rss_2.0Economics and Business Review FeedSciendo RSS Feed for Economics and Business Review and Business Review Feed to the thematic issue on digitalisation, big data, and artificial intelligence realized volatility through financial turbulence and neural networks<abstract> <title style='display:none'>Abstract</title> <p>This paper introduces and examines a novel realized volatility forecasting model that makes use of Long Short-Term Memory (LSTM) neural networks and the risk metric financial turbulence (FT). The proposed model is compared to five alternative models, of which two incorporate LSTM neural networks and the remaining three include GARCH(1,1), EGARCH(1,1), and HAR models. The results of this paper demonstrate that the proposed model yields statistically significantly more accurate and robust forecasts than all other studied models when applied to stocks with middle-to-high volatility. Yet, considering low-volatility stocks, it can only be confidently affirmed that the proposed model yields statistically significantly more robust forecasts relative to all other models considered.</p> </abstract>ARTICLEtrue intelligence—friend or foe in fake news campaigns<abstract> <title style='display:none'>Abstract</title> <p>In this paper the impact of large language models (LLM) on the fake news phenomenon is analysed. On the one hand decent text‐generation capabilities can be misused for mass fake news production. On the other, LLMs trained on huge volumes of text have already accumulated information on many facts thus one may assume they could be used for fact‐checking. Experiments were designed and conducted to verify how much LLM responses are aligned with actual fact‐checking verdicts. The research methodology consists of an experimental dataset preparation and a protocol for interacting with ChatGPT, currently the most sophisticated LLM. A research corpus was explicitly composed for the purpose of this work consisting of several thousand claims randomly selected from claim reviews published by fact‐ checkers. Findings include: it is difficult to align the respons‐ es of ChatGPT with explanations provided by fact‐checkers; prompts have significant impact on the bias of responses. ChatGPT at the current state can be used as a support in fact‐checking but cannot verify claims directly.</p> </abstract>ARTICLEtrue and data science: The tale of two accidentally parallel transitions<abstract> <title style='display:none'>Abstract</title> <p>Accidentally parallel at the beginning, the transition to value-based pricing and transition to pricing data science have blended harmoniously, changing the pricing landscape. Using the marketing capability approach, I show that the introduction of pricing data science is costly and requires higher management support. Despite its cost, algorithmic price optimisation allows one to react swiftly to changes in demand. The optimisation process is applied to inherently non-linear, multimodal, and right-skewed pricing data. Presenting the interactions between new computational techniques and value-data pricing, I concentrate on altered perceptions of price elasticity, value-driver estimations, and contract opportunity analysis.</p> </abstract>ARTICLEtrue rise of Generative AI and possible effects on the economy<abstract> <title style='display:none'>Abstract</title> <p>The aim of the paper is to analyse the likely implications of Generative AI (GAI) on various aspects of business and the economy. Amid the rapid growth and maturing of Generative AI technologies such as Large Language Models (like ChatGPT by OpenAI) a rapid growth of both immediate and potential applications can be seen. The implications for the economy and industries of this technological shift will be discussed. The foreseeable scenarios for the level and types of adoption that GAI might achieve—from useful analytical tool, invaluable assistant to the white-collar workers of the world to being trusted with a wide array of business and life-critical decision making. Both disruptive and premium service opportunities are foreseen. For instance, general purpose models may provide quality service—such as copywriting—to overserved customers leaving human writers as the premium option. In this context, overserved customers would be those who would be satisfied with a non-human, potentially less creative content. On the other hand highly specialized models—specifically trained in a given domain and with access to proprietary knowledge can possibly provide a premium service over that provided by human experts. It is expected that some jobs will be replaced by new AI applications. However, new workplaces will emerge. Not only the obvious expert-level data scientist roles but also low grade, “model supervisors”—people training the models, assessing the quality of responses given and handling escalations. Lastly new cybercrime risks emerging from the rise of GAI are discussed.