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Record W4393697382 · doi:10.5281/zenodo.10367509

KINGSWAY FINANCIAL SVCS INC

2023· dataset· en· W4393697382 on OpenAlexaboutno aff
Dan Linh

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typedataset
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

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. The company has: 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). 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). 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. 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. Portfolio Optimization Efficient Frontier Calculator Asset Correlations Backtest Portfolio Portfolio Visualizer Online Monte Carlo Simulation 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. 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. Summary of current business 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. Extended Warranty business: 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). 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. 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. Search Accelerator: 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). 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. 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. 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). 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. 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. 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). 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). Apple Stock Valuation Netflix Stock Valuation Microsoft Stock Valuation Meta Stock Valuation Tesla Stock Valuation Amazon Stock Valuation Citibank Stock Valuation Nvidia Stock Valuation AMD Stock Valuation Best Buy Stock Valuation Alphabet Stock Valuation Home Depot Stock Valuation JPMorgan Stock Valuation 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." 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 KF

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.270
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0040.001
Open science0.0020.005
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.276

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.233
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractyes

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