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527 results for “financial”
HiDy: A Large-scale Hierarchical Dynamic Financial Knowledge Base
<p>HiDy is a hierarchical, dynamic, robust, diverse, and large-scale financial KB that aims to provide various valuable financial knowledge as critical benchmarking data for fair model testing in different financial tasks. Specifically, HiDy currently contains 34 relation types, more than 506,444 relations, 17 entity types, and more than 51,095 entities. The scale of HiDy is steadily growing due to its continuous updates. To make HiDy easily accessible and retrieved, HiDy is organized in a well-formed financial hierarchy with four branches, <em>Macro</em>, <em>Meso</em>,<em> Micro</em>, and<em> Others</em>.</p> <p>We then give explanations on the various csv files as follows.</p> <ul> <li>"hidy.nodes.entity_type.csv" includes a mapping dictionary of entities with a specific entity type. The meta data is ID, name, (code), Label. For example, "hidy.nodes.company.csv" includes "0,东诚药业,002675.SZ,company", "1,大庆华科,000985.SZ,company".</li> <li>"hidy.relationships.relation_type.csv" includes quadruple knowledge with a specific relation type. The meta data is START_ID, END_ID, TYPE, time. For example, "hidy.relationships.cooperate.csv" includes "2197,245,cooperate,2019/9/17 12:00, "1165,756,cooperate,2020/8/2 17:10".</li> </ul> <p>For details of HiDy, please refer to our <a href="https://github.com/K-Quant/HiDy">GitHub</a>.</p>
Unlocking Efficiency: Assessing the Impact of Prompt Pay on Government Operations and Financial Transactions in Thailand
<p> </p> <p>1. Promtpay users</p> <p>2. Monetary Aggregates and Components</p> <p> </p> <p>Data collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (<em>Project ID: HORIZON MSCA-SE 101086381)</em></p>
ISSUES OF FINANCIAL PROVISION OF PRISON INSTITUTIONS IN RUSSIA IN THE EARLY XX CENTURY
<p><span>The article deals with the issues of material provision of prisons in the early XX century. It is an interesting historical fact that prisoners brought a certain income to the treasury by performing various types of socially useful work, which was encouraged by the authorities. A similar practice existed in the early twentieth century abroad, for example, in Switzerland, Great Britain, and the USA, where prisoners' labor was also actively used, bringing money to the state. In fact, these financial resources were used partly for the needs of places of deprivation of liberty, which contributed to their self-sufficiency. The Tsarist government in Russia rightly believed that, from a financial point of view, it was more profitable to force prisoners to work and bring income than to simply keep the entire prison population "under lock and key". In addition, later, after 1917 in Russia, labor re-education of prisoners was actively used during the years of Soviet power. At present, the Russian government is attempting to organize the so-called "public institute of probation" in order to activate labor employment of convicts. </span></p>
FINANCIAL INSIGHTS INTO HUMAN CAPITAL: ASSESSING EMPLOYEE CONTRIBUTION FROM HR-FINANCE PERSPECTIVE
<p>International Research Journal of <br>Management Sociology & Humanities</p> <p>ISSN 2277 – 9809 (online)<br>ISSN 2348 - 9359 (Print)</p> <p>Volume 15, Issue 9 , page 126-136 </p> <p>Name of the research paper : FINANCIAL INSIGHTS INTO HUMAN CAPITAL: ASSESSING EMPLOYEE CONTRIBUTION FROM HR-FINANCE PERSPECTIVE</p> <p>Authors <br>Suvendu Narayan Roy<br>Director, Knowgen Education Services Private Limited<br>Researcher, Faculty of Finance & Management<br>Orcid id : https://orcid.org/0000-0002-2801-0740<br>Soham Roy<br>Researcher & Partner, E & O Learnet and Learnet Manpower Solutions<br>Orcid id : https://orcid.org/0000-0002-3127-608</p>
INNOVATIVE MACHINE LEARNING APPROACHES TO FOSTER FINANCIAL INCLUSION IN MICROFINANCE
