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Book part
Publication date: 21 November 2014

Alex Maynard and Dongmeng Ren

We compare the finite sample power of short- and long-horizon tests in nonlinear predictive regression models of regime switching between bull and bear markets, allowing…

Abstract

We compare the finite sample power of short- and long-horizon tests in nonlinear predictive regression models of regime switching between bull and bear markets, allowing for time varying transition probabilities. As a point of reference, we also provide a similar comparison in a linear predictive regression model without regime switching. Overall, our results do not support the contention of higher power in longer horizon tests in either the linear or nonlinear regime switching models. Nonetheless, it is possible that other plausible nonlinear models provide stronger justification for long-horizon tests.

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Essays in Honor of Peter C. B. Phillips
Type: Book
ISBN: 978-1-78441-183-1

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Article
Publication date: 24 September 2021

Lu Yang

To capture the last hour momentum over the intraday session, the authors develop a trading strategy for the exchange-traded fund (ETF) that is effective because of the T+0…

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Abstract

Purpose

To capture the last hour momentum over the intraday session, the authors develop a trading strategy for the exchange-traded fund (ETF) that is effective because of the T+0 trading rule. This strategy generates annualized excess return of 9.673%.

Design/methodology/approach

In this study, the authors identify a last hour momentum pattern in which the sixth (seventh) half-hour return predicts the next half-hour return by employing high frequency 2012–2017 data from the China Securities Index (CSI) 300 and its ETF.

Findings

Overall, both the predictability and the trading strategy are statistically and economically significant. In addition, the strategy performs more strongly on high volatility days, high trading volume days, high order-imbalance days and days without economic news releases than on other days.

Originality/value

Noise trading, late-information trading, infrequent rebalancing and disposition effects from retail investors may account for this phenomenon.

Details

China Finance Review International, vol. 12 no. 1
Type: Research Article
ISSN: 2044-1398

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Abstract

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Financial Modeling for Decision Making: Using MS-Excel in Accounting and Finance
Type: Book
ISBN: 978-1-78973-414-0

Book part
Publication date: 30 November 2011

Massimo Guidolin

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of…

Abstract

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

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Missing Data Methods: Time-Series Methods and Applications
Type: Book
ISBN: 978-1-78052-526-6

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Article
Publication date: 11 April 2022

Emmanuel Hayble-Gomes

The purpose of this study is to explore and use artificial intelligence (AI) techniques for identifying the relevant attributes necessary to file a suspicious activity…

Abstract

Purpose

The purpose of this study is to explore and use artificial intelligence (AI) techniques for identifying the relevant attributes necessary to file a suspicious activity report (SAR) using historical customer transactions. This method is known as predictive modeling, a statistical approach which uses machine learning algorithm to predict outcomes by using historical data. The models are applied to a modified data set designed to mimic transactions of retail banking within the USA.

Design/methodology/approach

Machine learning classifiers, as a subset of AI, are trained using transactions that meet or exceed the minimum threshold amount that could generate an alert and report a SAR to the government authorities. The predictive models are developed to use customer transactional data to predict the probability that a transaction is reportable.

Findings

The performance of the machine learning classifiers is determined in terms of accuracy, misclassification, true positive rate, false positive rate and false negative rate. The decision tree model provided insight in terms of the attributes relevant for SAR filing based on the rule-based criteria of the algorithm.

Originality/value

This research is part of emerging studies in the field of compliance where AI/machine learning technology is used for transaction monitoring to identify relevant attributes for suspicious activity reporting. The research methodology may be replicated by other researchers, Bank Secrecy Act/anti-money laundering (BSA/AML) officers and model validation analysts for BSA/AML compliance models.

Details

Journal of Money Laundering Control, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1368-5201

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Book part
Publication date: 31 December 2010

Ricardo M. Sousa

Purpose – The purpose of this chapter is to assess the role of collateralizable wealth and systemic risk in explaining future asset returns.Methodology/approach – To test…

Abstract

Purpose – The purpose of this chapter is to assess the role of collateralizable wealth and systemic risk in explaining future asset returns.

Methodology/approach – To test this hypothesis, the chapter uses the residuals of the trend relationship among housing wealth and labor income to predict both stock returns and government bond yields. Specifically, it shows that nonlinear deviations of housing wealth from its cointegrating relationship with labor income, hwy, forecast expected future returns.

