In this chapter and Chapters 11, 12, and 13, we will discuss quantum-like models with financial applications in mind. The very last chapter of this book will analyze the sources of quantum-like processing in the brain. The concept of non-arbitrage, as we have indicated before (see Section 4.18.3) is of key importance in financial models. We reiterate its importance in this chapter. We also briefly mention a popular finance model. In the coming chapters, the wave function will sometimes have an information interpretation. Therefore we also discuss the informational aspects of the classical financial theory (see Section 10.1). This chapter is meant to introduce the reader to some of the key achievements in financial theory. By doing so, we hope to convince the reader, in later chapters, that some of those very achievements can possibly be reformulated with the help of the wave function approach. Section 10.3 in this chapter will give a flavor of how Fourier integration can possibly be used to model George Soros' concept of reflexivity. Relevance of the concepts of efficiency and non-arbitrage: a brief discussion With the 2008 financial crisis, the concept of efficiency may have been seriously questioned. Indeed the financial market in September 2008 did take a tremendous hit and the ensuing price movements would not be consistent with a so-called efficient market.
In this chapter and Chapters 11, 12, and 13, we will discuss quantum-like models with financial applications in mind. The very last chapter of this book will analyze the sources of quantum-like processing in the brain. The concept of non-arbitrage, as we have indicated before (see Section 4.18.3) is of key importance in financial models. We reiterate its importance in this chapter. We also briefly mention a popular finance model. In the coming chapters, the wave function will sometimes have an information interpretation. Therefore we also discuss the informational aspects of the classical financial theory (see Section 10.1). This chapter is meant to introduce the reader to some of the key achievements in financial theory. By doing so, we hope to convince the reader, in later chapters, that some of those very achievements can possibly be reformulated with the help of the wave function approach. Section 10.3 in this chapter will give a flavor of how Fourier integration can possibly be used to model George Soros' concept of reflexivity. Relevance of the concepts of efficiency and non-arbitrage: a brief discussion With the 2008 financial crisis, the concept of efficiency may have been seriously questioned. Indeed the financial market in September 2008 did take a tremendous hit and the ensuing price movements would not be consistent with a so-called efficient market.
This chapter attempts to delve deeper into the question on how quantum mechanical techniques can be brought closer into the realm of economics and finance. The (non-)Hermiticity of finance-based operators? Hermiticity of operators was discussed in Chapter 4 of the book. We again take up this very important concept in the context of financial asset pricing. It is a classical result from quantum mechanics that the existence of Hermiticity of the Hamiltonian operator is intimately linked with the notion of conservation of probability. The existence of Hermiticity is also known to be closely linked to the concept of spatial localization. Please see below. Baaquie [1] makes the important argument that the Black-Scholes Hamiltonian is non-Hermitian and this condition provides for the need to satisfy the martingale condition. Please recall that the martingale property was covered in Chapter 2, Section 2.9. It is also important to mention that Luigi Accardi has indicated that it is white noise which may be the cause of non-Hermiticity in a finance context. One can argue that within an economics/finance context, the equivalent of the state function, using Baaquie [1] [2], can be the option price function. A similar interpretation can also be found in the paper by Li and Zhang [3] (see also Haven [4]). Please note that the work of Li and Zhang is covered in Section 13.12 of this chapter.
In this talk we intend to provide for an overview of the uses we can make in financial economics of several quantum physical concepts. We introduce and briefly discuss the so called information wave function. We discuss how the information wave function can be of use in financial option pricing. We then briefly allude on how the information wave function can be used in arbitrage. We briefly discuss how the information wave concept can be connected to measures of information. We round off the paper on recent work we are doing with Andrei Khrennikov on i) testing probability interference in psychology (Khrennikov and Haven 2006 (I)) and ii) finding uses of ‘financial ’ nonlocality and entanglement (Khrennikov and Haven 2006 (II)). In summary this talk addresses interference (probability interference), financial entanglement and looks at using the wave function in a variety of ways within an economics/finance – political science – social interaction environment.
Abstract Since Adam Smith, economics has erected a grand edifice upon the foundations of rational agents, equilibrium analysis, and linear causality; however, the 2008 global financial crisis, the European sovereign debt crisis, and the COVID-19 pandemic have successively revealed its fundamental limitations when confronted with complex economic phenomena: bubbles that ought not to have existed, crises unforeseen by equilibrium models, non-linear ruptures brought about by technological revolutions, and self-fulfilling expectations—all of which call for a richer theoretical language. This book serves as the foundational introduction to the "Quantum Economics Textbook Series," as well as the first gateway into this entire series. It introduces the methodological framework, mathematical apparatus, and philosophical insights of quantum mechanics to economic analysis, grounding its approach in the core proposition of a practical ontology—that human economic practice itself possesses quantum attributes such as superposition, entanglement, constructibility, uncertainty, and emergence—thereby reconstructing the economic world as a quantum process of superposition and evolution. Six core concepts—superposition, entanglement, measurement collapse, the uncertainty principle, quantum transitions, and emergence—run throughout the work. Quantum economics does not represent a simple negation of classical theory; rather, it constitutes a dialectical sublation: under the classical limit—where decoherence is complete, the measurement basis is fixed, and entanglement vanishes—quantum theory precisely reduces to classical theory. This book comprises five parts and thirty chapters: Part I establishes the paradigm shift and theoretical framework, covering critiques of Newton's worldview, the quantum worldview, a foundational conceptual framework, mathematical foundations, and research methodologies; Part II reconstructs quantum microeconomic theory, encompassing consumers, producers, pricing, markets, the wave-particle duality of markets, quantum games, and welfare economics; Part III builds quantum macroeconomic theory, addressing national income accounting, money, inflation, economic cycles, policy, and open economies; Part IV explores quantum finance and market dynamics, including asset pricing, portfolio management, market sentiment, risk management, and trading theory; Part V extends the scope to organizations, innovation, institutions, development, economic ethics, and the digital economy, while offering insights into the future of this discipline. This volume is intended for undergraduate and graduate students, as well as researchers in economics and related fields, and serves as the essential first volume in the quantum economics series.