</p> </abstract>ARTICLEtrue for higher education in the era of widespread access to Generative AI<abstract> <title style='display:none'>Abstract</title> <p>The aim of this paper is to discuss the role and impact of Generative Artificial Intelligence (AI) systems in higher education. The proliferation of AI models such as GPT-4, Open Assistant and DALL-E presents a paradigm shift in information acquisition and learning. This transformation poses substantial challenges for traditional teaching approaches and the role of educators. The paper explores the advantages and potential threats of using Generative AI in education and necessary changes in curricula. It further discusses the need to foster digital literacy and the ethical use of AI. The paper’s findings are based on a survey conducted among university students exploring their usage and perception of these AI systems. Finally, recommendations for the use of AI in higher education are offered, which emphasize the need to harness AI’s potential while mitigating its risks. This discourse aims at stimulating policy and strategy development to ensure relevant and effective education in the rapidly evolving digital landscape.</p> </abstract>ARTICLEtrue data in monetary policy analysis—a critical assessment<abstract> <title style='display:none'>Abstract</title> <p>Over the last years the use of big data became increasingly relevant also for macroeconomic topics and specifically the conduct and analysis of monetary policy. The aim of this paper is to provide a survey of these applications and the relevant methods. The rationale for doing so is twofold. First, there is no straightforward definition of “big data”. Since macroeconomics and monetary policy analysis has a long tradition in quite sophisticated and data-intensive empirical applications the nature of the innovation big data is indeed bringing to the field is reflected upon. Second, concerning statistical / empirical methods the analysis of big data necessitates the use of different tools relative to traditional empirical macroeconomics which are in some cases a complement to more traditional methods. Hence big data in monetary policy is not just the application of well-established methods to larger data sets.</p> </abstract>ARTICLEtrue to fly to safety without overpaying for the ticket<abstract> <title style='display:none'>Abstract</title> <p>For most active investors treasury bonds (govs) provide diversification and thus reduce the risk of a portfolio. These features of govs become particularly desirable in times of elevated risk which materialize in the form of the flight-to-safety (FTS) phenomenon. The FTS for govs provides a shelter during market turbulence and is exceptionally beneficial for portfolio drawdown risk reduction. However, what if the unsatisfactory expected return from treasuries discourages higher bonds allocations? This research proposes a solution to this problem with Deep Target Volatility Equity-Bond Allocation (DTVEBA) that dynamically allocate portfolios between equity and treasuries. The strategy is driven by a state-of-the-art recurrent neural network (RNN) that predicts next-day market volatility. An analysis conducted over a twelve year out-of-sample period found that with DTVEBA an investor may reduce treasury allocation by two (three) times to get the same Sharpe (Calmar) ratio and overper-forms the S&amp;P500 index by 43% (115%).</p> </abstract>ARTICLEtrue of research co-created by Generative AI: Experimental evidence<abstract> <title style='display:none'>Abstract</title> <p>The introduction of ChatGPT has fuelled a public debate on the appropriateness of using Generative AI (large language models; LLMs) in work, including a debate on how they might be used (and abused) by researchers. In the current work, we test whether delegating parts of the research process to LLMs leads people to distrust researchers and devalues their scientific work. Participants (<italic>N</italic> = 402) considered a researcher who delegates elements of the research process to a PhD student or LLM and rated three aspects of such delegation. Firstly, they rated whether it is morally appropriate to do so. Secondly, they judged whether—after deciding to delegate the research process—they would trust the scientist (that decided to delegate) to oversee future projects. Thirdly, they rated the expected accuracy and quality of the output from the delegated research process. Our results show that people judged delegating to an LLM as less morally acceptable than delegating to a human (<italic>d</italic> = –0.78). Delegation to an LLM also decreased trust to oversee future research projects (<italic>d</italic> = –0.80), and people thought the results would be less accurate and of lower quality (<italic>d</italic> = −0.85). We discuss how this devaluation might transfer into the underreporting of Generative AI use.</p> </abstract>ARTICLEtrue adaptive market hypothesis and the return predictability in the cryptocurrency markets<abstract> <title style='display:none'>Abstract</title> <p>This study employs robust martingale difference hypothesis tests to examine return predictability in a broad sample of the 40 most capitalized cryptocurrency markets in the context of the adaptive market hypothesis. The tests were applied to daily returns using the rolling window method in the research period from May 1, 2013 to September 30, 2022. The results of this study suggest that the returns of the majority of the examined cryptocurrencies were unpredictable most of the time. However, a great part of them also suffered some short periods of weak-form inefficiency. The results obtained validate the adaptive market hypothesis. Additionally, this study allowed the observation of some differences in return predictability between the examined cryptocurrencies. Also some historical trends in weak-form efficiency were identified. The results suggest that the predictability of cryptocurrency returns might have decreased in recent years also no significant relationship between market cap and predictability was observed.