Tailored Investment Strategy Development
Budget: $250 – $750 USD
Our strategy flows together like the chemistry of a superball-winning team. Everything is accounted for, and nothing is contradicted. 58% Large Cap, 25% Small Cap, 17% ETF. First, conners 10k withdrawals will be funded by our portfolio dividends. Our portfolio dividends will be roughly 1.67% annually, which will more than account for the withdrawals since they start after year 3. Conner's goal of 1.5M is so large that we have to maximize growth with every investment, and that's why we will not invest in treasury bonds, low-yielding sectors, or hold cash equivalent assets. Now, this doesn't mean we won't be hedging our investments; that is what our well-planned, well-accounted-for diversification plan is for.
Let’s start at large cap stocks. Our large cap stocks will efficiently follow and exceed the growth of the S&P 500 and general stock market, which turns 12%yoy on average. Our Large cap stock selection process starts at the s&p500, which we apply an extensive quantitative filtration that leaves us with the stocks that scored in the top 30% of our index. Our offensive playbook starts with the general and moves to more specific areas as the situation changes. It contains large-cap companies, or sets, which are viewed as market veterans. They will make up 60-70% of the portfolio and provide the foundation for consistent growth and stability. Within these sets, we will judge the stocks through our proprietary quantitative index, which categorizes and ranks companies' performance by sector to ensure diversification. This index analyzes performance metrics specific to each industry, ensuring we evaluate each company relative to its competitive peers. To build the index, the top large-cap companies, or the S&P 500, were compiled. Each company was compared against others in its industry. Using this information, every company was given a score based on its value and momentum. Finally, a filter was applied to each industry, yielding the top 30% companies within them based on their combined value and momentum scores. This allowed a true judgment of each company purely within its competition pool. It leaves us with a diversified and strong playbook that aligns with our overall strategy.
Now the stocks go through the scouting report, our attempt at making sure every investment aligns with Conners core beliefs: locally driven companies that pay attention to sustainability and benefit communities. The Scouting Report index (SRI) is, at its core, an AI-driven model that combines a qualitative and quantitative approach to align every stock pick with Mr. Barwin's foundational values. We created the SRI because it would be unwise to rely entirely on ESG grades and ESG indices to tailor our report to Mr. Barwin's goals. We felt that typical ESG ratings were flawed due to their lack of standardized data and their inherent biases toward larger corporations. By avoiding the usual approach of filtering stocks based on ESG ratings, we created the SRI. The SRI’s unique approach uses AI to filter through companies' public 10-K filings to identify specific keywords we believe align with Mr. Barwin's Values.
As each 10-K filing is a different length. We knew we needed to develop a standardized method for analyzing each report. To regulate our process, we divided the number of times the targeted words came up by the total number of words. Providing a percentage, which we dubbed the composite sustainibitlity score. Higher scores indicated that the stock was more closely aligned with Mr. Barwin's ideals, while lower scores implied that the corporation's values were misaligned, regardless of financial performance.
To filter stocks after they had already passed through the SRI, our screening system excluded the bottom 40%. Some stocks that performed exceedingly well in our large-cap report were trimmed because they did not align with Mr. Barwin's values. One particular stock that stood out was Western Digital, WDC, which was our leading large-cap stock in the tech sector. Yet WDC performed poorly on the SRI, resulting in its disqualification from our portfolio.
Filter out bottom 30th percentile.
Now we need to weigh the percentage each sector represents in our large-cap portfolio. This is where our Sector Rotation Prediction Model (SRPM) is used. The SRPM is a machine learning model trained on 25 years of historical data. It uses economic metrics such as PMI, CPIyoy, 10-2Curve, Unemployment Rate, Fed Funds Rate, Rolling returns of each sector, and rolling volatility of each sector, to predict each sectors returns over the next six months. I can go into more explaining here. We then average the predictions with the current sector weights in the s&p500 to get our portfolio weights. It is also worth mentioning that we are not invested in low yielding sectors like real estate, materials, and utilities, they are also very slightly represented in the s&p500 anyway. Since our model predicts 6months into the future, and markets change constantly, we will rebalance our sector diversification every 6 months to ensure we are invested in the most growing and largest sectors. This Large Cap investment process allows us to most efficiently follow top companies. These stocks are sure to stay on top of their domain for the next 10 years to come, allowing us to stay represented in the top of the stock market, while being able to ensure Conner's core values are adhered to.
SMALL-CAP STRATEGY
Small-cap and emerging-market companies—our portfolio’s “rising stars”—represent 25% of Connor’s allocation and serve as the engine for long-term asymmetric returns. Unlike large-cap names, these earlier-stage firms offer exponential upside driven by innovation in AI, renewable energy, and biotechnology, making them essential to achieving Connor’s $1.5M target within 10 years. Our team built a structured quantitative scoring model and paired it with deep qualitative and TAM research, ensuring every selected company passed a multi-layered analytical process. We know that these small cap companies are best evaluated through qualitative research, especially because the particular companies that we were looking for were innovative companies with new technology that is useful. We also know that these investments can be risky, so we will keep a close eye on them and adjust our positions if issues with certain companies arise.
