Research articles for the 2021-04-29

A Study of Individual Investor Behaviour For Stocks and Gold in Anand City
Mehta, Ashish C.,Moradia, Abha
Investors’ primary goal is to increase their wealth. For long term wealth creation, stocks and gold are inevitable components of the investment portfolio. Generally individual investors have certain behavioral tendencies that affect their investment decisions and portfolio returns as a result. There has been a lot of research interest around this topic in the recent years. Many people invest in stocks but not all have the skills or the expertise to evaluate which stocks are good and which are not. Therefore, they rely on some cognitive and emotional decision-making methods to make a suitable investment decision for them. Gold investment is also guided by the traditions and customs rather than a rational and sound investment decision. This research paper mainly focuses on the factors that affect the individual investors’ investment decision making process for stocks and gold and eventually affect the portfolio performance. The investors’ preferences and behavior have been analyzed with the help of descriptive statistics, Mann-Whitney U Test and Principal Component Analysis statistical tools.

A general theory of option pricing: Explicit formulas
Alghalith, Moawia
We provide simple, explicit formulas for pricing both the European and American options. These formulas do not require any numerical/computational methods. Moreover, we provide these formulas under stochastic volatility, both stochastic volatility and stochastic interest rate, jumpsand simultaneous stochastic volatility, stochastic interest rate and jumps.

A methodological overview to defining and measuring “digital” financial literacy
Lyons, Angela,Kass-Hanna, Josephine
Researchers and financial practitioners alike recognize the importance of defining and measuring financial literacy (FL) to better understand its relationship to financial behavior and decision-making. Despite many efforts, there is still no widely accepted definition or methodological approach for measuring FL. The rapid expansion of digital financial services (DFS), which promises to enhance financial inclusion and improve personal financial management, has brought to light a new challenge: linking FL to digital literacy (DL) and assessing their dual effect on financial outcomes. Recent research has even proposed a framework to operationalize the emerging concept of digital financial literacy (DFL), as traditional FL definitions and metrics have become insufficient to capture the specificities of financial services within a digital context. This survey article discusses empirical research techniques being used to assess FL, DL, and most recently DFL. It highlights the characteristics and limitations of these approaches and suggests ways to address some of the challenges related to the construction, testing, weighting, and standardization of multidimensional measures, as well as methodological issues related to modeling and estimation. The article is a helpful guide to researchers and practitioners interested in FL in general and in the emerging concept of “digital” financial literacy.

Applying Convolutional Neural Networks for Stock Market Trends Identification
Ekaterina Zolotareva

In this paper we apply a specific type ANNs - convolutional neural networks (CNNs) - to the problem of finding start and endpoints of trends, which are the optimal points for entering and leaving the market. We aim to explore long-term trends, which last several months, not days. The key distinction of our model is that its labels are fully based on expert opinion data. Despite the various models based solely on stock price data, some market experts still argue that traders are able to see hidden opportunities. The labelling was done via the GUI interface, which means that the experts worked directly with images, not numerical data. This fact makes CNN the natural choice of algorithm. The proposed framework requires the sequential interaction of three CNN submodels, which identify the presence of a changepoint in a window, locate it and finally recognize the type of new tendency - upward, downward or flat. These submodels have certain pitfalls, therefore the calibration of their hyperparameters is the main direction of further research. The research addresses such issues as imbalanced datasets and contradicting labels, as well as the need for specific quality metrics to keep up with practical applicability. This paper is the full text of the research, presented at the 20th International Conference on Artificial Intelligence and Soft Computing Web System (ICAISC 2021)

Blockchain for SME Finance: Call for Empirical Testing
Yesseleva-Pionka, Mariya
For many countries worldwide, small and medium enterprises (SMEs) are the backbones of their economy, more than half of the overall world population is working for SMEs. Historically, research studies on SME lending have emphasised that SMEs are less transparent and have an information advantage in comparison to external lenders as many businesses are owner-managed and privately owned. Distributed Ledger Technology (DLT), also known as a blockchain, opens new opportunities to SMEs. It could be used as a network system to exchange financial and non-financial data for assessing SMEs' creditworthiness. Hence, blockchain could bridge the information/data gap between lenders and SMEs and enhance the overall financing speed. This article provides a theoretical overview of the policy developments for blockchain and DLTs with the final recommendations for empirical testing.

