Finance
Barksdale Announces Closing of Private Placement Financing
Vancouver, British Columbia–(Newsfile Corp. – July 26, 2024) – Barksdale Resources Corp. (TSXV: BRO) (“Barksdale” or the “Company“) is pleased to announce the closing of the second and final tranche (“Final Tranche“) of its previously announced non-brokered private placement offering (“Offering“) of units of the Company (“Units“) with the issuance of 14,674,683 Units for gross proceeds of $2,201,202.45. The first tranche (“First Tranche“) of the non-brokered private placement offering comprising 27,325,317 Units for gross proceeds of $4,098,798 closed on June 27, 2024 (see news release dated June 27, 2024). The Units sold in respect of the First Tranche and Final Tranche, together, total 42,000,000 for gross proceeds of $6,300,000.
Each Unit consists of one common share of Barksdale (a “Common Share“) and one Common Share purchase warrant (a “Warrant“), whereby each Warrant entitles the holder to acquire one Common Share at a price of $0.23 for a period of three years from the date of issuance.
Proceeds of the Offering will be used to finance exploration activities at the Company’s properties in Arizona as well as for working capital and general corporate purposes. Pursuant to the closing of the Offering, the Company paid an aggregate of (i) $199,516.60 in cash finder’s fees and issued an aggregate of 1,330,111 finder’s warrants to eligible finders in connection with the First Tranche, and (ii) $64,396.49 in cash finder’s fees and issued an aggregate of 429,309 finder’s warrants to eligible finders in connection with the Final Tranche. The finder’s fees in respect of the Offering, therefore, total $263,913.09 and 1,759,420 finder’s warrants. Each finder’s warrant entitles the holder to acquire one Common Share at a price of $0.23 until June 27, 2027 (First Tranche) or July 27, 2027 (Final Tranche).
All securities issued pursuant to the (i) First Tranche are subject to a statutory hold period expiring October 28, 2027, and (ii) Final Tranche are subject to a statutory hold period expiring November 27, 2027; each expiration date being the date that is four months and one day from the date of issuance. The Offering remains subject to TSX Venture Exchange final acceptance.
The securities described herein have not been, and will not be, registered under the United States Securities Act of 1933, as amended (the “U.S. Securities Act“), or any state securities laws, and may not be offered or sold within the United States except in compliance with the registration requirements of the U.S. Securities Act and applicable state securities laws or pursuant to available exemptions therefrom. This release does not constitute an offer to sell or a solicitation of an offer to buy of any securities in the United States.
Related Party Participation in the Offering
Certain insiders of the Company participated in the Offering. For details of insider participation in the First Tranche, please see news release dated June 27, 2024. In connection with the Final Tranche, Crescat Portfolio Management LLC, an insider of the Company as it has ownership of, or control or direction over, directly or indirectly, securities of Barksdale carrying more than 10% of the voting rights attached to all the Company’s outstanding voting securities, purchased 6,666,667 Units. In addition, Rick Trotman, Chief Executive Officer and Director of Barksdale, purchased 118,317 Units. The participation by insiders in the Offering constitutes a “related party transaction” as defined under Multilateral Instrument 61-101 – Protection of Minority Security Holders in Special Transactions (“MI 61-101“). The Company is relying on the exemptions from the valuation and minority shareholder approval requirements of MI 61-101 contained in sections 5.5(a) and 5.7(1)(a) of MI 61-101, as neither the fair market value of the securities purchased by insiders, nor the consideration for the securities paid by such insiders, will exceed 25% of the Company’s market capitalization. The Company did not file a material change report in respect of the related party transaction at least 21 days before the closing of either the First Tranche or the Final Tranche, which the Company deems reasonable in the circumstances in order to complete the Offering in an expeditious manner. The Offering was unanimously approved by the Company’s board of directors.
Barksdale Resources Corp., a 2023 OTCQX BEST 50 Company, is a base metal exploration company headquartered in Vancouver, B.C., that is focused on the acquisition, exploration and advancement of highly prospective base metal projects in North America. Barksdale is currently advancing the Sunnyside copper-zinc-lead-silver project in the Patagonia mining district of southern Arizona, which hosts several significant porphyry copper deposits as well as the adjoining world-class Hermosa carbonate-replacement lead-zinc-silver deposit which is under construction by a major mining company.