I. BUILDING THE SMALL-CAP MODEL
We created a systematic scoring model to prevent speculation and anchor decisions in financial reality. Each metric was normalized, weighted, and interpreted with attention to how small-cap financial structures differ from mature firms.
Core Metrics
Operating Cash Flow (OCF)
OCF shows whether a company’s core operations generate real liquidity or require ongoing capital infusion. Positive OCF signals scalable economics, while negative OCF is acceptable only when tied to disciplined reinvestment in future revenue.
Quarterly Revenue Growth (YoY)
This measures whether the market is adopting a company’s product or technology in real time. Rapid YoY growth indicates accelerating traction and is one of the strongest leading indicators of future valuation expansion.
Price-to-Book Ratio (P/B)
P/B gauges how the market values a company relative to its underlying assets—crucial in early-stage firms where earnings may not exist. A lower P/B suggests the market has not yet priced in future growth, creating an entry point before revaluation occurs.
II. QUALITATIVE + TAM ANALYSIS
After ranking companies quantitatively, we conducted deep qualitative reviews of leadership quality, product differentiation, competitive moats, and real-world feasibility. We then applied standardized TAM analysis, evaluating whether each company addresses a large, urgent, or rapidly expanding market and whether it can capture meaningful market share within a multi-year horizon. This phase required high-level collaboration, structured debate, and strict evidence-based reasoning, forcing us to refine assumptions until consensus formed.
Only companies that cleared **all three filters—quantitative score, qualitative validation, and TAM alignment—**were selected. The final list represented three emerging secular themes:
AI – foundational software, automation infrastructure, and high-growth computational technologies
Renewable Energy – scalable clean-energy systems enabling global energy transition
Biotechnology – solutions addressing unmet medical needs with breakthrough scientific platforms
These niche sectors are not so much represented in our large cap portion, so this gives us more divervification and exposure.
These sectors offer the highest innovation density, strongest long-term trends, and best alignment with Connor’s sustainability-driven goals.
The ETF section in our portfolio allows us to represent international markets, small cap companies as a whole, and sectors like materials and utilities which require a lot of holdings for proper exposure. This exposure gives us a hedge if us economies slow, Small cap companies usually thrive after recessions, and Low yielding sectors like utilities and materials allow us to maintain buying power when rebalancing, in case our other holdings stifle.
Our ETFs also have high yields, averaging 2.3% which contributes to our security in funding conners withdrawals.
Glide pathL if our portfolio ever reaches a value in which the future returns for last few (2) years is under 11%, we will deriskify by and buy into treasury bonds.
We will rebalance asset classes every 6 months, at the time of sector rebalancing. rebalancing provides constant uniform diversification but also provides leverage because we would be selling from asset classes that returned higher, so deriskifying from them.
Also everything has to be football themed. If you can try to add in football terms like in our midterm. Everything from our midterm has to not contradict our final report
The zip contains the rep we need
Let’s start at large cap stocks. Our large cap stocks will efficiently follow and exceed the growth of the S&P 500 and general stock market, which turns 12%yoy on average. Our Large cap stock selection process starts at the s&p500, which we apply an extensive quantitative filtration that leaves us with the stocks that scored in the top 30% of our index. Our offensive playbook starts with the general and moves to more specific areas as the situation changes. It contains large-cap companies, or sets, which are viewed as market veterans. They will make up 60-70% of the portfolio and provide the foundation for consistent growth and stability. Within these sets, we will judge the stocks through our proprietary quantitative index, which categorizes and ranks companies' performance by sector to ensure diversification. This index analyzes performance metrics specific to each industry, ensuring we evaluate each company relative to its competitive peers. To build the index, the top large-cap companies, or the S&P 500, were compiled. Each company was compared against others in its industry. Using this information, every company was given a score based on its value and momentum. Finally, a filter was applied to each industry, yielding the top 30% companies within them based on their combined value and momentum scores. This allowed a true judgment of each company purely within its competition pool. It leaves us with a diversified and strong playbook that aligns with our overall strategy.
Now the stocks go through the scouting report, our attempt at making sure every investment aligns with Conners core beliefs: locally driven companies that pay attention to sustainability and benefit communities. The Scouting Report index (SRI) is, at its core, an AI-driven model that combines a qualitative and quantitative approach to align every stock pick with Mr. Barwin's foundational values. We created the SRI because it would be unwise to rely entirely on ESG grades and ESG indices to tailor our report to Mr. Barwin's goals. We felt that typical ESG ratings were flawed due to their lack of standardized data and their inherent biases toward larger corporations. By avoiding the usual approach of filtering stocks based on ESG ratings, we created the SRI. The SRI’s unique approach uses AI to filter through companies' public 10-K filings to identify specific keywords we believe align with Mr. Barwin's Values.
As each 10-K filing is a different length. We knew we needed to develop a standardized method for analyzing each report. To regulate our process, we divided the number of times the targeted words came up by the total number of words. Providing a percentage, which we dubbed the composite sustainibitlity score. Higher scores indicated that the stock was more closely aligned with Mr. Barwin's ideals, while lower scores implied that the corporation's values were misaligned, regardless of financial performance.
