-values. The lower and upper bounds for the computed SPA values are presented in Tabel4and give additional insights related to the strategies included in our study. Table4 SPA test and lower and upper bounds Annualized returns were derived using: Index Best rule Lower bound SPA Upper bound where the fraction captures the average percent of trading days for the period. As can be seen from Table2 the best strategy for Athex Compos (the one yielding the highest excess return) is the SMA (1,25) the fastest among the 100 strategies that we have FTSE SMA(10,500) S&P SMA(10,500) DAX SMA(1,300) ATC SMA(1,25) ISBN:
5 The lower and upper bounds for the SPA test further support our findings about FTSE and Athex Compos. However, the larger discrepancies between the lower and the upper limit for the SPA test computed for DAX and S&P suggest that the result might be influenced by the inclusion of poor performing models, and this is more of a concern for the S&P family of strategies than for those applied on DAX. 4 Conclusions We have attempted to reject the null hypothesis of data mining for 100simple trading strategies applied on three mature markets and Greeceby using an adapted version of Hansen s test for Superior Predictive Ability. We have provided a detailed description of our bootstrap implementation of the test.the results stronger support our previous finding, that there might be other models (for example, random walk with a drift) that fit the FTSE index better than the SMA rules; and that data mining could not be ruled out as a possible cause for any apparent superior performance of the rules that we have tested on the FTSE. We could not reject however the null hypotheses of data mining for the performance of the SMA rules on S&P and DAX, although we did manage to find lower p-value by applying the SPA test. Thus we are forced to conclude that unintended data mining could have influenced our finding for the two markets: their apparent predictive ability could have been the result of survivorship bias manifested amongst trading rules. Of course, there is also a possibility that the SPA testpenalizes to harsh our tested strategies,though is less susceptible of type II errorsthan White s Reality Check. Surprisingly, all the SMA rules seem to exploit some inefficiencies of a less mature market as is the case of Athena Stock Exchange, and all the tests that we have applied, including the SPA test, suggests that this is not the spurious result of data mining. Consequently, we are more confident that the rules we have tested are able to behave remarkably different when applied to different markets. We suspect that the inclusion of dividends in an index makes it more suitable for technical trading strategies (as it is DAX in comparison with S&P or FTSE) but also that the maturity of a market might influence the profitability of these strategies (as they seem to behave remarkably better on Athens Stock Exchange as compared to the other three analyzed indices). Of course, remains to be seen which of the actual traits of a mature market makes it in fact un-exploitable for such simple strategies. Acknowledgements This research was supported by CNCS- UEFISCDI, Project number IDEI 303, code PN-II-ID-PCE References: [1] Alexander, S. (1961) Price Movements in Speculative Markets: Trends or Random Walks, Industrial Management Review, 2:2, 7-26 [2] Anghel, A. and Tudor, C. (2013) The profitability of technical trading rules: empirical application on mature stock markets, 8th Annual London Business Research Conference Proceedings [3] Brock, W., Lakonishok, J. and LeBaron, B. (1992) Simple Technical Trading Rules and the Stochastic Properties of Stock Returns, Journal of Finance, Volume 47, Issue 5, [4] Fama,E. and Blume, M. (1966) Filter Rules and Stock Market Trading, The Journal of Business, Volume 39, Issue 1, Part 2, [5] Goncalves, S. and R. de Jong (2003) Consistency of the Stationary Bootstrap under Weak Moment Conditions, Economics Letters, 81, [6] Hansen, P. (2005) A Test for Superior Predictive Ability, Journal of Business & Economic Statistics, 23:4, [7] Künsch, H.R. (1989) The Jackknife and the Bootstrap for General Stationary Observations, Annals of Statistics, Volume 17, Number 3, [8] Sullivan R., Timmermann A. and White H. Dangers of Data-Driven Inference: The Case of Calendar Effects in Stock Returns, University of California San Diego Discussion Paper [9] Tudor, C., (2008), An empirical study on risk-return tradeoff using GARCH-class models: evidence from Bucharest Stock Exchange, Proceedings of the ICBE International Conference, ISBN:
