This book focuses on modelling financial information flows and information-based asset pricing framework. After introducing the fundamental properties of the framework, it presents a short information-theoretic perspective with a view to quantifying the information content of financial signals, and links the present framework with the literature on asymmetric information and market microstructure by means of a dynamic, bipartite, heterogeneous agent network. Numerical and explicit analyses shed light on the effects of differential information and information acquisition on the allocation of profit and loss as well as the pace of fundamental price discovery. The dynamic programming method is used to seek an optimal strategy for utilizing superior information. Lastly, the book features an implementation of the present framework using real-world financial data.
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1 | (4) |
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4 | (1) |
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2 The Signal-Based Framework |
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5 | (28) |
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2.1 Modelling Information Flow |
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6 | (5) |
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2.2 The Signal-Based Price Process |
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11 | (9) |
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16 | (1) |
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2.2.2 Exponential Dividends |
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17 | (1) |
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2.2.3 Log-Normal Dividends |
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18 | (2) |
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2.3 Change of Measure and Signal-Based Derivative Pricing |
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20 | (7) |
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2.4 An Information-Theoretic Analysis |
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27 | (3) |
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2.5 Single Dividend-Multiple Market Factors |
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30 | (3) |
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31 | (2) |
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3 A Signal-Based Heterogeneous Agent Network |
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33 | (34) |
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37 | (2) |
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39 | (7) |
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3.3 Signal-Based Optimal Strategy |
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46 | (21) |
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3.3.1 Characterisation of Expected Profit |
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46 | (11) |
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3.3.2 Risk-Neutral Optimal Strategy |
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57 | (4) |
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3.3.3 Extension to Risk-Adjusted Performance |
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61 | (1) |
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3.3.4 Extension to Risk-Averse Utility |
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61 | (4) |
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65 | (2) |
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4 Putting Signal-Based Model to Work |
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67 | (26) |
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4.1 Multiple Dividends: Single Market Factor |
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67 | (1) |
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4.2 The Case for "Implied" Dividends |
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68 | (4) |
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4.2.1 Recovering the Gordon Model in Continuous Time |
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70 | (2) |
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4.3 Real-Time Information Flow |
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72 | (3) |
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4.4 Calibrating the Information Flow Rate |
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75 | (2) |
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4.5 Analytical Approximation to Signal-Based Price |
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77 | (16) |
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4.5.1 Extension to Multiple Signals |
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84 | (1) |
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4.5.2 Maximum-Likelihood Estimation of Earnings Model |
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84 | (4) |
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4.5.3 Information-Based Model Output |
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88 | (2) |
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90 | (3) |
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93 | (4) |
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5.1 Financial Signal Processing (FSP) |
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94 | (3) |
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96 | (1) |
| A Analytical Gamma Approximation to Log-Normal via Kullback--Leibler Minimisation |
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Dr. Nadi Serhan Aydn, FRM, is a graduate of the Institute of Applied Mathematics at Middle East Technical University and a lecturer at TED University in Ankara, Turkey. Formerly, he was a research fellow at Imperial College London and served as a senior research economist in an intergovernmental organization. His research interest lies in financial mathematics, risk management, stochastic & digital filtering, market microstructure, and frequency-domain analysis.