Do Hacker Groups Pose a Risk to Organizations? Study on Financial Institutions Targeted by Hacktivists
Keppo, Jussi,Niemela, Mikko
As organizations are increasingly engaged in the digital world with greater dependency on data, crime and activism have shifted from the streets to the internet. In this paper, we study the impact of activist hacking campaigns on financial institutions. We look into how target institutions’ deep web and dark web exposure in terms of different risk categories is affected by hacking campaigns, and the interactions of the risk categories during the campaigns. We show that, on average, hacking campaigns raise the target institutions’ deep web and dark web exposure by 62 percent per year during the first two years after the campaigns’ start date. Further, leaked employee passwords amplify the effect substantially, which allows us to forecast the institutions’ cyber exposure changes during and after the campaigns.

Do Hedge Funds Still Manipulate Stock Prices?
Cui, Xinyu,Kolokolova, Olga
We find no evidence that hedge funds manipulate stock prices from 2011 to 2019, while confirming strong stock price manipulation pattern previously documented between 2000 and 2010. Stocks held by hedge funds exhibit positive abnormal returns and then reversals at quarter end in the earlier period; however, there is no relation between hedge fund ownership and end-of-quarter stock returns in the later period. Hedge fund market discipline is related to proactive actions of regulators. End-of-quarter stock price manipulation decreases with the number of the Security and Exchange Commission litigation cases involving hedge funds in that quarter. Investor flows, on the contrary, react positively on last-trading-day of a quarter hedge fund portfolio returns, creating incentives for stock price manipulation by hedge funds.

FX Market Volatility
Anton Koshelev

This paper aims at solving FX market volatility modeling problem and finding the most becoming approach to this task. Validity of two competing approaches, classical econometric generalized conditional heteroscedasticity and mathematical (singular spectrum analysis and dynamical systems stability analysis) are tested on major currency pairs (EUR/USD, USD/JPY, GBP/USD) and unique high-frequency USD/RUB data. The study shows that both mathematical tools, understudied in econometric discourse, have a great potential in scope of discussed problematic, as for all experiments covered in this research, both of them show promising results.

Foreign Institutional Investment and Its Determinants in India
Mehta, Rekha,Jain, Dr. Akansha
The attempt is made with this paper to examine the affect of various determinants of Foreign Institutional Investment inflows on the investment pattern of FIIS and on foreign shareholders in India. This study has covered the period of global financial crises due to which , the determinant of FIIs such as money supply , inflation. Foreign exchange reserve and BSE sensex etc. had adversely influenced pattern of FIIs. The role of FII has been observed which was helpful for RBI and Government to frame economic policies and to maintain the balance of payments. For this study monthly data has been used ranging from April 2008 to March 2014. SPSS and EVIEWS software has also been used to test staionarity and regression models. So that objective behind this paper can be achieved. And further this research can contribute in others research so as to understand the different policies running in the economy.

From the Athenian silver to the bitcoin standard: Private money in a state-enforced free banking model
Bitros, Georgios (George) C.
Currently numerous market-driven cryptocurrencies challenge to dethrone the state-controlled supply of fiat money. The outcome hinges on the old question of whether privately produced money with or without government is possible. This paper revisits the issue by looking for insights in the successful precedent of classical Athens. It is found that in this case the government defined a unit of account (Attic drachma), linked it to a commodity (silver), and used it in its domestic and foreign transactions. Î'y drawing on its share of silver from the Laurion mines and other sources, the government acted as a major supplier of the said currency; and lastly, it enacted and enforced rules and regulations that aimed at safeguarding the integrity of the currency while leaving private markets free to co-determine the supply of the currency and credit, and the drachma to circulate in competition with all other foreign currencies within its borders. On this account and the evidence that the Attic drachma dominated for three centuries in the wider Mediterranean region, it is concluded that the application of the silver standard in classical Athens provides a most suitable framework of free banking after which to pattern the emerging regime of a privately produced bitcoin-based digital standard with amicable state sponsorship and enforcement. ]