6 SpiruHaretUniversitaty, Constanta, Ed. Muntenia [10] White, H. (2000) A Reality Check for Data-Snooping, Econometrica, vol. 68, No. 5, ISBN:
Maastricht University. High Frequency Trading Rules in Forex Markets - A Model Confidence Set Approach
Maastricht University School of Business and Economics High Frequency Trading Rules in Forex Markets - A Model Confidence Set Approach Author: Raffael Danielli Supervisor: Dr. Sébastien Laurent July 1,
More informationUsefulness of Moving Average Based Trading Rules in India
Usefulness of Moving Average Based Trading Rules in India S K Mitra Institute of Management Technology 35 Km Milestone, Katol Road, Nagpur 441 502, India Tel: 91-712-280-5000 E-mail: skmitra@imtnag.ac.in
More informationThe Stability of Moving Average Technical Trading Rules on the. Dow Jones Index
The Stability of Moving Average Technical Trading Rules on the Dow Jones Index Blake LeBaron Brandeis University NBER August 1999 Revised: November 1999 Abstract This paper analyzes the behavior of moving
More informationAn Empirical Comparison of Moving Average Envelopes and Bollinger Bands
An Empirical Comparison of Moving Average Envelopes and Bollinger Bands Joseph Man-joe Leung and Terence Tai-leung Chong Department of Economics, The Chinese University of Hong Kong November 8, 00 Abstract
More informationOptimization of technical trading strategies and the profitability in security markets
Economics Letters 59 (1998) 249 254 Optimization of technical trading strategies and the profitability in security markets Ramazan Gençay 1, * University of Windsor, Department of Economics, 401 Sunset,
More informationData-Snooping, Technical Trading Rule Performance, and the Bootstrap
THE JOURNAL OF FINANCE VOL. LIV, NO. 5 OCTOBER 1999 Data-Snooping, Technical Trading Rule Performance, and the Bootstrap RYAN SULLIVAN, ALLAN TIMMERMANN, and HALBERT WHITE* ABSTRACT In this paper we utilize
More informationCHAPTER 11: THE EFFICIENT MARKET HYPOTHESIS
CHAPTER 11: THE EFFICIENT MARKET HYPOTHESIS PROBLEM SETS 1. The correlation coefficient between stock returns for two non-overlapping periods should be zero. If not, one could use returns from one period
More informationJournal of Financial and Strategic Decisions Volume 13 Number 2 Summer 2000 DISEQUILIBRIUM IN ASIA-PACIFIC FUTURES MARKETS: AN INTRA-DAY INVESTIGATION
Journal of Financial and Strategic Decisions Volume 13 Number 2 Summer 2000 DISEQUILIBRIUM IN ASIA-PACIFIC FUTURES MARKETS: AN INTRA-DAY INVESTIGATION Mahendra Raj * Abstract This paper examines the weak
More informationFinancial Market Efficiency and Its Implications
Financial Market Efficiency: The Efficient Market Hypothesis (EMH) Financial Market Efficiency and Its Implications Financial markets are efficient if current asset prices fully reflect all currently available
More informationInternational Business & Economics Research Journal September 2007 Volume 6, Number 9
Profitable Technical Trading Rules For The Austrian Stock Market Massoud Metghalchi, (E-mail: MetghalchiM@uhv.edu), University of Houston, Victoria Yong Glasure, University of Houston, Victoria Xavier
More informationCONTENTS OF DAY 2. II. Why Random Sampling is Important 9 A myth, an urban legend, and the real reason NOTES FOR SUMMER STATISTICS INSTITUTE COURSE
1 2 CONTENTS OF DAY 2 I. More Precise Definition of Simple Random Sample 3 Connection with independent random variables 3 Problems with small populations 8 II. Why Random Sampling is Important 9 A myth,
More informationSAIF-2011 Report. Rami Reddy, SOA, UW_P
1) Title: Market Efficiency Test of Lean Hog Futures prices using Inter-Day Technical Trading Rules 2) Abstract: We investigated the effectiveness of most popular technical trading rules on the closing
More informationMarket Seasonality Historical Data, Trends & Market Timing
Market Seasonality Historical Data, Trends & Market Timing We are entering what has historically been the best season to be invested in the stock market. According to Ned Davis Research if an individual
More informationA Refined MACD Indicator Evidence against the Random Walk Hypothesis?