<p>This study examines the application of machine learning algorithms to enhance financial inclusion in microfinance, focusing on credit scoring, risk and fraud detection, and customer segmentation. We performed feature engineering and employed models such as Logistic Regression, Decision Trees, Random Forests, Gradient Boosting Machines (XGBoost and LightGBM), Support Vector Machines (SVM), Autoencoders, Isolation Forests, and K-means Clustering. LightGBM achieved the highest accuracy (89.6%) and AUC (0.92) in credit scoring, while Random Forests demonstrated strong performance in both loan approval (86.7% accuracy) and fraud detection (87.6% accuracy, AUC of 0.88). SVM also performed competitively, and unsupervised methods like Autoencoders and Isolation Forests showed potential for anomaly detection but required further refinement.K-means Clustering excelled in customer segmentation with a silhouette score of 0.72, enabling tailored services based on client demographics. Our findings highlight the significant impact of machine learning on improving credit scoring accuracy, reducing fraud risks, and enhancing customer service delivery in microfinance, thereby promoting financial inclusion for underserved populations. Ethical considerations and model interpretability are crucial, particularly for smaller institutions. This study advocates for the broader adoption of machine learning in the microfinance sector.</p>
Data Materials for Fortune Telling Studies: Positive Fortune Telling Enhances Men's Financial Risk Taking
<p>We share the data materials for three studies and a meta-analysis on the effects of positive fortune telling on enhancing men's financial risk taking. A paper entitled as "<strong>Positive Fortune Telling Enhances Men’s Financial Risk Taking</strong>" was written up for publication.</p>
Financial News dataset for text mining
<p>please cite this dataset by :</p> <p>Nicolas Turenne, Ziwei Chen, Guitao Fan, Jianlong Li, Yiwen Li, Siyuan Wang, Jiaqi Zhou (2021) Mining an English-Chinese parallel Corpus of Financial News, BNU HKBU UIC, technical report</p> <p> </p> <p>The dataset comes from Financial Times news website (https://www.ft.com/)</p> <p>news are written in both languages Chinese and English.</p> <p><a href="https://zenodo.org/api/files/74136928-3c77-4388-aa47-b1079efa1650/FTIE.zip?versionId=440c065e-1528-4f01-800d-541a0611ea7f">FTIE.zip</a> contains all documents in a file individually</p> <p><a href="https://zenodo.org/api/files/74136928-3c77-4388-aa47-b1079efa1650/FT-en-zh.rar">FT-en-zh.rar</a> contains all documents in one file</p> <p>Below is a sample document in the dataset defined by these fields and syntax : </p> <p>id;time;english_title;chinese_title;integer;english_body;chinese_body</p> <p> </p> <p>1021892;2008-09-10T00:00:00Z;FLAW IN TWIN TOWERS REVEALED;科学家发现纽约双子塔倒塌的根本原因;1;Scientists have discovered the fundamental reason the Twin Towers collapsed on September 11 2001. The steel used in the buildings softened fatally at 500?C – far below its melting point – as a result of a magnetic change in the metal. @ The finding, announced at the BA Festival of Science in Liverpool yesterday, should lead to a new generation of steels capable of retaining strength at much higher temperatures.;科学家发现了纽约世贸双子大厦(Twin Towers)在2001年9月11日倒塌的根本原因。由于磁性变化,大厦使用的钢在500摄氏度——远远低于其熔点——时变软,从而产生致命后果。 @ 这一发现在昨日利物浦举行的BA科学节(BA Festival of Science)上公布。这应会推动能够在更高温度下保持强度的新一代钢铁的问世。<br> </p> <p>The dataset contains 60,473 bilingual documents.</p> <p>Time range is from 2007 and 2020. </p> <p>This dataset has been used for parallel bilingual news mining in Finance domain.</p>
Dataset for Financial Literacy Bibliometrics
<p>Dataset for Financial Literacy Bibliometrics, extracted on 4th November 2022 according to the following queries. Duplicated values removed.</p> <table> <tbody> <tr> <td> <p>Web of Science</p> </td> <td> <p>((AK=(financial literacy)) AND LA=(English)) AND DT=(Article)</p> </td> </tr> <tr> <td> <p>Scopus</p> </td> <td> <p>AUTHKEY(financial literacy) AND (LIMIT-TO(DOCTYPE,”ar”)) AND<br> (LIMIT-TO(LANGUAGE,”English” ) )</p> </td> </tr> </tbody> </table> <p> </p> <p>Part of Ph.D. thesis in Management.</p>
