A Conceptual Model of Investor Behavior

Based on a survey of behavioral finance literature, this paper presents a descriptive model of individual investor behavior in which investment decisions are seen as an iterative process of interactions between the investor and the investment environment. This investment process is influenced by a number of interdependent variables and driven by dual mental systems, the interplay of which contributes to boundedly rational behavior where investors use various heuristics and may exhibit behavioral biases. In the modeling tradition of cognitive science and intelligent systems, the investor is seen as a learning, adapting, and evolving entity that perceives the environment, processes information, acts upon it, and updates his or her internal states. This conceptual model can be used to build stylized representations of (classes of) individual investors, and further studied using the paradigm of agent-based artificial financial markets. By allowing us to implement individual investor behavior, to choose various market mechanisms, and to analyze the obtained asset prices, agent-based models can bridge the gap between the micro level of individual investor behavior and the macro level of aggregate market phenomena. It has been recognized, yet not fully explored, that these models could be used as a tool to generate or test various behavioral hypothesis.behavioral finance;financial decision making;agent-based artificial financial markets;cognitive modeling;investor behavior

On the Authentic Notion, Relevance, and Solution of the Jeffreys-Lindley Paradox in the Zettabyte Era

The Jeffreys-Lindley paradox is the most quoted divergence between the frequentist and Bayesian approaches to statistical inference. It is embedded in the very foundations of statistics and divides frequentist and Bayesian inference in an irreconcilable way. This paradox is the Gordian Knot of statistical inference and Data Science in the Zettabyte Era. If statistical science is ready for revolution confronted by the challenges of massive data sets analysis, the first step is to finally solve this anomaly. For more than sixty years, the Jeffreys-Lindley paradox has been under active discussion and debate. Many solutions have been proposed, none entirely satisfactory. The Jeffreys-Lindley paradox and its extent have been frequently misunderstood by many statisticians and non-statisticians. This paper aims to reassess this paradox, shed new light on it, and indicates how often it occurs in practice when dealing with Big data

Conflicts in Bayesian Statistics Between Inference Based on Credible Intervals and Bayes Factors

In frequentist statistics, point-null hypothesis testing based on significance tests and confidence intervals are harmonious procedures and lead to the same conclusion. This is not the case in the domain of the Bayesian framework. An inference made about the point-null hypothesis using Bayes factor may lead to an opposite conclusion if it is based on the Bayesian credible interval. Bayesian suggestions to test point-nulls using credible intervals are misleading and should be dismissed. A null hypothesized value may be outside a credible interval but supported by Bayes factor (a Type I conflict), or contrariwise, the null value may be inside a credible interval but not supported by the Bayes factor (Type II conflict). Two computer programs in R have been developed that confirm the existence of a countable infinite number of cases, for which Bayes credible intervals are not compatible with Bayesian hypothesis testing

Testing Point Null Hypothesis of a Normal Mean and the Truth: 21st Century Perspective

Testing a point (sharp) null hypothesis is arguably the most widely used statistical inferential procedure in many fields of scientific research, nevertheless, the most controversial, and misapprehended. Since 1935 when Buchanan-Wollaston raised the first criticism against hypothesis testing, this foundational field of statistics has drawn increasingly active and stronger opposition, including draconian suggestions that statistical significance testing should be abandoned or even banned. Statisticians should stop ignoring these accumulated and significant anomalies within the current point-null hypotheses paradigm and rebuild healthy foundations of statistical science. The foundation for a paradigm shift in testing statistical hypotheses is suggested, which is testing interval null hypotheses based on implications of the Zero probability paradox. It states that in a real-world research point-null hypothesis of a normal mean has zero probability. This implies that formulated point-null hypothesis of a mean in the context of the simple normal model is almost surely false. Thus, Zero probability paradox points to the root cause of so-called large n problem in significance testing. It discloses that there is no point in searching for a cure under the current point-null paradigm

Designing a road traffic model for the cross-sectoral analysis of future national infrastructure