A Refined MACD Indicator Evidence against the Random Walk Hypothesis? By Gunter Meissner *, Albin Alex ** and Kai Nolte *** Abstract Rigorous testing of the widely used MACD indicator results in a surprisingly
More informationApplication of Markov chain analysis to trend prediction of stock indices Milan Svoboda 1, Ladislav Lukáš 2
Proceedings of 3th International Conference Mathematical Methods in Economics 1 Introduction Application of Markov chain analysis to trend prediction of stock indices Milan Svoboda 1, Ladislav Lukáš 2
More informationTechnical Trading-Rule Profitability, Data Snooping, and Reality Check: Evidence from the Foreign Exchange Market *
Technical Trading-Rule Profitability, Data Snooping, and Reality Check: Evidence from the Foreign Exchange Market * Min Qi Kent State University Yangru Wu Rutgers University and Hong Kong Institute for
More informationPITFALLS IN TIME SERIES ANALYSIS. Cliff Hurvich Stern School, NYU
PITFALLS IN TIME SERIES ANALYSIS Cliff Hurvich Stern School, NYU The t -Test If x 1,..., x n are independent and identically distributed with mean 0, and n is not too small, then t = x 0 s n has a standard
More informationChapter 9: Univariate Time Series Analysis
Chapter 9: Univariate Time Series Analysis In the last chapter we discussed models with only lags of explanatory variables. These can be misleading if: 1. The dependent variable Y t depends on lags of
More informationHedge Fund Returns: You Can Make Them Yourself!
Hedge Fund Returns: You Can Make Them Yourself! Harry M. Kat * Helder P. Palaro** This version: June 8, 2005 Please address all correspondence to: Harry M. Kat Professor of Risk Management and Director
More informationWorking Papers. Cointegration Based Trading Strategy For Soft Commodities Market. Piotr Arendarski Łukasz Postek. No. 2/2012 (68)
Working Papers No. 2/2012 (68) Piotr Arendarski Łukasz Postek Cointegration Based Trading Strategy For Soft Commodities Market Warsaw 2012 Cointegration Based Trading Strategy For Soft Commodities Market
More informationAN EVALUATION OF THE PERFORMANCE OF MOVING AVERAGE AND TRADING VOLUME TECHNICAL INDICATORS IN THE U.S. EQUITY MARKET
AN EVALUATION OF THE PERFORMANCE OF MOVING AVERAGE AND TRADING VOLUME TECHNICAL INDICATORS IN THE U.S. EQUITY MARKET A Senior Scholars Thesis by BETHANY KRAKOSKY Submitted to Honors and Undergraduate Research
More informationThe Day of the Week Effect: Evidence from the Athens Stock Exchange Using Parametric and Non-Parametric Tests
The Day of the Week Effect: Evidence from the Athens Stock Exchange Using Parametric and Non-Parametric Tests Dimitra Vatkali 1, Ioannis A. Michopoulos 2, Dimitrios S. Tinos 3 1 Department of Accounting
More informationYao Zheng University of New Orleans. Eric Osmer University of New Orleans
ABSTRACT The pricing of China Region ETFs - an empirical analysis Yao Zheng University of New Orleans Eric Osmer University of New Orleans Using a sample of exchange-traded funds (ETFs) that focus on investing
More informationThe Variability of P-Values. Summary
The Variability of P-Values Dennis D. Boos Department of Statistics North Carolina State University Raleigh, NC 27695-8203 boos@stat.ncsu.edu August 15, 2009 NC State Statistics Departement Tech Report
More informationTechnical analysis is one of the most popular methods
Comparing Profitability of Day Trading Using ORB Strategies on Index Futures Markets in Taiwan, Hong-Kong, and USA Yi-Cheng Tsai, Mu-En Wu, Chin-Laung Lei, Chung-Shu Wu, and Jan-Ming Ho Abstract In literature,
More informationInvestment Section INVESTMENT FALLACIES 2014
Investment Section INVESTMENT FALLACIES 2014 Simulation of Long-Term Stock Returns: Fat-Tails and Mean Reversion By Rowland Davis Following the 2008 financial crisis, discussion of fat-tailed return distributions
More informationTechnical Trading Rules in Emerging Stock Markets Stefaan Pauwels, Koen Inghelbrecht, Dries Heyman, and Pieter Marius
Technical Trading Rules in Emerging Stock Markets Stefaan Pauwels, Koen Inghelbrecht, Dries Heyman, and Pieter Marius Abstract Literature reveals that many investors rely on technical trading rules when
