LEADER 05129nam 2200721 450 001 9910456205403321 005 20211117205405.0 010 $a1-4426-7629-9 010 $a1-282-00939-7 010 $a9786612009396 024 7 $a10.3138/9781442676299 035 $a(CKB)2430000000000845 035 $a(OCoLC)666908899 035 $a(CaPaEBR)ebrary10195533 035 $a(SSID)ssj0000300449 035 $a(PQKBManifestationID)11226607 035 $a(PQKBTitleCode)TC0000300449 035 $a(PQKBWorkID)10251685 035 $a(PQKB)11429706 035 $a(CaPaEBR)417743 035 $a(CaBNvSL)thg00600196 035 $a(MiAaPQ)EBC3250422 035 $a(MiAaPQ)EBC4671637 035 $a(DE-B1597)464569 035 $a(OCoLC)944178071 035 $a(DE-B1597)9781442676299 035 $a(Au-PeEL)EBL4671637 035 $a(CaPaEBR)ebr11257342 035 $a(CaONFJC)MIL200939 035 $a(OCoLC)958565308 035 $a(EXLCZ)992430000000000845 100 $a20160922h19971997 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aInventing the Loyalists $ethe Ontario Loyalist tradition and the creation of usable pasts /$fNorman Knowles 210 1$aToronto, [Ontario] ;$aBuffalo, [New York] ;$aLondon, [England] :$cUniversity of Toronto Press,$d1997. 210 4$dİ1997 215 $a1 online resource (271 p.) 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-8020-7913-X 311 $a0-8020-0950-6 320 $aIncludes bibliographical references and index. 327 $tFrontmatter --$tContents --$tAcknowledgments --$tIntroduction --$t1. 'Chiefly landholders, farmers, and others': The Loyalist Reality --$t2. 'An ancestry of which any people might be proud': Official History, the Vernacular Past, and the Shaping of the Loyalist Tradition at Mid-Century --$t3. 'Loyalism is not dead in Adolphustown': Community Factionalism and the Adolphustown Loyalist Centennial Celebrations of 1884 --$t4. A sacred trust': The 1884 Toronto, Niagara, and Six Nations Loyalist Centennial Celebrations and the Politics of Commemoration --$t5. 'Fairy tales in the guise of history': The Loyalists in Ontario Publications, 1884-1918 --$t6. 'Object lessons': Loyalist Monuments and the Creation of Usable Pasts --$t7. 'A further and more enduring mark of honour': The Middle Class and the United Empire Loyalist Association of Ontario, 1896-1914 --$tConclusion --$tAppendices --$tNotes --$tSelect Bibliography --$tPicture Credits --$tIndex 330 $aThe Loyalists have often been credited with planting a coherent and unified tradition that has been passed on virtually unchanged to subsequent generations and that continues to define Ontario's political culture. Challenging past scholarship, Norman Knowles argues that there never has been consensus on the defining characteristics of the Loyalist tradition. He suggests that, in fact, the very concept of tradition has constantly been subject to appropriation by various constituencies who wish to legitimize their point of view and their claim to status by creating a usable past. The picture of the Loyalist tradition that emerges from this study is not of an inherited artefact but of a contested and dynamic phenomenon that has undergone continuous change. Inventing the Loyalists traces the evolution of the Loyalist tradition from the Loyalists' arrival in Upper Canada in 1784 until the present. It explores how the Loyalist tradition was produced, established, and maintained, delineates the roles particular social groups and localities played in constructing differing versions of the Loyalist past, and examines the reception of these efforts by the larger