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Abstract: Customers often show a tendency to prefer the status quo rather than switching to a new brand. Inertia equity is inferred from the difference between the price that will induce a customer to switch to a new brand and the price of the brand the customer has typically been using in the past (i.e., the anchoring price). Two studies demonstrated that inertia equity increases with the magnitude of the anchoring price, whereas the ratio of the inertial equity to the anchoring price decreases as the anchoring price increases. However, the hypothesis is retained that the logarithmic transformation of the inertia equity is a constant portion of the logarithmic transformation of the anchoring price. The asymmetric value function postulated by prospect theory was used to account for these relationships that link monetary variables (e.g., inertia equity) to consumer variables (e.g., inertia driver and inertia values).
Status quo bias, prospect theory, psychophysics of pricing, inertia equity, brand switching, experiments
Abstract: Irrational Exuberance (Greenspan, 1996; Shiller, 2000, 2006) depicts that the volatility of stock market may result partly from investor psychology and as a result investors may bias financial judgments with affective and non-fundamental variables. Yet one lacks a theoretical framework and methodology to separate the affective bias (psychology) from the fundamental sensitivity (economics) in stock or fund judgments. The author proposes a signal detection theory (SDT) framework and the SDT methodology to separate the bias from the sensitivity based on modeling and computing the data of 11012 mutual funds. With this SDT approach, a seemly good fund with a high average return rate actually may not be a good choice for future investment because its fundamental sensitivity is weak. Hence, the irrational exuberance for the fund may be corrected with the fundamental sensitivity indicator. The searchable results on the sensitivity (dprime) and bias (bias_c) for these funds are available at the YesWici.com website. Based on the SDT model, YesWici.com also presents an online Monte Carlo Simulation (MCS) forecasting engine to calculate and compare future fund performance.
Irrational Exuberance, investing, finance, yeswici.com, SDT, funds
Abstract: Investor confidence as risk propensity is measured with the SDT (Signal Detection Theory) formula. This piece presents theoretical relationships between risk propensity, implied volatility, and strike price (X), illustrated as the risk propensity surface against volatility and price. Derived from the volatility smile of the Black Scholes model and the SDT formula for risk propensity, one may predict that (1) risk propensity increases as volatility increases given stable price; (2) risk propensity increases as strike price increases given stable volatility; (3) above the money strike price increases as volatility increases given stable risk propensity; (4) the risk propensity surface may explain the volatility smile. The strategy derived from the risk propensity surface may lead to the creation of new financial instruments that consider both behavioral (risk propensity as investor confidence) and quantitative (volatility) ingredients.
Volatility, investor confidence, risk, choice modeling
Abstract: This article proposes a theoretical framework to categorize mobile marketing systems for marketing communication and then develops a novel m-Commerce technology to demonstrate the framework. Mobile marketing refers to the marketing communication activities such as advertising and customer retention that employ wireless devices and networks. Past research studies the consumer acceptance of the mobile technology. However, the under-studied is the mobile marketing systems that enable mobile marketing with computer and wireless technologies. Building on the relevance to Web-based and multi-channel marketing, the article firstly proposes the framework based on the mode of communication and the technology channels that enable the mobile communication. It then demonstrates the framework by inventing a new m-Commerce technology for marketing communication. The new technology uses a hybrid network and an m-Commerce computer application to display interactive messages on computer-mediated billboards. Mobile consumers can initiate the communication with marketers through the new technology that underscores the notion of reverse marketing.
mobile marketing system, reverse marketing, categorization, m-Commerce software, text messaging
Abstract: This study links the self-other asymmetry in judgment to the loss-gain asymmetry in choice. As a manifestation of loss aversion, a customer tends to stay with status quo (e.g., a home brand) rather than switch to a new brand because losing the status quo looms larger than gaining the new brand. Inertia equity assesses the difference between mental losses and gains. It is the difference between the price that will induce a customer to switch to a competitor brand and the price of the brand the customer has typically been using in the past (i.e., the anchoring price). We find that inertia equity is smaller when consumers evaluate peer customers (locus of others), than evaluating themselves (locus of self) to switch brands, which is coded as the locus effect on inertia equity. The asymmetric value function postulated by prospect theory is employed to describe inertia equity. The locus effect is then derived after linking the self-other asymmetry to the value function. The difference between valuing losses and gains in brand switching is larger for self-related than others’ items because consumers are more sensitive to self-related losses than others’ equivalent losses. It is also found that the locus effect is applicable to brands with various anchoring prices.
loss aversion, locus of evaluation, status quo bias, customer equity, brand switching
Abstract: This article uses the multinomial modeling technique (Batchelder 1998) to decompose the brand-switching matrix of the RLZ model and produces conditional probabilities for alternative decision outcomes (Rust, Lemon, and Zeithaml 2004; Rust, Lemon, and Narayandas 2005). It then links the probabilities with Bayes’ theorem and creates a new customer growth function on consumer choice (i.e., the customer retention rate). The growth function offers novel insights on customer growth from behavioral choice modeling perspective comparing to mainstream time-based adoption models (Bass 1969, 2004; Gupta, Lehmann, and Stuart 2004). These insights include three predictions that are corroborated with a peak analysis. The market share (k) of a brand determines the shape of the growth function. For small and medium brands, the relationship between customer growth and consumer choice follows an inverted-U curve. The peak of the growth curve may not exist if the market share of the brand is too large.
Abstract: Capital Asset Pricing Model (CAPM) has been a central theory in financial economics and financial practice for several decades (Sharpe 1964; French 2003). Building on the modern portfolio theory (Markowitz 1952, 1999), asset pricing theories estimate the rate of returns and risks of an asset with statistical metrics like means and standard deviations. With the advent of behavioral finance and investor psychology, more and more people realize that investor sentiment plays a crucial role in asset pricing. Yet one lacks sound real-time measures of the investor sentiment for an asset that matters in the asset's price movement. This piece develops the Sentiment Asset Pricing Model (SAPM) as a package of algorithms for an asset. SAPM calculates the investors' real-time estimates for given asset prices from the asset's option data with large samples. It then computes the asset prices for future dates from the sentiment estimates. The algorithms are implemented as a Web charting application that displays the future dates, asset prices, rate of returns, and detailed estimates for the asset prices in real-time. SAPM extends legacy asset pricing models with real-time empirical investor sentiment.
Abstract: The 2008 financial and economic crisis results from many drivers such as the collapse of investor confidence, credit risk on sub-prime mortgage, equity risk of stock volatility, and the lack of comprehensive data sets for algorithmic trading computer applications (Greenspan 2008). This calls for transforming financial risk management that includes empirical and evidence-based investor confidence assessment and management. This paper proposes a theoretical framework to derive an investor confidence indicator as investor decision criterion from the Black-Scholes Model and a well-respected decision theory, namely the signal detection theory. The implementation of the framework is demonstrated as computer applications at www.yeswici.com as the Investor Confidence Indicator (an iGoogle gadget), and the visualFundsRT (a Rich Internet Application (RIA) and a desktop application).
investor confidence, investor decision criterion, 2008 financial crisis, risk management
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