More informationA Study of the Relation Between Market Index, Index Futures and Index ETFs: A Case Study of India ABSTRACT
Rev. Integr. Bus. Econ. Res. Vol 2(1) 223 A Study of the Relation Between Market Index, Index Futures and Index ETFs: A Case Study of India S. Kevin Director, TKM Institute of Management, Kollam, India
More informationMachine Learning in Statistical Arbitrage
Machine Learning in Statistical Arbitrage Xing Fu, Avinash Patra December 11, 2009 Abstract We apply machine learning methods to obtain an index arbitrage strategy. In particular, we employ linear regression
More informationFalse Discovery Rates
False Discovery Rates John D. Storey Princeton University, Princeton, USA January 2010 Multiple Hypothesis Testing In hypothesis testing, statistical significance is typically based on calculations involving
More informationA Review of Cross Sectional Regression for Financial Data You should already know this material from previous study
A Review of Cross Sectional Regression for Financial Data You should already know this material from previous study But I will offer a review, with a focus on issues which arise in finance 1 TYPES OF FINANCIAL
More informationEVIDENCE IN FAVOR OF MARKET EFFICIENCY
Appendix to Chapter 7 Evidence on the Efficient Market Hypothesis Early evidence on the efficient market hypothesis was quite favorable to it. In recent years, however, deeper analysis of the evidence
More informationPredictability of Non-Linear Trading Rules in the US Stock Market Chong & Lam 2010
Department of Mathematics QF505 Topics in quantitative finance Group Project Report Predictability of on-linear Trading Rules in the US Stock Market Chong & Lam 010 ame: Liu Min Qi Yichen Zhang Fengtian
More informationStock Returns Following Profit Warnings: A Test of Models of Behavioural Finance.
Stock Returns Following Profit Warnings: A Test of Models of Behavioural Finance. G. Bulkley, R.D.F. Harris, R. Herrerias Department of Economics, University of Exeter * Abstract Models in behavioural
More informationDo Direct Stock Market Investments Outperform Mutual Funds? A Study of Finnish Retail Investors and Mutual Funds 1
LTA 2/03 P. 197 212 P. JOAKIM WESTERHOLM and MIKAEL KUUSKOSKI Do Direct Stock Market Investments Outperform Mutual Funds? A Study of Finnish Retail Investors and Mutual Funds 1 ABSTRACT Earlier studies
More informationChi Square Tests. Chapter 10. 10.1 Introduction
Contents 10 Chi Square Tests 703 10.1 Introduction............................ 703 10.2 The Chi Square Distribution.................. 704 10.3 Goodness of Fit Test....................... 709 10.4 Chi Square
More informationRecall this chart that showed how most of our course would be organized:
Chapter 4 One-Way ANOVA Recall this chart that showed how most of our course would be organized: Explanatory Variable(s) Response Variable Methods Categorical Categorical Contingency Tables Categorical
More informationBINOMIAL OPTIONS PRICING MODEL. Mark Ioffe. Abstract
BINOMIAL OPTIONS PRICING MODEL Mark Ioffe Abstract Binomial option pricing model is a widespread numerical method of calculating price of American options. In terms of applied mathematics this is simple
More informationFive Myths of Active Portfolio Management. P roponents of efficient markets argue that it is impossible
Five Myths of Active Portfolio Management Most active managers are skilled. Jonathan B. Berk 1 This research was supported by a grant from the National Science Foundation. 1 Jonathan B. Berk Haas School
More informationThe Dangers of Using Correlation to Measure Dependence
ALTERNATIVE INVESTMENT RESEARCH CENTRE WORKING PAPER SERIES Working Paper # 0010 The Dangers of Using Correlation to Measure Dependence Harry M. Kat Professor of Risk Management, Cass Business School,
More informationForecasting Chilean Industrial Production and Sales with Automated Procedures 1
