back start next
[start] [1] [2] [ 3 ] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51] [52] [53] [54] [55] [56] [57] [58] [59] [60] [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] [71]
3 APPENDICES «» 1. THE NOT-TO-BE-EXPECTED "ORDER" OF SPECTRAL RELATIONSHIPS IN STOCK PRICE DATA .............................188 The Implications of Fourier Analysis of Stock Prices 188 Course Frequency Structure 190 Fine Frequency Structure 190 Amplitude-Frequency Relationships 191 The Use of Comb Filters 191 The Variables Involved 196 Best EstinfKte of Spectral Line Spacing 196 The Line Spectral Model 199 II. EXTENSION OF "AVERAGE" RESULTS TO INDIVIDUAL ISSUES ... .201 A Basis For the Principle of Commonality 201 Spectral Signatures, Fundamentals, and Time Synchroniration 201 . THE SOURCE AND NATURE OF TRANSACTION INTERVAL EFFECTS ............................................. 204 Theoretical Yield-Rate Maximums vs. Transaction Interval 204 The Impact of Compounding 204 The Effect of Sinusoidal Rate Summation 206 IV. FREQUENCY RESPONSE CHARACTERISTICS OF A CENTERED MOVING AVERAGE .......................................207 Response Derivation 207 Response Characteristics 207 Application Implications 207 11. SPECTRAL ANALYSIS - HOW TO DO IT AND WHAT IT MEANS ......168 Why Numerical Analysis 169 The Meaning of a Frequency Spectrum 169 How to Do Fourier Analysis 171 Assembling Your Data 171 Separating Your Data Into Two Sequences 172 Determining the Frequencies in Your Analysis 172 Now Compute the Corresponding Amplitudes 173 How to Get Composite Amplitudes 175 The Kind of Results You Can Expect 175 How Numerical Filters Can Help You 175 VWiat You Must Know About Filter Operation 176 The Part of "Weits" in Numerical Filters 177 How to Design Your Own Numerical Filters 178 Applying Your Numerical Filter to Stock Prices 182 Take Advan tage of Curve Fitting 183 Fit Your Data With a Straight line 184 How to Use Other Kinds of Curve Fitting 185 Summarizing Numerical Analy sis 185
Response of the Inverse Centered Moving Average 210 V. PARABOUC INTERPOLATION ................................212 Three-Point Interpolation 212 Equation Derivation 213 VI. TRIGONOMETRIC CURVE FITTING ...........................215 Generalized Least-Square-Error Methods 215 Solving for Frequency 216 Computing Amplitudes 217 Determining Composity Amplitudes and Phases 217 BIBLIOGRAPHY ...........................................218 INDEX ....................................................220
List of illustrations Figure I-l. Typical Weekly "High-Low" Chart • 24 Figure 1-2. Ideal Transaction Timing 25 Figure II-l. The Magnitude-Duration Relationship • 34 Figure II-2. Price Fluctuations in The Dow Average • 36 Figure II-3. A Constant-Width Envelope: The Starting Point in Observational Analysis • 37 Figure IW. Nesting Envelopes • 39 Figure II-5. Another Envelope Technique • 40 Figure 11-6. An Example of Short-Duration Cyclicality • 41 Figure II-7. The Longer Duration Cycles Require Monthly Data • 42 Figure II-8. The Infamous "Bull-Bear" Cycle • 43 Figure II-9. The Time-Persistence of Cyclicality • 44 Figure IMO. The Principle of Variation at Work • 45 Figure -1 ]. Cyclicality and Numerical Analysis • 46 Figure 11-12. Before Envelope Analysis • 46 Figure II-l 3. Envelopes and Standard Packaging • 47 Figure 11-14. Nesting Down • 47 Figure 11-15. Nesting Up 48 Figure 11-16. Commonality and Standard Packaging • 49 Figure 111-1. Channel Formation • 53 Figure III-2. Adding Another Component • 54 Figure 1-3. The Summation Principle Applied • 54 Figure III-4. How Head and Shoulder Patterns Form • 55 Figure 1II-5. True Channels Are Curvilinear • 56 Figure -6. The Effect of Cycle Timing Change • 57 Figure III-7. How Double Tops (and Bottoms) Are Formed 58 Figure 1 -8. How Triangles Come About • 58 Figure -9. Triangle Analysis in Perkin-EImer • 60 Figure 111-10. Moving Averages Versus Time-Span - 63 Figure III-l 1. "Centering" a Moving Average 66 Figure IV-1. Plotting Helps - But Not Enough • 73 Figure IV-2. A First Envelope Adds Visibility • 74 Figure IV-3, A Second Envelope Clarifies the Picture Further • 75 Figure IV-4. Rough Prediction Using Envelopes * 76 Figure lV-5. How Resuks Compare With Prediction 77 Figure IV-6. Refining Predictions - By Going to Daily Data • 80
[start] [1] [2] [ 3 ] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51] [52] [53] [54] [55] [56] [57] [58] [59] [60] [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] [71]
|