Dataset for the climate-related financial policy index (CRFPI)
<p>Data on the climate-related financial policy index (CRFPI) - comprising the global climate-related financial policies adopted globally and the bindingness of the policy - are provided for 74 countries from 2000 to 2020. The data include the index values from four statistical models used to calculate the composite index as described in D’Orazio and Thole 2022. The four alternative statistical approaches were designed to experiment with alternative weighting assumptions and illustrate how sensitive the proposed index is to changes in the steps followed to construct it. The index data shed light on countries’ engagement in climate-related financial planning and highlight policy gaps in relevant policy sectors.</p> <p> </p>
Supporting data for "A service to help insurers understand the financial impacts of changing flood risk in Europe, based on PESETA IV"
<p>Supporting data for the paper "A service to help insurers understand the financial impacts of changing flood risk in Europe, based on PESETA IV".</p> <p><a href="https://doi.org/10.1016/j.cliser.2023.100395">https://doi.org/10.1016/j.cliser.2023.100395</a></p>
Extreme Climate Risks and Financial Tipping Points
<p>This repository contains the data and the source code for the simulations and figures of the article.</p> <p><strong>Abstract</strong></p> <p>Designing climate change policies requires considering the feedback loops between mitigation and adaptation, since more mitigation efforts today will trigger lower adaptation costs. In this framework, carbon taxes are often seen as promising tools but at the risk of financially overburdening the private sector, depriving it of important economic resources. However, analyzing the financial feasibility of mitigation-adaptation policies using conventional Integrated Assessment Models (IAM) is limited, as they do not simultaneously endogenize economic growth, emissions, and damages.</p> <p>Here, we present IDEE (Integrated Dynamic Environment-Economic), a new IAM based on the coupling of an Earth Model of Intermediate Complexity and a non-linear macroeconomic model in continuous time. Then, we analyze the simultaneous effects of carbon taxes and public spending, both on climate and on the world economy. We show that, above a warming about +2.3°C, damages drastically foster the need for additional investments in productive capital—an adaptation necessity—that potentially leads private firms to a debt overhang and a worldwide cascade of defaults. This suggests that the Paris Agreement target should not only be motivated by the climatic non-linearities and tipping points arising beyond the +2°C threshold, but also by the emergence of financial tipping points. We also show that, provided public subsidies are high enough, a tax of usd 300 per tCO<sub>2</sub>e by 2030 enables reaching net-zero emissions in 2050, preventing firms from suffering global bankruptcy.</p> <p>We anticipate IDEE to be a starting point for a new class of IAMs that better represent the reciprocal feedback loops between the environment and the economy.</p>
Financial dimensions of global zoonotic disease risks
<p>The CSV files contain cleaned lists of companies and shareholders used in the study "<strong>Financial dimensions of global zoonotic disease risks</strong>". Code for the analysis is available at <a href="https://github.com/juanrocha/finance_tipping">https://github.com/juanrocha/finance_tipping</a> and a preprint of the study at <a href="https://beijer.kva.se/publication/financial-dimensions-of-global-zoonotic-disease-risks/">https://beijer.kva.se/publication/financial-dimensions-of-global-zoonotic-disease-risks/</a></p>
CONFIDENCE-AI Financial Education for Caregivers (CONFIDENCE-AI)
ClinicalTrials.gov study NCT06134180. IPD Sharing: YES. Countries: 1. Publications: 3.