community. Rejecting both consensual and hegemonic models, Knowles presents a pluralistic understanding of the invention of tradition as a complex process of social and cultural negotiation by which different groups, interests, and generations compete with each other over the content, meaning, and uses of the past. He demonstrates that in Ontario, many groups, including filiopietistic descendants, political propagandists, status-conscious professionals, reform-minded women, and Native peoples, invested in the creation of the Loyalist tradition. By exploring the ways in which the Loyalist past was, and still is, being negotiated, Inventing the Loyalists revises our understanding of the Loyalist tradition and provides insight into the politics of commemoration. 606 $aUnited Empire loyalists 606 $aUnited Empire loyalists$xHistoriography 607 $aOntario$xSocial life and customs$y19th century 607 $aOntario$xHistoriography 608 $aElectronic books. 615 0$aUnited Empire loyalists. 615 0$aUnited Empire loyalists$xHistoriography. 676 $a971.3 700 $aKnowles$b Norman James$f1963-$0941973 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910456205403321 996 $aInventing the Loyalists$92125400 997 $aUNINA LEADER 09278nam 2200577 450 001 996464519103316 005 20231110211541.0 010 $a3-030-64155-4 035 $a(CKB)5590000000516479 035 $a(MiAaPQ)EBC6647519 035 $a(Au-PeEL)EBL6647519 035 $a(OCoLC)1257705410 035 $a(PPN)258061340 035 $a(EXLCZ)995590000000516479 100 $a20220321d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAnalyzing financial data and implementing financial models using R /$fClifford S. Ang 205 $a2nd ed. 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (476 pages) 225 1 $aSpringer Texts in Business and Economics 300 $aIncludes index. 311 $a3-030-64154-6 327 $aIntro -- Preface -- Acknowledgments -- Contents -- 1 Prices -- 1.1 Price Versus Value -- 1.2 Importing Price Data from Yahoo Finance -- 1.3 Checking the Data -- 1.3.1 Check the Start and End Data -- 1.3.2 Plotting the Data -- 1.3.3 Checking the Dimension -- 1.3.4 Outputting Summary Statistics -- 1.3.5 Checking the Ticker Symbol -- 1.4 Basic Data Manipulation Techniques -- 1.4.1 Keeping and Deleting One Row -- 1.4.2 Keeping First and Last Rows -- 1.4.3 Keeping Contiguous Rows -- 1.4.4 Keeping One Column -- 1.4.5 Deleting One Column -- 1.4.6 Keeping Non-Contiguous Columns -- 1.4.7 Keeping Contiguous Columns -- 1.4.8 Keeping Contiguous and Non-Contiguous Columns -- 1.4.9 Keeping Rows and Columns -- 1.4.10 Subsetting Time Series Data Using Dates -- 1.4.11 Converting to Weekly Prices -- 1.4.12 Converting to Monthly Prices -- 1.5 Comparing Capital Gains Between Securities -- 1.5.1 Alternative 1-Using xts-Style Chart -- 1.5.2 Alternative 2-Plotting Four Mini-Charts -- 1.5.3 Alternative 3-Using ggplot to Plot FourMini-Charts -- 1.6 Simple and Exponential Moving Averages -- 1.7 Volume-Weighted Average Price -- 1.8 Plotting a Candlestick Chart -- 1.9 2-Axis Price and Volume Chart -- 1.10 Further Reading -- References -- 2 Individual Security Returns -- 2.1 Price Returns -- 2.2 Total Returns -- 2.3 Logarithmic Total Returns -- 2.4 Winsorization and Truncation -- 2.5 Cumulating Multi-Day Returns -- 2.5.1 Cumulating Arithmetic Returns -- 2.5.2 Cumulating Logarithmic Returns -- 2.5.3 Comparing Price Return and Total Return -- 2.6 Weekly Returns -- 2.7 Monthly Returns -- 2.8 Comparing Performance of Multiple Securities -- 2.8.1 Using Normalized Prices -- 2.8.2 Using Cumulative Returns -- 3 Portfolio Returns -- 3.1 Portfolio Returns the Long Way -- 3.2 Portfolio Returns Using Matrix Algebra -- 3.3 Constructing Benchmark Portfolio Returns. 