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Book part
Publication date: 29 January 2024

Ariq Idris Annaufal, April Lia Dina Mariyana and Ratna Roostika

The financial sector’s growing interest in leveraging artificial intelligence (AI) for forecasting has been noted in recent years. In this chapter, we delve into the application…

Abstract

The financial sector’s growing interest in leveraging artificial intelligence (AI) for forecasting has been noted in recent years. In this chapter, we delve into the application of AI in financial forecasting within Indonesia’s stock market. Our primary focus is to assess how AI’s prediction potential can impact investors and financial regulators in this context. Our review spans existing literature on AI and financial forecasting, recent developments in the Indonesian stock market, and ethical and regulatory concerns that surround AI in finance. Our analysis indicates that AI can enhance forecast accuracy in Indonesia’s stock exchange; however, we must also consider limitations and challenges.

Details

Digital Technology and Changing Roles in Managerial and Financial Accounting: Theoretical Knowledge and Practical Application
Type: Book
ISBN: 978-1-80455-973-4

Keywords

Book part
Publication date: 5 April 2024

Ziwen Gao, Steven F. Lehrer, Tian Xie and Xinyu Zhang

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and…

Abstract

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

Article
Publication date: 4 January 2024

Trung Hai Le

This paper investigates how various strategies for combining forecasts, both simple and optimised approaches, are compared with popular individual risk models in estimating…

Abstract

Purpose

This paper investigates how various strategies for combining forecasts, both simple and optimised approaches, are compared with popular individual risk models in estimating value-at-risk (VaR) and expected shortfall (ES) in emerging market at alternative risk levels.

Design/methodology/approach

Using the case study of the Vietnamese stock market, the author produced one-day-ahead VaR and ES forecast from seven individual risk models and ten alternative forecast combinations. Next, the author employed a battery of backtesting procedures and alternative loss functions to evaluate the global predictive accuracy of the different methods. Finally, the author investigated the relative performance over time of VaR and ES forecasts using fluctuation test.

Findings

The empirical results indicate that, although combined forecasts have reasonable predictive abilities, they are often outperformed by one individual risk model. Furthermore, the author showed that the complex combining methods with optimised weighting functions do not perform better than simple combining methods. The fluctuation test suggests that the poor performance of combined forecasts is mainly due to their inability to cope with periods of instability.

Research limitations/implications

This study reveals the limitation of combining strategies in the one-day-ahead VaR and ES forecasts in emerging markets. A possible direction for further research is to investigate whether this finding holds for multi-day ahead forecasts. Moreover, the inferior performance of combined forecasts during periods of instability motivates further research on the combining strategies that take into account for potential structure breaks in the performance of individual risk models. A potential approach is to improve the individual risk models with macroeconomic variables using a mixed-data sampling approach.

Originality/value

First, the authors contribute to the literature on the forecasting combinations for VaR and ES measures. Second, the author explored a wide range of alternative risk models to forecast both VaR and ES with recent data including periods of the COVID-19 pandemic. Although forecast combination strategies have been providing several good results in several fields, the literature of forecast combination in the VaR and ES context is surprisingly limited, especially for emerging market returns. To the best of the author’s knowledge, this is the first study investigating predictive power of combining methods for VaR and ES in an emerging market.

Details

The Journal of Risk Finance, vol. 25 no. 1
Type: Research Article
ISSN: 1526-5943

Keywords

Open Access
Article
Publication date: 21 March 2024

Warisa Thangjai and Sa-Aat Niwitpong

Confidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty…

Abstract

Purpose

Confidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.

Design/methodology/approach

The construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.

Findings

The GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.

Originality/value

This research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.

Details

Asian Journal of Economics and Banking, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2615-9821

Keywords

Article
Publication date: 13 July 2023

Qiang Lu, Yihang Zhou, Zhenzeng Luan and Hua Song

This study empirically investigates how ambidextrous innovations and their balancing affect the supply chain financing performance (SCFP) of small and medium-sized enterprises…

Abstract

Purpose

This study empirically investigates how ambidextrous innovations and their balancing affect the supply chain financing performance (SCFP) of small and medium-sized enterprises (SMEs), based on signaling theory. Moreover, this study explores the moderating effect of the breadth and depth of digital technology deployment on the relationship between ambidextrous innovations and the SCFP of SMEs.