Identification of Peer Effects with Miss-specified Peer Groups: Missing Data and Group Uncertainty
Christiern Rose

We consider identification of peer effects under peer group miss-specification. Our model of group miss-specification allows for missing data and peer group uncertainty. Missing data can take the form of some individuals being entirely absent from the data, and the researcher need not have any information on these individuals and may not even know that they are missing. We show that peer effects are nevertheless identifiable under mild restrictions on the probabilities of observing individuals, and propose a GMM estimator to estimate the peer effects. In practice this means that the researcher need only have access to an individual/household level sample with group identifiers. The researcher may also be uncertain as to what is the relevant peer group for the outcome under study. We show that peer effects are nevertheless identifiable provided that the candidate peer groups are nested within one another (e.g. classroom, grade, school) and propose a non-linear least squares estimator. We conduct a Monte-Carlo experiment to demonstrate our identification results and the performance of the proposed estimators in a setting tailored to real data (the Dartmouth room-mate data).

Interpretable Machine Learning for Real Estate Market Analysis
Lorenz, Felix,Willwersch, Jonas,Cajias, Marcelo,Fuerst, Franz
While Machine Learning (ML) excels at predictive tasks, its inferential capacity is limited due to its complex non-parametric structure. This paper aims to elucidate the analytical behavior of ML through Interpretable Machine Learning (IML) in a real estate context. Using a hedonic ML approach to predict unit-level residential rents for Frankfurt, Germany, we apply a set of model-agnostic interpretation methods to decompose the rental value drivers and plot their trajectories over time. Living area and building age are the strongest predictors of rent, followed by proximity to CBD and neighborhood amenities. Our approach is able to detect the critical distances to these centers beyond which rents tend to decline more rapidly. Conversely, close proximity to hospitality facilities as well as public transport is associated with rental discounts. Overall, our results suggest that IML methods provide insights into algorithmic decision-making by illustrating the relative importance of hedonic variables and their relationship with rental prices in a dynamic perspective.

Long-Horizon Stock Returns Are Positively Skewed
Farago, Adam,Hjalmarsson, Erik
At long horizons, multiplicative compounding induces strong-to-extreme positive skewness into stock returns; the magnitude of the effect is primarily determined by single-period volatility. Consequently, at horizons greater than five years, returns --individual or portfolio-- will be positively skewed under reasonable parametrizations. From an investor perspective, the strong positive skewness implies that the mean compound return will serve as a poor guide for typical long-horizon outcomes. Moreover, the large effects of compounding on higher-order moments are shown to affect the validity of Taylor expansions used to approximate preferences for skewness, when applied to returns of annual or longer horizons.

Macroeconomic Determinants of Loan Delinquencies: Evidence from the US Peer-to-Peer Lending Market
Nigmonov, Asror,Shams, Syed ,Alam, Khorshed
The study documented in this paper utilises a probit regression analysis to empirically investigate the key macroeconomic factors that influence credit risk in the peer-to-peer (P2P) lending market. By aggregating the United States (US) state-level data with LendingClub’s loan book covering the period from 2008â€"2019, this study examines multiple factors related to default risks of loans issued by P2P lending platforms. Our results show that a higher interest rate and inflation increase the probability of default in the P2P lending market. We also find that the impact of interest rate on the probability of default is significantly higher for loans with lower ratings. The study’s outcomes, by paving the way to future market best practices, are applicable to P2P lending platforms and investors in their default estimation of loans.

Marxism, Logic and the Rate of Profit
Robin Hirsch

It is argued that Marxism, being based on contradictions, is an illogical method. More specifically, we present a rejection of Marx's thesis that the rate of profit has a long-term tendency to fall.