Search Behavior can Affect Financial Decision Results: A Behavior Study of Google Trends Data and Linguistic Scale
As search engines have become the main information resources of our daily life, studies about search behavior on the internet have gained great popularity with the growing knowledge of how the search behavior itself can affect our daily decisions, e.g. what to purchase, where to travel and even how to define beauty. However, there is no consensus conclusion whether the search behavior itself or the linguistic meaning behind it that can affect their decision. After analyzing the linguistic meanings of 13,915 English words obtained from Google Trends and its profit gained from the US house market by automatic transactions. It is found that linguistic meanings can affect financial decision results as word clusters with supervised machine learning methods.
Financial Capability Scale (FCS)
<p>The University of Wisconsin’s Center for Financial Security developed the Financial Capability Scale (FCS) with the support and Annie E. Casey Foundation in 2011 with the goal of developing a standardized client financial capability scale that could be used in the field with clients as well as in evaluation studies. In 2013, the Center conducted field testing to establish the reliability and validity of the scale. The FCS has been shown to be associated with credit use, credit scores, savings and financial attitudes. Although designed for financial coaching, the FCS can be applied to other financial capability services and programs.</p>
KINGSWAY FINANCIAL SVCS INC
<p>KFS was written up by fiverocks in May 2020 and has been a home run - up over 5x since his write up at $1.75 per share and exceeding the price target on his initial thesis, which has largely played out.</p><p> </p><p>The company has:</p><p>Re-started investor outreach (IR page now revamped, investor days/earnings calls being held) after years of zero outbound IR. (The company was also dark on its financial statements at the time of previous write up).</p><p>Sold down several non-strategic real estate assets and re-allocated the funds to purchasing operating companies (both extended warranty and as part of search accelerator program). </p><p>Simplified its balance sheet, with debt coming down massively as large amounts were associated with the non-core real estate assets, while the company also bought back the majority of its Trups preferred debt at discount to face value.</p><p>Unfortunately, I haven't partaken in most of the investment gains to date, only buying shares late last year. However thanks to the author's yeoman's work in the comments section, I noticed he was continually raising his price target, as the initial thesis was playing out and a new one was emerging. </p><ul><li><a href="https://valueinvesting.io/portfolio-optimization">Portfolio Optimization</a></li><li><a href="https://valueinvesting.io/efficient-frontier">Efficient Frontier Calculator</a></li><li><a href="https://valueinvesting.io/asset-correlations">Asset Correlations</a></li><li><a href="https://valueinvesting.io/backtest-portfolio">Backtest Portfolio</a></li><li><a href="https://valueinvesting.io/portfolio-visualizer">Portfolio Visualizer</a></li><li><a href="https://valueinvesting.io/monte-carlo-simulation">Online Monte Carlo Simulation</a></li></ul><p>One of my favourite signals for further investigating an idea on VIC, is when the author of an idea continues to pound the table on an idea that has already multi-bagged, given they have every incentive to hit that author exit recommendation button and take the W. It worked out well for me on XPEL, HMHC and HQI, where I either didn't take a position on initial recommendation (or took a very small position), but still managed to make some money, thanks to the respective author's subsequent cheerleading, as the thesis played out and evolved. </p><p>Thanks to fiverock's cheerleading on KFS, its clear after looking into the company that there is still plenty of meat left on the bone, as the thesis evolves from the complex to the simple thesis outlined by management, towards a potential compounding machine at a very reasonable starting valuation, as the company continues to build out its search accelerator business. </p><p> </p><p><strong>Summary of current business</strong></p><p>KFS's much simplified business now consists of two operating segments - 1) the Extended Warranty Business (68% ebitda) and 2) Search Accelerator (32% ebitda). I'll cover the extended warranty briefly, but its search accelerator business that really has the potential to make the stock a multi-bagger from here.</p><p><i>Extended Warranty business:</i></p><p>Kingsway's Extended warranty business operates under four different companies - three targeting the automotive sector (87% ebitda - IWS, PWI and Penn) and one targeting mechanical (13% of ebitda - Trinity). </p><p>KFS likes the extended warranty business, due to its lack of capital intensity + the sticky nature of relationships with customers (for example, the automotive extended warranties are mostly sold by credit union/dealer partners with credit union in particular exhibiting little churn). Furthermore, the nature of claims are fairly predictable, typically relating to mechanical failure (eg, transmission issues), with high quality data and little tail risk, resulting in predictable and recurring revenues.