Forecasting Chilean Industrial Production and Sales with Automated Procedures 1 Rómulo A. Chumacero 2 February 2004 1 I thank Ernesto Pastén, Klaus Schmidt-Hebbel, and Rodrigo Valdés for helpful comments
More informationTechnical Analysis and the London Stock Exchange: Testing Trading Rules Using the FT30
Technical Analysis and the London Stock Exchange: Testing Trading Rules Using the FT30 Terence C. Mills* Department of Economics, Loughborough University, Loughborough LE11 3TU, UK This paper investigates
More informationThe NCAA Basketball Betting Market: Tests of the Balanced Book and Levitt Hypotheses
The NCAA Basketball Betting Market: Tests of the Balanced Book and Levitt Hypotheses Rodney J. Paul, St. Bonaventure University Andrew P. Weinbach, Coastal Carolina University Kristin K. Paul, St. Bonaventure
More informationOptimal Risk Management Before, During and After the 2008-09 Financial Crisis
Optimal Risk Management Before, During and After the 2008-09 Financial Crisis Michael McAleer Econometric Institute Erasmus University Rotterdam and Department of Applied Economics National Chung Hsing
More informationI.e., the return per dollar from investing in the shares from time 0 to time 1,
XVII. SECURITY PRICING AND SECURITY ANALYSIS IN AN EFFICIENT MARKET Consider the following somewhat simplified description of a typical analyst-investor's actions in making an investment decision. First,
More informationCHARACTERISTICS OF INVESTMENT PORTFOLIOS PASSIVE MANAGEMENT STRATEGY ON THE CAPITAL MARKET
Mihaela Sudacevschi 931 CHARACTERISTICS OF INVESTMENT PORTFOLIOS PASSIVE MANAGEMENT STRATEGY ON THE CAPITAL MARKET MIHAELA SUDACEVSCHI * Abstract The strategies of investment portfolios management on the
More informationMonte Carlo testing with Big Data
Monte Carlo testing with Big Data Patrick Rubin-Delanchy University of Bristol & Heilbronn Institute for Mathematical Research Joint work with: Axel Gandy (Imperial College London) with contributions from:
More informationIs there Information Content in Insider Trades in the Singapore Exchange?
Is there Information Content in Insider Trades in the Singapore Exchange? Wong Kie Ann a, John M. Sequeira a and Michael McAleer b a Department of Finance and Accounting, National University of Singapore
More informationAbout Hedge Funds. What is a Hedge Fund?
About Hedge Funds What is a Hedge Fund? A hedge fund is a fund that can take both long and short positions, use arbitrage, buy and sell undervalued securities, trade options or bonds, and invest in almost
More informationEarnings Forecasts and the Predictability of Stock Returns: Evidence from Trading the S&P Joel Lander, Athanasios Orphanides and Martha Douvogiannis Board of Governors of the Federal Reserve System January
More informationTesting The Quantity Theory of Money in Greece: A Note
ERC Working Paper in Economic 03/10 November 2003 Testing The Quantity Theory of Money in Greece: A Note Erdal Özmen Department of Economics Middle East Technical University Ankara 06531, Turkey ozmen@metu.edu.tr
More informationROBUST TRADING RULE SELECTION AND FORECASTING ACCURACY
ROBUST TRADING RULE SELECTION AND FORECASTING ACCURACY Harald Schmidbauer / Angi Rösch / Tolga Sezer / Vehbi Sinan Tunalıoğlu c 2011 Harald Schmidbauer / Angi Rösch / Tolga Sezer / Vehbi Sinan Tunalıoğlu
More informationEmpirical Analysis of an Online Algorithm for Multiple Trading Problems
Empirical Analysis of an Online Algorithm for Multiple Trading Problems Esther Mohr 1) Günter Schmidt 1+2) 1) Saarland University 2) University of Liechtenstein em@itm.uni-sb.de gs@itm.uni-sb.de Abstract:
More informationMarket Efficiency and Stock Market Predictability
Mphil Subject 301 Market Efficiency and Stock Market Predictability M. Hashem Pesaran March 2003 1 1 Stock Return Regressions R t+1 r t = a+b 1 x 1t +b 2 x 2t +...+b k x kt +ε t+1, (1) R t+1 is the one-period
More informationAn Exploration of Simple Optimized Technical Trading Strategies
Charoenwong 1 An Exploration of Simple Optimized Technical Trading Strategies Ben G. Charoenwong* Abstract This paper studies the behavior and statistical properties of three simple trading strategies.
