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We present a unified, market-complete model that integrates both Bachelier and Black–Scholes–Merton frameworks for asset pricing. The model allows for the study, within a unified framework, of asset pricing in a natural world that experiences the possibility of negative security prices or riskless rates. Unlike the classical Black–Scholes–Merton, we show that option pricing in the unified model differs depending on whether the replicating, self-financing portfolio uses riskless bonds or a single riskless bank account. We derive option price formulas and extend our analysis to the term structure of interest rates by deriving the pricing of zero-coupon bonds, forward contracts, and futures contracts. We identify a necessary condition for the unified model to support a perpetual derivative. Discrete binomial pricing under the unified model is also developed. In every scenario analyzed, we show that the unified model simplifies to the standard Black–Scholes–Merton pricing under specific limits and provides pricing in the Bachelier model limit. We note that the Bachelier limit within the unified model allows for positive riskless rates. The unified model prompts us to speculate on the possibility of a mixed multiplicative and additive deflator model for risk-neutral option pricing. [ABSTRACT FROM AUTHOR]

In this paper we build a methodology for pricing of insurance-linked securities which are tied to multiple natural catastrophe perils. As a representative example, we construct a multi-peril catastrophe (CAT) bond which can be linked to the industry loss indices or actual losses incurred by an insurer. We provide pricing formulas for such CAT bonds. We illustrate the introduced methodology on the US natural catastrophe data obtained from Property Claim Services (PCS). Within this dataset, we specifically examine two types of risks: losses associated with wind and thunderstorm events, and those linked to winter storm events. Then, we fit and validate the underlying compound non-homogeneous Poisson processes taking into account the fact that the data are left-truncated. The best fitted loss distributions appear to be Burr and Generalised Extreme Value and for the first peril and log-normal for the second. Finally, we visualise the zero-coupon CAT bond prices for the selected best-fitted models. [ABSTRACT FROM AUTHOR]

This study investigates the contradiction in the finding of a positive distress risk premium in Vassalou and Xing's study and the finding of a negative distress risk premium, i.e., a distress risk discount, in several other studies. Using the default likelihood measure calculated following Vassalou and Xing's procedure for 1965–2023, we show that excluding outliers and including the time period beyond the end of Vassalou and Xing's sample period in 1999 makes a difference in the results. Overall, using portfolio sorting and Fama-MacBeth regressions, this study supports the existence of a distress risk discount. This study also documents that the financial distress risk is negatively reflected in security prices even after accounting for size and book-to-market risk factors. Furthermore, it demonstrates that the negative distress risk premium is strong and persistent across economic expansions, recessions, and the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

We derive a two-dimensional partial differential equation to examine the pricing of convertible bonds and callable convertible bonds. We combine the well-known geometric Brownian motion process for the evolution of the stock price with a recently developed pull-to-par model for bond price evolution. To account for default risk, we price securities using a default-adjusted discount rate. As an illustration, we provide some solutions and sensitivity analysis using a finite difference approximation to the partial differential equation. [ABSTRACT FROM AUTHOR]

Bruce Jacobs, Ken Levy, and Harry Markowitz shared similar interests and did complementary work. This led to collaboration, debate, and building upon each other's ideas and research. They had a prodigious relationship of over 30 years, bridging the gap between theory and practice. Bruce individually, and then with Harry, distinguished between portfolio insurance and portfolio theory. Bruce and Ken estimated security expected returns using cross-sectional analysis, and Harry used that methodology for portfolio management. Bruce and Ken used Harry's methods for portfolio construction, and they jointly explored the value of using constraints in portfolio optimization and addressed the optimality and optimization of long–short portfolios. Bruce, Ken, and Harry jointly developed an asynchronous, discrete-time, dynamic market simulator, JLMSim, to explain the behavior of security prices and to find equilibrium expected returns. Bruce and Ken extended portfolio theory to account for the unique risks of leverage and applied investor volatility aversion and leverage aversion to portfolio choice. The optimal portfolio lies within an efficient region and on a three-dimensional efficient surface. Harry concurred that the mean–variance model is a special case of the mean–variance-leverage model. Bruce and Ken used the mean–variance-leverage model to address the optimal amount of leverage in 130–30-type portfolio strategies. Bruce and Ken would challenge Harry, and Harry would challenge Bruce and Ken, and out of that would often come something interesting and useful. [ABSTRACT FROM AUTHOR]