Abstract Finance stands as the most quantitatively advanced branch of the social sciences, yet it is also the most humbling. The mean-variance efficient frontier, the CAPM, arbitrage pricing relations, the Black–Scholes–Merton option formula and the efficient market hypothesis possess unparalleled formal elegance, yet they suffered a broad empirical refutation in the global financial crisis of 2008. Standard models understate tail risk, misinterpret the comovement of assets that are supposed to diversify risk, and presuppose liquidity and rationality — two features that vanish precisely when they are most needed. Behavioural finance has documented an extensive catalogue of cognitive biases, yet it remains a patchwork of isolated effects without a unified calculus of probability. This monograph is the first systematic work to establish quantum finance. It argues that the mathematical formalism of quantum mechanics supplies this missing unified structure: financial states are state vectors in Hilbert space; beliefs and valuations coexist as superposed amplitudes; trading and information disclosure act as measurements that collapse states in accordance with the Born rule; assets and markets are entangled with one another; and path interference generates fat tails and herding, which are inadmissible under classical additive probability. The book comprises six parts and thirty chapters in total. Part I lays the philosophical and formal foundations. Starting from the paradigm crisis, it proceeds through quantum probability and quantum stochastic processes to construct the analytical framework built on three core pillars of quantum finance. Part II develops quantum asset pricing, covering the quantum CAPM, quantum APT, quantum option pricing and the term structure of interest rates. Part III advances quantum risk management, including quantum risk measures, Value-at-Risk (VaR) and expected shortfall, stress testing, credit risk and phase transitions in systemic risk. Part IV addresses quantum portfolios and asset allocation, ranging from the quantum meanvariance frontier, strategic and tactical asset allocation, to factor investing and hedge fund strategies. Part V turns to market microstructure and behavioural finance, examining quantum order flows, marketmaker liquidity, quantum prospect theory and market sentiment. Part VI looks ahead to frontier avenues: quantum computational optimization, quantum machine learning, blockchains, postquantum cryptography and quantuminformed financial regulation. This book rigorously recovers the classical CAPM, efficient market hypothesis and Gaussian risk models as special cases under decoherence limits. It is intended for graduate students and faculty in finance and financial engineering, as well as researchers and practitioners working on asset pricing, risk management, portfolio allocation and financial regulation.
This book had as its main objective to discuss how (quantum) physics concepts could be used in a relevant way in the modeling of a variety of economics/finance and psychology-based processes. For those readers who have come to read this conclusion, without skipping its “backbone,” we would like to say “thank you” for keeping with us until the end. The possible claim, which says that social science driven dynamics would need analyzing with techniques from the natural sciences, may well have been tempered with this book. Although quantum physics as a theory of prediction is one of the most successful theories humankind ever devised, it is not unreasonable to suggest that the number of foundational interpretations of quantum physics is sizable. Consider now the following natural question: “How can a theory with such a wide diversity of foundational interpretations serve to inform and elucidate a non-cognate area of research (like economics or psychology)?” Indeed, we should want to stand upright and write, without hesitation, the words “non-cognate.” No theory in social science can come close to the predictive power of quantum physics. However, very few social science theories are “plagued” by foundational issues of such depth as quantum physics. As an example, consider the issue of why quantum physical wave functions do have to be complex valued. Do physicists have a satisfactory answer to this very basic question? A theory of such predictive power has a partial differential equation, the Schrödinger equation, which cannot be rigorously derived. However, in economics and finance we strive to build theoretical edifices with the utmost respect for rigor.
Quantum finance is a new paradigm in financial market operations, based on the principles of quantum computing in solving highly complex problems with efficiency. This chapter covers some basic ideas of quantum finance through its historical context and the role it has played in surmounting the limitations of classical financial models. The chapter commences with the basics, including the need for an introduction to quantum mechanics, qubits, quantum gates, and algorithms that can be applied in financial applications to demonstrate how quantum computing can change finance. We discuss some of those areas where this technology is bound to make an impact: in portfolio optimization, quantum computing considers several combinations of assets at a time; in risk management, improved models will allow for better predictions of market movements; in derivative pricing, the calculations are faster and more accurate; and in fraud detection, it enables one to execute better analysis of transactional data. The advancement of quantum finance is linked to the need to resolve ethical, legal, and technical obstacles while integrating with traditional financial institutions. The financial markets will be a key source of innovation that will drive efficiency, precision, and invention to previously unheard-of heights, given the direction that quantum computing technology is expected to take. Thus, the chapter ends with a call for more study and cooperation to fully grasp quantum finance's transformative potential.