Design/methodology/approach

A mixed-methods design is used, including a qualitative study and a quantitative study. Qualitative data have been collected from six multi-cases in different industries. Questionnaire data have been collected from 259 SMEs in China, and a multiple regression model is used to verify the research hypotheses.

Findings

The findings indicate that, in supply chain financing, both exploitative innovation and exploratory innovation are helpful in improving the SCFP of SMEs. For resource-constrained SMEs, a relative balance between exploitative innovation and exploratory innovation can help improve SCFP. The breadth of digital technology deployment can strengthen the relationship between exploitative innovation and SCFP, while the depth of digital technology deployment can weaken the relationship between exploratory innovation and SCFP. In addition, increasing the depth of digital technology deployment strengthens the positive correlation between the relative balance of ambidextrous innovations and SCFP.

Practical implications

To effectively obtain supply chain financing, SMEs can either concentrate their limited resources on a single type of innovation or use relative balance strategies to simultaneously pursue two innovations. In addition, in the process of obtaining supply chain financing by ambidextrous innovations, SMEs should appropriately deploy digital technologies.

Originality/value

This study first deconstructs the impact mechanism of ambidextrous innovation capabilities on SCFP based on signaling theory, and then discusses the balancing effect of ambidextrous innovations on SCFP in the cases of resource-constrained SMEs. This study also goes further and finds the negative moderating effect of digital technology deployment in the process of supply chain financing.

Details

International Journal of Operations & Production Management, vol. 44 no. 2
Type: Research Article
ISSN: 0144-3577

Keywords

Article
Publication date: 28 June 2022

Maqsood Ahmad

This article aims to systematically review the literature published in recognized journals focused on cognitive heuristic-driven biases and their effect on investment management…

2054

Abstract

Purpose

This article aims to systematically review the literature published in recognized journals focused on cognitive heuristic-driven biases and their effect on investment management activities and market efficiency. It also includes some of the research work on the origins and foundations of behavioral finance, and how this has grown substantially to become an established and particular subject of study in its own right. The study also aims to provide future direction to the researchers working in this field.

Design/methodology/approach

For doing research synthesis, a systematic literature review (SLR) approach was applied considering research studies published within the time period, i.e. 1970–2021. This study attempted to accomplish a critical review of 176 studies out of 256 studies identified, which were published in reputable journals to synthesize the existing literature in the behavioral finance domain-related explicitly to cognitive heuristic-driven biases and their effect on investment management activities and market efficiency as well as on the origins and foundations of behavioral finance.

Findings

This review reveals that investors often use cognitive heuristics to reduce the risk of losses in uncertain situations, but that leads to errors in judgment; as a result, investors make irrational decisions, which may cause the market to overreact or underreact – in both situations, the market becomes inefficient. Overall, the literature demonstrates that there is currently no consensus on the usefulness of cognitive heuristics in the context of investment management activities and market efficiency. Therefore, a lack of consensus about this topic suggests that further studies may bring relevant contributions to the literature. Based on the gaps analysis, three major categories of gaps, namely theoretical and methodological gaps, and contextual gaps, are found, where research is needed.

Practical implications

The skillful understanding and knowledge of the cognitive heuristic-driven biases will help the investors, financial institutions and policymakers to overcome the adverse effect of these behavioral biases in the stock market. This article provides a detailed explanation of cognitive heuristic-driven biases and their influence on investment management activities and market efficiency, which could be very useful for finance practitioners, such as an investor who plays at the stock exchange, a portfolio manager, a financial strategist/advisor in an investment firm, a financial planner, an investment banker, a trader/broker at the stock exchange or a financial analyst. But most importantly, the term also includes all those persons who manage corporate entities and are responsible for making their financial management strategies.