</p> </abstract>ARTICLEtrue or engage? Effective paths to net zero from the U.S. perspective<abstract> <title style='display:none'>Abstract</title> <p>The aim of this article is to critically review and evaluate two ESG-based investment strategies—divestment and engagement for alignment of investment portfolios with climate change mitigation goals of the United Nations. The article compares both approaches in terms of their effectiveness of decarbonization, using the case study method. First, the case on fossil fuels divestment by Harvard Management Company is analysed. The second case study discusses shareholder engagement endeavors by Engine No. 1 hedge fund and its investment in ExxonMobil. The findings indicate that divestment may have non-immediate impact on corporate behavior and carries political and legal retribution risks. Engagement, on the other hand, presents itself as a more plausible option as it takes less time to deploy and, therefore, can produce more immediate and impactful results. Nevertheless, both divestment and engagement can play mutually supportive roles in addressing climate change by the investment industry.</p> </abstract>ARTICLEtrue frontiers in customers’ relations with banks<abstract> <title style='display:none'>Abstract</title> <p>The widespread use of digital technologies in banking allows banks to obtain and analyse huge amounts of data from different communication channels. While this phenomenon is conducive to improving the quality of services it also increases the risk of privacy breaches. The aim of this study is to identify what factors determine consumer acceptance of banks’ use of public access personal data found on social media accounts. The results indicate the importance of the financial incentive and consumers’ assessment of banks’ information activities regarding the processing of personal data. Determinants relating to the technological sophistication of respondents were also found to be significant, with a particular focus on the ethical evaluation of decisions made by Artificial Intelligence algorithms. The results of the work may be used by banks in practice to adapt the area of personal data management to the requirements of e-privacy and Trustworthy Artificial Intelligence.</p> </abstract>ARTICLEtrue introduction and welfare: A common, invalid anti-tariff argument<abstract> <title style='display:none'>Abstract</title> <p>President Trump imposed tariffs in 2017 on several of China’s exports, notably steel. Many papers opposed these tariffs by using a common, invalid argument: rather than arguing these tariffs reduced U.S. welfare, they argue U.S. consumers and businesses pay the tariffs, a different, rhetorical issue. Their main evidence of harm is increases in imported goods’ after-tariff U.S. prices, especially relative to other goods’ U.S. prices. In a standard, small general equilibrium model (two countries, two goods, two factors), this price evidence is wholly ambiguous—it is even consistent with the view that Trump’s tariff was optimal, increasing U.S. welfare. Even sophisticated papers are similarly ambiguous. All fail because they neglect how government uses tariff revenue. Relying on fallacious arguments makes the free-trade position look weak and encourages protectionism.</p> </abstract>ARTICLEtrue the stability of a certain Keynes-Metzler-Goodwin monetary growth model<abstract> <title style='display:none'>Abstract</title> <p>The article has three aims. The first aim is to develop an improved version of the Keynes-Metzler-Goodwin (the KMG) monetary growth model originally presented and analysed in a series of publications by Carl Chiarella, Peter Flaschel and Willi Semler. The improvement of the model is obtained by modifying some of its equations in a way which ensures that they reflect real macroeconomic dependencies more properly. The equations that have been modified describe final demand expectations, determinants of production decisions, fixed capital accumulation, tax revenues, government budget deficit and money demand. The second aim is to transform the model into an intensive form described by seven non-linear differential equations and determine its unique steady state which shows proportions between variables on the balanced growth path. The third ultimate aim is to present a mathematical proof that the new improved version of the KMG model is locally asymptotically stable.</p> </abstract>ARTICLEtrue is talent? Implications of talent definitions for talent management practice<abstract> <title style='display:none'>Abstract</title> <p>Although talent is considered imperative for gaining a competitive advantage, talent management programs’ effectiveness is unknown. It is believed that consensus on a strong theoretical underpinning for identifying talent and its general definition is yet to be achieved among academia and practitioners. This lack of integration and agreement on a single definition among scholars lead to more confusion which inhibits the advancement of talent management scholarship. The notion also requires renewed attention in the post-pandemic era because everything may not go back to normal as pre-pandemic. This study addresses the gap and focuses on reviewing the existing scholarship on talent definitions and its conceptualization in one place. The study also aims to present the potential implications of talent definition on talent management practices. Among the various implications discussed, it is argued that a single approach to talent definition makes the company vulnerable as it is not using the full potential of talent management. Finally, based on this in-depth review, the study will highlight potential critical research areas towards which the scholarship of talent may be extended.