<p>Cyber risk is a significant concern for all types of businesses. The consequences of a cyber attack can be quite severe. Investing in security to mitigate the impact of such risks is a crucial task, both in terms of the frequency and the severity of cyber incidents. In this paper, we propose a practical application of the Gordon and Loeb model, thereby suggesting a methodology to estimate risk exposure and reconsidering some investment evaluation metrics. Our findings strongly support the claim that maximizing the expected net benefit of an investment solely at the optimal level is not sufficient for sound decision-making. On the contrary, incorporating metrics that evaluate the benefit in relation to risk and consider worst-case scenarios offers deeper insights.</p>

<abstract> <p>Previous studies have extensively investigated the effects of online word-of-mouth (eWOM) factors such as volume and valence on product sales. However, studies of the effect of eWOM factors on product prices are lacking. It is necessary to examine how various eWOM factors can either explain or affect product prices. The objective of this study is to suggest explanatory and predictive analytics using a regression analysis and ensemble-based machine learning methods for eWOM factors and hotels booking prices. This study utilizes publicly available data from a hotel booking site to build a sample of eWOM factors. The final study sample was comprised of 927 hotels. The important eWOM factors found to affect hotel prices are the review depth and the review rating, which are moderated by a number of reviews to affect prices. The effect of the number of positive words is moderated by the review helpfulness to affect the price. The review depth and rating, along with the number of reviews, should be considered in the design of hotel services, as these provide the rationale for adjusting the prices of various aspects of hotel services. Furthermore, the comparison results when applying various ensemble-based machine learning methods to predict prices using eWOM factors based on a 46-fold cross-validation partition method indicated that ensemble methods (bagging and boosting) based on decision trees outperformed ensemble methods based on k-nearest neighbor methods and neural networks. This shows that bagging and boosting methods are effective ways to improve the prediction performance outcomes when using decision trees. The explanatory and predictive analytics using eWOM factors for hotel booking prices offers a better understanding in terms of how the accommodation prices of hotel services can be explained and predicted by eWOM factors.</p> </abstract>

<p>We analyze how the sentiment of financial news can be used to predict stock returns and build profitable trading strategies. Combining the textual analysis of financial news headlines and statistical methods, we build multi-class classification models to predict the stock return. The main contribution of this paper is twofold. Firstly, we develop a performance evaluation metric to compare multi-class classification methods, taking into account the precision and accuracy of the models and methods. By maximizing the metric, we find optimal combinations of models and methods and select the best approach for prediction and decision-making. Secondly, this metric enables us to construct profitable option trading strategies, which can also be used as an assessment tool to analyze models' prediction power. We apply our methodology to historical data from Apple stock and financial news headlines from Reuters from January 1, 2012 to May 31, 2019. During validation (May 31, 2018, to May 31, 2019), our models consistently outperformed the market, with two-class one-stage models yielding returns between 30% and 45%, compared to the S & P500 index's 1.73% return over the same period.</p>

<abstract><p>An extended interval is a range $ A = [\underline{A}, \overline{A}] $ where $ \underline{A} $ may be bigger than $ \overline{A} $. This is not really natural, but is what has been used as the definition of an extended interval so far. In the present work we introduce a new, natural, and very intuitive way to see an extended interval. From now on, an extended interval is a subset of the Cartesian product $ {\mathbb R}\times {\mathbb Z}_2 $, where $ {\mathbb Z}_2 = \{0, 1\} $ is the set of directions; the direction $ 0 $ is for increasing intervals, and the direction $ 1 $ for decreasing ones. For instance, $ [3, 6]\times\{1\} $ is the decreasing version of $ [6, 3] $. Thereafter, we introduce on the set of extended intervals a family of metrics $ d_\gamma $, depending on a function $ \gamma(t) $, and show that there exists a unique metric $ d_\gamma $ for which $ \gamma(t)dt $ is what we have called an "adapted measure". This unique metric has very good properties, is simple to compute, and has been implemented in the software $ R $. Furthermore, we use this metric to {define variability for random extended intervals. We further study extended interval-valued ARMA} time series and prove the Wold decomposition theorem for stationary extended interval-valued times series.</p></abstract>