More informationHow to time the stock market Using Artificial Neural Networks and Genetic Algorithms
How to time the stock market Using Artificial Neural Networks and Genetic Algorithms Donn S. Fishbein, MD, PhD Neuroquant.com ABSTRACT Despite considerable bad press that market timing has received in
More informationA Statistical Analysis of Popular Lottery Winning Strategies
CS-BIGS 4(1): 66-72 2010 CS-BIGS http://www.bentley.edu/csbigs/vol4-1/chen.pdf A Statistical Analysis of Popular Lottery Winning Strategies Albert C. Chen Torrey Pines High School, USA Y. Helio Yang San
More informationTrading Strategies on Trial: A Comprehensive Review of 21 Practical Trading Strategies Over 56 Listed Stocks
Trading Strategies on Trial: A Comprehensive Review of 21 Practical Trading Strategies Over 5 Listed Stocks Shu-Heng Chen AI-ECON Research Center Department of Economics National Chengchi University Taipei,
More informationThe Technical Analysis Method of Moving Average Trading: Rules That Reduce the Number of Losing Trades. Marcus C. Toms
The Technical Analysis Method of Moving Average Trading: Rules That Reduce the Number of Losing Trades Marcus C. Toms A thesis submitted for the degree of Doctor of Philosophy Department of Electrical,
More informationUnderstanding Confidence Intervals and Hypothesis Testing Using Excel Data Table Simulation
Understanding Confidence Intervals and Hypothesis Testing Using Excel Data Table Simulation Leslie Chandrakantha lchandra@jjay.cuny.edu Department of Mathematics & Computer Science John Jay College of
More informationECON 351: The Stock Market, the Theory of Rational Expectations, and the Efficient Market Hypothesis
ECON 351: The Stock Market, the Theory of Rational Expectations, and the Efficient Market Hypothesis Alejandro Riaño Penn State University June 8, 2008 Alejandro Riaño (Penn State University) ECON 351:
More informationSTATISTICAL SIGNIFICANCE OF RANKING PARADOXES
STATISTICAL SIGNIFICANCE OF RANKING PARADOXES Anna E. Bargagliotti and Raymond N. Greenwell Department of Mathematical Sciences and Department of Mathematics University of Memphis and Hofstra University
More informationHow To Determine If Technical Currency Trading Is Profitable For Individual Currency Traders
Is Technical Analysis Profitable for Individual Currency Traders? Boris S. Abbey and John A. Doukas * Journal of Portfolio Management, 2012, 39, 1,142-150 Abstract This study examines whether technical
More informationHow To Understand The Theory Of Active Portfolio Management
Five Myths of Active Portfolio Management Most active managers are skilled. Jonathan B. Berk Proponents of efficient markets argue that it is impossible to beat the market consistently. In support of their
More informationCHAPTER 11: THE EFFICIENT MARKET HYPOTHESIS
CHAPTER 11: THE EFFICIENT MARKET HYPOTHESIS PROBLEM SETS 1. The correlation coefficient between stock returns for two non-overlapping periods should be zero. If not, one could use returns from one period
More informationInternational Journal of Information Technology, Modeling and Computing (IJITMC) Vol.1, No.3,August 2013
FACTORING CRYPTOSYSTEM MODULI WHEN THE CO-FACTORS DIFFERENCE IS BOUNDED Omar Akchiche 1 and Omar Khadir 2 1,2 Laboratory of Mathematics, Cryptography and Mechanics, Fstm, University of Hassan II Mohammedia-Casablanca,
More informationIs the Forward Exchange Rate a Useful Indicator of the Future Exchange Rate?
