Details

Prediction Revisited


Prediction Revisited

The Importance of Observation
1. Aufl.

von: Mark P. Kritzman, David Turkington, Megan Czasonis

19,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 01.06.2022
ISBN/EAN: 9781119895596
Sprache: englisch
Anzahl Seiten: 240

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Beschreibungen

<p><b>A thought-provoking and startlingly insightful reworking of the science of prediction</b> <p>In <i>Prediction Revisited: The Importance of Observation</i>, a team of renowned experts in the field of data-driven investing delivers a ground-breaking reassessment of the delicate science of prediction for anyone who relies on data to contemplate the future. The book reveals why standard approaches to prediction based on classical statistics fail to address the complexities of social dynamics, and it provides an alternative method based on the intuitive notion of relevance. <p>The authors describe, both conceptually and with mathematical precision, how relevance plays a central role in forming predictions from observed experience. Moreover, they propose a new and more nuanced measure of a prediction’s reliability. <i>Prediction Revisited also </i>offers: <ul> <li>Clarifications of commonly accepted but less commonly understood notions of statistics</li> <li>Insight into the efficacy of traditional prediction models in a variety of fields</li> <li>Colorful biographical sketches of some of the key prediction scientists throughout history</li> <li>Mutually supporting conceptual and mathematical descriptions of the key insights and methods discussed within</li> </ul> <br/> <p>With its strikingly fresh perspective grounded in scientific rigor, <i>Prediction Revisited </i>is sure to earn its place as an indispensable resource for data scientists, researchers, investors, and anyone else who aspires to predict the future from the data-driven lessons of the past.
<p>Timeline of Innovations ix</p> <p>Essential Concepts xi</p> <p>Preface xv</p> <p><b>1 Introduction 1</b></p> <p>Relevance 2</p> <p>Informativeness 3</p> <p>Similarity 4</p> <p>Roadmap 4</p> <p><b>2 Observing Information 7</b></p> <p>Observing Information Conceptually 7</p> <p>Central Tendency 8</p> <p>Spread 9</p> <p>Information Theory 10</p> <p>The Strong Pull of Normality 14</p> <p>A Constant of Convenience 17</p> <p>Key Takeaways 18</p> <p>Observing Information Mathematically 20</p> <p>Average 20</p> <p>Spread 21</p> <p>Information Distance 24</p> <p>Observing Information Applied 26</p> <p>Appendix 2.1: On the Inflection Point of the Normal Distribution 32</p> <p>References 39</p> <p><b>3 Co-occurrence 41</b></p> <p>Co-occurrence Conceptually 41</p> <p>Correlation as an Information-Weighted Average of Co-occurrence 46</p> <p>Pairs of Pairs 49</p> <p>Across Many Attributes 50</p> <p>Key Takeaways 52</p> <p>Co-occurrence Mathematically 54</p> <p>The Covariance Matrix 58</p> <p>Co-occurrence Applied 59</p> <p>References 66</p> <p><b>4 Relevance 67</b></p> <p>Relevance Conceptually 67</p> <p>Informativeness 68</p> <p>Similarity 72</p> <p>Relevance and Prediction 73</p> <p>How Much Have You Regressed? 74</p> <p>Partial Sample Regression 76</p> <p>Asymmetry 80</p> <p>Sensitivity 86</p> <p>Memory and Bias 87</p> <p>Key Takeaways 88</p> <p>Relevance Mathematically 90</p> <p>Prediction 95</p> <p>Equivalence to Linear Regression 97</p> <p>Partial Sample Regression 100</p> <p>Asymmetry 102</p> <p>Relevance Applied 107</p> <p>Appendix 4.1: Predicting Binary Outcomes 114</p> <p>Predicting Binary Outcomes Conceptually 114</p> <p>Predicting Binary Outcomes Mathematically 116</p> <p>References 121</p> <p><b>5 Fit 123</b></p> <p>Fit Conceptually 123</p> <p>Failing Gracefully 125</p> <p>Why Fit Varies 126</p> <p>Avoiding Bias 129</p> <p>Precision 130</p> <p>Focus 133</p> <p>Key Takeaways 134</p> <p>Fit Mathematically 136</p> <p>Components of Fit 138</p> <p>Precision 139</p> <p>Fit Applied 143</p> <p><b>6 Reliability 149</b></p> <p>Reliability Conceptually 149</p> <p>Key Takeaways 153</p> <p>Reliability Mathematically 155</p> <p>Reliability Applied 163</p> <p>References 168</p> <p><b>7 Toward Complexity 169</b></p> <p>Toward Complexity Conceptually 169</p> <p>Learning by Example 170</p> <p>Expanding on Relevance 171</p> <p>Key Takeaways 175</p> <p>Toward Complexity Mathematically 177</p> <p>Complexity Applied 183</p> <p>References 183</p> <p><b>8 Foundations of Relevance 185</b></p> <p>Observations and Relevance: A Brief Review of the Main Insights 186</p> <p>Spread 187</p> <p>Co-occurrence 187</p> <p>Relevance 188</p> <p>Asymmetry 188</p> <p>Fit and Reliability 189</p> <p>Partial Sample Regression and Machine Learning Algorithms 189</p> <p>Abraham de Moivre (1667–1754) 190</p> <p>Pierre-Simon Laplace (1749–1827) 192</p> <p>Carl Friedrich Gauss (1777–1853) 193</p> <p>Francis Galton (1822–1911) 195</p> <p>Karl Pearson (1857–1936) 197</p> <p>Ronald Fisher (1890–1962) 199</p> <p>Prasanta Chandra Mahalanobis (1893–1972) 200</p> <p>Claude Shannon (1916–2001) 202</p> <p>References 206</p> <p><b>Concluding Thoughts 209</b></p> <p>Perspective 209</p> <p>Insights 210</p> <p>Prescriptions 210</p> <p>Index 211</p>
<p><b>MEGAN CZASONIS</b> is Managing Director and Head of Portfolio Management Research at State Street Associates.</p> <p><b>MARK KRITZMAN </b>is a Founding Partner and CEO of Windham Capital Management. He is also a Founding Partner of State Street Associates and teaches a graduate course at the Massachusetts Institute of Technology. <p><b>DAVID TURKINGTON </b>is Senior Managing Director and Head of State Street Associates.
<p><b>A thought-provoking and startlingly insightful reimagination of the science of prediction</b></p> <p>In <i>Prediction Revisited: The Importance of Observation,</i> a team of renowned finance and risk experts at the top of their game describes a ground-breaking realignment of the connection between past experiences and future outcomes. The book reveals why standard approaches to prediction based on classical statistics fail to address the complexities of social dynamics, and it maps out an elegant prediction system based on a novel measure of statistical relevance. <p>Drawing upon information theory and an obscure yet profound mathematical equivalence, the authors describe, both conceptually and with mathematical precision, how relevance plays a central role in forming predictions. Additionally, they introduce a new and more nuanced measure of a prediction’s reliability, enabling researchers to fine tune their responses to specific predictions. <p><i>Prediction Revisited </i>also: <ul><li>Illuminates many commonly accepted but less commonly understood notions of statistics</li> <li>Reveals several valuable yet previously unrecognized mathematical equivalences </li> <li>Includes colorful biographical sketches of some of the key scientists whose contributions paved the path to relevance-based prediction</li> <li>Enables access to the mathematically minded reader as well as those who prefer an intuitive and conceptual discussion of the book’s key ideas</li></ul> <p>With its strikingly fresh perspective grounded in scientific rigor, <i>Prediction Revisited</i> is a must-read for anyone who aspires to reach a new level of understanding and mastery of data-driven prediction.

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