1,721,032 research outputs found
Essays on machine learning for economics and finance
Get PDFEconometrics and machine learning are quite close and related concepts. Nowadays, it is always more
important to extract value from raw data, and distilling actionable insights from quantitative values as
well as qualitative features. In order to deal with these topics, the first chapters (Chapter 1 - 4) are going
to introduce the new wave called machine learning or big data and they will explain the most common
techniques used in the field, respectively regression, clustering, model selection, and tree-based models
(Chapter 2); time series analysis (Chapter 3); and eventually forecasting model with shrinkage methods
(Chapter 4). Then, three applications are going to be provided.
In Chapter 5, it is going to be shown an example of big dataset for the insurance vertical. Rothschild
and Stiglitz ([30]) argued that people signal their risk profile through their insurance demand, i.e. individuals
with a high risk profile would buy insurance as much as they can, while people who are not
going to buy any insurance are the ones with a lower risk profile. This issue is commonly known as
adverse selection. Even if their prediction seems to work quite well in a lot of different markets, Cutler et
al. ([13]) proved that there exist some insurance markets in United States in which the expected result
is completely different. In the wake of this study, we provide empirical evidences that there are some
European insurance markets in which the low risk profile agents are the ones who buy more insurance.
In Chapter 6, a second application is going to be provided. It has been studies the effect of behavioural
biases on entrepreneurial choices to insure their firms against kinds of corporate risks. It has been used a
large sample of Italian Small and Medium sized - finding that they under-insure themselves. The dataset
allows to link corporate insurance choices with the personal traits of the entrepreneur and his household’s
financial choices.
In Chapter 7, finally, an application to financial markets is going to be shown. Bollen et al. ([10])
reintroduced the idea of formulating prediction based on the general sentiment of the investors, even if
they originally exploited microblogging data. The purpose of this study is to verify whether social data
may have a predictive power for the stock prices, returns, and volumes. The analysis has been implemented
for different large technology companies, and the robustness has been tested through a ten-days
rolling window. The evidence shows that there is some intrinsic value in these new features, and that
both the sentiment and the amount of tweets posted online can improve the forecast given by a baseline
autoregressive model. Some additional variations have been tested eventually with the same dataset
Stock jumps: Analyzing traditional and behavioral perspectives
Full text linkOur aim is to define the concept of stock jumps from a practitioner's perspective and to give an insightful overview of the topic. We provide different technical and practical definitions from distinct points of view: mathematical, risk managerial, trading and investing. We verify the robustness of some common stylised facts for three major stock indices, and we derive an approximated jumps distribution. We finally provide some innovative insights from a behavioral perspective, and how to account for behavioral biases in this context
The Power of Micro-Blogging: How to Use Twitter to Predict the Stock Market
No full textThe availability of new data and techniques enriched the existing extensive literature on the
importance of investors’ sentiment and on his impact of the stock price oscillations. The purpose
of this paper is to exploit micro-blogging data in order to construct a new index-tracking variable
that may be used to earn some insights on the Nasdaq-100’s future movements. The results are
promising: the models augmented with the newly created variable show an incremented
explanatory power with respect to the benchmark
Balancing Risk and Learning Opportunities in Corporate Venture Capital Investments: Evidence from the Biopharmaceutical Industry
No full textWhen seizing new investment opportunities, CVC investors face a tension between learning rewards and risks in the form of market and technological uncertainties. Based on an inductive qualitative study relying upon a unique, longitudinal dataset of 260 CVC deals carried out by the top CVC investors in the biopharmaceutical industry between 2003 and 2013, we argue that the extent to which a CVC investor (and its corporate sponsor) may learn from new ventures depends on the nature of its risk attitude and, more in general, on its portfolio diversification (low risk) or concentration (high risk) strategy. In so doing, we identify four typologies of CVC portfolio strategies that allow for growth and learning options available to the parent sponsor, showing that there is a curvilinear (U-shaped) relationship between learning propensity and portfolio diversification. We also develop a tool for determining a CVC opportunity set that may help a fund to optimally allocate capital based on its own risk-return preferences. Theory for CVC decision-making is advanced by furthering two propositions requiring future empirical validation