</p><p>Whilst KFS has in the recent past been able to acquire extended warranty businesses at attractive valuations, they see current valuations as probably too high to contemplate acquisitions here in near future and will be focusing on organic growth.</p><p><i>Search Accelerator:</i></p><p>KFS's search accelerator business 'officially' commenced as a separate operating segment with the purchase of the Ravix business (accounting/HR outsourcing) in 2021, under Timi Okah, their first operator-in-residence. KFS CEO JT Fitzgerald is quite experienced with the search fund model, having been a post-MBA search fund CEO himself, while also being an active investor in the space . Since the acquisition of Ravix, KFS has also purchased CSuite (financial executive search) and SNS (nurse staffing). There are also 4.5 operators-in-residence (OIRs) that are currently searching for businesses to acquire (the 0.5 being Charles Joyce, who is working on initiatives to improve search efficiency and OIR recruitment at holdco level, but is also interested in acquiring a business at some point).</p><p>Before describing the current state of affairs for KFS's search accelerator business, I think its worthwhile to give a brief history of the search fund model, its history of lucrative returns and why I believe KFS's search accelerator is well-positioned to perform well.</p><p>Search Funds (also knows as 'Entrepreneurs through acquisition' or ETA) are typically set up by freshly minted, high potential MBAs who have the ambition of running their own company, but are lacking in both financial resources and experience. Typically at least some of the search fund principals will have experience in both search funds and/or running a business and in addition to investing are also there to provide mentorship and guidance to the searcher. Whilst the asset class has grown rapidly since the early 80s, professional search investors are still a relatively small group and mostly know each other. </p><p>In terms of size, KFS is looking to purchase businesses with ebitda between $1.5mn-$3mn- at the sweet spot of being too small for a mid-market PE fund, whilst also being out of reach for majority of ex-HNWI individuals. In terms of types of business they are looking to acquire, KFS (and search funds in general) look to acquire businesses with predictable, recurring revenues and low operational complexity (given they will be managed by rookie CEOs). </p><p>Searchers are typically looking to purchase businesses from owners approaching retirement age, who are looking for both exit liquidity and succession planning (JT has used the phrase 'succession capital' to describe the solution that searchers like KFS are providing to retiring entrepreneurs.) Deals can be sourced in multiple ways including business brokers, cold approaching and purchase of databases. Its typically a very time consuming process, with several rocks needing to be turned over, and can take take anywhere up to two years. </p><p>In terms of deal economics, at KFS the OIRs are given a very modest salary and resources to initiate their search (at more traditional search funds, the searchers have to raise a fund to cover search costs, typically upto two years). On consummation of a deal, the searcher is given some equity in the business, with this amount increasing over time, and the amount typically linked to certain return goals. It's also typically structured in such a way that KFS's equity is preferred and they need to be made whole on their investment (+ modest single digit return), before the searcher participates in any upside. While the searcher CEO is paid a salary, it is usually fairly modest and given the typical talent level, well below their opportunity cost if they had entered the corporate world post-MBA, so the carried interest is really their main way of making bank and aligning the incentives between searcher and KFS. It makes more financial + reputational sense for a searcher/OIR to either end or extend their search than consummate a mediocre/bad deal.</p><p>Searchers typically look to create value in the business once acquired, either by pursuing additional revenue channels, cutting costs and/or investing in the business. Given that the previous owner is typically approaching retirement age, in several instances the company is a lifestyle business and not necessarily cost or revenue-optimised, so there is usually some low hanging fruit to improve profitability. Early signs at Ravix look promising (although its probably too soon to judge). </p><p>Stanford Business School releases a study every couple of years on the financial performance of search funds. The most recent study was 2022, which looked at the performance of 546 search funds in US and Canada between 1984 to 2021. In total, the aggregate pre-tax IRR and MOIC of all funds in the study was 35% and 5.2x, respectively, with this number including 1) search funds that failed to consummate an acquisition (34% of total funds) and 2) search funds that produced a loss (27% of funds that consummated an acquisition), so potential returns could be higher if these left tail outcomes can be mitigated (in the most recent investor day, JT discusses how he believes the KFS model can avoid some of the pitfalls associated with traditional search funds).