Is the Forward Exchange Rate a Useful Indicator of the Future Exchange Rate? Emily Polito, Trinity College In the past two decades, there have been many empirical studies both in support of and opposing
More informationNeural Network Stock Trading Systems Donn S. Fishbein, MD, PhD Neuroquant.com
Neural Network Stock Trading Systems Donn S. Fishbein, MD, PhD Neuroquant.com There are at least as many ways to trade stocks and other financial instruments as there are traders. Remarkably, most people
More informationSimple Random Sampling
Source: Frerichs, R.R. Rapid Surveys (unpublished), 2008. NOT FOR COMMERCIAL DISTRIBUTION 3 Simple Random Sampling 3.1 INTRODUCTION Everyone mentions simple random sampling, but few use this method for
More informationPredicting the Stock Market with News Articles
Predicting the Stock Market with News Articles Kari Lee and Ryan Timmons CS224N Final Project Introduction Stock market prediction is an area of extreme importance to an entire industry. Stock price is
More informationThree Investment Risks
Three Investment Risks Just ask yourself, which of the following risks is the most important risk to you. Then, which order would you place them in terms of importance. A. A significant and prolonged fall
More informationFrom Saving to Investing: An Examination of Risk in Companies with Direct Stock Purchase Plans that Pay Dividends
From Saving to Investing: An Examination of Risk in Companies with Direct Stock Purchase Plans that Pay Dividends Raymond M. Johnson, Ph.D. Auburn University at Montgomery College of Business Economics
More informationStock Market Trading - A Review of Technical Analysis
Technical Analysis and the Stochastic Properties of the Jordanian Stock Market Index Return Muhannad A. Atmeh Department of Accounting Hashemite University, Jordan and Ian M. Dobbs The Business School
More informationPeer Reviewed. Abstract
Peer Reviewed William J. Trainor, Jr.(trainor@etsu.edu) is an Associate Professor of Finance, Department of Economics and Finance, College of Business and Technology, East Tennessee State University. Abstract
More informationIntroduction to. Hypothesis Testing CHAPTER LEARNING OBJECTIVES. 1 Identify the four steps of hypothesis testing.
Introduction to Hypothesis Testing CHAPTER 8 LEARNING OBJECTIVES After reading this chapter, you should be able to: 1 Identify the four steps of hypothesis testing. 2 Define null hypothesis, alternative
More informationBootstrapping Big Data
Bootstrapping Big Data Ariel Kleiner Ameet Talwalkar Purnamrita Sarkar Michael I. Jordan Computer Science Division University of California, Berkeley {akleiner, ameet, psarkar, jordan}@eecs.berkeley.edu
More informationProfitability of Applying Simple Moving Average Trading Rules for the Vietnamese Stock Market
Journal of Business & Management Volume 2, Issue 3 (2013), 22-31 ISSN 2291-1995 E-ISSN 2291-2002 Published by Science and Education Centre of North America Profitability of Applying Simple Moving Average
More informationThe analysis on the Evolution of Capital Market basically in Romania during 1995 November 2011
The analysis on the Evolution of Capital Market basically in Romania during 1995 November 2011 Mădălina - Gabriela ANGHEL Artifex University 47 Economu Cezarescu Street Bucharest, Romania madalinagabriela_anghel@yahoo.com
More informationInvestment Portfolio Philosophy
Investment Portfolio Philosophy The performance of your investment portfolio and the way it contributes to your lifestyle goals is always our prime concern. Our portfolio construction process for all of
More informationAffine-structure models and the pricing of energy commodity derivatives
Affine-structure models and the pricing of energy commodity derivatives Nikos K Nomikos n.nomikos@city.ac.uk Cass Business School, City University London Joint work with: Ioannis Kyriakou, Panos Pouliasis
More informationTechnical Analysis with a Long Term Perspective: Trading Strategies and Market Timing Ability
Technical Analysis with a Long Term Perspective: Trading Strategies and Market Timing Ability Dušan ISAKOV * University of Fribourg, Switzerland Didier MARTI ** University of Fribourg, Switzerland First
More informationCHANCE ENCOUNTERS. Making Sense of Hypothesis Tests. Howard Fincher. Learning Development Tutor. Upgrade Study Advice Service
CHANCE ENCOUNTERS Making Sense of Hypothesis Tests Howard Fincher Learning Development Tutor Upgrade Study Advice Service Oxford Brookes University Howard Fincher 2008 PREFACE This guide has a restricted
More informationHow To Find Out If People Overweigh Their Professionally Close Stocks
Do individual investors have asymmetric information based on work experience? Motivation Trend towards increased investor autonomy how well do people perform as their own money managers? Individuals own
More informationMultiple Kernel Learning on the Limit Order Book
JMLR: Workshop and Conference Proceedings 11 (2010) 167 174 Workshop on Applications of Pattern Analysis Multiple Kernel Learning on the Limit Order Book Tristan Fletcher Zakria Hussain John Shawe-Taylor
More informationon share price performance
THE IMPACT OF CAPITAL CHANGES on share price performance DAVID BEGGS, Portfolio Manager, Metisq Capital This paper examines the impact of capital management decisions on the future share price performance
More informationExamining the Relationship between ETFS and Their Underlying Assets in Indian Capital Market
2012 2nd International Conference on Computer and Software Modeling (ICCSM 2012) IPCSIT vol. 54 (2012) (2012) IACSIT Press, Singapore DOI: 10.7763/IPCSIT.2012.V54.20 Examining the Relationship between
More informationFinancial Risk Forecasting Chapter 8 Backtesting and stresstesting
Financial Risk Forecasting Chapter 8 Backtesting and stresstesting Jon Danielsson London School of Economics 2015 To accompany Financial Risk Forecasting http://www.financialriskforecasting.com/ Published
More informationNetwork quality control
Network quality control Network quality control P.J.G. Teunissen Delft Institute of Earth Observation and Space systems (DEOS) Delft University of Technology VSSD iv Series on Mathematical Geodesy and
More informationDepartment of Economics
Department of Economics Working Paper Do Stock Market Risk Premium Respond to Consumer Confidence? By Abdur Chowdhury Working Paper 2011 06 College of Business Administration Do Stock Market Risk Premium
More informationCharles University, Faculty of Mathematics and Physics, Prague, Czech Republic.