Measuring the Value of Corporate Cash Holdings against Predictable and Unpredictable Negative Shocks
Hori, Keiichi,Aono, Kohei
This paper explores how cash mitigates predictable and unpredictable adverse cash flow shocks to firms using the financial data of Japanese firms. We find that (i) cash has no value after the predicted shock regardless of the severity of the financial constraint, (ii) after the unpredicted shock, the value of cash for the financially constrained firms is larger than that for the unconstrained firms, and (iii) the value of cash is similar between the two shocks for the unconstrained firms, while the value is larger when the unpredicted shock occurs than when the predicted shock occurs for the constrained firms.

Modeling Managerial Search Behavior based on Simon's Concept of Satisficing
Friederike Wall

Computational models of managerial search often build on backward-looking search based on hill-climbing algorithms. Regardless of its prevalence, there is some evidence that this family of algorithms does not universally represent managers' search behavior. Against this background, the paper proposes an alternative algorithm that captures key elements of Simon's concept of satisficing which received considerable support in behavioral experiments. The paper contrasts the satisficing-based algorithm to two variants of hill-climbing search in an agent-based model of a simple decision-making organization. The model builds on the framework of NK fitness landscapes which allows controlling for the complexity of the decision problem to be solved. The results suggest that the model's behavior may remarkably differ depending on whether satisficing or hill-climbing serves as an algorithmic representation for decision-makers' search. Moreover, with the satisficing algorithm, results indicate oscillating aspiration levels, even to the negative, and intense - and potentially destabilizing - search activities when intra-organizational complexity increases. Findings may shed some new light on prior computational models of decision-making in organizations and point to avenues for future research.

Modelling Net Loan Loss with Bayesian and Frequentist Regression Analysis
Nathan Thomas Provost

We create two distinct nonlinear regression models relating net loan loss (as an outcome) to several other financial and sociological quantities. We consider these data for the time interval between April 1st 2011 and April 1st 2020. We also include temporal quantities (month and year) in our model to improve accuracy. One model follows the frequentist paradigm for nonlinear regression, while the other follows the Bayesian paradigm. By using the two methods, we obtain a rounded understanding of the relationship between net loan losses and our given financial, sociological, and temporal variables, improving our ability to make financial predictions regarding the profitability of loan allocation.

Modelling and Forecasting of the Nigerian Stock Exchange.
Yahayah, Ibraheem Abiodun
In this research work, we discuss Nigerian stock price and model it usingVariance-Gamma distribution. We compare the model with closely relateddistributions and test the goodness of fit. Finally, we compare Nigerian stockprice model with Johannesburg stock exchange model.

Monetary Policy and the Equity Term Structure
Golez, Benjamin,Matthies, Ben
We study the impact of monetary policy on the term structure of equity prices. We find that short-term and long-term equity prices respond in opposite ways to changes in monetary policy. Following an unanticipated cut in the target federal funds rate, short-term equity prices fall while long-term equity prices rise on average. This pattern could arise if policy decisions signal information about economic conditions. We examine this mechanism and find that the price change of the short-term equity asset in the 30-minute window around an FOMC announcement significantly predicts both macroeconomic growth and professional forecast errors over subsequent quarters.

Nonparametric Test for Volatility in Clustered Multiple Time Series
Erniel B. Barrios,Paolo Victor T. Redondo

Contagion arising from clustering of multiple time series like those in the stock market indicators can further complicate the nature of volatility, rendering a parametric test (relying on asymptotic distribution) to suffer from issues on size and power. We propose a test on volatility based on the bootstrap method for multiple time series, intended to account for possible presence of contagion effect. While the test is fairly robust to distributional assumptions, it depends on the nature of volatility. The test is correctly sized even in cases where the time series are almost nonstationary. The test is also powerful specially when the time series are stationary in mean and that volatility are contained only in fewer clusters. We illustrate the method in global stock prices data.