We examine how design features and labels of complex financial securities affect pricing and performance. Hence, we utilize the security design features required by the European Union's Securitization Regulation and the optional STS label ("Simple, Transparent, and Standardized"). Based on a unique dataset of European securitizations with 31 million quarterly loan observations, we find that investors hardly consider the features but rely on the existence of the label, although the latter has no performance-increasing effect. Our results reveal that investors neglect a proper risk assessment and misinterpret the easily accessible label as a signal of superior performance. [ABSTRACT FROM AUTHOR]

We propose a novel factor model for option returns. Option exposures are estimated nonparametrically, and factor risk premia can vary nonlinearly with states. The model is estimated using regressions with minimal assumptions on factor and option return dynamics. We estimate the model using index options to characterize the conditional risk premia for factors of interest, such as the market return, market variance, tail and intermediary risk factors, higher moments, and the VIX term structure slope. Together, market return and variance explain more than 90% of option return variation. Unconditionally, the magnitude of the variance risk premium is plausible. It displays pronounced time variation, spikes during crises, and always has the expected sign. [ABSTRACT FROM AUTHOR]

We develop a fast Monte Carlo simulation (MCS) for pricing equity-linked securities (ELS) with time-dependent volatility and interest rate. In this paper, we extend a recently developed fast MCS for pricing ELS. In the previous model, both the volatility and interest rate were constant. However, in the real finance market, volatility and interest rate are time-dependent parameters. In this work, we approximate the time-dependent parameters by piecewise constant functions and apply Brownian bridge technique. We present some numerical results of the proposed method. The computational results demonstrate the fastness of the proposed algorithm with equivalent accuracy with standard MCS. It is important for traders and hedgers considering derivatives to evaluate prices and risks quickly and accurately. Therefore, our algorithm will be very useful to practitioners in the ELS market. [ABSTRACT FROM AUTHOR]

Purpose: The study examines the mediating role of accounting information quality (AQ), a proxy for firms' information risk, in firms' business strategy and the cost of equity (COE) nexus to highlight how AQ provides a mechanism through which a company's business strategy affects its COE. Design/methodology/approach: The research study utilises data from 12,100 firm-year observations of United States (US) non-financial firms from 2001 to 2017, drawn from multiple databases, and employs the bootstrapping method of mediation analysis to test the indirect effect of AQ on the business strategy–COE relationship. The authors rely on Miles and Snow's two pure business strategy typologies, prospectors and defenders and use innate accrual quality and implied COE models to measure AQ and COE, respectively. Findings: The results suggest that AQ partially mediates the relationship between business strategy and COE. The authors document that while innovative-oriented prospector firms have a lower AQ and a higher implied COE, efficiency-oriented defenders are associated with a higher AQ and lower COE. The higher (lower) COE of prospector (defender) firms is observed to be partly due to their lower (higher) AQ. The results indicate that while the idiosyncratic risk implied in firms' strategic orientation can directly influence their COE, the business strategy implications on firms' COE can be indirect through their AQ, a source of information risk. Research limitations/implications: Due to data limitation, it was not possible to measure all possible methods of measuring implied COE. Practical implications: The paper highlights the role of firm's business strategy in pricing decisions by investors. Originality/value: The paper contributes to the existing literature by providing evidence that AQ, a proxy for information risk, is a mechanism through which business strategy affects firms' COE. The authors thus complement extant literature to empirically test the information risk effect inherent in strategic orientation on security pricing. [ABSTRACT FROM AUTHOR]

We use the prices of credit card asset-backed securities to study the market risk premium associated with unsecured consumer credit risk. We find that the market incorporates a substantial credit risk premium into the prices of these securities. Furthermore, there has been a major repricing of unsecured consumer credit risk since the 2007–2009 financial crisis. We find evidence that this increase is linked to balance-sheet costs imposed by postcrisis changes in regulations that have placed credit card securitizations back onto issuer balance sheets. These regulatory changes may have added more than 100 basis points to the cost of unsecured household credit. [ABSTRACT FROM AUTHOR]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We document that global board reforms are associated with a significant reduction in the underpricing of initial public offerings (IPOs). The effect is amplified for IPOs with greater agency problems and mitigated for IPOs certified by reputable intermediaries, IPOs with greater disclosure specificity, and IPOs in countries with better shareholder protection and stringent financial reporting regulations. Furthermore, global board reforms have led to an improvement in the long-term market performance, proceeds, and subscription level of IPOs and have enhanced board independence in the issuing firms. Our findings suggest that global board reforms have strengthened board oversight in the issuing firms, leading to less underpriced IPOs. [ABSTRACT FROM AUTHOR]