ON BEHALF OF BARKSDALE RESOURCES CORP
Rick Trotman
President, CEO and Director
Rick@barksdaleresources.com
Terri Anne Welyki
Vice President of Communications
778-238-2333
TerriAnne@barksdaleresources.com
For more information please phone 778-558-7145, email info@barksdaleresources.com or visit www.BarksdaleResources.com.
Neither TSX Venture Exchange nor its Regulation Services Provider (as that term is defined in the policies of the TSX Venture Exchange) accepts responsibility for the adequacy or accuracy of this release.
CAUTIONARY STATEMENT REGARDING FORWARD-LOOKING INFORMATION: This news release contains certain “forward-looking information” and “forward-looking statements” (collectively “forward-looking statements”) within the meaning of applicable securities legislation. Forward-looking statements are frequently, but not always, identified by words such as “expects”, “anticipates”, “believes”, “intends”, “estimates”, “potential”, “possible”, and similar expressions, or statements that events, conditions, or results “will”, “may”, “could”, or” should” occur or be achieved. All statements, other than statements of historical fact, included herein, without limitation, statements relating to TSX Venture Exchange approval and the use of proceeds from the Offering are forward-looking statements. There can be no assurance that such statements will prove to be accurate, and actual results and future events could differ materially from those anticipated in such statements. Forward-looking statements reflect the beliefs, opinions and projections on the date the statements are made and are based upon a number of assumptions and estimates that, while considered reasonable by Barksdale, are inherently subject to significant business, economic, competitive, political and social uncertainties and contingencies. Many factors, both known and unknown, could cause actual results, performance or achievements to be materially different from the results, performance or achievements that are or may be expressed or implied by such forward-looking statements and the Company has made assumptions and estimates based on or related to many of these factors. Such factors include, without limitation, the ability to obtain necessary approvals, the ability to complete proposed exploration work, the results of exploration, continued availability of capital, and changes in general economic, market and business conditions. Readers should not place undue reliance on the forward-looking statements and information contained in this news release concerning these items. Barksdale does not assume any obligation to update the forward-looking statements of beliefs, opinions, projections, or other factors, should they change, except as required by applicable securities laws.
// NOT FOR DISTRIBUTION TO UNITED STATES NEWSWIRE SERVICES OR FOR DISSEMINATION IN THE UNITED STATES //
To view the source version of this press release, please visit https://www.newsfilecorp.com/release/218027
In a world of interconnected financial markets, policymakers and regulators face the complex task of identifying and addressing risks before they escalate into crises. The 2008-09 global crisis and recent episodes of market dysfunction highlight the need for early warning tools to detect vulnerabilities in real time. However, predicting financial market stress remains challenging, as traditional econometric models often fail to capture the complex, nonlinear dynamics and interconnectedness of modern financial systems.
Recent advances in artificial intelligence (AI) provide new tools to address these challenges. AI methods excel at analysing high-dimensional datasets and uncovering hidden patterns. While they are widely applied in asset pricing (Kelly et al. 2024), they are increasingly used for financial stability monitoring (Fouliard et al. 2021, du Plessis and Fritsche 2025). However, the ‘black box’ nature of AI models has limited their ability to generate actionable policy insights.
This article highlights recent research (Aldasoro et al. 2025, Aquilina et al. 2025) that advances the deployment of AI tools to anticipate financial market stress. These studies demonstrate the potential of AI to forecast market stress and dysfunction, offering both methodological innovations and actionable insights for policymakers by addressing the black-box issue.
The challenge of anticipating financial market stress
Financial market stress can take many forms, including liquidity shortages, price dislocations, and breakdowns in arbitrage relationships. Events such as the 1998 LTCM crisis, the 2008-09 global crisis, and the 2020 ‘dash for cash’ highlight the systemic risks posed by market dysfunction. These disruptions often begin in specific market segments, such as foreign exchange or money markets, but can quickly spread throughout the financial system, threatening its stability. Increasingly, stress has also shifted from traditional banks to non-bank financial intermediaries, reflecting the evolving nature of financial intermediation.