Entrepreneurs’ behavioural biases, risk misperception and company underinsurance
Full text linkWe analyse the effect of behavioural biases on entrepreneurs’ decisions to insure their firms against different kinds of corporate risks. We use a large sample of 2,295 Italian small and medium enterprises (SMEs), finding that they under-insure themselves. Since SMEs should insure more – in proportion – compared to bigger companies, analysing the reasons for this underinsurance is relevant to improve entrepreneurs’ decisions and help their firms, but also from a policy-making point of view. We link corporate insurance choices with the entrepreneurs’ personal characteristics and behavioral traits as well as with their households’ financial choices. Our methodology uses stepwise regressions to discern which variables are statistically significant. In our results, we find that entrepreneurs not only underinsure their firms but also themselves, thus exposing themselves, their firms and their families to high idiosyncratic risk. We find that these suboptimal decisions are affected by behavioural biases such as overconfidence, over-optimism, risk misperceptions, and stubbornness, even though in a not straightforward manner. We measure both the overall effect on the number of insurances underwritten and on the specific type of insurance contract. In general, we find that relatively bigger firms do buy more insurance, and that trust in insurance companies is a key driver to insurance purchasing, as well as the estimated probability of suffering damages in the future. In contrast, entrepreneurs do underwrite fewer insurance contracts if their firms caused or suffered damages in the past, but also if they possess personal insurances, thus treating them as substitutes for firm insurance. Since SMEs represent a very important part not only of the Italian economy but also of the economy of many other countries, analyzing their insurance-related decisions is relevant because understanding the determinants that may lead entrepreneurs to mitigate the risks they face is beneficial not only for them and their firms but also for the economy as a whole
Hacking the venture industry: An Early-stage Startups Investment framework for data-driven investors
Full text linkInvesting in early-stage companies is incredibly hard, especially when no data are available to support the decision process. Venture capitalists often rely on gut feeling or heuristics to reach a decision, which is biased and potentially harmful. This work proposes a new data-driven framework to help investors be more effective in selecting companies with a higher probability of success. We built upon existing interdisciplinary research and augmented it with further analysis on more than 600,000 companies over a 20-year timeframe. The resulting framework is therefore a smart checklist of 21 relevant features that may help investors to select the companies more likely to succeed
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Stroke care in Italy: An overview of strategies to manage acute stroke in COVID-19 time
No full textPurpose: To analyse structural and non-structural modifications of acute stroke care pathways undertaken at healthcare institutions across the regions of Italy due to the coronavirus disease 2019 (COVID-19) pandemic. Methods: Research on National decrees specific for the pandemic was carried out. The stroke pathways of four Italian regions from North to South, such as Lombardy, Veneto, Lazio and Campania, were analysed before and after the pandemic outbreak. Findings: On 29 February 2020, the Italian Minister of Health issued national guidelines on how to address the COVID-19 emergency. Stroke management was affected and required changes, basically resulting in the need to prioritise the ongoing COVID-19 emergency. In the most affected regions, the closure of departments and hospitals led to a complete reorganisation of previously functioning stroke networks. With the closure of several Stroke Units and Stroke Centres, the transportation time to hospital lengthened significantly, especially for the outlying populations. Discussion: The COVID-19 pandemic outbreak has been spreading rapidly in Italy and placing an overwhelming burden on healthcare systems. In response to this, political and healthcare decision-makers worked together to develop and implement efforts to sustain the national healthcare system while fighting the pandemic. Stroke care pathways changed during the pandemic and different organisational models were applied in the most affected regions. Conclusions: Stroke treatment pathways will need to be redesigned so to guarantee that severe and acute disease patients do not lose their rights to the access and delivery of care during the COVID-19 pandemics
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