This paper presents a UK national road traffic model developed as part of the ITRC MISTRAL - a large interdisciplinary project of the Infrastructure Transitions Research Consortium (ITRC). The proposed model includes passenger and freight vehicle flows on major UK roads and predicts future demand in the form of an inter-zonal origin-destination matrix, using and elasticity-based simulation approach. An important part of the model is the network assignment step during which predicted flows are assigned to the road network. This allows for the assessment of road capacity utilisation and facilitates the identification of "pinch points" where future infrastructure investments might be targeted. Several policy interventions are studied in the paper, including road expansion with additional lanes, new road development and vehicle electrification. The model also explicitly considers cross-sectoral interdependencies with other infrastructure networks, primarily with the energy sector where the transport sector is the largest consumer, the digital communications sector, water supply and waste management. In future extensions, the model will also be able to estimate the environmental footprint and assess the risk and resilience of the transport network. This model has the potential to inform policy makers about the long-term performance of UK road infrastructure, considering a range of possible future scenarios for population growth, technological innovation and climate change

Modeling investor optimism with fuzzy connectives

Optimism or pessimism of investors is one of the important characteristics that determine the investment behavior in financial markets. In this paper, we propose a model of investor optimism based on a fuzzy connective. The advantage of the proposed approach is that the influence of different levels of optimism can be studied by varying a single parameter. We implement our model in an artificial financial market based on the LLS model. We find that more optimistic investors create more pronounced booms and crashes in the market, when compared to the unbiased efficient market believers of the original model. In the case of extreme optimism, the optimistic investors end up dominating the market, while in the case of extreme pessimism, the market reduces to the benchmark model of rational informed investors

The healing mechanism for excited molecules near metallic surfaces

Radiation damage prevents the ability to obtain images from individual molecules. We suggest that this problem can be avoided for organic molecules by placing them in close proximity with a metallic surface. The molecules will then quickly dissipate any electronic excitation via their coupling to the metal surface. They may therefore be observed for a number of elastic scattering events that is sufficient to determine their structure.Comment: 4 pages, 4 figures. Added reference

A Conceptual Model of Investor Behavior

Based on a survey of behavioral finance literature, this paper presents a descriptive model of individual investor behavior in which investment decisions are seen as an iterative process of interactions between the investor and the investment environment. This investment process is influenced by a number of interdependent variables and driven by dual mental systems, the interplay of which contributes to boundedly rational behavior where investors use various heuristics and may exhibit behavioral biases. In the modeling tradition of cognitive science and intelligent systems, the investor is seen as a learning, adapting, and evolving entity that perceives the environment, processes information, acts upon it, and updates his or her internal states. This conceptual model can be used to build stylized representations of (classes of) individual investors, and further studied using the paradigm of agent-based artificial financial markets. By allowing us to implement individual investor behavior, to choose various market mechanisms, and to analyze the obtained asset prices, agent-based models can bridge the gap between the micro level of individual investor behavior and the macro level of aggregate market phenomena. It has been recognized, yet not fully explored, that these models could be used as a tool to generate or test various behavioral hypothesis

Behavioral Finance and Agent-Based Artificial Markets

Studying the behavior of market participants is important due to its potential impact on asset prices and the dynamics of financial markets. The idea of individual investors who are prone to biases in judgment and who use various heuristics, which might lead to anomalies on the market level, has been explored within the field of behavioral finance. In this dissertation, we analyze market-wise implications of investor behavior and their irrationalities by means of agent-based simulations of financial markets. The usefulness of agent-based artificial markets for studying the behavioral finance topics stems from their ability to relate the micro-level behavior of individual market participants (represented as agents) and the macro-level behavior of the market (artificial time-series). This micro-macro mapping of agent-based methodology is particularly useful for behavioral finance, because that link is often broken when using other methodological approaches. In this thesis, we study various biases commented in the behavioral finance literature and propose novel models for some of the behavioral phenomena. We provide mathematical definitions and computational implementations for overconfidence (miscalibration and better-than-average effect), investor sentiment (optimism and pessimism), biased self-attribution, loss aversion, and recency and primacy effects. The levels of these behavioral biases are related to the features of the market dynamics, such as the bubbles and crashes, and the excess volatility of the market price. The impact of behavioral biases on investor performance is also studied

Sustainable revenue management

The introduction of public transport smart cards means it is now possible to forecast how behavioural change stimulators, such as time-variable pricing, will impact passenger activity. This is an invaluable tool for managing revenue in a sustainable way, not just in the public transport sector, but also for every industry constrained by peak-loading capacity