Climate change is a financial risk to companies, and those that disregard this will have to deal with losses due to extreme weather events, new regulations, or the disruption of the supply chain. Companies must disclose climate-related financial implications to enable informed decision-making by investors and other stakeholders. Any indifference or disregard for climate-related disclosures will harm the investors who may trade based on insufficient information and prices of securities that do not account for climate risks. In India, disclosures are mandated for companies under the Companies Act 2013 and various Securities and Exchange Board of India Regulations. This paper will explore the adequacy of the current disclosure regime concerning climate change and argue that, although information regarding climate risks falls within the ambit of the current set of mandated disclosures, it is insufficient. The paper concludes by making a case for mandatory climate disclosures under the Companies Act. [ABSTRACT FROM AUTHOR]

We employ a battery of model evaluation tests for a broad set of GARCH-MIDAS models and account for data snooping bias. We document that inferences based on standard tests for GM variance components can be misleading. Our data mining free results show that the gain of macro-variables in forecasting total (long-run) variance by GM models is overstated (understated). Estimation of different components of volatility is crucial for designing differentiated investing strategies, risk management plans and pricing derivative securities. Therefore, researchers and practitioners should be wary of data-mining bias, which may contaminate a forecast that may appear statistically validated using robust evaluation tests. [ABSTRACT FROM AUTHOR]

We study the extent to which credit index (CDX) options are priced consistent with S&P 500 (SPX) equity index options. We derive analytical expressions for CDX and SPX options within a structural credit‐risk model with stochastic volatility and jumps using new results for pricing compound options via multivariate affine transform analysis. The model captures many aspects of the joint dynamics of CDX and SPX options. However, it cannot reconcile the relative levels of option prices, suggesting that credit and equity markets are not fully integrated. A strategy of selling CDX volatility yields significantly higher excess returns than selling SPX volatility. [ABSTRACT FROM AUTHOR]

We propose an asset pricing factor model constructed with semiparametric characteristics-based mispricing and factor loading functions. We approximate the unknown functions by B-splines sieve where the number of B-splines coefficients is diverging. We estimate this model and test the existence of the mispricing function by a power enhanced hypothesis test. The enhanced test solves the low power problem caused by diverging B-splines coefficients, with the strengthened power approaching one asymptotically. We also investigate the structure of mispricing components through Hierarchical K-means Clusterings. We apply our methodology to CRSP (Center for Research in Security Prices) and Compustat data for the U.S. stock market with one-year rolling windows during 1967–2017. This empirical study shows the presence of mispricing functions in certain time blocks. We also find that distinct clusters of the same characteristics lead to similar arbitrage returns, forming a "peer group" of arbitrage characteristics. [ABSTRACT FROM AUTHOR]

Following the COVID-19 pandemic, the healthcare sector has emerged as a resilient and profitable domain amidst market fluctuations. Consequently, investing in healthcare securities, particularly through mutual funds, has gained traction. Existing research on predicting future prices of healthcare securities has been predominantly reliant on historical trading data, limiting predictive accuracy and scope. This study aims to overcome these constraints by integrating a diverse set of twelve external factors spanning economic, industrial, and company-specific domains to enhance predictive models. Employing Long Short-Term Memory (LSTM) and Multiple Linear Regression (MLR) techniques, the study evaluates the effectiveness of this multifaceted approach. Results indicate that incorporating various influencing factors beyond historical data significantly improves price prediction accuracy. Moreover, the utilization of LSTM alongside this comprehensive dataset yields comparable predictive outcomes to those obtained solely from historical data. Thus, this study highlights the potential of leveraging diverse external factors for more robust forecasting of mutual fund prices within the healthcare sector. [ABSTRACT FROM AUTHOR]