Optimal bidding on hourly and quarter-hourly day-ahead electricity price auctions: trading large volumes of power with market impact and transaction costs
Michał Narajewski,Florian Ziel

Electricity exchanges offer several trading possibilities for market participants: starting with futures products through the spot market consisting of the auction and continuous part, and ending with the balancing market. This variety of choice creates a new question for traders - when to trade to maximize the gain. This problem is not trivial especially for trading larger volumes as the market participants should also consider their own price impact. The following paper raises this issue considering two markets: the hourly EPEX Day-Ahead Auction and the quarter-hourly EPEX Intraday Auction. We consider a realistic setting which includes a forecasting study and a suitable evaluation. For a meaningful optimization many price scenarios are considered that we obtain using bootstrap with models that are well-known and researched in the electricity price forecasting literature. The own market impact is predicted by mimicking the demand or supply shift in the respectful auction curves. A number of trading strategies is considered, e.g. minimization of the trading costs, risk neutral or risk averse agents. Additionally, we provide theoretical results for risk neutral agents. Especially we show when the optimal trading path coincides with the solution that minimizes transaction costs. The application study is conducted using the German market data, but the presented methods can be easily utilized with other two auction-based markets. They could be also generalized to other market types, what is discussed in the paper as well. The empirical results show that market participants could increase their gains significantly compared to simple benchmark strategies.

Avoyan, Ala,Ribeiro, Mauricio,Schotter, Andrew,Schotter, Elizabeth R.,Vaziri, Mehrdad,Zou, Minghao
When time is scarce, we need to plan how to allocate our attention across decision tasks. To study this problem, we present subjects with pairs of games between which they have to allocate a fixed amount of decision time (attention). We then let subjects play each pair of games without time constraint and use eye-tracking to measure how much time subjects spend playing each game in a pair. We find that subjects’ planned and actual attention allocation differ. We identify the determinants of this difference and show that this discrepancy can be payoff relevant in games where choice is time-dependent.

Personal Income Taxes and Small Business Lending
Wang, Weichao
I investigate whether and how personal income taxes impact bank lending to small businesses in the U.S. My empirical design exploits staggered statutory changes of state personal income tax rate as identification, and compares loans granted in geographically adjacent counties across state borders with relative tax changes from 2001 to 2018. My estimates show that a higher personal income tax rate has significantly negative effects on loan outcomes for small businesses, including smaller loan size, shorter maturity, higher default probability and loan charge-off ratio. Moreover, the total number and amount of small business loans granted significantly decrease in high-tax localities. These findings provide novel evidences suggesting tax-induced reduction in business income and capital constrains local banks’ lending to small businesses.

Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS
Saeed Nosratabadi,Sina Ardabili,Zoltan Lakner,Csaba Mako,Amir Mosavi

Advancing models for accurate estimation of food production is essential for policymaking and managing national plans of action for food security. This research proposes two machine learning models for the prediction of food production. The adaptive network-based fuzzy inference system (ANFIS) and multilayer perceptron (MLP) methods are used to advance the prediction models. In the present study, two variables of livestock production and agricultural production were considered as the source of food production. Three variables were used to evaluate livestock production, namely livestock yield, live animals, and animal slaughtered, and two variables were used to assess agricultural production, namely agricultural production yields and losses. Iran was selected as the case study of the current study. Therefore, time-series data related to livestock and agricultural productions in Iran from 1961 to 2017 have been collected from the FAOSTAT database. First, 70% of this data was used to train ANFIS and MLP, and the remaining 30% of the data was used to test the models. The results disclosed that the ANFIS model with Generalized bell-shaped (Gbell) built-in membership functions has the lowest error level in predicting food production. The findings of this study provide a suitable tool for policymakers who can use this model and predict the future of food production to provide a proper plan for the future of food security and food supply for the next generations.