To filter stocks after they had already passed through the SRI, our screening system excluded the bottom 40%. Some stocks that performed exceedingly well in our large-cap report were trimmed because they did not align with Mr. Barwin's values. One particular stock that stood out was Western Digital, WDC, which was our leading large-cap stock in the tech sector. Yet WDC performed poorly on the SRI, resulting in its disqualification from our portfolio.
Filter out bottom 30th percentile.
Now we need to weigh the percentage each sector represents in our large-cap portfolio. This is where our Sector Rotation Prediction Model (SRPM) is used. The SRPM is a machine learning model trained on 25 years of historical data. It uses economic metrics such as PMI, CPIyoy, 10-2Curve, Unemployment Rate, Fed Funds Rate, Rolling returns of each sector, and rolling volatility of each sector, to predict each sectors returns over the next six months. I can go into more explaining here. We then average the predictions with the current sector weights in the s&p500 to get our portfolio weights. It is also worth mentioning that we are not invested in low yielding sectors like real estate, materials, and utilities, they are also very slightly represented in the s&p500 anyway. Since our model predicts 6months into the future, and markets change constantly, we will rebalance our sector diversification every 6 months to ensure we are invested in the most growing and largest sectors. This Large Cap investment process allows us to most efficiently follow top companies. These stocks are sure to stay on top of their domain for the next 10 years to come, allowing us to stay represented in the top of the stock market, while being able to ensure Conner's core values are adhered to.
SMALL-CAP STRATEGY
Small-cap and emerging-market companies—our portfolio’s “rising stars”—represent 25% of Connor’s allocation and serve as the engine for long-term asymmetric returns. Unlike large-cap names, these earlier-stage firms offer exponential upside driven by innovation in AI, renewable energy, and biotechnology, making them essential to achieving Connor’s $1.5M target within 10 years. Our team built a structured quantitative scoring model and paired it with deep qualitative and TAM research, ensuring every selected company passed a multi-layered analytical process. We know that these small cap companies are best evaluated through qualitative research, especially because the particular companies that we were looking for were innovative companies with new technology that is useful. We also know that these investments can be risky, so we will keep a close eye on them and adjust our positions if issues with certain companies arise.
I. BUILDING THE SMALL-CAP MODEL
We created a systematic scoring model to prevent speculation and anchor decisions in financial reality. Each metric was normalized, weighted, and interpreted with attention to how small-cap financial structures differ from mature firms.
Core Metrics
Operating Cash Flow (OCF)
OCF shows whether a company’s core operations generate real liquidity or require ongoing capital infusion. Positive OCF signals scalable economics, while negative OCF is acceptable only when tied to disciplined reinvestment in future revenue.
Quarterly Revenue Growth (YoY)
This measures whether the market is adopting a company’s product or technology in real time. Rapid YoY growth indicates accelerating traction and is one of the strongest leading indicators of future valuation expansion.
Price-to-Book Ratio (P/B)
P/B gauges how the market values a company relative to its underlying assets—crucial in early-stage firms where earnings may not exist. A lower P/B suggests the market has not yet priced in future growth, creating an entry point before revaluation occurs.
II. QUALITATIVE + TAM ANALYSIS
After ranking companies quantitatively, we conducted deep qualitative reviews of leadership quality, product differentiation, competitive moats, and real-world feasibility. We then applied standardized TAM analysis, evaluating whether each company addresses a large, urgent, or rapidly expanding market and whether it can capture meaningful market share within a multi-year horizon. This phase required high-level collaboration, structured debate, and strict evidence-based reasoning, forcing us to refine assumptions until consensus formed.
Only companies that cleared **all three filters—quantitative score, qualitative validation, and TAM alignment—**were selected. The final list represented three emerging secular themes:
AI – foundational software, automation infrastructure, and high-growth computational technologies
Renewable Energy – scalable clean-energy systems enabling global energy transition
Biotechnology – solutions addressing unmet medical needs with breakthrough scientific platforms
These niche sectors are not so much represented in our large cap portion, so this gives us more divervification and exposure.
These sectors offer the highest innovation density, strongest long-term trends, and best alignment with Connor’s sustainability-driven goals.
The ETF section in our portfolio allows us to represent international markets, small cap companies as a whole, and sectors like materials and utilities which require a lot of holdings for proper exposure. This exposure gives us a hedge if us economies slow, Small cap companies usually thrive after recessions, and Low yielding sectors like utilities and materials allow us to maintain buying power when rebalancing, in case our other holdings stifle.
Our ETFs also have high yields, averaging 2.3% which contributes to our security in funding conners withdrawals.
Glide pathL if our portfolio ever reaches a value in which the future returns for last few (2) years is under 11%, we will deriskify by and buy into treasury bonds.
We will rebalance asset classes every 6 months, at the time of sector rebalancing. rebalancing provides constant uniform diversification but also provides leverage because we would be selling from asset classes that returned higher, so deriskifying from them.
Also everything has to be football themed. If you can try to add in football terms like in our midterm. Everything from our midterm has to not contradict our final report
The zip contains the rep we need