<abstract> <p>For ten simple horizontal merger tests, I analytically derive the probability of blocking a merger. The assumptions are the symmetry of diversion ratios, price-cost margins, and efficiency gains, and the uniform joint distribution of these variables. The robustness analyses suggest that the limitations arising from the simplifying assumptions are only moderate. Based on these results, I provide an easy-to-use Excel calculator (without VBA macro), with which the probabilities associated with any product market and price increase threshold can be quickly calculated. Additionally, I propose a paired testing protocol for unknown demand systems by linking linear- and isoelastic-demand indicators through the threshold or the alarm probability.</p> </abstract>

<p>High-frequency tick data have proved helpful for forecasting volatility across asset classes. In the finite samples typically faced by practitioners, however, noise inherent in tick-level prices creates inaccuracies in model parameter estimates and resulting forecasts. A remedy proposed to alleviate these measurement errors is to include higher-order moments, more specifically the realized quarticity, in volatility prediction models. In this paper, we investigate the relevance of this approach in foreign exchange markets, as represented by EURUSD and USDJPY data from 2010 to 2022. Using well-established realized volatility models, we find that including realized quarticity leads to higher precision in daily, weekly, and monthly out-of-sample forecasts. These results are robust across estimation windows, evaluation metrics, and model specifications.</p>

<abstract> <p>The reduction of carbon emissions has attracted significant global attention. This paper empirically analyzes the dynamic nonlinear linkages among carbon markets, green bonds, clean energy, and electricity markets by constructing DCC-GARCH and TVP-VAR-SV models, and places the four markets under a unified framework to analyze the volatility risk from a time-varying perspective, thereby enriching the research on China's carbon market and renewable energy sector. We found that extreme events have a significant impact on the dynamic connectivity among the four markets. The analysis of the shock impact indicates that the carbon market has a positive effect on the power market in the short and medium terms, but has a mitigating impact in the long term. Especially, when the other markets are hit, the carbon market has evident fluctuation in 2020. The green bond market has a positive influence on the carbon market, whereas the power market demonstrates adverse effects in the short and medium terms. The New Energy Index negatively impacts the power market in the short and medium terms, but is expected to have a positive effect after 2020, highlighting the growing need for renewable energy in the power system transformation. According to the findings mentioned above, we put forward appropriate recommendations.</p> </abstract>

<abstract> <p>In this study we investigate alternative <italic>interpretable machine learning</italic> ("IML") models in the context of <italic>probability of default</italic> ("PD") modeling for the large corporate asset class. IML models have become increasingly prominent in highly regulated industries where there are concerns over the unintended consequences of deploying black box models that may be deemed conceptually unsound. In the context of banking and in wholesale portfolios, there are challenges around using models where the outcomes may not be explainable, both in terms of the business use case as well as meeting model validation standards. We compare various IML models (deep neural networks and explainable boosting machines), including standard approaches such as logistic regression, using a long and robust history of corporate borrowers. We find that there are material differences between the approaches in terms of dimensions such as model predictive performance and the importance or robustness of risk factors in driving outcomes, including conflicting conclusions depending upon the IML model and the benchmarking measure considered. These findings call into question the value of the modest pickup in performance with the IML models relative to a more traditional technique, especially if these models are to be applied in contexts that must meet supervisory and model validation standards.</p> </abstract>

<abstract> <p>In this article, we describe the novel properties of Bitcoin and green financial assets and empirically examine the connectedness between Bitcoin and two green financial assets (i.e., carbon emissions, green bonds) and two representative markets of conventional assets (i.e., oil and emerging stock). This study also analyzes whether Bitcoin, carbon, green bonds, oil, and emerging stock assets can hedge against any market turbulence. From observed findings, Bitcoin was not an effective substitute for green bond assets. Thus, Bitcoin is not a valuable hedge instrument to substitute green bonds to mitigate climate risks. More precisely, the findings of the study show that carbon assets outperform emerging stock assets amidst the COVID-19 crisis, while the stock markets incurred significant losses. Crucially, the innovative findings also played an important role for policymakers interested in decarbonizing the crypto-assets.</p> </abstract>