Regional poverty in Bulgaria in the period 2008-2019
Iva Raycheva

Background: Poverty among the population of a country is one of the most disputable topics in social studies. Many researchers devote their work to identifying the factors that influence it most. Bulgaria is one of the EU member states with the highest poverty levels. Regional facets of social exclusion and risks of poverty among the population are a key priority of the National Development Strategy for the third decade of 21st century. In order to mitigate the regional poverty levels it is necessary for the social policy makers to pay more attention to the various factors expected to influence these levels. Results: Poverty reduction is observed in most areas of the country. The regions with obviously favorable developments are Sofia district, Pernik, Pleven, Lovech, Gabrovo, Veliko Tarnovo, Silistra, Shumen, Stara Zagora, Smolyan, Kyustendil and others. Increased levels of poverty are found for Razgrad and Montana districts. It was fond that the reduction in the risk of poverty is associated to the increase in employment, investment, and housing. Conclusion: The social policy making needs to be aware of the fact that the degree of exposition to risk of poverty and social exclusion significantly relates to the levels of regional employment, investment and housing.

Regulating Virtual Currency Payment Systems
Animashaun, Sijuade
This paper examines the functioning of virtual currencies as payment systems through crypto-currency exchanges and the likely impact their integration with traditional payment systems may have on the interdependent global payment systems. Being a potential global transformational phenomenon, should virtual payment systems be regulated like other traditional intermediaries to manage the risks from their operations? Which regulator has the requisite regulatory architecture to comprehend the fast-evolving dynamics of the innovative payment solution and better manage the risks? These are some of the questions attempted in this paper. The paper also examines the role played by central banks as the major regulator of payment intermediaries and their limitations on multinational financial institutions and payment activities. Finally, the paper suggests the adoption of international regulatory bodies as the major regulatory authority for the virtual exchanges in ensuring global cooperation and coordinated implementation of any developed action plan while fostering financial innovation.

Social Media Disclosure and Stock Price Synchronicity
Zhou, Xi,Chen, Kun,Lai, Shufang,Yifan, Fan
According to the efficient market hypothesis, stock price is determined by market information. Since 2010, a new social media disclosure, called “Hudongyi” has been launched by Shenzhen Stock Exchange. The social media provides an inter-communication feature for information disclosure compared with traditional firm announcements. So, can the social media disclosure provide additional firm-specific information to the market? To investigate the research question, we design surprise index and specificity index based on the Q&A texts on “Hudongyi”, and empirically analyze the relationships between the two indexes and the stock price synchronicity. The results showed that there are significant negative effects of the two indexes on synchronization of stock price. That is, the more information the messages published by companies on the platform contain, or the higher the proportion of information related to the company itself contain, the lower the synchronization. The findings provide important theoretical and practical implications.

Sparse Grid Method for Highly Efficient Computation of Exposures for xVA
Lech A. Grzelak

Every x-adjustment in the so-called xVA financial risk management framework relies on the computation of exposures. Considering thousands of Monte Carlo paths and tens of simulation steps, a financial portfolio needs to be evaluated numerous times during the lifetime of the underlying assets. This is the bottleneck of every simulation of xVA. In this article, we explore numerical techniques for improving the simulation of exposures. We aim to decimate the number of portfolio evaluations, particularly for large portfolios involving multiple, correlated risk factors. The usage of the Stochastic Collocation (SC) method, together with Smolyaks sparse grid extension, allows for a significant reduction in the number of portfolio evaluations, even when dealing with many risk factors. The proposed model can be easily applied to any portfolio and size. We report that for a realistic portfolio comprising linear derivatives, the expected reduction in the portfolio evaluations may exceed 6000 times, depending on the dimensionality and the required accuracy. We give illustrative examples and examine the method with realistic multi-currency portfolios.