Traditional early warning systems, which were primarily designed to predict full-blown crises, have had mixed success. These models often suffer from high false positive rates and struggle to account for the nonlinear interactions and feedback loops that amplify shocks during periods of stress.
Machine learning (ML) offers a promising alternative, particularly for generating early warning signals. Unlike traditional models, ML algorithms can process vast datasets, identify complex relationships, and adapt to changing market conditions. The studies discussed here demonstrate the potential of these tools to anticipate market stress and provide policymakers with timely warnings.
Using machine learning to model the tail behaviour of financial market conditions
Aldasoro et al. (2025) present a novel framework for predicting financial market stress using machine learning. The study begins by constructing market condition indicators (MCIs) for three key US markets critical to financial stability: Treasury, foreign exchange, and money markets. These indicators (illustrated in Figure 1) capture dislocations in liquidity, volatility, and arbitrage conditions.
Figure 1 Market condition indices for US Treasury, foreign exchange, and money markets
Notes: This figure shows the five-day moving average of market condition indices for the US Treasury, money, and foreign exchange (FX) markets (upper, middle, and lower panels respectively). The sample period is from 01/01/2003 to 31/05/2024.
The paper employs random forest models, a popular tree-based machine learning algorithm, to forecast the full distribution of future market conditions. This approach uses multiple decision trees and averages their predictions, reducing the risk of overfitting. The results are noteworthy: random forest models outperform traditional time-series approaches, particularly in predicting tail risks over longer time horizons (up to 12 months). This is especially evident in forecasting foreign exchange market conditions (Figure 2).
Figure 2 Forecast accuracy of random forest and autoregressive models
Notes: This figure compares quantile losses between the random forest and autoregressive models based on out-of-sample predictions across forecast horizons. Negative values indicate better performance of the random forest model.
To address the black-box issue, the study uses Shapley value analysis to explain the main factors driving market stress predictions. The analysis reveals that macroeconomic expectations and uncertainty, particularly around monetary policy, are significant contributors to market vulnerability. Liquidity conditions and the state of the global financial cycle also play critical roles. This approach not only improves predictive accuracy but also provides actionable insights for policymakers, enabling them to respond proactively to the build-up of vulnerabilities.
Combining machine learning with large language models
Aquilina et al. (2025) take a different approach by integrating numerical data with textual information using large language models (LLMs). The study focuses on deviations from triangular arbitrage parity (TAP) in the euro-yen currency pair, a key indicator of dysfunction in the foreign exchange market. By combining recurrent neural networks (RNNs) with LLMs, the authors develop a two-stage framework to forecast market stress and identify its underlying drivers.
The recurrent neural network detects periods of heightened triangular arbitrage parity deviations up to 60 working days in advance, effectively predicting market dysfunctions that may occur within a one-month window. Out-of-sample testing on 3.5 years of data demonstrates the model’s practical value. For example, the model identified elevated risks before the March 2023 banking turmoil, despite being trained only on data up to the end of 2020 (Figure 3). However, it did not predict the market anomaly caused by the onset of COVID-19, as the event’s origins were external to the financial system.
Figure 3 Predictive accuracy of market dysfunction episodes
Notes: True data: 20-day average of the daily euro-yen triangular arbitrage parity difference with the US dollar as the vehicle currency, calculated on a minute-by-minute basis. The vertical red dashed line represents the end of the training period, end-2020; everything to the right of this line is considered pseudo out-of-sample.
To address the black-box challenge, Aquilina et al. (2025) develop a new architecture for recurrent neural network models that dynamically assigns weights to input variables. This allows the model to identify which indicators are most important for predicting future market conditions at any given time. These weights can then be fed into an LLM to search financial news and commentary for contextual information, helping to uncover potential triggers of market stress.
For instance, during the March 2023 banking turmoil, the model flagged elevated risks in euro liquidity and cross-currency arbitrage. Guided by these signals, the LLM identified news articles discussing tightening dollar funding conditions and rising geopolitical tensions. This targeted approach transforms opaque statistical forecasts into narrative explanations that policymakers can understand and act upon.