WDS'09 Proceedings of Contributed Papers, Part I, 148 153, 2009. ISBN 978-80-7378-101-9 MATFYZPRESS Volatility Modelling L. Jarešová Charles University, Faculty of Mathematics and Physics, Prague, Czech
More informationChapter 1 INTRODUCTION. 1.1 Background
Chapter 1 INTRODUCTION 1.1 Background This thesis attempts to enhance the body of knowledge regarding quantitative equity (stocks) portfolio selection. A major step in quantitative management of investment
More informationFOR MANY YEARS cconomists, Statisticians, and teachers
Random Walks in Stock Market Prices by Eugene F. Fama FOR MANY YEARS cconomists, Statisticians, and teachers of finance have been interested in developing and testing models of stock price behavior. One
More informationDiscussion of Capital Injection, Monetary Policy, and Financial Accelerators
Discussion of Capital Injection, Monetary Policy, and Financial Accelerators Karl Walentin Sveriges Riksbank 1. Background This paper is part of the large literature that takes as its starting point the
More information11.2 Monetary Policy and the Term Structure of Interest Rates
518 Chapter 11 INFLATION AND MONETARY POLICY Thus, the monetary policy that is consistent with a permanent drop in inflation is a sudden upward jump in the money supply, followed by low growth. And, in
More informationTHE CDS AND THE GOVERNMENT BONDS MARKETS AFTER THE LAST FINANCIAL CRISIS. The CDS and the Government Bonds Markets after the Last Financial Crisis
THE CDS AND THE GOVERNMENT BONDS MARKETS AFTER THE LAST FINANCIAL CRISIS The CDS and the Government Bonds Markets after the Last Financial Crisis Abstract In the 1990s, the financial market had developed
More informationIncorporating Commodities into a Multi-Asset Class Risk Model
Incorporating Commodities into a Multi-Asset Class Risk Model Dan dibartolomeo, Presenting Research by TJ Blackburn 2013 London Investment Seminar November, 2013 Outline of Today s Presentation Institutional
More information12 April 2007. Hedging for regulated AER
12 April 2007 Hedging for regulated businesses AER Project Team Tom Hird (Ph.D.) NERA Economic Consulting Level 16 33 Exhibition Street Melbourne 3000 Tel: +61 3 9245 5537 Fax: +61 3 8640 0800 www.nera.com
More informationThe Equity Premium in India
The Equity Premium in India Rajnish Mehra University of California, Santa Barbara and National Bureau of Economic Research January 06 Prepared for the Oxford Companion to Economics in India edited by Kaushik
More informationTime Series Analysis
JUNE 2012 Time Series Analysis CONTENT A time series is a chronological sequence of observations on a particular variable. Usually the observations are taken at regular intervals (days, months, years),
More informationThe profitability of MACD and RSI trading rules in the Australian stock market
Safwan Mohd Nor (Malaysia), Guneratne Wickremasinghe (Australia) The profitability of MACD and RSI trading rules in the Australian stock market Abstract This study investigates the profitability of two
More information