Stakeholder dynamics in residential solar energy adoption: findings from focus group discussions in Germany
Fabian Scheller,Isabel Doser,Emily Schulte,Simon Johanning,Russell McKenna,Thomas Bruckner

Although there is a clear indication that stages of residential decision making are characterized by their own stakeholders, activities, and outcomes, many studies on residential low-carbon technology adoption only implicitly address stage-specific dynamics. This paper explores stakeholder influences on residential photovoltaic adoption from a procedural perspective, so-called stakeholder dynamics. The major objective is the understanding of underlying mechanisms to better exploit the potential for residential photovoltaic uptake. Four focus groups have been conducted in close collaboration with the independent institute for social science research SINUS Markt- und Sozialforschung in East Germany. By applying a qualitative content analysis, major influence dynamics within three decision stages are synthesized with the help of egocentric network maps from the perspective of residential decision-makers. Results indicate that actors closest in terms of emotional and spatial proximity such as members of the social network represent the major influence on residential PV decision-making throughout the stages. Furthermore, decision-makers with a higher level of knowledge are more likely to move on to the subsequent stage. A shift from passive exposure to proactive search takes place through the process, but this shift is less pronounced among risk-averse decision-makers who continuously request proactive influences. The discussions revealed largely unexploited potential regarding the stakeholders local utilities and local governments who are perceived as independent, trustworthy and credible stakeholders. Public stakeholders must fulfill their responsibility in achieving climate goals by advising, assisting, and financing services for low-carbon technology adoption at the local level. Supporting community initiatives through political frameworks appears to be another promising step.

The Effect of Marketing Investment on Firm Value and Systematic Risk
Musaab Mousa,Saeed Nosratabadi,Judit Sagi,Amir Mosavi

Analyzing the financial benefit of marketing is still a critical topic for both practitioners and researchers. Companies consider marketing costs as a type of investment and expect this investment to be returned to the company in the form of profit. On the other hand, companies adopt different innovative strategies to increase their value. Therefore, this study aims to test the impact of marketing investment on firm value and systematic risk. To do so, data related to four Arabic emerging markets during the period 2010-2019 are considered, and firm share price and beta share are considered to measure firm value and systematic risk, respectively. Since a firm's ownership concentration is a determinant factor in firm value and systematic risk, this variable is considered a moderated variable in the relationship between marketing investment and firm value and systematic risk. The findings of the study, using panel data regression, indicate that increasing investment in marketing has a positive effect on the firm value valuation model. It is also found that the ownership concentration variable has a reinforcing role in the relationship between marketing investment and firm value. It is also disclosed that it moderates the systematic risk aligned with the monitoring impact of controlling shareholders. This study provides a logical combination of governance-marketing dimensions to interpret performance indicators in the capital market.

The impact of past pandemics on CO$_2$ emissions and transition to renewable energy
Michal Brzezinski

We estimate the short- to medium term impact of six major past pandemic crises on the CO2 emissions and energy transition to renewable electricity. The results show that the previous pandemics led on average to a 3.4-3.7% fall in the CO2 emissions in the short-run (1-2 years since the start of the pandemic). The effect is present only in the rich countries, as well as in countries with the highest pandemic death toll (where it disappears only after 8 years) and in countries that were hit by the pandemic during economic recessions. We found that the past pandemics increased the share of electricity generated from renewable sources within the fiveyear horizon by 1.9-2.3 percentage points in the OECD countries and by 3.2-3.9 percentage points in countries experiencing economic recessions. We discuss the implications of our findings in the context of CO2 emissions and the transition to renewable energy in the post-COVID-19 era.

The role of Common Agricultural Policy (CAP) in enhancing and stabilising farm income: an analysis of income transfer efficiency and the Income Stabilisation Tool
Biagini Luigi,Simone Severini