Policy implications and conclusions
While much more research into these issues is needed, these approaches show the promise of leveraging AI tools for financial stability monitoring and analysis.
- First, our work has shown that machine learning models are useful in forecasting future conditions of various markets.
- Second, the integration of numerical and textual data through machine learning and large language models provides a richer understanding of market dynamics. Policymakers can use these tools to monitor emerging risks in real time, combining quantitative forecasts with qualitative insights from financial news and commentary.
- Finally, the interpretability of machine learning models is critical for their adoption in policy settings. Techniques like Shapley value analysis and variable-specific weighting not only improve the transparency of forecasts but also provide actionable information about the drivers of market stress.
Overall, these approaches represent a significant step forward in leveraging AI to detect vulnerabilities in financial markets. By combining different methods, the studies offer novel tools for forecasting market stress and understanding its underlying drivers. However, these methods are not without limitations, such as the risk of overfitting and the need for substantial computational resources. Policymakers and regulators should invest in the necessary data and infrastructure to fully harness the potential of these tools.
References
Aldasoro, I, P Hördahl and S Zhu (2022), “Under pressure: market conditions and stress”, BIS Quarterly Review (19): 31–45.
Aldasoro, I, P Hördahl, A Schrimpf and X S Zhu (2025), “Predicting Financial Market Stress with Machine Learning”, BIS Working Papers No. 1250.
Aquilina, M, D Araujo, G Gelos, T Park and F Pérez-Cruz (2025), “Harnessing Artificial Intelligence for Monitoring Financial Markets”, BIS Working Papers No. 1291.
Du Plessis, E and U Fritsche (2025), “New forecasting methods for an old problem: Predicting 147 years of systemic financial crises”, Journal of Forecasting 44 (1): 3-40.
Fouliard, J, M Howell, H Rey and V Stavrakeva (2021), “Answering the queen: Machine learning and financial crises”, NBER Working Paper 28302.
Huang, W, A Ranaldo, A Schrimpf and F Somogyi (2025), “Constrained liquidity provision in currency markets”, Journal of Financial Economics 167: 104028.
Kelly, B, S Malamud and K Zhou (2024), “The Virtue of Complexity in Return Prediction”, Journal of Finance 79: 459-503.
Pasquariello, P (2014), “Financial Market Dislocations”, Review of Financial Studies 27(6): 1868–1914.
US-based bank holding company First Financial Bancorp has completed its previously announced acquisition of Westfield Bancorp and its subsidiary, Westfield Bank.
In June, First Financial agreed to acquire Westfield Bancorp from Ohio Farmers Insurance Company in a cash-and-stock deal valued at $325m.
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With the addition of Westfield Bank, First Financial’s total assets now stand at $20.6bn, strengthening its presence in the Midwest region of the US.
The acquisition is said to expand First Financial’s commercial banking and wealth management services in Northeast Ohio.
Westfield Bank’s retail locations and related services will now operate as part of First Financial’s network.
These branches will retain their current branding until the completion of conversion process, which is expected to occur in March 2026.
The conversion will merge the two banks’ products, processes and operating systems.
Westfield Bank clients will continue to receive services through existing channels, and will receive information about account conversions in the coming months.
First Financial president and CEO Archie Brown said: “This is an exciting step in the growth of First Financial, as the addition of Westfield Bank opens new possibilities for growth and profitability for us in an attractive market.
“We can now bring our wide range of solutions in consumer, commercial, specialty lending and wealth management to new clients, while expanding our geographic footprint for our current clients.
“The First Financial team is thrilled to welcome the Westfield Bank team members to the First Financial family.”
The transaction follows First Financial’s recent expansion activities in the Midwest.
In 2023, the company established a commercial lending presence in Northeast Ohio.
Earlier this year, First Financial announced BankFinancial, the parent company of BankFinancial, National Association, in Chicago, Illinois.
Furthermore, the company has also established a commercial banking presence in Grand Rapids, Michigan.
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