The efficient capital markets hypothesis (EMH) posits that security prices incorporate all available information in capital markets. Nevertheless, real stock markets often exhibit speculative behavior due to information asymmetry and the limited rationality of investors. This paper employs statistical analysis, a multiple regression approach, and robustness tests to investigate the impact of investor attention and accounting information comparability on stock returns. We collected monthly data from all Chinese A-share stocks listed on the main board of the Shanghai Stock Exchange for the period 2017–2021. Our findings reveal a significant positive correlation between current investor attention and current monthly stock returns and a significant negative correlation between lagged investor attention and current monthly stock returns. Moreover, accounting information comparability serves as a substantial moderator, amplifying the positive effect of current investor attention on current stock returns and mitigating the negative impact of lagged investor attention. We investigate the indicator of accounting information comparability from the perspective of investor attention. Significantly, we use accounting information comparability as a moderating variable for the first time to assess its influence on stock returns. Our results demonstrate that accounting information comparability significantly contributes to mitigating excessive share price declines and stimulating share price increases. This discovery also acts as an internal driver for listed companies to proactively improve accounting information comparability. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to measure the trade price impact of a recent regulatory disclosure intervention in municipal securities secondary markets, which required broker-dealers to disclose securities trading information on a near-real-time and continuing basis. Design/methodology/approach: The author analyzes trade price outcomes in the preintervention and postintervention regimes using a suite of time series estimations that give heteroskedasticity-robust standard errors (Prais–Winsten and Cochrain–Orcutt), accommodate higher-order lag structure in the error term (autoregressive integrated moving average) and account for volatility clustering in the time series (generalized autoregressive conditional heteroskedasticity). Findings: Results show that regulatory disclosure intervention significantly improved trade price efficiency in municipal securities secondary markets as daily trade price differential and volatility both declined market-wide after the disclosure intervention. Research limitations/implications: The sample consists of trades in State of California general obligation bonds; therefore, empirical findings may not be generalizable to other states, local governments and different types of bonds. Practical implications: The findings highlight voluntary information disclosure as a practical and effective mechanism in disclosure regulation of municipal securities secondary markets. Originality/value: Only a small body of work exists that examines information disclosure regulation in municipal securities secondary markets; therefore, this paper expands knowledge on the topic and should provide renewed impetus for regulatory efforts aimed at improving the efficiency of municipal capital markets. [ABSTRACT FROM AUTHOR]

Machine learning has been used in many areas today. It thrives at reviewing large volumes of data and identifying patterns and trends that might not be apparent to a human. Given the huge potential benefit and the amount of data available in the financial market, it is not surprising to see machine learning applied to various financial products. While future prices of financial securities are extremely difficult to forecast, we study from a different angle. Instead of trying to forecast the future prices, we apply machine learning algorithms to predict the direction of future price movement, in particular, whether a sector Exchange Traded Fund (ETF) would outperform or underperform the market in the next week. We apply several machine learning algorithms for this prediction. The algorithms are Decision Tree (DT), Gaussian Naive Bayes (GNB), Neural Networks (NN). We show that these machine learning algorithms, have some predictive power in forecasting out-performance and under-performance out of sample. We also try to explore whether it is possible to utilize the predications from these algorithms to outperform the buy-and-hold strategy of S&P 500 index. The trading strategy to explore out-performance predictions does not differ from S&P 500 index, but the trading strategy to explore under-performance predictions can earn higher returns than simply holding S&P 500 index out of sample. [ABSTRACT FROM AUTHOR]

This article provides an interdisciplinary analysis of the interconnected challenges stemming from military deterrence strategies in the Indo- Pacific region, with a specific emphasis on their health and environmental impacts. The focus is on illuminating how military actions in the Western Pacific and South China Sea not only influence geopolitical stability but also carry profound implications for disabled veterans and Indigenous communities. Employing a diverse methodology that integrates federal documents, local news sources, scholarly literature, and fieldwork on Guam, this article utilizes the principles of procedural justice and genuine security as frameworks to comprehend the historical complexities and contemporary challenges in the non- self- governing Mariana Islands. Recommendations are put forth for inclusive dialogue and proactive policy changes that respect the voices and well- being of those most affected. This article serves as a clarion call for a paradigm shift in our approaches to security and justice, especially in contexts where military, health, and environmental concerns intersect. [ABSTRACT FROM AUTHOR]

We examine how public firms listed in North American stock exchanges acquire technology companies during 2010–2020. Combining data from Standard and Poor's (S&P), Refinitiv, Compustat, and Center for Research in Security Prices, and utilizing a unique S&P taxonomy that classifies tech mergers and acquisitions (M&As) by tech categories and business verticals, we show that 13.1% of public firms engage in any tech M&A in the S&P data, while only 6.75% of public firms make any (tech or nontech) M&A in Refinitiv. In both data sets, the acquisitions are widespread across sectors of the economy, but tech acquirers in the S&P data are on average younger, more investment efficient, and more likely to engage in international acquisitions than general acquirers in Refinitiv. Within the S&P data, deals in each M&A‐active tech category tend to be led by acquirers from a specific sector; the majority of target companies in tech M&As fall outside the acquirer's core area of business; and firms are, in part, driven to acquire tech companies because they face increased competition in their core areas. [ABSTRACT FROM AUTHOR]