</p> </abstract>ARTICLEtrue determination, Global Value Chains and role played by wage bargaining schemes: The case of Poland<abstract> <title style='display:none'>Abstract</title> <p>This study examines the linkages between GVC involvement and wages in Poland given different wage bargaining schemes. The analysis is based on microdata from the European Structure of Earnings Survey for Poland combined with sectoral data from the World Input-Output Database. In particular, two measures of GVC involvement were used: the share of foreign value added (FVA) to export and the measure of traditional offshoring. The institutional settings are represented by the wage bargaining scheme which reflects the level at which the collective pay is agreed. The results show that despite the lack of a significant relationship between the sectoral involvement in GVC and the level of wages in Poland, on average workers covered by the collective pay agreement receive higher wages. Moreover, the wage-GVC nexus is conditioned on the type of pay agreements: the positive wage effect from national agreements is eliminated for a certain range of GVC intensity.</p> </abstract>ARTICLEtrue governance, excess-cash and firm value: Evidence from ASEAN-5<abstract> <title style='display:none'>Abstract</title> <p>This study investigates the role of the country- and firm-level governance practices on the relationship between excess-cash and firm value in ASEAN-5 markets. Using the Generalized Method of Moment models and a sample of 578 firms from 2010 to 2020 the study finds that excess-cash reduces firm value, indicating high agency costs and low firm value. However, excess-cash motivated by managerial ownership, founder CEO, board independence, shareholder rights and creditor rights increase firm value while excess-cash due to managerial entrenchment and CEODuality reduce firm value. In the sub-sample analyses the study finds that entrenched managers and board size play a less effective role in wasting excess-cash in low-excess-cash firms while independent directors play a higher monitoring role in high-excess-cash firms. In addition, governance at the country-level is more effective than at the firm-level in improving the value of excess-cash in large firms. The study offers unique evidence on the relationship between excess-cash and firm value by integrating corporate governance practices at the firm- and country-levels. The research aids practitioners, academics, policymakers and investors in developing the best liquidity policies to enhance business performance.</p> </abstract>ARTICLEtrue returns and liquidity after listing switch on the Warsaw Stock Exchange<abstract> <title style='display:none'>Abstract</title> <p>The aim of the article is to evaluate the market reaction to the change of listing venue of companies moving from the alternative market to the regulated market of the Warsaw Stock Exchange. To do so, we investigated 71 switches, and their effect on market returns and liquidity. While the transfer itself creates a negative market reaction, the announcement of the transfer of a company and the institutional confirmation by the supervision of the company’s readiness for this transfer resulting from the approval of the prospectus creates positive market reactions. As a result of the transfer of companies there is an improvement in the liquidity of the shares. The empirical findings of the study could assist managers and investors in understanding the impact of stock exchange migration on returns and the liquidity of shares in the shorter and longer term.</p> </abstract>ARTICLEtrue the effect of credit on monetary policy with Markov regime switching: Evidence from Turkey<abstract> <title style='display:none'>Abstract</title> <p>This paper analyses the effect of credit on monetary policy responses for different regimes in Turkey. To do so, the Taylor rule augmented with the credit gap is estimated by using a Markov regime switching model from January 2006 to December 2019. The empirical findings identify two regimes: the low- and high-interest rate regimes. The prevalence of the former indicates policy authorities’ growth priorities. Furthermore, differing responses across the regimes reflect that the Central Bank of the Republic of Turkey has an asymmetric policy stance. In the low-interest rate regime, the monetary policy only significantly responds to inflation. In the high-interest rate regime, both inflation and credit have significant positive impacts on interest rate setting. This indicates that credit conditions affected the tightening of the monetary policy stance in Turkey despite the use of macroprudential tools after the global financial crisis.</p> </abstract>ARTICLEtrue