Since its inception, the E.U.'s Common Agricultural Policy (CAP) aimed at ensuring an adequate and stable farm income. While recognizing that the CAP pursues a larger set of objectives, this thesis focuses on the impact of the CAP on the level and the stability of farm income in Italian farms. It uses microdata from a high standardized dataset, the Farm Accountancy Data Network (FADN), that is available in all E.U. countries. This allows if perceived as useful, to replicate the analyses to other countries. The thesis first assesses the Income Transfer Efficiency (i.e., how much of the support translate to farm income) of several CAP measures. Secondly, it analyses the role of a specific and relatively new CAP measure (i.e., the Income Stabilisation Tool - IST) that is specifically aimed at stabilising farm income. The assessment of the potential use of Machine Learning procedures to develop an adequate ratemaking in IST. These are used to predict indemnity levels because this is an essential point for a similar insurance scheme. The assessment of ratemaking is challenging: indemnity distribution is zero-inflated, not-continuous, right-skewed, and several factors can potentially explain it. We address these problems by using Tweedie distributions and three Machine Learning procedures. The objective is to assess whether this improves the ratemaking by using the prospective application of the Income Stabilization Tool in Italy as a case study. We look at the econometric performance of the models and the impact of using their predictions in practice. Some of these procedures efficiently predict indemnities, using a limited number of regressors, and ensuring the scheme's financial stability.

Trading Volume, Information Releases, and the Returns to Equity Option Straddles
Neururer, Thaddeus,Papadakis, George
In this paper we investigate the relationship between past trading volume and variance risk premiums (VRPs) around earnings announcement (EAs). Theoretical models suggest opposing relationships between trading volume and VRPs. Using a large sample of straddle returns, we find higher VRPs for firms around EAs with higher trading volume. This relationship holds conditional to other factors suggested to predict VRPs and appears specific to the EA period. Further tests reveal that the result is driven by excess options trading and options trading continues to predict straddle returns conditional on excess stock trading, option open interest, analyst dispersion, and realized earnings surprises. Our main results suggest a one-standard deviation increase in abnormal log options trading volume is associated with a 170 to 190 basis points drop in realized straddle returns around EAs. Finally, we find excess option trading is a stronger predictor of EA option returns for smaller firms and firms with tighter equity bid-ask spreads.

What determines cross-country differences in fintech and bigtech credit markets?
Kowalewski, Oskar,Pisany, Paweł,Slazak, Emil
This study is an investigation of the determinants of the development of technology-driven alternative credit markets, that is, fintech and bigtech credit. Using a data sample from 94 countries from 2013â€"2019, we confirmed the relevance of the availability of credit data, both the traditional and alternative types, with the latter being known as the so-called “digital footprint.” Furthermore, we have provided evidence to confirm the positive role of strengthening Internet privacy protections in fostering the development of the fintech credit market, which may not necessarily be the case for the bigtech credit market. We have also shown that the growth of the fintech and bigtech credit market is preceded by a rising paytech services market. Furthermore, we have found that the development of fintech credit services is fostered by the strength of both principal institutions, like the rule of law, and credit-specific institutions, especially in terms of insolvency framework effectiveness, while, for the bigtech credit market, only the latter matters. Interestingly, we have also found that various national cultural profiles can boost the development of fintech and bigtech credit services. Lastly, we have shown that the fintech credit market develops faster in countries characterized by high levels of societal distrust toward banks and that the opposite seems to be the case with the bigtech credit market.

Where to Refuel: Modeling On-the-way Choice of Convenience Outlet
Ari Pramono,Harmen Oppewal

This paper introduces on-the-way choice of retail outlet as a form of convenience shopping. It presents a model of on-the-way choice of retail outlet and applies the model in the context of fuel retailing to explore its implications for segmentation and spatial competition. The model is a latent class random utility choice model. An application to gas station choices observed in a medium-sized Asian city show the model to fit substantially better than existing models. The empirical results indicate consumers may adopt one of two decision strategies. When adopting an immediacy-oriented strategy they behave in accordance with the traditional gravity-based retail models and tend to choose the most spatially convenient outlet. When following a destination-oriented strategy they focus more on maintaining their overall trip efficiency and so will tend to visit outlets located closer to their main destination and are more susceptible to retail agglomeration effects. The paper demonstrates how the model can be used to inform segmentation and local competition analyses that account for variations in these strategies as well as variations in consumer type, origin and time of travel. Simulations of a duopoly setting further demonstrate the implications.