We study the problem of a seller (e.g. a bank) who is privately informed about the quality of her asset and wants to exploit gains from trade with uninformed buyers (e.g. investors) by issuing securities backed by her asset cash flows. In our setting, buyers post menus of contracts to screen the seller, but the seller cannot commit to trade with only one buyer, i.e. markets are non-exclusive. We show that non-exclusive markets behave very differently from exclusive ones: (1) separating contracts are never part of equilibrium; (2) mispricing of claims faced by the seller is always greater than in exclusive markets; (3) there is always a semi-pooling equilibrium where all sellers issue the same debt contract priced at average -valuation, and sellers of low -quality assets issue remaining cash flows at low -valuation; (4) market liquidity can be higher or lower than in exclusive markets, but (5) the average quality of originated assets is always lower. Our model's predictions are consistent with empirical evidence on the issuance and pricing of mortgage-backed securities, and we use the theory to evaluate recent reforms aimed at enhancing transparency and exclusivity in markets. [ABSTRACT FROM AUTHOR]

The object of the investigation is a model of the incomplete financial market. It includes a bank deposit with a known interest rate and basic risky securities. The instant interest rate and volatility are governed by a hidden market regime, represented by some finite-state Markov jump process. The paper presents a solution to two problems. The first one consists of the characterization of the derivatives based on the existing market securities, which are valid to complete the considered market. It is determined that for the market completion, it is sufficient to add the number of derivatives equal to the number of possible market regimes. A generalization of the classic Black–Scholes equation, describing the evolution of the fair derivative price, is obtained along with the structure of a self-financing portfolio, replicating an arbitrary contingent claim in the market. The second problem consists of the online estimation of the market regime, given the observations of both the underlying and derivative prices. The available observations are either a combination of the time-discretized risky security prices or some high-frequency multivariate point processes associated with these prices. The paper presents the numerical algorithms of the market regime tracking for both observation types. The comparative numerical experiments illustrate the high quality of the proposed estimates. [ABSTRACT FROM AUTHOR]

Relentless attacks on Ukraine's fuel facilities threaten energy security and raise the prices of all petroleum products. Manufacture of biodiesel from waste food and processing industries can meet some of the demand. Based on experimental and production studies, the main characteristics of fat-containing waste, particularly technical animal fat (TAF), are determined, and the technological aspects of the production of diesel biofuels from waste from food and processing industries are characterised. The hardware and technological configuration of the mobile plant for the production of biodiesel has been developed and its technical support is proposed. [ABSTRACT FROM AUTHOR]

Copyright of Cuadernos del Centro de Estudios de Diseño y Comunicación is the property of Cuadernos del Centro de Estudios de Diseno y Comunicacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Contrary to the claims made by several authors, a financial market model in which the price of a risky security follows a reflected geometric Brownian motion is not arbitrage-free. In fact, such models violate even the weakest no-arbitrage condition considered in the literature. Consequently, they do not admit numéraire portfolios or equivalent risk-neutral probability measures, which makes them unsuitable for contingent claim valuation. Unsurprisingly, the published option pricing formulae for such models violate classical no-arbitrage bounds. [ABSTRACT FROM AUTHOR]

The process of attempting to estimate the future prices of particular stocks by utilizing historical data and various analytical tools, including deep learning algorithms, is called stock price prediction. Insurance providers' overall approach and decisions to manage their risks, enhance their profitability, and give value to their policyholders are referred to as the insurance strategy. It requires various things to be considered, including underwriting procedures, pricing strategies, product creation, risk analysis, claims administration, and investment choices. This study proposed optimizing an insurance strategy and predicting securities prices using a deep learning algorithm. Initially, the real stock data sources for Microsoft Corporation (MSFT) were gathered from Ping An Insurance Company of China (PAICC) and the Shanghai-based National Association of Securities Dealers Automated Quotation (NASDAQ). Normalization is the procedure used to preprocess data for the raw data. We suggest an Enhanced dragonfly-optimized deep neural network (EDODNN) with stock price forecasting and insurance. The outcomes demonstrate that the proposed model outperforms the current methodology and achieves accuracy, precision, recall, F1 score, R2, and RMSE. To display the effectiveness of the suggested system, its performance is compared to more established methods to obtain the highest level of efficiency for the research. [ABSTRACT FROM AUTHOR]

This paper proposes a mathematical model of financial security prices in continuous time with bubbles in which prices may diverge and crash in finite time. Just before the bubbles burst, prices increase super-exponentially. In addition, a discrete-time excess demand model is proposed to provide a micro-foundation for the continuous-time model. The derived discrete-time security price model has the same characteristics as the continuous-time price model and expresses the finite-time singularity. Furthermore, based on the excess demand model, an agentbased simulation is performed to check the price behavior. As expected, we can confirm that prices can diverge in finite time and increase super-exponentially. [ABSTRACT FROM AUTHOR]