Originality/value

Currently, no recent study exists, which reviews and evaluates the empirical research on cognitive heuristic-driven biases displayed by investors. The current study is original in discussing the role of cognitive heuristic-driven biases in investment management activities and market efficiency as well as the history and foundations of behavioral finance by means of research synthesis. This paper is useful to researchers, academicians, policymakers and those working in the area of behavioral finance in understanding the role that cognitive heuristic plays in investment management activities and market efficiency.

Details

International Journal of Emerging Markets, vol. 19 no. 2
Type: Research Article
ISSN: 1746-8809

Keywords

Open Access
Article
Publication date: 5 June 2023

Tadhg O’Mahony, Jyrki Luukkanen, Jarmo Vehmas and Jari Roy Lee Kaivo-oja

The literature on economic forecasting, is showing an increase in criticism, of the inaccuracy of forecasts, with major implications for economic, and fiscal policymaking…

Abstract

Purpose

The literature on economic forecasting, is showing an increase in criticism, of the inaccuracy of forecasts, with major implications for economic, and fiscal policymaking. Forecasts are subject to the systemic uncertainty of human systems, considerable event-driven uncertainty, and show biases towards optimistic growth paths. The purpose of this study is to consider approaches to improve economic foresight.

Design/methodology/approach

This study describes the practice of economic foresight as evolving in two separate, non-overlapping branches, short-term economic forecasting, and long-term scenario analysis of development, the latter found in studies of climate change and sustainability. The unique case of Ireland is considered, a country that has experienced both steep growth and deep troughs, with uncertainty that has confounded forecasting. The challenges facing forecasts are discussed, with brief review of the drivers of growth, and of long-term economic scenarios in the global literature.

Findings

Economic forecasting seeks to manage uncertainty by improving the accuracy of quantitative point forecasts, and related models. Yet, systematic forecast failures remain, and the economy defies prediction, even in the near-term. In contrast, long-term scenario analysis eschews forecasts in favour of a set of plausible or possible alternative scenarios. Using alternative scenarios is a response to the irreducible uncertainty of complex systems, with sophisticated approaches employed to integrate qualitative and quantitative insights.

Research limitations/implications

To support economic and fiscal policymaking, it is necessary support advancement in approaches to economic foresight, to improve handling of uncertainty and related risk.

Practical implications

While European Union Regulation (EC) 1466/97 mandates pursuit of improved accuracy, in short-term economic forecasts, there is now a case for implementing advanced foresight approaches, for improved analysis, and more robust decision-making.

Social implications

Building economic resilience and adaptability, as part of a sustainable future, requires both long-term strategic planning, and short-term policy. A 21st century policymaking process can be better supported by analysis of alternative scenarios.

Originality/value

To the best of the authors’ knowledge, the article is original in considering the application of scenario foresight approaches, in economic forecasting. The study has value in improving the baseline forecast methods, that are fundamental to contemporary economics, and in bringing the field of economics into the heart of foresight.

Details

foresight, vol. 26 no. 1
Type: Research Article
ISSN: 1463-6689

Keywords

Article
Publication date: 1 September 2023

Jueshuai Wang

This paper aims to enhance the Global Projection Model (GPM) developed by the International Monetary Fund by constructing a GPM4 model that includes the United States of America…

Abstract

Purpose

This paper aims to enhance the Global Projection Model (GPM) developed by the International Monetary Fund by constructing a GPM4 model that includes the United States of America, the Eurozone, Japan and China.

Design/methodology/approach

This article introduces the United States of America, the Eurozone, Japan and China into a comprehensive global forecasting model, analyzing the impact of liquidity management in G3 economies on nine key macroeconomic variables in China.

Findings

The findings reveal that the liquidity management strategies employed by major economies do exert a certain influence on China's major macroeconomic variables. Different types of liquidity shocks elicit varying effects. Monetary shocks exhibit the strongest instantaneous impact, while credit conditions and policy rate shocks contribute more significantly to China's long-term macroeconomic fluctuations. However, no single shock stands out as the dominant factor.