327 $a3.3.1 Quarterly Returns the Long Way -- 3.3.2 Quarterly Returns the Shorter Way -- 3.3.3 Equal-Weighted Portfolio -- 3.3.4 Value-Weighted Portfolio -- 3.3.5 Daily Portfolio Returns -- 3.4 Time-Weighted Rate of Return -- 3.5 Money-Weighted Rate of Return -- 3.6 Further Reading -- Reference -- 4 Risk -- 4.1 Risk-Return Trade-Off -- 4.2 Individual Security Risk -- 4.2.1 Standard Deviation and Variance -- 4.3 Portfolio Risk -- 4.3.1 Two Assets Using Manual Approach -- 4.3.2 Two Assets Using Matrix Algebra -- 4.3.3 Multiple Assets -- 4.4 Value-at-Risk -- 4.4.1 Gaussian VaR -- 4.4.2 Historical VaR -- 4.5 Expected Shortfall -- 4.5.1 Gaussian ES -- 4.5.2 Historical ES -- 4.5.3 Comparing VaR and ES -- 4.6 Alternative Risk Measures -- 4.6.1 Parkinson -- 4.6.2 Garman-Klass -- 4.6.3 Rogers, Satchell, and Yoon -- 4.6.4 Yang and Zhang -- 4.6.5 Comparing the Risk Measures -- 4.7 Further Reading -- References -- 5 Factor Models -- 5.1 CAPM -- 5.2 Market Model -- 5.3 Rolling Window Regressions -- 5.4 Betas on Different Reference Days -- 5.5 Fama-French Three Factor Model -- 5.6 Testing for Heteroskedasticity -- 5.7 Testing for Non-Normality -- 5.8 Testing for Autocorrelation -- 5.9 Event Studies -- 5.9.1 Example: Drop in Tesla Stock After 1Q 2019 Earnings Release on April 24, 2019 -- 5.9.2 Single Step Event Study -- 5.9.3 Two Stage Event Study -- 5.9.4 Sample Quantiles/Non-Parametric -- 5.10 Selecting Best Regression Variables -- 5.10.1 Create Dataset of Returns -- 5.10.2 Forward Step Approach -- 5.10.3 Backward Step Approach -- 5.10.4 Suppressing Steps in Output -- 5.11 Further Reading -- References -- 6 Risk-Adjusted Portfolio Performance Measures -- 6.1 Portfolio and Benchmark Data -- 6.2 Sharpe Ratio -- 6.3 Roy's Safety First Ratio -- 6.4 Treynor Ratio -- 6.5 Sortino Ratio -- 6.6 Information Ratio -- 6.7 Combining Results -- 6.8 Further Reading -- References. 327 $a7 Markowitz Mean-Variance Optimization -- 7.1 Two Assets the Long Way -- 7.2 Two Assets Using Quadratic Programming -- 7.3 Multiple Assets Using Quadratic Programming -- 7.4 Effect of Allowing Short Selling -- 7.5 Further Reading -- References -- 8 Equities -- 8.1 Company Financials -- 8.2 Projections -- 8.2.1 Projecting Based on Historical Trends -- 8.2.2 Analyzing Projections Prepared by Third Parties -- 8.2.3 Analyzing Growth Rates Embedded in Projections -- 8.2.4 Analyzing Projections Using Regression Analysis -- 8.3 Equity Risk Premium -- 8.4 Unlevering Betas -- 8.5 Sensitivity Analysis -- 8.6 Relative Valuation Using Regression Analysis -- 8.7 Identifying Significant Shifts in Stock Returns -- 8.7.1 t-Test: Testing Difference in Average Returns -- 8.7.2 Identifying Breakpoints -- 8.7.3 Chow Test -- 8.7.4 Test Equality of Two Betas -- 8.8 Further Reading -- References -- 9 Fixed Income -- 9.1 Economic Analysis -- 9.1.1 Real GDP -- 9.1.2 Unemployment Rate -- 9.1.3 Inflation Rate -- 9.2 US Treasuries -- 9.2.1 Shape of the US Treasury Yield Curve -- 9.2.2 Slope of the US