Purpose: Signals, e.g. information released by firms about new products attract the attention and scrutiny of customers, competitors and other stakeholders. In product management, an important area of research focuses on the economic value of such signals. However, extant studies consider valuation effects of product signals independently, and largely ignore how the value of a product signal at launch depends upon prior preannouncements. This study aims to investigate how the dependence of new product development (NPD) signals on past preannouncements affects firms' security prices. Design/methodology/approach: The study develops a conceptual model that draws upon information asymmetry theories, i.e. signaling and agency theory to hypothesize the effect of firms' product introduction announcements on security prices given two antecedent preannouncement types (costless and costly signals). Hypotheses are tested by conducting an event study analysis on a sample of 149 matched observations (product introduction announcement preceded by a certain type of preannouncement). Findings: Empirical results confirm the hypothesis that positive valuation effects are observed during product launch that is preceded by initial costless product signaling. In contrast, for ex ante costly product signaling, launch events are not diagnostic enough to affect value. Since organizations' NPD communications can revise investors' prior beliefs, they need to be understood in more detail and managed strategically. Research limitations/implications: Valuation metrics can be noisy with a potential to influence information events. In addition, product introduction signals may be deployed more frequently in certain fast-paced industries, e.g. hi-tech. Practical implications: Managers can incorporate signal dependence in product communications. For example, in costless ex ante product signaling situations, initial economic loss may be recovered through launch announcements. Furthermore, when costly signals have been used earlier, firms may economize on promotion costs during launch. Originality/value: Past research has focused on assessing the economic value of new product signals independently, i.e. as discrete events. Absent is an examination of valuation effects due to the dependence of launch signals on prior preannouncements. This paper addresses the dependence gap, and empirical results show that even if firms do not deploy product signals ex ante, value can be created through ex post launch announcements. [ABSTRACT FROM AUTHOR]

In the electricity business, power evacuation from source to load is a challenging task when there is contingency and agencies have to swirl around for solutions in terms of rescheduling generator dispatch, load slashing, adding of network, etc. The more challenging scenario arises when the independent operator wants to re-dispatch a generator during the contingent situation. So, in this paper, we focus on a new generator rescheduling technique with congestion price and transmission security as an intervention. The significant intention of this paper involves restricting the load thereby dispatching a particular generator or a set of generators with low cost and secure transmission line. In addition to this, the network jamming is unpredictable and does not follow any pattern, but power supply in some zones is disrupted due to hidden reasons. Therefore, a macroscopic or holistic approach is adopted for congestion forecasting through demand schedule, gathering, minimum as well as maximum drawls. Here, two significant factors namely the transmission utilization charges as well as transmission congestion charges for predicting the congestion of a transmission line are evaluated. Finally, the experimental analysis to determine transmission utilization charges, transmission congestion charges, cost function and generation supply as well as demand balance with congestion optimization. [ABSTRACT FROM AUTHOR]

We show how a three-strike option portfolio can be used to trade the difference between the instantaneous variance rate and the implied variance rate, the difference between the instantaneous covariation rate and the implied slope, or the difference between the instantaneous variance rate of volatility and the implied convexity. We label each one of these strategies as vol, skew, and smile trades. Our results yield precise financial interpretations of particular measures of the level, slope, and curvature of a BMS implied variance curve. We provide empirical evidence that the average returns of the vol and smile (skew) trades are negative (positive) and that the returns of the skew and smile trades cannot be explained by the CAPM. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Business Science (JOBS) / İşletme Bilimi Dergisi (JOBS) is the property of Journal of Business Science / Isletme Bilimi Dergisi (JOBS) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rent regulation has a significant impact on tenant–landlord relations and the overall functioning of the private rented sector. Different forms of rent regulation—in relation to rent levels, rent increases, security of tenure, etc.—also affect the quality, the social composition and, ultimately, the size of the private rented sector. Together they affect the character of much urban regeneration and renewal. The introduction in Portugal of more flexible rent regimes that aimed to gradually replace open-ended tenancies with freely negotiated contracts led researchers to classify the country as a free market system. In this paper, by using a mixed methods approach that combined desk-based research with census data and in-depth interviews, we test the) classification of Portugal's rented sector as a free market against empirical evidence and examine the impacts of the main rent regulation regimes on social sustainability-oriented urban regeneration. Our results show that open-ended contracts, which were signed before the 1990s, still account for a significant part of the private rented sector, thus the classification of Portugal's rent regulation regime as a free-market system does not capture the country's most significant features. This is particularly evident in inner-city Lisbon, where various extreme rent regimes (in terms of contract duration, tenant security and prices) coexist, giving rise to tensions between housing quality and demographic shifts that threaten the overall social sustainability of the city. [ABSTRACT FROM AUTHOR]