Originality/value

This paper attempts to expand the GPM model developed by the International Monetary Fund and build a GPM4 model including China, the United States of America, the Eurozone and Japan. For the first time, the GPM model was used to analyze the spillover effects of liquidity management in major economies on China's macroeconomy and revealed the impact of non-price factors such as credit conditions on China's macroeconomic variables.

Details

Kybernetes, vol. 53 no. 2
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 27 June 2023

Fatemeh Binesh, Amanda Mapel Belarmino, Jean-Pierre van der Rest, Ashok K. Singh and Carola Raab

This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network.

Abstract

Purpose

This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network.

Design/methodology/approach

Using three data sets from upper-midscale hotels in three locations (i.e. urban, interstate and suburb), from January 1, 2018, to August 31, 2020, three long-term, short-term memory (LSTM) models were evaluated against five traditional forecasting models.

Findings

The models proposed in this study outperform traditional methods, such that the simplest LSTM model is more accurate than most of the benchmark models in two of the three tested hotels. In particular, the results show that traditional methods are inefficient in hotels with rapid fluctuations of demand and ADR, as observed during the pandemic. In contrast, LSTM models perform more accurately for these hotels.

Research limitations/implications

This study is limited by its use of American data and data from midscale hotels as well as only predicting ADR.

Practical implications

This study produced a reliable, accurate forecasting model considering risk and competitor behavior.

Theoretical implications

This paper extends the application of game theory principles to ADR forecasting and combines it with the concept of risk for forecasting during uncertain times.

Originality/value

This study is the first study, to the best of the authors’ knowledge, to use actual hotel data from the COVID-19 pandemic to determine an appropriate neural network forecasting method for times of uncertainty. The application of Shapley value and operational risk obtained a game-theoretic property-level model, which fits best.

Details

International Journal of Contemporary Hospitality Management, vol. 36 no. 4
Type: Research Article
ISSN: 0959-6119

Keywords

Article
Publication date: 2 November 2022

Clio Ciaschini and Maria Cristina Recchioni

This work aims at designing an indicator for detecting and forecasting price volatility and speculative bubbles in three markets dealing with agricultural and soft commodities…

Abstract

Purpose

This work aims at designing an indicator for detecting and forecasting price volatility and speculative bubbles in three markets dealing with agricultural and soft commodities, i.e. Intercontinental Exchange Futures market Europe, (IFEU), Intercontinental Exchange Futures market United States (IFUS) and Chicago Board of Trade (CBOT). This indicator, designed as a demand/supply odds ratio, intends to overcome the subjectivity limits embedded in sentiment indexes as the Bull and Bears ratio by the Bank of America Merrill Lynch.

Design/methodology/approach

Data evidence allows for the parameter estimation of a Jacobi diffusion process that models the demand share and leads the forecast of speculative bubbles and realised volatility. Validation of outcomes is obtained through the dynamic regression with autoregressive integrated moving average (ARIMA) error. Results are discussed in comparison with those from the traditional generalized autoregressive conditional heteroskedasticity (GARCH) models. The database is retrieved from Thomson Reuters DataStream (nearby futures daily frequency).

Findings

The empirical analysis shows that the indicator succeeds in capturing the trend of the observed volatility in the future at medium and long-time horizons. A comparison of simulations results with those obtained with the traditional GARCH models, usually adopted in forecasting the volatility trend, confirms that the indicator is able to replicate the trend also providing turning points, i.e. additional information completely neglected by the GARCH analysis.

Originality/value

The authors' commodity demand as discrete-time process is capable of replicating the observed trend in a continuous-time framework, as well as turning points. This process is suited for estimating behavioural parameters of the agents, i.e. long-term mean, speed of mean reversion and herding behaviour. These parameters are used in the forecast of speculative bubbles and realised volatility.

Details

Review of Behavioral Finance, vol. 16 no. 1
Type: Research Article
ISSN: 1940-5979

Keywords

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