</p><ul><li><a href="https://valueinvesting.io/AAPL/valuation/intrinsic-value">Apple Stock Valuation</a></li><li><a href="https://valueinvesting.io/NFLX/valuation/intrinsic-value">Netflix Stock Valuation</a></li><li><a href="https://valueinvesting.io/MSFT/valuation/intrinsic-value">Microsoft Stock Valuation</a></li><li><a href="https://valueinvesting.io/META/valuation/intrinsic-value">Meta Stock Valuation</a></li><li><a href="https://valueinvesting.io/TSLA/valuation/intrinsic-value">Tesla Stock Valuation</a></li><li><a href="https://valueinvesting.io/AMZN/valuation/intrinsic-value">Amazon Stock Valuation</a></li><li><a href="https://valueinvesting.io/C/valuation/intrinsic-value">Citibank Stock Valuation</a></li><li><a href="https://valueinvesting.io/NVDA/valuation/intrinsic-value">Nvidia Stock Valuation</a></li><li><a href="https://valueinvesting.io/AMD/valuation/intrinsic-value">AMD Stock Valuation</a></li><li><a href="https://valueinvesting.io/BBY/valuation/intrinsic-value">Best Buy Stock Valuation</a></li><li><a href="https://valueinvesting.io/GOOGL/valuation/intrinsic-value">Alphabet Stock Valuation</a></li><li><a href="https://valueinvesting.io/HD/valuation/intrinsic-value">Home Depot Stock Valuation</a></li><li><a href="https://valueinvesting.io/JPM/valuation/intrinsic-value">JPMorgan Stock Valuation</a></li></ul><p>In 2022, KFS set up a strategic advisory board to the search accelerator business comprising Will Thorndike (investor and author of 'Outsiders') and Tom Joyce (ex CEO of Danaher). In the words of the press release, "the goal of the Advisory Board will be to assist the KSX CEOs and Operators-in-residence with their strategic thinking, acquisition opportunity analysis, operational execution, and capital allocation decisions, as well as to provide advice, experience, and expanded networks."</p><p>I'm sure Thorndike is known to many on VIC for writing the bible on capital allocation. What is less well known is that Thorndike is an important player in the search fund community. His firm Housatonic Partners pioneered institutional investment in the space and according to one Thorndike speaker bio, he had invested in the majority of search funds that had a top twenty outcome (typical 10x-ers+). He's also already helped KFS add an OIR to their bench (Davide Zanchi). Furthermore, subsequent to his appointment on the advisory board, an investment firm he co-manages (Sun Mountain Partners LLC) made an investment in 800k KFS shares (worth ~$7mn), providing an additional endorsement to KFS's search accelerator business model.</p><p>Tom Joyce was the CEO of Danaher between 2014 and 2020. During that period, Danaher crushed the S&P 500, whilst its considered one of American businesses' greatest success stories. Danaher's history involves being one of the early pioneers in the US of importing kaizen/lean manufacturing techniques from Japan. What's unique to Danaher compared to its Japanese peers, is that it specifically built out a toolkit of techniques (referred to as Danaher Business Systems or DBS), that it would use in its M&A strategy, to massively improve operating performance of acquired companies that it would believe would greatly benefit from DBS. Kingsway is also building out its toolkit called Kingsway Business Systems (no points for originality), likely aimed at the more business service oriented companies that are favoured by search funds. As JT mentioned on KFS most recent earnings call, "we've been using Danaher Business system tools for a long time, but to have the guy that actually helped build those teaching us how to use them the right way is really powerful." </p><p>JT mentioned on the most recent earnings call that the search accelerator is expected to do 2-3 acquisitions per year and they are looking to have 4-5 OIRs searching at any point in time.</p><p>When taking account the favourable historical returns of the search fund asset class, the pedigree the advisory board and the small sample size of success to date at KFS, this bodes well for a favourable outcome and possibility that the search accelerator business could be a compounding machine for years to come. </p><p><strong>Other Investment Strengths:</strong></p><p><i>Insider ownership/insider buying:</i> Insiders own over 60% of KFS shares and the insider buying over the past couple of years has been constant, even after a massive amount of share appreciation.