The present paper proposes new international portfolio optimization problems when the foreign exchange rates and the future security prices are modelled by uncertain variables, given by experts' estimations and predicted by experts instead of historical data. The use of uncertain measure is justified. We provide Mean-VaR models for international portfolio Some real-world constraints such as portfolio diversification and transaction costs were taken into consideration. In addition, equivalent deterministic forms have been proposed when security prices and exchange rates follow certain types of uncertainty distributions. Finally, numerical applications are given to establish the impact of reality factors considered on uncertain international portfolio investment. [ABSTRACT FROM AUTHOR]

We analyze how Dodd-Frank-mandated risk retention affects the information investors extract from issuers' retention choices in the CMBS market. We show that the required retention level is both binding and stringent. Although this implies issuers cannot signal using the level of retention, we provide a model showing that signaling can occur by varying the retention structure. The model is consistent with spreads being empirically lower in deals with a purely first-loss retention structure. A stated concern of rulemakers is asymmetric information. However, we show that, post-crisis, the level of asymmetric information in this market is quite low. [ABSTRACT FROM AUTHOR]

Using a novel database on venue short sales and market design characteristics, we ask: Where do short sellers exploit their information advantage? Consistent with the prediction of Zhu (2014), we find that exchange short sales comprise a larger proportion of trading and are more informative about future prices than dark-pool short sales, particularly when there is greater competition among short sellers to trade and in the presence of short-lived information. When examining market design characteristics, we find that dark pools offering volume-weighted average price crossing attract more short sales, whereas those offering block trading attract fewer short sales. [ABSTRACT FROM AUTHOR]

We develop a systemic, information-theoretic model of competitive capital-market functioning that can account for the observed statistical regularities in cross-sectional distributions of the logarithm of Tobin's q for US non-financial corporations since 1962. The model considers capital markets as a self-organizing system driven by competitive interactions among investors and corporate managers. The persistent pattern of organization we observe in those distributions is primarily defined by the efforts of corporate managers to appropriate arbitrage capital gains defined by heterogeneity across individual measures of the logarithm of Tobin's q. Competition ensures the structures of security prices shaped by those efforts reflect an aggregate tradeoff between the gross returns and costs they pose to corporate managers. The distributions are also influenced by the endogenous, competitive formation of the opportunity cost of capital corporations face, which is conditioned by what investors come to expect to be a typical or general expected rate of return on assets across all corporations. In addition to offering an economic account of what we observe, the resulting framework defines new conceptualizations and aggregate measures of the informational and allocative performance of capital markets. Those suggest the performance of US capital markets has deteriorated since the 1980s. [ABSTRACT FROM AUTHOR]

Most firms face some form of competition in product markets. The degree of competition a firm faces feeds back into its cash flows and affects the values of the securities it issues. Through its effects on stock prices, product market competition affects the prices of options on equity and leads to an inverse relationship between equity returns and volatility, generating a negative volatility skew in option prices. Using a large sample of U.S. equity options, we provide empirical support for this finding and demonstrate the importance of accounting for product market competition when explaining the cross-sectional variation in option skew. Received June 28, 2017; editorial decision October 23, 2018 by Editor Andrew Karolyi. Authors have furnished supplementary Internet Appendices, which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

We use the exponential Ornstein–Uhlenbeck model to predict the stock price dynamics over some finite time horizon of interest. The predictions are the key to the investors in a financial market because they provide vital reference information for decision making. We estimated all the parameters of the model (mean reversion speed, long-run mean, and the volatility) using the data from Stanbic Uganda Holdings Limited. We used the parameters to forecast the stock price and the associated mean absolute percentage error (MAPE). The predictions were compared against those by the ARMA-GARCH model. We also found the 95 % prediction intervals before and during the COVID-19 pandemic. Results indicate that the exponential Ornstein–Uhlenbeck stochastic model gives very accurate and reliable predictions with a MAPE of 0.4941 %. All the forecasted stock prices were within the prediction region established. This was not the case during the COVID-19 pandemic; the predicted stock prices are higher than the actual prices, indicating the severe impact COVID-19 inflicted on the stock market. [ABSTRACT FROM AUTHOR]

