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Hey all, I wanted to share a project I’ve been working on that focuses on using fundamental data from financial reports to build portfolios that are rotated on a quarterly basis. I feel this is in contrast to many algotrading strategies posted here that rely on high-frequency trading or short-term swing trading, and I thought it would be helpful to show how these methods can be applied to a low-frequency, fundamental-only approach.
I’ve just started paper trading this model this quarter and plan to deploy it live within the next few months. While I’m going to be purposely vague on the exact model and predictors used to protect my "secret sauce," I’m happy to answer any questions about the process to the best of my ability.
My model essentially pulls quarterly report data from companies listed on the S&P 100 (using SimFin; list does not include banks due to their reporting structure), uses data from those statements to predict the return of a stock in the two months following the quarterly report. Some of the predictors are pulled directly from the quarterly reports, while others are calculated/derived from several fundamentals. The model projects, based on those predictors, what the 2 month return will be.
At the end of the quarter, I take a look at all the projected returns (regardless of whether the 2 month timeframe has passed), rank them and choose the top 10, and buy them with the weightings based on their rankings. For instance, the top ranked stock is roughly 18% of my portfolio, while the 10th rank stock is roughly 3%. I then hold until the end of the next quarter where I repeat the process.
In terms of returns, I am only able to currently present backtesting results from 2019 Q2; you can see the results in the table below, relative to SPY.
|**Quarter/Year**|**Portfolio Return**|**SPY Return**|**Portfolio Capital**|**SPY Capital**|
|:-|:-|:-|:-|:-|
||
|2019 Q2|2.65%|2.92%|1.027|1.029|
|2019 Q3|\-0.15%|0.03%|1.025|1.029|
|2019 Q4|17.93%|8.10%|1.209|1.113|
|2020 Q1|\-12.83%|\-20.33%|1.054|0.887|
|2020 Q2|29.58%|24.35%|1.365|1.102|
|2020 Q3|15.93%|8.18%|1.583|1.193|
|2020 Q4|3.30%|10.72%|1.635|1.320|
|2021 Q1|7.52%|5.60%|1.758|1.394|
|2021 Q2|3.92%|7.44%|1.827|1.498|
|2021 Q3|1.91%|0.06%|1.862|1.499|
|2021 Q4|11.45%|10.20%|2.075|1.652|
|2022 Q1|1.22%|\-5.18%|2.101|1.567|
|2022 Q2|\-21.41%|\-16.78%|1.651|1.304|
|2022 Q3|\-2.74%|\-5.15%|1.605|1.237|
|2022 Q4|21.82%|5.91%|1.956|1.310|
|2023 Q1|11.95%|6.51%|2.190|1.395|
|2023 Q2|2.99%|8.42%|2.255|1.512|
|2023 Q3|2.65%|\-3.49%|2.315|1.460|
|2023 Q4|19.47%|11.41%|2.765|1.626|
|2024 Q1|3.52%|10.78%|2.863|1.802|
|2024 Q2|1.71%|3.89%|2.912|1.872|
|2024 Q3|10.56%|5.16%|3.219|1.968|
|2024 Q4|\-1.16%|2.21%|3.182|2.012|
|2025 Q1|4.28%|\-5.09%|3.318|1.909|
|2025 Q2|13.44%|10.84%|3.764|2.116|
|2025 Q3|20.05%|8.08%|4.519|2.287|
|2025 Q4|6.29%|2.83%|4.803|2.352|
|2026 Q1|\-4.88%|\-5.16%|4.569|2.231|
The final backtesting results show a **357%** return (SPY returns **123%**) over that time. The model also beat SPY in 68% of all quarters tested (19/28).
Looking at yearly returns:
|**Year**|**Portfolio Annual Return**|**SPY Annual Return**|Outperformance|
|:-|:-|:-|:-|
||
|**2019**|\+20.90%|\+11.30%|\+9.60%|
|**2020**|\+35.30%|\+18.70%|\+16.60%|
|**2021**|\+26.90%|\+25.10%|\+1.80%|
|**2022**|\-5.76%|\-20.70%|\+14.94%|
|**2023**|\+41.40%|\+24.20%|\+17.20%|
|**2024**|\+15.10%|\+23.70%|\-8.60%|
|**2025**|\+50.90%|\+16.90%|\+34.00%|
|**2026 (YTD)**|\-4.88%|\-5.16%|\+0.28%|
We can see on a yearly basis that the model beats SPY 6/7 years (not including this year and acknowledging that 2019 is a shortened year in my backtesting). On a risk-adjusted basis (calculated from quarterly returns), both the annualized Sharpe and Sortino ratios significantly outperform SPY.
|**Metric**|**Portfolio**|**SPY**|**Improvement**|
|:-|:-|:-|:-|
||
|**Sharpe Ratio**|**1.15**|0.75|\+53%|
|**Sortino Ratio**|**1.61**|1.05|\+53%|
What happens if we change the number of picks?
|**Strategy**|**Total Return**|**Mean Quarterly**|**Quarterly SD**|
|:-|:-|:-|:-|
||
|**1 Pick**|**+810.92%**|10.00%|19.22%|
|**5 Picks**|**+418.36%**|6.68%|11.65%|
|**10 Picks**|**+356.88%**|6.11%|10.66%|
|**20 Picks**|**+268.87%**|5.20%|9.54%|
|**SPY (Ref)**|**+123.00%**|3.30%|8.98%|
Decreasing the number of picks tends to increase the return, but also increases the volatility (as should be expected with increasing concentration). The 5 - 10 pick zone seems to be a nice balance between high returns but also manageable variance.
I'd also like to add that the most interesting thing to me is that I get these results despite often picking stocks that are past the 2-month prediction horizon used by the model itself. For instance say a report is released in January and predicts 2 months ahead (March), i'm only buying the stock at the end of March, past the prediction period. This to me further speaks to the model's strength of picking strong stocks overall.
It's also important to note that in my backtesting, I use a list of S&P 100 constituents from the previous year. So for instance, for 2022, I'm using the companies listed in 2021. This is obviously imperfect as it doesn't account for new constituents added during the year, but is better than using the current list across years.
I'm also publicly documenting my journey/picks for free, though I'm not sure if I can share that link without it counting as "self-promotion"; perhaps the mods can give me some clarity on that and I can add a link to the page in the comments.
Anyways, that's what I have. I'm excited for it and I hope it works long-term. I'd love to hear some thoughts and feedback from you folks!