Findings – Using data for a set of industrialized countries, the chapter finds that when the housing wealth-to-income ratio falls, investors demand a higher risk premium for stocks. As for government bond returns: (i) when they are seen as a component of asset wealth, investors react in the same manner and (ii) if, however, investors perceive the increase in government bond returns as signaling a future rise in taxes or a deterioration of public finances, then they interpret the fall in the housing wealth-to-income ratio as a fall in future bond premia. Finally, this work shows that the occurrence of crisis episodes amplifies the transmission of housing market shocks to financial markets.

Originality/value of chapter – These findings are novel. They also open new and challenging avenues for understanding the dynamics of the relationship between the housing sector, stock market and government bond developments, and the banking system.

Details

Nonlinear Modeling of Economic and Financial Time-Series
Type: Book
ISBN: 978-0-85724-489-5

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Book part
Publication date: 13 March 2013

John F. Kros, Evelyn Brown, Rhonda Joyner, Paul Heath and Laura Helms

The application of forecasting to health care is not new. A frequent issue in many Inpatient Rehabilitation Facilities (IRFs) is the fluctuating and unpredictable census…

Abstract

The application of forecasting to health care is not new. A frequent issue in many Inpatient Rehabilitation Facilities (IRFs) is the fluctuating and unpredictable census. With scarce resources, particularly physical therapists and occupational therapists, this unpredictability makes appropriate scheduling of these resources challenging. This research addresses the issue of patient admissions in an inpatient rehabilitation facility attached to an 861 bed level-one trauma hospital. The goal is to develop a predictive model for the IRF's Census to assist in resource planning (e.g., labor, beds, and materials).

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-78190-331-5

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Article
Publication date: 25 November 2013

Michael Levi and Matthew Leighton Williams

– This paper aims to map out multi-agency partnerships in the UK information assurance (UKIA) network in the UK.

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Abstract

Purpose

This paper aims to map out multi-agency partnerships in the UK information assurance (UKIA) network in the UK.

Design/methodology/approach

The paper surveyed members of the UKIA community and achieved a 52 percent response rate (n=104). The paper used a multi-dimensional scaling (MDS) technique to map the multi-agency cooperation space and factor analysis and ordinary least squares regression to identify predictive factors of cooperation frequency. Qualitative data were also solicited via the survey and interviews with security managers.

Findings

Via the quantitative measures, the paper locates gaps in the multi-agency cooperation network and identifies predictors of cooperation. The data indicate an over-crowded cybersecurity space, problems in apprehending perpetrators, and poor business case justifications for SMEs as potential inhibitors to cooperation, while concern over certain cybercrimes and perceptions of organisational effectiveness were identified as motivators.

Practical implications

The data suggest that the neo-liberal rationality that has been evoked in other areas of crime control is also evident in the control of cybercrimes. The paper concludes divisions exist between the High Policing rhetoric of the UK's Cyber Security Strategy and the (relatively) Low Policing cooperation outcomes in “on the ground” cyber-policing. If the cooperation outcomes advocated by the UK Cyber Security Strategy are to be realised, UKIA organisations must begin to acknowledge and remedy gaps and barriers in cooperation.

Originality/value

This paper provides the first mixed-methods evidence on the multi-agency cooperation patterns amongst the UKIA community in the UK and highlights significant gaps in the network.

Details

Information Management & Computer Security, vol. 21 no. 5
Type: Research Article
ISSN: 0968-5227

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Article
Publication date: 7 January 2020

Ahmed Bouteska

The purpose of this paper is to study a novel and direct measurement of investor sentiment index in the Tunisian stock market that overcomes the weaknesses of a well-known…

Abstract

Purpose

The purpose of this paper is to study a novel and direct measurement of investor sentiment index in the Tunisian stock market that overcomes the weaknesses of a well-known investor sentiment index by Baker and Wurgler (2006, 2007).

Design/methodology/approach

Based on the data of 43 firms of the Tunisian stock market index (Tunindex) over the period 2004–2016, the author constructs a monthly investor sentiment that reflects both the economic fundamentals and the investor sentiment components. Seven indirect indicators collected from investor sentiment literature and Tunisian stock exchange were analyzed. Specifically, after accounting to remove the sentiment component for macroeconomic factors, the author estimates each sentiment proxy with a number of controlling variables. The residual from the estimation is used to define the author’s measure of excessive investor sentiment. To determine the best timing of sentiment indicators, the author employs a factor sentiment series as the first principal component of these total seven sentiment proxies and their lags of a month. Furthermore, by capturing the highest saturations with the first factor analysis, the author regressed each selected indicator’s lead or one-month lag in a second linear principal component analysis to reach the author’s Tunisian market’s total sentiment index.