Treasury Yield Curve -- 9.2.3 Real Yields on US Treasuries -- 9.2.4 Expected Inflation Rates -- 9.2.5 Mean Reversion -- 9.3 Principal Components Analysis -- 9.4 Investment Grade Bond Spreads -- 9.4.1 Time Series of Spreads -- 9.4.2 Spreads and Real GDP Growth -- 9.5 Bond Valuation -- 9.5.1 Valuing Bonds with Known Yield to Maturity -- 9.5.2 Bond Valuation Function -- 9.5.3 Finding the Yield to Maturity -- 9.6 Duration and Convexity -- 9.6.1 Calculating Duration and Convexity -- 9.6.2 Duration and Convexity Functions -- 9.6.3 Comparing Estimates of Value to Full Valuation -- 9.7 Short Rate Models -- 9.7.1 Vasicek -- 9.7.2 Cox, Ingersoll, and Ross -- 9.8 Further Reading -- References -- 10 Options -- 10.1 Obtaining Options Chain Data -- 10.2 Black-Scholes-Merton Options Pricing Model. 327 $a10.2.1 BSM Function -- 10.2.2 Put-Call Parity -- 10.2.3 The Greeks -- 10.3 Implied Volatility -- 10.3.1 Implied Volatility Function -- 10.3.2 Volatility Smile -- 10.3.3 Gauging Market Risk -- 10.4 The Cox, Ross, and Rubinstein Binomial OPM -- 10.4.1 CRR: The Long Way -- 10.4.2 CRR Function -- 10.5 American Option Pricing -- 10.5.1 CRR Binomial Tree -- 10.5.2 Bjerksund-Stensland Approximation -- 10.6 Further Reading -- References -- 11 Simulation -- 11.1 Simulating Stock Prices Using Geometric Brownian Motion -- 11.1.1 Simulating Multiple Ending Stock Price Paths -- 11.1.2 Comparing Theoretical to Empirical Distributions -- 11.2 Simulating Stock Prices with and Without Dividends -- 11.3 Simulating Stocks with Correlated Prices -- 11.4 Value-at-Risk Using Simulation -- 11.5 Monte Carlo Pricing of European Options -- 11.6 Monte Carlo Option Pricing Using Antithetic Variables -- 11.7 Exotic Option Valuation -- 11.7.1 Asian Options -- 11.7.2 Lookback Options -- 11.7.3 Barrier Options -- 11.8 Further Reading -- References -- 12 Trading Strategies -- 12.1 Efficient Markets Hypothesis -- 12.1.1 Autocorrelation Test -- 12.1.2 Variance Ratio Test -- 12.1.3 Runs Test -- 12.2 Technical Analysis -- 12.2.1 Trend: Simple Moving Average Crossover -- 12.2.2 Volatility: Bollinger Bands -- 12.2.3 Momentum: Relative Strength Index -- 12.3 Building a Simple Trading Strategy -- 12.4 Machine Learning Techniques -- 12.4.1 General Steps to Apply ML -- 12.4.2 k-Nearest Neighbor Algorithm -- 12.4.3 Regression and k-Fold Cross Validation -- 12.4.4 Artificial Neural Networks -- 12.5 Further Reading -- References -- A Getting Started with R -- A.1 Installing R -- A.2 The R Working Directory -- A.3 R Console Output -- A.4 R Editor -- A.5 Packages -- A.6 Basic Commands -- A.7 The R Workspace -- A.8 Vectors -- A.9 Combining Vectors -- A.10 Matrices -- A.11 data.frame. 327 $aA.12 Date Formats -- B Pre-Loaded Code -- C Constructing a Hypothetical Portfolio (Monthly Returns) -- D Constructing a Hypothetical Portfolio (Daily Returns) -- Index. 410 0$aSpringer Texts in Business and Economics 606 $aRisk management 606 $aEconomics, Mathematical 606 $aStatistics 606 $aR (Computer program language) 615 0$aRisk management. 615 0$aEconomics, Mathematical. 615 0$aStatistics. 615 0$aR (Computer program language). 676 $a332 700 $aAng$b Clifford S.$0769911 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996464519103316 996 $aAnalyzing financial data and implementing financial models using R$91570178 997 $aUNISA