</p><p><i>NOL:</i> KFS still has over $600mn in NOLs, meaning it won't have to pay federal taxes for the rest of the decade. If KFS sees some traction in building out the search accelerator, the ability to save on federal tax and re-invest pre-tax profits at attractive IRRs could further supercharge KFS operating income over the next few years. </p><p> </p><p><strong>Valuation</strong></p><p> </p><p>Management note that run-rate ebitda of the operating businesses is in $18-19mn range. If we assume that over the next five years that management is able to compound earnings at between 15-25% (we think its possible for this rate to be higher in the next few years), and the market re-rates the multiple in recognising the growth from newly acquired search accelerator businesses (to, say between 15-20x exit multiple), we derive a value of between $550mn-1.1bn in five years (or 17-35% 5 year IRR).</p><p> </p><p><strong>Reason for opportunity</strong></p><p>Small market cap / no sell-side coverage</p><p>Company only recently started investor outreach (Earnings call in November 2022 call, was the first since 2009)</p><p>The initial 'complex to simple' thesis has largely played out, so possibility the shares are in holding pattern until there is more investors on board the revised thesis. Furthermore, with shares up 5x since fiverock's 2020 write up, its a tough chart to bu</p>
Data from: Community Credit Photovoice project on trust in consumer financial services
<p>The Community Credit research project explores pathways for trusted collaboration between credit unions and the communities they serve. To understand the experiences of people historically underserved by the consumer financial services industry, we focused in particular on the lived experience of low-income residents in Southern California. As part of a larger, mixed-methods study, in 2022 we conducted a five-week photovoice workshop exploring participants' economic lives and experiences with consumer financial services. Photovoice is a community-based participatory research method in which participants narrate and analyze their lived experiences using photography. This data set contains the transcripts from the photovoice sessions after excluding any personally identifying data.</p> <p>All study materials and procedures were approved by the University of California, Irvine Office of Human Research Protections and the Institutional Review Board (protocol ID 20216839). This material is based upon work supported by the National Science Foundation under Grant No. 2137567. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.</p>
Data from: Community Credit interviews on trust in consumer financial services
<p>The Community Credit research project explores pathways for trusted collaboration between credit unions and the communities they serve. To understand the experiences of people historically underserved by the consumer financial services industry, we focused in particular on the lived experience of low-income residents in Southern California. As part of a larger, mixed-methods study, in 2022 we conducted 30 semi-structured interviews exploring people's everyday financial practices and their attitudes toward consumer financial services providers. This data set contains the transcripts from the interviews after excluding any personally identifying data.</p> <p>All study materials and procedures were approved by the University of California, Irvine Office of Human Research Protections and the Institutional Review Board (protocol ID 20216839). This material is based upon work supported by the National Science Foundation under Grant No. 2137567. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.</p>
LLMs for Automatic Financial Statement Analysis
<p>Financial Accounting provides standards for the recording and valuation of economic elements, facilitating the representation of financial activity in standardized models called Financial Statements, such as the Balance Sheet or the Income Statement. Financial Statements are analyzed using different techniques, including the use of ratios or indicators that reveal vital information for understanding, controlling, and planning the financial management of an organization. While it is feasible to achieve a certain degree of automation in Financial Statement Analysis, especially in the computation of financial ratios, human intervention remains essential to extract conclusions that serve as the basis for decision-making. This exploratory study aims to automate the analysis process through the employment of Large Language Models. Leveraging Financial Statements and indicators calculated automatically as the base knowledge, this study applies these language models to autonomously generate an understandable Financial Statement Analysis.</p>
Resources for "Enterprise's strategies to improve financial capital under a climate change scenario – evidence of the leading country"
<p>The dataset and code deposited here are resources used for analysis in the study titled "Enterprise’s strategies to improve financial capital under a climate change scenario – evidence of the leading country"</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.