Findings

The results show that all employed indicators may reflect the investor sentiment on the Tunisian stock market. The findings also indicate significant evidence that the author’s sentiment index takes into consideration the political and economic events such as the Jasmine Revolution experienced by Tunisia during the period from January 2, 2004 to December 30, 2016. Moreover, investor sentiment index flow appears to be one leading mechanism for the performance of Tunindex.

Originality/value

Results found have clearly shown that the author’s seven indirect indicators can reflect investor sentiment in the Tunisian context. The various sentiment proxies are bullish indicators of investor sentiment. Brown and Cliff (2004) argue that the higher bull/bear ratio, the more investor sentiment is bullish. An important value of price–earnings ratio implies that the level of investor confidence as for change in market is also important. Liquidity measured by trading volume, market turnover ratio and liquidity ratio reflects individual investor sentiment. Otherwise, it seems that investors only invest when they are optimistic and reduce market liquidity once they became pessimistic. The monthly response rate to initial public offerings (IPOs) represents a bullish sentiment indicator. Indeed, the more optimistic investors are, the higher the response rate to IPOs. Investor satisfaction also reflects investor sentiment. In other words, a high level of satisfaction translates an important level of optimism. In addition, the author also recognizes that the authors’ Tunisian sentiment index follow general trend of stock market prices and appears to be an important determinant of Tunindex returns during the period of study, from January, 2004 to December, 2016. The author suggests investor sentiment can help predict Tunindex returns, distinguishing between turbulent and tranquil periods in the financial market. The graphical illustration of monthly investor sentiment index shows that it captures extreme events such as the Tunisian revolution of January, 2011, also known as the Jasmine revolution which marked the start of the Arab Spring and the consequences of economic and political turmoil in Tunisia that have disrupted economic activity in the next few years. Like all research work, the current research paper has certain limitations. The choice of control variables allowing the author to separate sentiment component of that fundamental might be criticized. Moreover, there is no unanimous number of control variables but they are chosen according to data availability. The author also believes that one of the study’s weaknesses is that the author has not examined the impact of investor sentiment on the Tunisian stock market. For future interesting avenues of research, the author proposes, first, to study the effect of investor sentiment on financial asset returns and check, second, if sentiment factor constitutes an additional source of business risk valued by the marketplace.

Article
Publication date: 14 March 2016

Ming-Chieh Wang and Jin-Kui Ye

The purpose of this paper is to examine whether the conditionally expected return on size-based portfolios in an emerging market (EM) is determined by the country’s world…

Abstract

Purpose

The purpose of this paper is to examine whether the conditionally expected return on size-based portfolios in an emerging market (EM) is determined by the country’s world risk exposure. The authors analyze the degree of financial integration of 23 emerging equity markets grouped into five size portfolios using the conditional international asset pricing model with both world and domestic market risks. The authors also compare the model’s fitness on the predictability of portfolio returns by using world and EM indices.

Design/methodology/approach

This study investigates whether large-cap stocks are priced globally and whether mid- and small-cap stocks are strongly influenced by domestic risk factors. The authors first examine the predictability of large-, mid-, and small-cap stock portfolio returns by using global and local variables, and next compare the model fitness by using world and EM indices on the prediction of size-based stock returns. Finally, the authors test whether the world price of covariance risk is the same for different portfolios.

Findings

The authors find that the conditional expected returns of large-cap stocks should be priced by global variables. Mid- and small-cap stocks are influenced by domestic variables rather than global variables, and their returns are priced by local residual risks. The test of the conditional asset pricing model shows that the largest stocks have the smallest mean absolute pricing errors (MAE), and their pricing errors are lower in large markets than in small markets. Third, the EM index offers more predictability for the excess returns of mid- and small-cap stocks than the world market index, but the explanatory power of this index does not increase for large-cap stocks.

Originality/value

EMs in the past were known as segment markets, with local risk factors more important than global risk factors, suggesting significant benefits from adding EMs to global portfolios. It would be interesting to examine whether financial integration differs for various firm sizes in the markets.

Details

Managerial Finance, vol. 42 no. 3
Type: Research Article
ISSN: 0307-4358

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