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This study aims to evaluate the effects of service quality, security, and price perception on consumer trust and purchasing decisions on Tokopedia. Additionally, this study also aims to examine the mediating role of consumer trust in the relationship between the variables of service quality, security, price perception, and purchase decisions. The research was conducted in Yogyakarta using the snowball sampling method to select a sample of Tokopedia customers. A total of 250 respondents participated in the study. Data collection was carried out through questionnaires using a Likert scale of 1-5. Data analysis was performed using the Partial Least Squares (PLS) method with Smart PLS 3.3.3 software. The results showed that the security variable and price perception significantly influenced consumer trust, while service quality had a significant negative relationship with purchasing decisions. However, the variables of service quality, security, and price perception did not have a significant influence on purchasing decisions. Consumer trust was found to mediate the effects of security and price perception on purchasing decisions, but it was unable to mediate the effect of service quality. Overall, Tokopedia needs to improve communication regarding service quality, ensure a high level of security, consider other factors that influence purchasing decisions, and continue to build consumer trust. [ABSTRACT FROM AUTHOR]

In the Markowitz mean–variance portfolio optimization problem, the estimation of the inverse covariance matrix is not trivial and can even be intractable, especially when the dimension is very high. In this paper, we propose a linear-programming portfolio optimizer (LPO) to solve the Markowitz optimization problem in both low-dimensional and high-dimensional settings. Instead of directly estimating the inverse covariance matrix Σ − 1 , the LPO method estimates the portfolio weights Σ − 1 μ through solving an l 1 -constrained optimization problem. Moreover, we further prove that the LPO estimator asymptotically yields the maximum expected return while preserving the risk constraint. To offer a practical insight into the LPO approach, we provide a comprehensive implementation procedure of estimating portfolio weights via the Dantzig selector with sequential optimization (DASSO) algorithm and selecting the sparsity parameter through cross-validation. Simulations on both synthetic data and empirical data from Fama–French and the Center for Research in Security Prices (CRSP) databases validate the performance of the proposed method in comparison with other existing proposals. [ABSTRACT FROM AUTHOR]

This study compares credit spreads and pricing determinants of securitization vis‐à‐vis covered bonds. Our analysis reveals that although ratings are the most important pricing determinant for asset‐backed securities (ABS) and mortgage‐backed securities (MBS) investors place relatively more importance on contractual, macroeconomic and banks' characteristics rather than ratings in pricing covered bonds. We find evidence of a mispricing effect in structured finance markets: ABS and MBS have higher credit spreads than similarly rated public‐covered bonds and mortgage‐covered bonds and security prices reflect information beyond credit ratings. We find no evidence of borrowing costs affecting banks' choice between securitization and covered bonds. [ABSTRACT FROM AUTHOR]

We discuss the dynamic mean-variance (MV) problem for pairs trading with the assumptions that one of the security prices satisfies a stochastic volatility model (SVM) and the corresponding price spread follows an Ornstein–Uhlenbeck (OU) process. We provide a semi-closed-form of the optimal strategy based on the solution of a PDE, which is difficult to solve explicitly. Thus, we assume that one of the security prices satisfies the Scott model, a fast-mean-reverting volatility model, and give a closed-form approximation for the optimal strategy. Empirical studies, by using historical data from Chinese security markets, show that the Scott model produces a more stable strategy by better capturing mean-reverting volatility. [ABSTRACT FROM AUTHOR]

This study investigates the reactions of stock prices to earnings announcements of the companies listed on the Colombo Stock Exchange (CSE), Sri Lanka. To verify the informational value of announcements, and possess market efficiency, the standard event study method was utilized. 158 companies were used to collect the data considering five years period, from 2015 to 2019. There were 334 earnings announcements for the sample period. Panel Ordinary Least Square analysis and the market model were used to estimate each company's expected and abnormal returns. The results indicated that investors react immediately and positively to good news announcements while negative reactions to bad news with lag reactions. Surprisingly, it could be noted that there was a sudden reaction in the market day before the good news announcement. Overall, there was a substantial price change of all the securities on the announcement day ignoring whether it is good news or bad news. This proves that earnings announcements possess informational value. Further, the share prices change on the event day shows that the market reacts immediately to the earnings information confirming that CSE follows a semi-strong form efficient hypothesis for earnings announcements. This is important for stakeholders to make investment decisions and financial decisions. Further, regulatory agencies need to build and implement rules and regulations in the market. [ABSTRACT FROM AUTHOR]